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RESEARCH ARTICLE
Quantitative estimates of the regulatory
influence of long non-coding RNAs on global
gene expression variation using TCGA breast
cancer transcriptomic data
Xiaoman XieID
1
, Saurabh Sinha
2,3
*
1Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois,
United States of America, 2Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and
Emory University, Georgia Institute of Technology, Atlanta, Georgia, United States of America, 3H. Milton
Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia,
United States of America
*saurabh.sinha@bme.gatech.edu
Abstract
Long non-coding RNAs (lncRNAs) have received attention in recent years for their regula-
tory roles in diverse biological contexts including cancer, yet large gaps remain in our under-
standing of their mechanisms and global maps of their targets. In this work, we investigated
a basic unanswered question of lncRNA systems biology: to what extent can gene expres-
sion variation across individuals be attributed to lncRNA-driven regulation? To answer this,
we analyzed RNA-seq data from a cohort of breast cancer patients, explaining each gene’s
expression variation using a small set of automatically selected lncRNA regulators. A key
aspect of this analysis is that it accounts for confounding effects of transcription factors
(TFs) as common regulators of a lncRNA-mRNA pair, to enrich the explained gene expres-
sion for lncRNA-mediated regulation. We found that for 16% of analyzed genes, lncRNAs
can explain more than 20% of expression variation. We observed 25–50% of the putative
regulator lncRNAs to be in ‘cis’ to, i.e., overlapping or located proximally to the target gene.
This led us to quantify the global regulatory impact of such cis-located lncRNAs, which was
found to be substantially greater than that of trans-located lncRNAs. Additionally, by includ-
ing statistical interaction terms involving lncRNA-protein pairs as predictors in our regres-
sion models, we identified cases where a lncRNA’s regulatory effect depends on the
presence of a TF or RNA-binding protein. Finally, we created a high-confidence lncRNA-
gene regulatory network whose edges are supported by co-expression as well as a plausible
mechanism such as cis-action, protein scaffolding or competing endogenous RNAs. Our
work is a first attempt to quantify the extent of gene expression control exerted globally by
lncRNAs, especially those located proximally to their regulatory targets, in a specific biologi-
cal (breast cancer) context. It also marks a first step towards systematic reconstruction of
lncRNA regulatory networks, going beyond the current paradigm of co-expression networks,
and motivates future analyses assessing the generalizability of our findings to additional bio-
logical contexts.
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OPEN ACCESS
Citation: Xie X, Sinha S (2024) Quantitative
estimates of the regulatory influence of long non-
coding RNAs on global gene expression variation
using TCGA breast cancer transcriptomic data.
PLoS Comput Biol 20(6): e1012103. https://doi.
org/10.1371/journal.pcbi.1012103
Editor: Shaun Mahony, Penn State University: The
Pennsylvania State University, UNITED STATES
Received: January 19, 2023
Accepted: April 24, 2024
Published: June 5, 2024
Copyright: ©2024 Xie, Sinha. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The source code and
data used to produce the results and analyses
presented in this manuscript are available from Git
repository: https://github.com/UIUCSinhaLab/
lncRNARN.git.
Funding: This work was supported by the National
Institutes of Health (grant R35GM131819 to SS
and XX). The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Author summary
Many studies have reported the role of long non-coding RNAs in regulating gene expres-
sion, yet there has not been a systematic assessment of the extent to which gene expression
variance can be explained by lncRNA regulation. lncRNA-mRNA co-expression networks
are commonly constructed using pair-wise correlations, but these approaches do not
claim to quantify lncRNA-driven regulation and may be confounded by shared transcrip-
tional regulators of lncRNA-mRNA pairs. Here, we used a multivariable linear regression
model to predict mRNA expression using lncRNAs’ expression in a large breast cancer
transcriptomic dataset from TCGA, and obtained estimates for the overall gene expression
variance under lncRNA regulation. We also integrated orthogonal criteria such as genome
proximity and protein binding information to refine these estimates and identified
lncRNA-mRNA associations that are both statistically significant and biologically
supported.
Introduction
Long non-coding RNAs (lncRNA) are a class of RNA transcripts that are longer than 200bp
and not translated to ORFs longer than 100 amino acids [1]. Over 10,000 lncRNAs have been
annotated in the human genome, yet their functional characterization lags far behind that of
protein-coding genes. There have been speculations that most lncRNAs are by-products of
leaky transcription and other cellular processes, without important functions [2,3]. At the
same time, there are many studies demonstrating evidence of lncRNA function and it is gener-
ally believed that such functions are carried out via their regulatory influence on other genes
[4–7]. Accordingly, numerous attempts at determining co-expression networks involving
lncRNAs and protein-coding genes have been published [8–12]. Despite these efforts, a simple
but fundamental question remains unanswered regarding lncRNA-driven regulation: to what
extent can gene expression variation across individuals be attributed to lncRNA-driven regula-
tion? Our work attempts to answer this key question by systematic statistical analysis of a large
transcriptomic data set and reconstruction of a conservative lncRNA regulatory network,
where putative lncRNA-mRNA relationships are inferred while the regulation of TFs and
other lncRNAs are controlled and additional criteria, such as genomic proximity and protein-
dependent co-expression are imposed.
lncRNAs as a class have received special attention in the context of cancer. Several lncRNAs
have been found to have critical roles in cell proliferation, invasion and apoptosis [13,14] [15–
18]. The Cancer LncRNA Census, as part of the ICGC/TCGA Pan-Cancer Analysis, reported
122 lncRNAs to have causal function in cancer phenotypes [19]. Some of the well-studied
lncRNAs such as MALAT1 and NEAT1 are known for their causal impact on breast cancer
metastasis [20–23]. Given their emerging significance in breast cancer, we conducted our sys-
tematic characterization of the extent of lncRNA regulatory influence using transcriptomic
data from a cohort of breast cancer patients in the Cancer Genome Atlas (TCGA). We hoped
that this may lead to a useful catalog of important lncRNA regulators of this disease, in addi-
tion to providing broadly applicable insights into the central question raised above.
A quantitative assessment of lncRNA regulation can be performed by examining co-expres-
sion patterns in cohort-level transcriptomic data and determining putative lncRNA regulators
of protein-coding genes. Previous studies constructed co-expression networks between
lncRNAs and protein coding genes using methods such as weighted correlation network
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Regulatory influence of long non-coding RNAs on global gene expression variation
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Competing interests: The authors have declared
that no competing interests exist.
analysis (WGCNA) [5,24], examining correlations between the expression levels of each
lncRNA-mRNA pair [25]. However, co-expression between a lncRNA and a mRNA does not
imply that the lncRNA regulates the gene. One possible reason is that both the lncRNA and
the mRNA are regulated by the same transcription factor (TF). Being co-regulated by the same
TF regulators may cause high correlation between the expression levels between two tran-
scripts, e.g., a lncRNA and an mRNA. Thus, accounting for evidence of relationships with TFs
when detecting lncRNA-mRNA co-expression is expected to enrich for regulatory interactions
between lncRNAs and mRNAs. Another important consideration in building regulatory net-
works from expression analysis is the possibility of multiple regulators influencing a target
gene; indeed, this is the common practice in building TF-gene regulatory networks, where
multi-variable regression/classification models are used to explain a target gene’s expression as
a function of multiple TFs [26–29]. A co-expression network that examines each lncRNA-
mRNA pair independently for evidence of correlated expression ignores this facet of regula-
tion, and is likely to have false as well as missing edges as a result. Finally, a methodological
challenge that arises when building lncRNA-mRNA regulatory networks is that there are a
large number of lncRNAs to select the true regulators from and this poses statistical difficulties,
especially due to extensive co-expression among lncRNAs themselves. A possible solution is to
demand that a single mRNA’s expression is explained using only a small set of regulators, cho-
sen from the full complement of lncRNAs in a data-driven manner.
We sought to address the above shortcomings of co-expression analysis in determining
lncRNA-mRNA regulatory relationships and estimating the fraction of expression variation in
TCGA breast cancer samples that can be explained using lncRNAs as regulators. Notably, a
diversity of mechanisms has been reported for lncRNA-driven regulation, including interfer-
ence in transcription by lncRNA genes overlapping the target gene [30], cis-regulatory action
by lncRNAs located proximally to a gene [31,32], trans-effects such as competing endogenous
RNA (ceRNA) [33], etc. [34] We sought to objectively estimate the extent to which each mech-
anism is borne out in the TCGA breast cancer data, adopting a simple strategy: modeling each
target gene’s expression while limiting the candidate lncRNA regulators to those for which
there is plausible evidence of a particular mechanism being at play. Moreover, there are reports
in the literature that the regulatory influence of lncRNAs may in some cases depend on the
presence of proteins such as the DNA binding TFs or RNA-binding proteins (RBPs), e.g., if
the lncRNA acts as a scaffold for assembly of complexes involving such proteins along with
DNA and/or RNA. We hypothesized that such a protein-dependent regulatory relationship
between a lncRNA and its target genes may leave its footprint in the expression data, by way of
“interaction terms” in a multi-variate regression of gene expression against lncRNA and TF/
RBP expression. We therefore sought to detect such examples of protein-dependent lncRNA
action via statistical testing of interaction terms.
With the above goals in mind, we analyzed the transcriptome data for 13,963 protein cod-
ing genes and 1,079 lncRNAs of 1,217 breast cancer tumor samples from TCGA. We assessed
how much variance of the gene expression can be explained by lncRNAs using linear regres-
sion models that treat expression values of selected lncRNAs as independent variables (“fea-
tures”). Importantly, we accounted for potential confounding effects of TFs as common
regulators of a co-expressed lncRNA-mRNA pair in performing the regression analysis. We
repeated the analysis using lncRNAs that can be potentially associated with the target gene
under a specific mechanistic assumption, such as lncRNAs that overlap the gene, or lncRNAs
that are located in the same Topologically Associating Domain (TAD) as the target gene, or
lncRNAs that may compete with the target gene for the same microRNA, i.e., the ceRNA
mechanism. In any modeling step where a large number of candidate regulators were possible,
we controlled for model complexity by adopting the Elastic Net approach [35] that ensures
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sparsity of selected features, and by assessing the percentage of expression variance explained
in an unseen part of the data set. Statistically significant interactions between lncRNA and
RBPs/TFs were detected by first training models to explain the target gene expression using
both lncRNAs and the class of proteins, and then assessing the added value of including inter-
action terms in the model.
Combining the results of the above-mentioned analyses, we finally created a lncRNA-
mRNA regulatory network with edges being lncRNA-mRNA connections that are strongly
supported by the above models and by additional evidence of a plausible regulatory mecha-
nism. About 25%-50% of the predicted edges are in ‘cis’ to, i.e., overlapping or located proxi-
mally to the target gene. We found that lncRNAs with prominent roles in the reconstructed
network are supported by literature-based evidence to have important regulatory roles in the
context of breast cancer. We also made the intriguing observation that genes targeted by spe-
cific lncRNAs, according to the network, are highly enriched for functions related to protein
translation. In summary, this work presents a comprehensive and rigorous analysis of expres-
sion relationships involving lncRNAs and protein coding genes, along with TFs and RBPs,
borne out by TCGA breast cancer data. It also presents a blueprint for identification of signifi-
cant regulatory lncRNAs and their regulons in any data set of individual-level expression
variation.
Results
Breast cancer transcriptomes reveal extensive co-expression involving
lncRNAs
We analyzed the transcriptomes of 1,217 primary tumor samples from breast cancer patients
(source: TCGA), with the goal of identifying regulatory relationships between lncRNAs and
mRNAs. TCGA HTSeq-counts data was preprocessed to remove lowly expressed genes, nor-
malize expression values and control for common confounding variables, such as age, sex, eth-
nicity and race. (See Methods for details of data processing and normalization.) The processed
data included expression values for 13,963 mRNAs and 1,079 lncRNAs (Fig 1A). In a prelimi-
nary analysis, we calculated Pearson correlation coefficients (PCC) for each lncRNA-mRNA
pair, and found extensive co-expression (Fig 1B), with 30% of all pairs at a PCC of absolute
value greater than 0.17 (Bonferroni corrected p-value of 0.05). An example of a pair with
strong correlation is the lncRNA MALAT1, a key regulator in breast cancer [22], and the
mRNA MATR3, an RNA-binding protein with tumor suppressive function in breast cancer
[36] (Fig 1C). The extensive co-expression was also noted among pairs of lncRNAs (Fig 1D),
with ~36.5% of all pairs at a PCC of absolute value greater than 0.15 (Bonferroni corrected p-
value of 0.05), with the majority of these co-expressed pairs being positively correlated. The
extensive co-expression observed among lncRNAs and mRNAs is consistent with previous
analyses of cancer data sets [8,9,11], including the TCGA breast cancer cohort [12,37], and
provides a natural starting point for the rest of this work.
lncRNAs can explain a significant portion of gene expression variation
Our next goal was to estimate how much of the variation in gene expression can be explained
using lncRNAs. This is an important step towards quantifying the potential regulatory influ-
ence of lncRNAs as a class. To this end, we first trained a linear model to predict each gene’s
expression as a weighted sum of multiple lncRNAs’ expression levels. With over 1000 lncRNAs
being candidate predictors and ~1,200 samples available for modeling, it was imperative that
we constrain the model’s complexity and avoid “over-fitting” the data. We therefore used the
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Elastic Net algorithm to select ~10 informative lncRNAs for each target gene (mRNA) and used
only the selected short-list of lncRNAs in modeling that gene (see Methods). The median R
2
(percentage of variance explained) was noted at 0.52 (Fig A in S1 Text), meaning that for most
genes over half of the variance in expression can be explained using ~10 selected lncRNAs. To
ensure that this observation is not the result of over-fitting, we performed a 5-fold cross-valida-
tion of the model. The test R
2
values (Fig 2A) had a median of 0.47, which is close to the 0.52
value noted above. Fig 2B shows that the test R
2
value of a gene is generally close to the overall
R
2
value observed when training and evaluating the linear model on all samples, indicating that
the results are likely not the result of over-fitting. The high R
2
values noted here are also mir-
rored in high values of Pearson Correlation Coefficient (PCC) between predicted and observed
expression levels (Figs B and C in S1 Text), with a median PCC of 0.66.
The observation that a substantial portion of the variance of mRNA expression can be
explained using small numbers of lncRNAs suggests extensive regulation by lncRNAs. But
such lncRNA-mRNA covariation also reflects the effects of shared regulators such as transcrip-
tion factors (TFs) that influence the expression of both the mRNA and the lncRNA. To arrive
at a more conservative estimate of the extent of influence and to enrich the statistically identi-
fied lncRNA-mRNA pairs for those with direct regulatory relationships (lncRNA regulates
mRNA), we considered two options: (a) impose mechanistic prior knowledge, as is done by
demanding the presence of TF binding sites in gene promoters when reconstructing TF-gene
regulatory networks [38], and/or (b) remove the effect of other regulatory mechanisms, e.g.,
TF-driven regulation of a lncRNA-mRNA pair, by treating them as statistical confounders.
Fig 1. Co-expression among mRNAs and lncRNAs. (A) Graphical Abstract of Workflow. RNA-Seq data for 1,217
primary tumor samples from 1,092 breast cancer patients were obtained from TCGA-BRCA cohort. The processed
transcriptomic data (see Methods) include expression profiles for 1,079 lncRNAs and 13,963 mRNAs. Using ElasticNet
regression, significant lncRNA, TF, and RBP regulators were identified. These significant lncRNAs with their
supporting mechanisms or involved in interaction terms were then used in an Ordinary Least Squares (OLS)
regression model to construct the final lncRNA-regulatory network. (B) Histogram of Pearson correlation coefficients
of lncRNA-mRNA pairs. Among ~15M pairs analyzed, ~4.5M pairs had absolute value of Pearson correlation above
0.17, which corresponds to Bonferroni corrected p-value of 0.05. (C) Density scatter plot of expression of lncRNA
MALAT1 and mRNA MATR3. MALAT1 and MATR3 are highly correlated with Pearson correlation of 0.58
(Bonferroni corrected p-value of 1.45e-109). (D) Histogram of pairwise Pearson correlations between lncRNA-
lncRNA pairs. There are ~170K pairs positively correlated with correlation greater than 0.15 and 42K negatively
correlated pairs with correlation lower than -0.15 (Bonferroni corrected p-value of 0.05).
https://doi.org/10.1371/journal.pcbi.1012103.g001
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Fig 2. LncRNA-mRNA co-expression network. (A) Histogram of test R
2
when mRNA expression values were
modeled using lncRNA. For each mRNA, Elastic Net algorithm was used to select ~10 most significant lncRNAs as
predictors, which were then used to predict the mRNA’s expression using ordinary least squares (OLS) regression.
Average test R
2
in a 5-fold cross-validation was calculated for each mRNA. (Each “fold” involved selecting predictor
lncRNAs and training the model on 80% of samples and testing on the remaining 20%.) (B) Density scatter plot of test
R
2
and overall (“training”) R
2
for the models. Overall R
2
is calculated by training the model on all samples and
calculating its R
2
on all samples. Average test R
2
values were generally similar to the corresponding overall R
2
values of
the same gene, indicating that overfitting is not a major concern. (C) Histogram of pairwise Pearson correlations for
TF-mRNA pairs. About 6.6M of the pairs (37.57% of all pairs) have a PCC with absolute value greater than 0.17
(Bonferroni corrected p-value of 0.05). (D) Histogram of test R
2
when mRNA expression values were modeled using
TFs selected using Elastic Net, in a five-fold cross validation. (E) Scatter plot of test R
2
values for models utilizing TFs
or lncRNAs (x and y axes respectively) as predictors. Each point represents an mRNA whose expression is predicted by
the models. PCC between two axes is 0.82 (p-value <2.2e-16). (F) Histogram of test R
2
values (average over five folds
of cross-validation) when lncRNAs were used to model mRNA expression values from which TF-based predictions
have been subtracted away.
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We first chose to adopt the latter approach of controlling for shared regulators (TFs). A cor-
relation analysis (Fig 2C) revealed that ~6.6M TF-mRNA pairs (37.6% of all pairs) have a PCC
with absolute value greater than 0.17 (Bonferroni corrected p-value of 0.05), highlighting their
extensive co-expression as expected. We then trained linear models to predict mRNA expres-
sion using ~10 selected TFs (from all 1,255 TFs included in the data set) and evaluated these
using 5-fold cross validation. Test R
2
values, shown for all target genes in Fig 2D, have a
median of 0.52, which is somewhat higher than the median R
2
value of 0.47 in comparable
evaluations of lncRNA-based predictions. Direct comparison of R
2
values from TF-based and
lncRNA-based models (Fig 2E) suggests that genes tend to be predictable to similar degrees
using either class of predictors (TFs or lncRNAs). We also observed genes that are far more
predictable using lncRNAs only or using TFs only (off-diagonal points in Fig 2E). Focusing on
the 109 mRNAs that are more predictable using lncRNAs (y–x >0.2 and y >0.5 in Fig 2E)
revealed that the most common significant lncRNA predictors of these target genes are
AC108449.2 and AC073896.4 (S1 and S2 Tables), increasing our confidence in their func-
tional regulatory roles. For example, AC073896.4 is sense overlapping to gene SMARCC2,
which is one of the core subunits of human SWI/SNF complex, which is known to be associ-
ated with breast cancer [39]. Examining the most significant (p-value <1E-10) lncRNA-
mRNA relationships involving the above set of 109 mRNAs, we observed that 23.7% (65/274)
were overlapping pairs and 39.8% (109/274) were pairs with genes within 1 Mbp of each other.
These observations are consistent with reported mechanisms of lncRNA action that involve
gene overlap [40,41], including in breast cancer context [42], as well as proximal chromatin
localization [43]. We visit these observations again in a later section.
Finally, for each mRNA we computed the residuals from its TF-based model, i.e., subtracted
from each original expression value the prediction from the TF-based model, and trained
lncRNA-based models on these residuals. In other words, we now sought to use lncRNAs to
explain the expression variance remaining after the putative effects of TFs had been subtracted
away. Fig 2F shows the distribution of test R
2
values for all genes. (Also see Fig D in S1 Text
for overall R
2
values.) Clearly, these estimates of percentage variance explained are substan-
tially lower than those from Fig 2A (before TF effects were subtracted), with a median R
2
of
0.12 compared to 0.47, which is consistent with our expectation that much of the lncRNA-
mRNA covariation can be explained as the result of TF-driven regulation. At the same time,
we noted that the R
2
value was 0.2 or more for 1,869 mRNAs (16.2% of all); i.e., for a substan-
tial number of mRNAs, at least 20% of the variance may be the result of lncRNA-driven regula-
tion. While this threshold of 20% is somewhat arbitrary, it does represent a highly significant
level of statistical association: a corresponding null distribution estimated empirically by per-
muting the expression levels of target genes across samples revealed that none of the 11,531
target genes can be modeled at a level better than this threshold (Fig E in S1 Text).
The high R
2
values observed above for a subset of target genes might still reflect mostly
their co-expression with lncRNAs as opposed to regulatory influence of those lncRNAs, due to
unknown shared regulators whose effects could not be subtracted away. We thus performed a
“control” exercise where we used a random set of 1,079 enzyme-encoding mRNAs in place of
the 1,079 lncRNAs as predictors and obtained R
2
values for all genes in an analogous manner.
Since enzyme-encoding mRNAs do not represent a regulatory class, prediction of a target
gene’s expression using these mRNAs likely reflects co-expression rather than regulation.
Repeating this control exercise 100 times, each time with a different random choice of 1,079
enzyme-encoding mRNAs as predictors, we obtained a null distribution of R
2
values for each
target gene; this allowed us to assign empirical p-values to the R
2
value achieved for each target
gene when using lncRNAs as predictors. We observed 699 genes to have empirical p-value <=
0.01 (578 genes have p-value <0.01, S3 Table), i.e., where at most one of the 100 control
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exercises using enzyme-encoding mRNAs achieved an R
2
value better than that seen with
lncRNA predictors. These 699 genes comprise 6.1% of the 11,531 target genes analyzed, signifi-
cantly higher than the chance expectation of 1% (Binomial test p-value <1.11e-302). This sug-
gests that the high R
2
values observed using lncRNA predictors do not merely represent co-
expression and are likely to reflect lncRNA-related regulation.
In summary, our analyses provide a rough estimate of the part of transcriptomic variation
that may be under lncRNA regulation, and points to the existence of a large number of genes
under such regulation, even after accounting for the confounding effects of common
regulators.
Categorization of putative lncRNA regulators based on location relative to
target gene
We next sought to characterize putative lncRNA regulators identified by our models above in
terms of potential mechanisms of action. There have been several reports of cis-regulatory
effects of lncRNAs (Fig 3A), e.g., antisense lncRNAs that regulate neighboring genes pre-tran-
scriptionally, transcriptionally or post-transcriptionally [30]. Indeed, global maps of
lncRNA-DNA interaction show that nucleus-retained lncRNAs tend to localize on proximal
chromatin regions [43]. On the other hand, an often-reported mechanism of action is the so-
called competing endogenous RNA (ceRNA) [44], which manifests in trans. It refers to the sit-
uation where a microRNA regulates a target mRNA post-transcriptionally and a lncRNA capa-
ble of binding the microRNA competes for the latter, thereby indirectly regulating the mRNA
(Fig 3A). To assess the relative frequencies of cis and trans regulatory mechanisms, we consid-
ered, for each target gene, the following categories of associated lncRNAs: those (A) sense or
antisense overlapping with target gene (“overlapping”), (B) located within the same topologi-
cally association domain (TAD) detected from Hi-C data from MCF10A breast cancer cell
lines [45]; [46] as the target gene (“same-TAD”), (C) located within 1 Mbp of the target gene
(“proximal”), (D) located on the same chromosome as the target gene (“same-chromosome”)
and (E) competing endogenous RNAs (“ceRNAs”) predicted to share a microRNA regulator
with the target gene, based on microRNA-mRNA and microRNA-lncRNA interactions [47].
In our context, competing endogenous RNA refers to a lncRNA-mRNA pair that are targets of
the same miRNA in the same cellular contexts (see Methods). We focused on the target
mRNAs whose expression could be well explained using lncRNA regulators (R
2
>= 0.2 and
empirical p-value <0.01, S3 Table), and examined their strongest putative lncRNA regulators
(p-value <= 1E-10 in OLS regression, and pair-wise correlation greater than 0.1 in absolute
value) in terms of the above categories. This yielded 643 lncRNA-mRNA associations (S4
Table), of which 500 (77.8%) were positive and 143 (22.2%) were negative relationships. Of
these, 171 pairs have correlation lower than 0.2 and thus might not be detected by traditional
co-expression based methods.
We noted that 177 (27.5%) of these pairs were overlapping lncRNA-mRNA pairs, all but
one of which were positively correlated. Similarly, 228 (35.5%) of the 643 pairs were within the
same TAD as each other; 54.0% and 62.7% were proximal and on the same chromosome
(respectively) as each other, almost always positively correlated (Table 1). In other words, the
discovered associations had large fractions of pairs located in “cis” and such pairs were over-
whelmingly found to be positively related pairs. We also confirmed that these large fractions
are not expected merely by chance (S2 Text). We noted a more balanced ratio of positively
correlated pairs (51.5%) and negatively correlated pairs (48.5%) for the 293 trans lncRNA-
mRNA pairs.
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Fig 3. Contribution of lncRNAs from different “mechanistic” categories. (A) Schematic of lncRNA regulation
mechanisms. The figure shows the four classes of lncRNAs acting in cis with the target gene and one type of trans-
acting lncRNAs (ceRNA). (B) Overlapping lncRNA-mRNA pairs (blue) are more correlated than overlapping pairs of
mRNAs (red). X-axis shows the Pearson Correlation Coefficient (PCC) and the Y-axisshows for any value of PCC
what fraction of lncRNA-gene pairs have correlation above that value. (CDF refers to cumulative distribution
function.) (C) Number of genes whose expression was modeled using lncRNAs from each category. For instance, only
588 genes had an overlapping lncRNA to use as predictor, thus only these genes could be modeled. (D,E) Number of
genes (D) and fraction of genes (E) that were modeled well (test R
2
>0.2, on average across five folds of cross-
validation) using lncRNAs from different categories. The fraction (E) is calculated as the ratio of respective numbers
from C and D. (F) Normalized expression of gene LRP1 in four groups of samples where lncRNA TMPO-AS1 and the
TF EBF1 are expressed at above (high) or below (low) their respective median level. In each category label, the first ‘H/
L’ indicates high/low expression of TF and the second ‘H/L’ corresponds to the lncRNA. LRP1 expression is
significantly lower in samples where both the TF and the lncRNA are highly expressed (HH) compared with the other
three groups where either the TF or the lncRNA or both are lowly expressed (t-test p-values are shown for each
between group comparison). The interaction term representing EBF1 and TMPO-AS1 has a p-value of 3.23E-10 in
modeling gene LRP1. (G) Gene MYL12A expression is significantly higher in samples with high levels of RBP FXR1
and lncRNA OIP5-AS1 (HH) compared to the three other groups (HL, LH or LL) where either regulator has low
expression. The interaction term representing FXR1 and OIP5-AS1 has a p-value of 7.8E-17 in modeling gene
MYL12A.
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A previous study by Chen et al. [48] has reported greater expression correlation between
overlapping protein-coding genes than between non-overlapping pairs of genes. We therefore
asked if the prevalence of overlapping pairs (and by generalization, the cis-located pairs) noted
above may simply reflect co-expression rather than regulator-target relationships. To answer
this, we considered 3,715 pairs of overlapping protein-coding genes (PCGs) and computed
pairwise correlations to obtain a null distribution, which we then contrasted with a corre-
sponding distribution of 615 overlapping lncRNA-mRNA pairs selected in an unbiased man-
ner, without regard to their expression profiles. As shown in Fig 3B, the two distributions are
significantly different (Kolmogorov-Smirnoff test p-value 2.1e-14). For instance, 250 (41%) of
the 615 overlapping lncRNA-mRNA pairs have a correlation coefficient >= 0.3, a ~1.6 fold
enrichment over the corresponding fraction of 24% seen among overlapping PCG pairs (Bino-
mial test p-value of 1.8e-19). In other words, a random overlapping lncRNA-mRNA pair has a
significantly higher expression correlation than a random overlapping PCG pair, making such
overlaps a marker of potential regulatory relationship. This also suggests that the 177 overlap-
ping lncRNA-mRNA pairs identified by us (Tables 1and S4) are enriched for regulatory
pairs.
Taken together, our statistical analyses suggest various modes of cis-regulatory action as a
prominent theme in lncRNA-driven regulation. We also noted 288 lncRNA-mRNA pairs (of
the 643 in S4 Table) whose genes were located more than 1 Mbp away on the same chromo-
some or on different chromosomes. These were evenly split between positive and negative cor-
relations (146 and 142 respectively), in sharp contrast to cis-located pairs, which were
predominantly positively correlated. (See S3 Text for selected examples of such pairs that have
literature support for their regulatory relationship.) Overall, of the 288 significant lncRNA-
mRNA pairs that were located distally or in trans, only 19 were designated as ceRNA in our
analysis, leaving the vast majority of such pairs mechanistically unresolved.
cis-located lncRNAs explain a substantial part of gene expression variation
Above, we observed a prevalence of cis location in a lncRNA regulatory network that was
obtained without regard to lncRNA location or mechanism. We next sought to quantify the
percentage of mRNA expression variation that can be explained by lncRNA regulators limited
to each of the above-defined categories of cis action. To this end, we repeated the above model-
ing exercise (Fig 2F) while limiting the candidate set of lncRNAs for a target gene to each of
the five categories–overlapping, same-TAD, proximal, same-chromosome or ceRNA. The
newly imposed restrictions on candidate lncRNAs used for predicting a given gene’s expres-
sion led to reductions in the number of genes analyzed (Fig 3C). We next examined the
Table 1. lncRNA-mRNA pairs categorized by relative genomic locations. For mRNAs that could be modeled using
lncRNAs with R
2
>= 0.2 and empirical p-value <0.01, we considered putative lncRNA regulators with OLS p-value <
= 1E-10 and pairwise correlation coefficient >= 0.1 in absolute value. Shown are the number of pairs in each category
and the number (of those) that are positively correlated. “Non-proximal” refers to pairs on different chromosomes or
more than 1 Mbp apart on the same chromosome.
Number of Annotated Pairs
(out of 643 pairs)
Positively Correlated
Overlapping 177 (27.5%) 176
Same TAD 228 (35.4%) 228
Proximal 347 (54.0%) 346
Same Chromosome 403 (62.7%) 394
ceRNA 41 (6.4%) 31
Non-proximal 296 (46.0%) 154
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number (Fig 3C) and fraction (Fig 3D) of target genes that could be modeled “well” (average
test R
2
>= 0.2 in 5-fold cross validation) using each of the five categories of candidate
lncRNAs. The “overlapping” category yielded the highest fraction, with ~16.7% of the 588
genes modeled using this class of lncRNAs reaching the R
2
>= 0.2 threshold. This fraction is
similar to the fraction of genes modeled well (16.2%) when the unrestricted class of all 1079
lncRNAs is used as candidates. Among the 588 genes that do have at least one overlapping
lncRNA, 303 genes can be modeled well using all lncRNAs as candidates and 32% of these can
also be modeled well using just the overlapping lncRNA(s) (Fig F in S1 Text). In other words,
if an mRNA’s expression can be predicted using lncRNAs and the mRNA has an overlapping
lncRNA, then that overlapping lncRNA is likely to be a significant predictor. (Also see Fig G
in S1 Text) Note that it is possible for such pairs to be co-regulated by a shared regulator,
rather than the lncRNA regulating the mRNA, but our strategy of subtracting the predicted
effects of TFs prior to modeling makes this less likely. These results reinforce the observations
made above and in the literature [49] that lncRNA-gene overlaps are a significant marker of
and potentially tied to the underlying mechanisms of lncRNA regulatory function.
When modeling a gene using candidate lncRNAs in the “same-TAD” (typically, 1.8 candi-
dates per gene) or “proximal” (typically, 2.0 candidates per gene) categories, the fraction of
genes with R
2
>= 0.2 fell to 2–4% (Fig 3E), much smaller than the 16.7% observed with “over-
lapping” lncRNAs. This is not surprising: a TAD often has multiple genes and it is possible
that a lncRNA within the same TAD regulates only one of those genes, driving the above-men-
tioned fraction down. Indeed, we found that ~20% of lncRNA-harboring TADs include at
least one gene that can be modeled well using lncRNAs in the same TAD. In contrast, among
TADs harboring enzyme-encoding genes, only 8.8% include at least one gene that can be mod-
eled well using enzymes in the same TAD.
Enlarging the candidate sets even further, we noted that ~8.0% of analyzed genes can be
predicted with R
2
>= 0.2 using lncRNAs from the same chromosome (typically, using ~11.5
regulators per gene). One way to interpret this observation is that while ~1,900 (~16%, Fig 2F)
of analyzed genes could be modeled well using cis- as well as trans-acting lncRNAs, about 50%
of these are amenable to expression prediction using only (putatively) cis-acting lncRNAs
from the same chromosome.
Finally, gene expression prediction using lncRNAs in the ceRNA category led to only ~2%
(246 out of 10556) of analyzed genes being modeled well, even though an average of 11
lncRNAs were considered as candidate regulators for each gene (S6 Table). We found 45
genes that have R
2
equal to 0.2 or greater and empirical p-value <0.01 (S4 Text) in such
modeling. Notable among these, lncRNA GAS5 was selected as one of the significant predic-
tors for 18 out of the 45 mRNAs (Table 2), including 12 genes that encode ribosomal proteins,
including PRS18, RPL31, RPL30, etc.. A previous study found that the overexpression of GAS5
facilitates cell apoptosis in breast cancer and increases the sensitivity to treatments [50], and it
has been shown to act via the ceRNA mechanism [51]. It belongs to the 5’-TOP gene family
that includes all ribosomal proteins [50], and five of its twelve putative ribosomal protein tar-
gets were also found to be among the top 20 genes related with GAS5 in kidney renal clear cell
carcinoma [52].
Statistical interaction analysis reveals potential protein mediators of
lncRNA action
Beyond cis-regulatory and ceRNA mechanisms, lncRNAs are also known to function by acting
as scaffolds for RNA- and/or DNA-binding proteins [34]. We hypothesized that such mecha-
nisms may be detectable in the current data if we simultaneously examine expression
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variations of a target gene (mRNA), candidate lncRNA regulators and (mRNA of) potential
protein mediators. Here, we report our findings from such an analysis where we detected sig-
nificant statistical interactions between lncRNAs and either TFs or RNA-binding proteins
(RBPs) in modeling mRNA expression.
Interactions with TFs: Above, we used a set of ~10 automatically selected lncRNAs and, sep-
arately, a set of ~10 selected TFs to model each target mRNA (Fig 2F and Fig 2D respectively).
We now included both sets as covariates and re-trained a linear model to predict the target
mRNA’s expression across all 1,217 samples as a function of these predictors. Starting with this
combined “lncRNA + TF predictors” model as a baseline, we tested each lncRNA-TF pair for
evidence of interaction by including their product as an additional covariate, re-training the
model and extracting the statistical significance of the interaction term. Such interaction terms
were added to the model iteratively, using the Akaike Information Criterion (AIC) as a mea-
sure of improved fit (Step-AIC method [53]). Inclusion of interaction terms improved the R
2
values achieved by the linear model, as expected, but the AIC criterion assured us that
improvements were not merely due to additional free parameters. The median R
2
value
achieved over all target mRNAs was now at 0.69, compared to 0.67 (t-test p-value = 2.68e-27)
from the baseline model without interactions. Summarizing all significant interactions (S7–
S10 Tables, limited to target genes whose R
2
values were above median), we noted that the TF
SPI1 is involved in the most interactions (88, compared to a median of 5, see S11 Table). This
is consistent with literature reports of this TF being associated with several lncRNAs [54–56].
We further focused on significant lncRNA-TF interaction terms where the target gene is
Table 2. Predicted GAS5 targets with ceRNA mechanism of action. Shown are target genes of GAS5 whose expres-
sion could be modeled using putative ceRNA regulators yielding R
2
of 0.2 or greater, with an empirical p-value <0.01,
i.e., where none of the 100 modeling attempts using equally many random lncRNAs yielded a better R
2
. (*GAS5 target
genes which are also included in the top 20 genes related to GAS5 in [27]).
mRNA Test R
2
OLS p-value of GAS5
RPS18 *0.39 2.23E-37
RPL31 *0.35 1.06E-23
RPL15 0.33 7.20E-04
RPL30 *0.33 1.05E-28
RPL5 *0.30 4.21E-09
RPS4X 0.28 7.46E-09
SLC25A6 0.27 7.94E-06
RPS20 0.27 2.07E-12
RPL24 0.25 1.13E-61
RPS15A 0.24 4.80E-06
RPL34 *0.24 2.91E-58
LY6G5B 0.23 7.67E-11
RPS12 0.22 9.69E-04
RPSA 0.21 1.11E-12
RRM2B 0.21 2.47E-14
EIF1 0.21 2.08E-03
ABCF1 0.21 2.51E-03
TRAF2 0.20 3.40E-07
In summary, we found multiple lines of evidence suggesting cis action as a dominant mechanistic theme in lncRNA-
driven regulation, but relatively limited evidence in supporting of the trans-acting ceRNA mechanism. (Also see
Discussion.)
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differentially expressed (t-test p-value <= 0.05) between samples with high (above median)
lncRNA and TF levels compared to samples that have low (below median) levels of either puta-
tive regulator. These 1,058 short-listed terms (S12 Table) present putative cases where the
simultaneous presence of lncRNA and TF has an effect on target gene expression. In Fig 3F,
we present the case of an interaction term involving the lncRNA TMPO-AS1 and the TF EBF1
(Bonferroni corrected p-value 3.23E-10), used in modeling the expression of gene LRP1.
TMPO-AS1 has been implicated in several cancers, is activated by EBF1 and also indirectly
activates EBF1 by sponging the microRNA miR-98-5p in bladder cancer [57]. Moreover, the
predicted target, LRP1, is a repressor of the Wnt signaling pathway [58] and TMPO-AS1 has
been shown to activate the Wnt pathway [59]. These observations together suggest that a
repressive effect of TMPO-AS1 on LRP1 in the presence of TF EBF1 underlies the lncRNA’s
activating influence on the Wnt pathway.
Interactions with RBPs: We performed a similar analysis to systematically detect
lncRNA-RBP statistical interactions in prediction of mRNA expression (S13 Table). The
inclusion of RBP interaction terms significantly enhanced the performance of the model,
increasing the median R-squared value from 0.29 to 0.33 (t-test p-value: 1.51e-199). Summa-
rizing all significant interactions (S14–S16 Tables, limited to target genes whose R
2
values
were above median), we observed the RBPs ESRP1 and ESRP2 to be among the top four RBPs
by frequency of detected interactions. ESRP1 has been reported as controlling ER+ breast can-
cer [60], and ESRP1/ESRP2 were noted as being the most strongly down-regulated RBPs in
epithelial-to-mesenchymal transition (EMT) in a cell culture model of breast cancer [61]. The
most frequent RBP (by frequency) per our analysis–PCBP1 –has also been shown to regulate
EMT in breast cancer [62]. Next, as for TFs, we next short-listed 2,425 interaction terms with
the above-mentioned pattern of differential target gene expression between categories of sam-
ples defined by high and low expression of lncRNA and RBP (S17 Table). One such term
involves the lncRNA OIP5-AS1 and the RBP FXR1, with a p-value of 7.8E-17 when used for
modeling the target gene MYL12A. As shown in Fig 3G,samples with high levels of the
lncRNA and the RBP have significantly higher expression of the target gene MYL12A than
those with low levels of either regulator, especially the lncRNA. Indeed, OIP5-AS1 has been
shown to bind to FXR1 in order to regulate other genes, activate the Wnt signaling pathway
and contribute to cell proliferation [63,64]. Another example is the interaction term for
lncRNA SNHG11 and RBP SNRPB2, found significant when modeling the target gene
RPRD1B (Bonferroni corrected p = 5.5E-17). We noted that the lncRNA (SNHG11) is located
in cis to (<400 Kbp separated from) the target gene (RPRD1B), adding support to the regula-
tory relationship. We also examined the interaction terms associated with the lncRNA
MALAT-1, which is among the best studied lncRNAs in breast cancer context [22,65]. Our
analysis (S17 Table) finds 12 such RBPs, including SRRM1, SRRM2, and SRSF1, which have
been shown to interact with MALAT-1 [46,66].
To summarize, extending the linear model for gene expression to include interaction terms
allowed us to generate hypotheses regarding potential mechanisms by which a lncRNA may
exert its regulatory influence, mediated by or while competing with RNA binding proteins or
transcription factors.
A multi-evidence lncRNA regulatory network
In the analyses above, we relied on expression modeling to identify potential lncRNA regula-
tors of a gene, and then annotated them with features such as cis-location, ceRNA, or statistical
interactions with proteins, that provide supporting evidence or plausible mechanisms for a
subset of these regulators. Our final goal was to integrate these two complementary sources of
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information: to model each target gene using only the small set of candidate lncRNA regula-
tors for which there exists supporting evidence found in the various annotation steps above.
Specifically, we limited the candidate regulators for each target gene to those that were statisti-
cally significant in above-trained models for that gene (p-value of OLS coefficient <= 0.01)
and had been annotated with any of the location categories of “overlapping”, “same-TAD”,
“proximal” (S4 Table) or the mechanistic category of “ceRNA” (empirical p-value = 0, S6
Table) or had been found in a statistical interaction term involving a TF (S7 Table) or an RBP
(S13 Table). As a result, for each target gene there were about two candidate lncRNAs, which
were used in an ordinary least square regression to fit the gene’s expression profile. The R
2
val-
ues in this exercise were comparable to those obtained when utilizing all lncRNAs as candi-
dates (Fig 4A), even though the number of candidates was two orders of magnitude smaller,
with 271 (5.0%) of the 5,364 genes modeled meeting the R
2
>= 0.2 threshold. We focused on
these target genes (mRNAs), constructing a regulatory network comprising their significant
lncRNA predictors (p-value of coefficient <= 0.01). This network (S18 Table) includes 508
lncRNAs regulating 232 mRNAs, a total of 1157 lncRNA-mRNA regulatory relationships, with
an average of 2.3 targets per lncRNA and 5.0 regulators per mRNA. We calculated the correla-
tion between the lncRNA regulator and the mRNA target for the edges in the predicted net-
work and, separately, for 1157 randomly sampled pairs, using RNA-seq data for 54 tissues
from GTEx (S22 Table). The edges have significantly higher correlation compared to the ran-
dom pairs in breast mammary tissue with t-test p-value of 2.1e-10, suggesting significantly
higher co-expression for predicted lncRNA-mRNA regulatory pairs. Only 199 edges would be
detected by the co-expression based regulatory network from the same transcriptomic dataset
where 1157 most correlated lncRNA-mRNA pairs among the same 508 lncRNAs and 232
mRNAs are defined as lncRNA-mRNA associations. In the co-expression network, each
lncRNA has 4.2 targets on average, which is significantly higher (p-value: 1.04e-7) than the
average number of targets per lncRNA in the regulatory network, suggesting that co-expres-
sion-based approaches might be in favor of common regulators whose expression are highly
correlated with many targets and fail to find regulatory lncRNAs with more specific function.
We further validated a subset of the putative lncRNA-mRNA pairs using experimentally
measured chromatin interaction from GRID-seq assays on the breast cancer epithelium cell
line MDA231 [67,68]. These data provided RNA-DNA interaction information for 23 of the
lncRNAs present in the multi-evidence network. The network includes 111 edges involving
these lncRNAs, 29 (26.1%, hypergeometric test p-value: 5.7e-8, see Methods) of which are sup-
ported by the GRID-seq data as interactions between lncRNA and proximal region (1 Mbp) of
the target gene (S18 Table Column “GRID-seq”). Notably, all of the nine predicted edges
involving MALAT1 and six of the seven predicted targets of NEAT1 are supported by GRID-
seq data.
The network included 549 ceRNAs and 514 cis-acting (overlapping, same-TAD or proxi-
mal) lncRNAs. We noted a clear enrichment of the “overlapping” category among the edges
with the strongest p-values from expression modeling (Fig 4B). Each edge is supported by
some form of evidence, e.g., location near or in cis with target gene, ceRNA evidence, statistical
interaction with a TF or RBP, in addition to its expression being predictive of the target’s
expression, making this a higher confidence, multi-evidence lncRNA regulatory network.
Among the 388 lncRNAs with cis-acting edges in the network, 271 overlap the regulatory
genetic regions of human mammary epithelial cells obtained from GenenHancer [69]. Given
that 745 of the 1074 lncRNAs analyzed in this work overlap the regulatory regions, the
lncRNAs reported with cis-edges in the network showing no enrichment for enhancer marks
(Odds ratio: (271/388)/(745/1079) = 1.01, p-value:0.64), suggesting cis-acting lncRNAs
reported in the network don’t merely mark active regulatory regions.
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We observed 53 of the 508 lncRNAs in this network to have 25 or more targets (>10% of
the 232 target mRNAs in the network, see S19 Table) and significantly higher expression com-
pared to other lncRNAs (p-value: 1.92e-6), suggesting a possible prioritization of lncRNAs
with the most regulatory role. Indeed, these “hubs” included several lncRNAs well known for
their role in breast cancer, e.g., MALAT1 [22,65] at rank 17, SNHG29 [70] at rank 2, SNHG16
[71] at rank 19, GAS5 [50,72] at rank 1, XIST [73,74] at rank 11, NEAT1 [75,76] at rank 23,
NORAD [17,77] and others. (It is noteworthy that the majority of connections associated with
these lncRNAs are predominantly trans-regulatory interactions, see S21 Table and Fig I in S1
Text) Pursuing the above observation systematically, we obtained 30 breast cancer-related
lncRNAs from the LncRNADisease database [72], of which 10 were included in the high confi-
dence regulatory network. Four of these 10 were among the 53 high-degree regulators noted
above, and a Gene Set Enrichment Analysis (GSEA) revealed a significant association (p-
value <0.027) between the breast cancer-related lncRNAs and the degree of a lncRNA in the
network, as shown in Fig 4C.
Next, we functionally characterized the predicted targets of each high-degree regulator
(lncRNA) through gene set analysis. We first conducted enrichment tests against a previously
published set of genes that are differentially expressed (DE) between benign (“M1”) and highly
tumorigenic (“M4”) states of a widely used breast cancer progression cell line series [18]
derived from MCF10A mammary epithelial cells. We found two of the lncRNAs—GAS5 and
SNHG29—to have targets significantly enriched in the M1-M4 DE genes (FDR <0.05), with
Hypergeometric test p-value 5E-6 (Fig 4D) and 6E-4 respectively. Expanding the enrichment
tests to iterate over an entire compendium of biological pathways from the REACTOME data-
base [78], we observed a set of four lncRNAs (OIP5-AS1, GAS5, ZFAS1, SNHG29) to have
their targets enriched for several pathways related to translation processes (Fig 4E,S20 Table,
and Fig J in S1 Text). For instance, the lncRNA GAS5, a tumor suppressor that is down-regu-
lated in mammary carcinoma [79], has 20 targets in the lncRNA-mRNA network and 12 of
these putative targets belong to the pathway “peptide chain elongation” (Bonferroni corrected
p-value 3.23e-15). Indeed, this lncRNA is believed to regulate translation of specific proteins
such as c-Myc by interacting with the translation initiation complex protein eIF4F [80], which
is one of its predicted targets in our network.
The regulatory network comprises edges with directionality, i.e., not only is each lncRNA-
mRNA relationship assigned a significant p-value in expression modeling, the sign of the coef-
ficient in the linear model tells us if the relationship is activating or inhibitory. We noted 782
activating and 355 inhibitory edges overall in the network and identified four lncRNAs with a
significant departure from the global ratio in one direction or the other (Binomial test p-
value <0.01, see Figure H in S1 Text). These included the lncRNA SNHG29, that has 19 tar-
gets in the network, of which 18 (94.7%) represent activating relationships.
While the majority of lncRNAs were initially thought to lack protein-coding potential,
emerging evidence suggests that certain lncRNAs can indeed produce functional peptides [1].
These peptides, often referred to as "micropeptides," are short sequences of amino acids
encoded by specific regions within lncRNA transcripts. They can be translated from non-
canonical open reading frames (ORFs) and have been shown to have regulatory roles in vari-
ous biological processes. Micropeptides derived from lncRNAs can associate with ribosomes
and be involved in translation regulation or other ribosome-associated functions.
Overall, while the majority of lncRNAs are non-coding, there is growing evidence support-
ing a connection between certain lncRNAs and the production of ribosome-associated pep-
tides, expanding the functional repertoire of these RNA molecules beyond their non-coding
roles.
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Fig 4. Expression modeling and regulatory network using selected lncRNAs with location- or mechanism-based
supporting evidence. (A) R
2
values using selected lncRNAs (on average ~2 lncRNAs per target gene) were comparable
to those obtained when utilizing all lncRNAs as candidates. (B) Network edges (lncRNA-gene pairs) in the
“overlapping” category are strongly enriched among the edges with the strongest p-values from expression modeling.
X-axis represents the network edges sorted by p-value from expression modeling, and Y-axis shows the number of
“overlapping” lncRNA-gene pairs among the top x network edges, for each value of x. (C) Breast cancer related
lncRNAs are enriched among lncRNAs with high degrees in the regulation network. Shown is the standard graphical
output from the GSEA tool. X-axis represents lncRNAs sorted by the number of putative gene targets (edges in the
network). Breast cancer-related lncRNAs from the literature are marked by vertical bars in the middle panel, and the
top panel (green curve0 shows the “enrichment score” statistic calculated by GSEA as a function of the rank order. This
analysis shows that breast cancer-related lncRNAs tend to have significantly more targets compared to other lncRNAs,
in our network. (D) Venn diagram of predicted GAS5 targets that are also differentially expressed genes (DEGs)
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In summary, the regulatory network constructed here is based on both expression modeling
and prior evidence of plausible mechanisms (cis-action, ceRNA or protein-mediated action),
and we found several additional lines of evidence supporting its compendium of regulatory
relationships: enrichment of the highest degree regulators for reported involvement in breast
cancer, association of target gene sets with DE genes from a breast cancer progression cell line
series and with translation-related as well as other relevant biological processes, and statisti-
cally significant biases towards either activating or inhibitory relationships.
Discussion
This study was aimed at systematically studying lncRNAs’ impact on gene expression variation
among individuals in a breast cancer cohort, in the hope of teasing apart potentially causal
effects from chance correlations, reaching statistical estimates of the extent to which gene
expression may be under lncRNA regulation, and taking the first steps towards lncRNA regu-
latory network reconstruction considering both expression correlation and potential regula-
tion mechanisms. Importantly, the specifics of our findings here, such as percentage of
variation explained or the predicted regulatory interactions, need not generalize to other bio-
logical contexts and further work is needed to assess the extent of their generalizability and
context-specificity. We performed the study on data from a cohort of breast cancer tumor
samples, in part because of easy availability of data (over a thousand samples) and partly
because of the emerging significance of lncRNA regulation in cancer [81–84]. In comparison
to prior work reporting on co-expression networks derived from pairwise (lncRNA-gene) cor-
relation analysis [8,9,11], we adopted a strategy of multivariable regression of target genes
using lncRNAs as predictors, borrowing inspiration from how transcriptional regulatory net-
works (TRNs) of TF-gene relationships are commonly reconstructed today [26,29,85]. While
linear as well as non-linear models have been successfully used in TRN reconstruction, we
adopted linear models for ease of interpretability and ability to estimate significance of individ-
ual predictors. We used regularization (Elastic Net) to ensure that each gene is modeled using
a small selection of candidate lncRNA regulators and relied on cross-validation to assess how
well target gene expression can be modeled using lncRNAs.
An important feature of our methodology was to treat transcription factors as potential
confounders for regulatory modeling and limit the assessment of lncRNA-driven regulation to
the gene expression variation that cannot be explained using TFs. It is likely that this step
removed part of the expression variance caused by lncRNA regulation, but we chose to be con-
servative with lncRNAs’ regulatory impact, given that TFs, compared to lncRNAs, are better
studied gene expression regulators and expression-based models of TF-gene regulatory net-
works are an accepted strategy to quantifying their effects. We did not remove the influence of
shared RBPs, which can also act as confounders for lncRNA-mRNA relationships, because
RBP-mediated regulation is a potential mechanism through which a lncRNA may regulate the
expression of the target gene. Regressing out the impact of RBP from mRNA expression might
lead to the failure to identify regulatory lncRNAs whose function depends on RBP binding.
We found that for over 16% of genes analyzed, lncRNAs can explain more than 20% of
expression variation, even after accounting for possible co-regulation by TFs. We performed
various statistical assessments and comparisons of this number. For instance, repeating a simi-
lar analysis with an equally large set of enzyme-encoding mRNAs in place of lncRNAs as regu-
lators, we could establish a null distribution that yielded an empirical p-value of 0.01 for the
between M1 and M4 cell lines. (E) Target genes of four high-degree lncRNAs (more than 25 targets each) were
enriched in REACTOME pathways related to translation processes.
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lncRNA-driven regulation of over 6% of analyzed genes. (Such an extent of inferred regulatory
effect is expected by chance for only 1% of genes.)
Using simple criteria to filter the regulators identified during regression modeling, we
obtained 643 high confidence lncRNA-gene regulatory relationships, and though these were
identified in an unbiased manner, purely from expression data, we observed a clear tendency
for the regulator lncRNA to be located in ‘cis’ to the gene. For instance, for 27.5%, 35.5%, 54%
and 62.7% of the above pairs, the lncRNA is overlapping, located in the same TAD as, proximal
(1 Mbp) to, or on the same chromosome as the target gene, respectively (Table 1). Using suit-
able controls, we confirmed that these percentages are not merely a reflection of cis-located
gene pairs having a tendency for co-expression.
Furthermore, by repeating the regression analysis using only the lncRNAs from a specific
location category (overlapping, same-TAD, proximal, or same chromosome) as predictors, we
were able to assess the overall regulatory importance of each category (Fig 3E). We noted that
over 16% genes that have an overlapping lncRNA can be modeled well using that lncRNA,
indicating lncRNA-gene overlaps as a reliable marker of regulatory function. About 20% of
lncRNA-harboring TADs were found to include at least one gene that can be modeled well
using lncRNAs in the same TAD (compared to 8.8% by chance). Similarly, about 8% of all
genes could be modeled well using lncRNAs from the same chromosome. Thus, while ~1,800
of analyzed genes could be modeled well using an unrestricted choice of lncRNAs, about 50%
of these are amenable to expression prediction using only (putatively) cis-acting lncRNAs
from the same chromosome. While we found multiple lines of evidence in favor of cis-action
by lncRNA regulators, we did not observe nearly as much influence belonging to the trans-act-
ing, competing endogenous RNA (ceRNA) mechanism. For instance, among the 643 lncRNA-
gene pairs identified in an unbiased manner, only 19 (~3%) were plausible ceRNAs. Similarly,
when repeating the modeling of target gene expression using only ceRNAs, only ~2% (246 out
of 10556) of analyzed genes could be modeled well. These observations may point to short-
comings of the ceRNA compendium we relied on (derived from experimental miRNA binding
data but based on computational predictions of competing lncRNA-miRNA and mRNA-
miRNA pairs) or to unknown methodological limitation that limits our ability to detect
ceRNA relationships from expression data alone.
The functional role of competing endogenous RNAs (ceRNAs) has been extensively investi-
gated, with reports of numerous lncRNA-miRNA-mRNA triplets, and led us to employ it as a
plausible regulatory mechanism in our study. However, there have also been doubts raised
regarding the extent of their influence [86], and our findings of limited predictive value of this
class of candidate regulators raise similar doubts. The low predictive value may also be attrib-
uted to the fact that ceRNA candidates used in our models have relatively limited prior sup-
port, being based on computational predictions of competition for common miRNAs; our
working definition–the shared targeting of lncRNAs and mRNAs by the same miRNA within
the same cell line–does not guarantee the formation of definitive ceRNA triplets. As experi-
mentally verified ceRNA pairs become available in the future, their integration with our analy-
sis holds the promise of enhancing the accuracy of predicting lncRNAs’ trans regulatory
effects.
It is believed that lncRNAs may interact with TFs and RBPs as scaffolds as part of their reg-
ulatory activity. Motivated by this, we statistically analyzed the significance of interaction
terms involving lncRNA-TF pairs or lncRNA-RBP pairs as additional features in modeling a
target gene’s expression. This helped us identify thousands of cases where a lncRNA’s regula-
tory effect may depend on the expression level of another regulator such as a TF or an RBP
(S12 and S17 Tables). The TF SPI1 and the RBPs ESRP1, ESRP2 and PCBP1 featured
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Regulatory influence of long non-coding RNAs on global gene expression variation
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frequently in detected statistical interactions, suggesting additional mechanistic roles for these
proteins in breast cancer.
Finally, we created a high confidence network of lncRNA regulatory influences, where the
lncRNA is not only a significant predictor of target gene expression but is also located proxi-
mally to the gene, or is potentially supported by an RNA or DNA-binding protein, or is a puta-
tive ceRNA (S18 Table). Top regulators in this network are enriched for breast cancer-related
lncRNA (Fig 4C) and their targets are enriched for genes differentially expressed in breast can-
cer progression (M1-M4 cell lines). Interestingly, several of the top regulators have a signifi-
cant bias in the sign (positive or negative) of co-expression with their targets (Fig H in S1
Text), further supporting their regulatory roles since chance co-expression of a lncRNA with
dozens to hundreds of genes is not expected to have a significant bias in directionality of
correlation.
Our analyses were performed using RNA-seq data from TCGA, obtained using an earlier
processing pipeline that relied on the “HTSeq-count” tool to count reads mapping to genes.
This tool, now superseded by STAR-2, may map the same reads to multiple overlapping genes,
which may potentially affect our discovery of statistical relationships between overlapping
lncRNA-mRNA pairs. However, when we re-calculated lncRNA-mRNA expression correla-
tions using the up-to-date expression quantification data that address the above issue, we
found the correlations to be largely in agreement with corresponding correlations from the
previous version of TCGA data used in our study (see Fig K in S1 Text). This was true for the
entire collection of lncRNA-mRNA pairs deemed as putative regulatory pairs in our work,
including for overlapping pairs. This suggests that the lncRNA regulatory compendium
reported by us should remain largely unaffected by the transition of TCGA processing
pipelines.
Our work is a first step towards systematic reconstruction of lncRNA regulatory networks.
The variety of mechanisms that have been reported for lncRNA-driven regulation make this
an exciting challenge, and we expect future efforts to integrate available information on such
mechanisms, such as ceRNAs, cis-action, protein-mediated mechanisms, etc., into the network
inference. Methods for utilization for mechanistic prior information have been successfully
demonstrated in the context of TF regulatory networks [28] and will be a key strategy towards
enriching lncRNA-gene co-expression networks for causal relationships. Such networks can
furnish testable hypotheses regarding specific lncRNA regulators of a gene of interest and also
prioritize lncRNA with extensive global regulatory roles in a biological process, ultimately pro-
viding a more complete picture of the functions of this important class of non-coding RNAs.
Methods
Expression data collection and pre-processing
Transcriptome profiling data from 1092 patients with breast invasive carcinoma were obtained
from The Cancer Genome Atlas (TCGA) breast cancer cohort (TCGA-BRCA project). The
dataset consisted of RNA-Seq expression measurements and clinical data for 1217 primary
tumor samples. The HTSeq-counts data encompassed measurements for 60488 RNAs on the
gene level. RNA-Seq data processing steps are documented at the GDC portal (URL https://
docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/). To ensure
reliable analysis, RNAs with counts per million (CPM) greater than 1 in fewer than 500 sam-
ples were removed due to low expression, resulting in a filtered expression dataset containing
15766 RNAs with adequate expression levels. RNA annotations were retrieved from the
Ensembl database (Ensembl 104: May 2021) [87]. The filtered matrix comprised expression
data for 13963 protein-coding genes and 1079 long non-coding RNAs (lncRNAs). Note that
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Regulatory influence of long non-coding RNAs on global gene expression variation
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since TCGA RNA-seq data collection involved a polyA enrichment step, lncRNAs that are not
polyadenylated will not be suitable represented in the analyzed data. The raw HTSeq-counts
were normalized using TMM normalization in the R package “EdgeR”. The TMM-normalized
expression values were then log-transformed and standardized to have zero mean and variance
equals to 1. Confounder variables including age, sex, ethnicity and race for patients were
obtained from the clinic data for the same cohort. The transformed normalized expression val-
ues of all RNAs were modeled using the confounder variables via simple linear regression
models and residuals from these confounder-based models were used for further modeling.
Regression models of expression data
We trained linear models to predict each gene’s expression using different sets of predictors,
which included lncRNAs, TFs or confounder variables. ElasticNet algorithm was used to select
~10 predictors (features) when there were more than 10 candidates. An ordinary linear
squares (OLS) regression model was then trained using the selected features. For genes with
fewer than 10 predictors, an OLS model was used directly to predict the gene’s expression
using all the predictors, without any feature selection. For most analyses here, 5-fold cross vali-
dation was used and test R
2
averaged across the five folds was used to measure the performance
of the model. After cross validation, the same process (ElasticNet followed by OLS model) was
repeated on the entire data set and the resulting R
2
was reported as the training or “overall” R
2
.
Transcription factors as confounders
We obtained identities of 1665 human transcription factors (TFs) from HumanTFDB [88].
Expression profiles for 1255 of these TFs were available in the expression data set. We first
used Elastic Net algorithm to select ~10 TF regulators to predict the expression of each gene. If
the gene being modeled is a TF itself, it was removed from the predictor set when training the
Elastic Net model. The gene’s expression was then modeled using the selected TFs. Residuals
from the TF-based model were then subjected to modeling using lncRNA predictors as
described above.
Statistical lncRNA-RBP interaction analysis
We first modeled mRNA residuals from the TF-based models using ~10 RNA-binding protein
(RBP) genes selected by the Elastic Net algorithm out of a candidate set of 351 RBPs obtained
from RBPDB V1.3.1 [89]. We then used the candidate set of ~10 lncRNAs and ~10 RBPs to
model each mRNA’s residual from the TF-based model. The model with the lncRNA and RBP
predictors forms the baseline model for each mRNA. We then identified significant
lncRNA-RBP interaction terms iteratively using the Step-AIC method [53]. We reported the
R
2
values from the lncRNA-based models, RBP-based models, lncRNA+RBP (baseline) models
and lncRNA-RBP interaction models.
Location and mechanism categorization of lncRNAs
We categorized putative lncRNAs based on the following relationships to target genes: 1)
Overlapping: LncRNA overlaps the gene on either DNA strand. Locations of protein coding
genes and lncRNAs were obtained from Ensembl BioMart. 2) Same TAD: TAD boundaries
from Hi-C data for MCF-10A mammary epithelial cell line were used to define TADs [45].
LncRNAs in the same TAD with the gene were defined as “same TAD” lncRNAs. 3) Same
Chromosome: LncRNAs in the same chromosome as the target gene were included in the
“same chromosome” category. 4) ceRNAs: Experimentally supported miRNA-gene
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interactions were downloaded from TarBase v.8 [47]. Relationships between miRNAs and
lncRNAs were obtained from lncBase v.2 [90]. LncRNAs that are related to the same miRNAs
as the target gene in the same cell lines were considered to be the competing endogenous
RNAs (ceRNAs).
GRID-Seq enrichment test
RNA-chromatin interactions in the breast cancer epithelium cell line MDA231 were obtained
from GRID-seq profile GSE82312. Twenty three lncRNAs are measured in both GRID-seq
and TCGA RNA-seq data. Hypergeometric test was used to measure the overall enrichment of
lncRNA interacted genes in predicted edges:
1. Universe: size 318849 = (23 lncRNA included in both our study and GRID-seq) X (13963
genes included with TCGA RNA-seq measurements)
2. Set A: size 27757 = lncRNA-mRNA interaction pairs identified by GRID-seq, through bind-
ing evidence of lncRNA on 1mb region around the gene.
3. Set B: size 111 = predicted edges for 23 lncRNAs included in both studies
4. Intersection between Set A and Set B: size 29.
5. P-value: 5.738724e-08
Pathway enrichment test
For lncRNAs with more than 25 putative target genes, associations between the target gene set
and pathway gene sets from Reactome database [91] were detected via hypergeometric test of
overlap between the two gene sets and FDR-corrected p-values were reported.
Supporting information
S1 Table. mRNA-lncRNA pairs satisfying the following properties: (1) the mRNA’s expres-
sion can be accurately (R2 >= 0.5) modeled using lncRNAs, (2) the R2 using lncRNA pre-
dictors is at least 0.2 greater than the R2 using TF predictors, (3) the lncRNA was one of
the ~10 selected predictors of the mRNA’s expression and (4) its coefficient in an OLS
model of the mRNA’s expression was assigned a p-value <= 0.05. Gene.ID, Gene.Name:
identity of target mRNA. lncRNA.ID, lncRNA.Name: identity of predictor lncRNA. OLS.p-
value: Statistical significance of coefficient of lncRNA as a covariate in an OLS model of target
mRNA.
(XLSX)
S2 Table. Number of times each lncRNA occurs as a significant predictor in S1 Table.
(XLSX)
S3 Table. Target genes (mRNA) whose expression could be modeled accurately (R2 empiri-
cal p-value <= 0.01) using lncRNAs. Empirical p-value was computed for each mRNA by
comparing the R2 value obtained using lncRNAs as predictors to R2 values obtained using
enzyme-encoding RNAs as predictors. 100 replicates of the “control” using enzyme-encoding
RNAs were performed, allowing us to infer empirical p-values at a resolution of 0.01. The 699
genes (mRNAs) reported here comprise 6.1% of the 11,531 target genes analyzed.
(XLSX)
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S4 Table. Details of 643 mRNA-lncRNA pairs meeting the following criteria: the mRNA
can be modeled using lncRNAs with R2 >= 0.2 and empirical p-value <0.01, the lncRNA
has an OLS p-value <= 1E-10 in the linear model for the mRNA, and their pairwise corre-
lation coefficient is >= 0.1 in absolute value. Gene.ID, Gene.Name: identify of target gene
(mRNA); lncRNA.ID, lncRNA.Name: identity of lncRNA; OLS P-value: p-value of coefficient
of lncRNA in OLS model of mRNA; R2: test R2 of model, averaged over five folds of cross-vali-
dation; Columns G-K indicate relationship between mRNA and lncRNA; Columns L-N indi-
cate if an overlapping pair is tandem (same direction), convergent or divergent; dist: distance
between lncRNA and mRNA genes, -1 means overlapping, ‘X’ means not on the same chro-
mosome; Column P (“Corr Coeff”) is Pearson correlation coefficient between mRNA and
lncRNA expression.
(XLSX)
S5 Table. Statistical assessment of degree of correlation seen in lncRNA-mRNA pairs
defined by various criteria of relative location (“category”). For each category, a random set
of lncRNA-mRNA pairs belonging to that category was first selected, without regard to their
expression profiles. Column B shows the size of this random set and Column C shows the
number of members of this set for which the Pearson Correlation coefficient (PCC) in expres-
sion profiles was found to be >= 0.3. Next, we selected a random set of mRNA-mRNA pairs
belonging to the category (e.g., mRNA pairs located in the same TAD if the category is “same-
TAD”) as a background set; columns D and E respectively show the size of this random set of
mRNA pairs and the subset thereof for which the mutual expression correlation (PCC) is >=
0.3. The latter two numbers allow us to define a baseline frequency of co-expression (PCC >=
0.3) between two genes related to each other by a particular definition (e.g., in the same TAD),
which is then used to assess the statistical significance (Binomial p-value) of the number of
lncRNA-mRNA pairs reported in Column C.
(XLSX)
S6 Table. Target genes and R2 values from models using putative ceRNAs as regulators. R2
values are average of test R2 from five folds of cross validation. Empirical p-values were calcu-
lated by repeating the modeling 100 times using random sets of lncRNAs as candidate regula-
tors.
(XLSX)
S7 Table. All statistically significant (Bonferroni corrected p-value <= 0.05) lncRNA-TF
interaction terms detected when modeling expression of a gene (Gene.id, Gene.name)
using a linear model with selected number of lncRNA and TF covariates as well as an inter-
action term involving the lncRNA and TF.
(XLSX)
S8 Table. Frequency lncRNA-TF pairs detected as statistically significant terms. “Count”
represents the number of target genes for which the particular lncRNA-TF pair had a signifi-
cant interaction term.
(XLSX)
S9 Table. Frequency with which each lncRNA appears among significant lncRNA-TF inter-
action terms. For each lncRNA (lncRNA.id, lncRNA.name), Column C (#gene) shows the dis-
tinct target genes for which an interaction term involving this lncRNA and some TF was
statistically significant; Column D (#tf) shows the number of distinct TFs represented in these
interaction terms. (Note: the same lncRNA, paired with the TF, may be significant for more
than one target gene, and the same target gene may have more than one interaction terms
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involving the same TF.)
(XLSX)
S10 Table. Frequency with which each TF appears among significant lncRNA-TF interac-
tion terms. For each TF (TF.id, TF.name), Column C (#gene) shows the distinct target genes
for which an interaction term involving this TF and some lncRNA was statistically significant;
Column D (#lncRNA) shows the number of distinct lncRNAs represented in these interaction
terms.
(XLSX)
S11 Table. All significant interaction terms involving the TF SPI1. For each such interaction
term, the target gene is reported in columns A,B (gene.id, gene.name), the interacting lncRNA
in columns C,D (lncrna.id, lncrna.name), and the Bonferroni corrected p-value for the interac-
tion term involving SPI1 (TF) and that lncRNA in prediction of the target gene is shown in
Column G.
(XLSX)
S12 Table. Selection of significant (target gene, lncRNA, TF) triplets such that the
lncRNA-TF pair forms a statistically significant (Bonferroni corrected p-value <0.01)
interaction term in modeling the target gene’s expression. Columns A-F indicate the id and
name of the target gene, lncRNA and TF in that order, while column G reports the Bonferroni
corrected p-value for the interaction term. Columns H-Q report on the p-value of differential
expression of the target genes between two groups of samples defined as: HH = samples with
high levels of lncRNA and high levels of TF; HL = samples with high levels of lncRNA and low
levels of TF; LH = samples with low levels of lncRNA and high levels of TF; LL = samples with
low levels of lncRNA and low levels of TF. Thus columns labeled as HH-LH report on differen-
tial expression (of target gene) between the HH and LH groups, etc. For each contrast, column
with label suffixed with “.direction” reports on whether the average gene expression is higher
(1) or lower (-1) in the first group compared to the second group; column with label suffixed
with “.p” reports the p-value from a T-test between the two groups. The selection of triplets
shown here meet the additional criteria that the HH-HL, HH-LH, and HH-LL contrasts are
statistically significant (p-value <0.05) and have the same direction of differential expression
(all 1 or all -1).
(XLSX)
S13 Table. All statistically significant (Bonferroni corrected p-value <0.01) lncRNA-RBP
interaction terms detected when modeling expression of a gene (Gene.id, Gene.name)
using a linear model with selected number of lncRNA and TF covariates as well as an inter-
action term involving the lncRNA and TF.
(XLSX)
S14 Table. Frequency lncRNA-RBP pairs detected as statistically significant terms.
“Count” represents the number of target genes for which the particular lncRNA-RBP pair had
a significant interaction term.
(XLSX)
S15 Table. Frequency with which each lncRNA appears among significant lncRNA-RBP
interaction terms. For each lncRNA (lncRNA.id, lncRNA.name), Column C (#gene) shows
the distinct target genes for which an interaction term involving this lncRNA and some RBP
was statistically significant; Column D (#rbp) shows the number of distinct RBPs represented
in these interaction terms.
(XLSX)
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S16 Table. Frequency with which each RBP appears among significant lncRNA-RBP inter-
action terms. For each RBP (RBP.id, RBP.name), Column C (#gene) shows the distinct target
genes for which an interaction term involving this RBP and some lncRNA was statistically sig-
nificant; Column D (#lncRNA) shows the number of distinct lncRNAs represented in these
interaction terms.
(XLSX)
S17 Table. Selection of significant (target gene, lncRNA, RBP) triplets such that the
lncRNA-RBP pair forms a statistically significant (Bonferroni corrected p-value <0.01)
interaction term in modeling the target gene’s expression. Columns A-F indicate the id and
name of the target gene, lncRNA and RBP in that order, while column G reports the Bonfer-
roni corrected p-value for the interaction term. Columns H-Q report on the p-value of differ-
ential expression of the target genes between two groups of samples defined as: HH = samples
with high levels of lncRNA and high levels of RBP; HL = samples with high levels of lncRNA
and low levels of RBP; LH = samples with low levels of lncRNA and high levels of RBP;
LL = samples with low levels of lncRNA and low levels of RBP. Thus columns labeled as
HH-LH report on differential expression (of target gene) between the HH and LH groups, etc.
For each contrast, column with label suffixed with “.direction” reports on whether the average
gene expression is higher (1) or lower (-1) in the first group compared to the second group;
column with label suffixed with “.p” reports the p-value from a T-test between the two groups.
The selection of triplets shown here meet the additional criteria that the HH-HL, HH-LH, and
HH-LL contrasts are statistically significant (p <0.05) and have the same direction of differen-
tial expression (all 1 or all -1).
(XLSX)
S18 Table. Multi-evidence lncRNA regulatory network. Columns A,B indicate target gene,
columns C,D indicate regulator lncRNA, column E indicates p-value of OLS coefficient. Col-
umns F-L indicate the type of additional supporting evidence for the relationship. Column M
indicate if the interaction is supported by GRID-seq data.
(XLSX)
S19 Table. lncRNA regulon sizes in the multi-evidence regulatory network. Columns A,B
indicate lncRNA, column C indicates the number of predicted regulatory targets (mRNAs) of
that lncRNA.
(XLSX)
S20 Table. REACTOME pathway associations for gene targets of each lncRNA in the
multi-evidence regulatory network. Columns A,B indicate lncRNA, column C indicates the
REACTOME pathway, Columns D-G are the cardinality of universe, lncRNA regulon, path-
way, overlap set respectively, with Column H and G being the Hypergeometric test p-value
without and with Bonferroni correction.
(XLSX)
S21 Table. lncRNA regulon sizes in the trans (ceRNA) regulatory network. Columns A,B
indicate lncRNA, column C indicates the number of predicted regulatory targets (mRNAs) of
that lncRNA.
(XLSX)
S22 Table. Cross-tissue correlation of regulatory edges from the multi-evidence regulatory
network (S18 Table) using RNA-seq data from GTEx.
(XLSX)
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S1 Text. Figure A in S1 Text. Histogram of R
2
values when mRNA expression was modeled
using lncRNAs only. Figure B in S1 Text. Histogram of Pearson Correlation Coefficient
between real and model-predicted expression values on test samples. Figure C in S1 Text.
Density scatter plot of Pearson Correlation Coefficients between predicted and real expression
values. Figure D in S1 Text. Histogram of overall R
2
values when residuals from TF-based
models were predicted using lncRNA. Figure E in S1 Text. Histogram of test R
2
values model-
ing permuted expression. Figure F in S1 Text. Histogram of test R
2
using overlapping
lncRNAs only. Figure G in S1 Text. Scatter plot of test R
2
values of models using all lncRNAs
and those using overlapping lncRNAs only. Figure H in S1 Text. Comparison of negative tar-
gets and positive targets. Figure I in S1 Text. Comparison of cis targets and trans targets.
Figure J in S1 Text. Complete heatmap of lncRNA target genes in REACTOME pathways
which have more than 50 genes. Figure K in S1 Text. Expression correlations between regula-
tory lncRNA-mRNA pairs (S18 Table), calculated using STAR counts (y axis) or HTSeq-
counts (x-axis).
(DOC)
S2 Text. Null distribution of R2 values when mRNA expression was predicted using non-
regulatory enzyme expression.
(DOC)
S3 Text. Examples of trans lncRNA-mRNA pairs that have literature support for their reg-
ulatory relationship.
(DOC)
S4 Text. Empirical p-value calculation for ceRNA lncRNA regulators.
(DOC)
Author Contributions
Conceptualization: Saurabh Sinha.
Data curation: Xiaoman Xie.
Formal analysis: Xiaoman Xie.
Funding acquisition: Saurabh Sinha.
Investigation: Saurabh Sinha.
Methodology: Xiaoman Xie, Saurabh Sinha.
Project administration: Saurabh Sinha.
Supervision: Saurabh Sinha.
Validation: Xiaoman Xie.
Visualization: Xiaoman Xie.
Writing – original draft: Xiaoman Xie.
Writing – review & editing: Saurabh Sinha.
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