Single-cell and Bulk RNA-Seq reveal angiogenic heterogeneity and microenvironmental features to evaluate prognosis and therapeutic response in lung adenocarcinoma

Abstract

Background
Angiogenesis stands as a pivotal hallmark in lung adenocarcinoma (LUAD), intricately shaping the tumor microenvironment (TME) and influencing LUAD progression. It emerges as a promising therapeutic target for LUAD, affecting patients’ prognosis. However, its role in TME, LUAD prognosis, and its clinical applicability remain shrouded in mystery.

Methods
We employed integrated single-cell and bulk transcriptome sequencing to unravel the heterogeneity of angiogenesis within LUAD cells. Through “consensus clustering”, we delineated distinct angiogenic clusters and deciphered their TME features. “Monocle2” was used to unravel divergent trajectories within malignant cell subpopulations of LUAD. Additionally, regulon submodules and specific cellular communication patterns of cells in different angiogenic states were analyzed by “pyscenic” and “Cellchat” algorithms. The “univariate Cox” and “LASSO” algorithms were applied to build angiogenic prognostic models. Immunohistochemistry (IHC) on clinical samples validated the role of model factors in LUAD angiogenesis. We utilized CTRP 2.0 and PRISM databases for pinpointing sensitive drugs against lung adenocarcinoma.

Results
Two clusters for the activation of angiogenesis were identified, with Cluster 1 showing a poor prognosis and a pro-cancerous TME. Three differentiated states of malignant epithelial LUAD cells were identified, which had different degrees of angiogenic activation, were regulated by three different regulon submodules, and had completely different crosstalk from other cells in TME. The experiments validate that SLC2A1 promotes angiogenesis in LUAD. ARS (Angiogenesis related score) had a high prognostic value; low ARSs showed immunotherapy benefits, whereas high ARSs were sensitive to 15 chemotherapeutic agents.

Conclusion
The assessment of angiogenic clusters helps to determine the prognostic and TME characteristics of LUAD. Angiogenic prognostic models can be used to assess the prognosis, immunotherapeutic response, and chemotherapeutic drug sensitivity of LUAD.

Full text

Single-cell and Bulk RNA-Seq
reveal angiogenic heterogeneity
and microenvironmental features
to evaluate prognosis and
therapeutic response in
lung adenocarcinoma
Lijuan Tang
1,2
†
, Zhike Chen
3
†
, Jian Yang
3
†
, Qifan Li
3
,
Sichu Wang
1,2
, Taoming Mo
2,4
, Weibiao Zeng
3
*,
Hao Ding
3
*and Shu Pan
3,5
*
1
Dalian Medical University, Dalian, China,
2
Department of Pathology, Affiliated Hospital of Nantong
University, Nantong, China,
3
Department of Thoracic Surgery, The First Affiliated Hospital of Soochow
University, Suzhou, China,
4
Medical School of Nantong University, Nantong, China,
5
Suzhou Gene
Pharma Co., Ltd, Suzhou, China
Background: Angiogenesis stands as a pivotal hallmark in lung adenocarcinoma
(LUAD), intricately shaping the tumor microenvironment (TME) and influencing
LUAD progression. It emerges as a promising therapeutic target for LUAD,
affecting patients’prognosis. However, its role in TME, LUAD prognosis, and its
clinical applicability remain shrouded in mystery.
Methods: We employed integrated single-cell and bulk transcriptome
sequencing to unravel the heterogeneity of angiogenesis within LUAD cells.
Through “consensus clustering”, we delineated distinct angiogenic clusters and
deciphered their TME features. “Monocle2”was used to unravel divergent
trajectories within malignant cell subpopulations of LUAD. Additionally, regulon
submodules and specific cellular communication patterns of cells in different
angiogenic states were analyzed by “pyscenic”and “Cellchat”algorithms. The
“univariate Cox”and “LASSO”algorithms were applied to build angiogenic
prognostic models. Immunohistochemistry (IHC) on clinical samples validated
the role of model factors in LUAD angiogenesis. We utilized CTRP 2.0 and PRISM
databases for pinpointing sensitive drugs against lung adenocarcinoma.
Results: Two clusters for the activation of angiogenesis were identified, with
Cluster 1 showing a poor prognosis and a pro-cancerous TME. Three
differentiated states of malignant epithelial LUAD cells were identified, which
had different degrees of angiogenic activation, were regulated by three different
regulon submodules, and had completely different crosstalk from other cells in
TME. The experiments validate that SLC2A1 promotes angiogenesis in LUAD. ARS
(Angiogenesis related score) had a high prognostic value; low ARSs showed
immunotherapy benefits, whereas high ARSs were sensitive to 15
chemotherapeutic agents.
Frontiers in Immunology frontiersin.org01
OPEN ACCESS
EDITED BY
Hailin Tang,
Sun Yat-sen University Cancer Center
(SYSUCC), China
REVIEWED BY
Jing-Sheng Cai,
Peking University People’s Hospital, China
Zhigang Zhou,
First Affiliated Hospital of Jinan University,
China
Zijian Zhou,
Fudan University, China
*CORRESPONDENCE
Weibiao Zeng
18270881242@163.com
Hao Ding
353249221@qq.com
Shu Pan
panshu@suda.edu.cn
†
These authors have contributed equally to
this work
RECEIVED 09 December 2023
ACCEPTED 23 January 2024
PUBLISHED 08 February 2024
CITATION
Tang L, Chen Z, Yang J, Li Q, Wang S, Mo T,
Zeng W, Ding H and Pan S (2024) Single-cell
and Bulk RNA-Seq reveal angiogenic
heterogeneity and microenvironmental
features to evaluate prognosis and
therapeutic response in
lung adenocarcinoma.
Front. Immunol. 15:1352893.
doi: 10.3389/fimmu.2024.1352893
COPYRIGHT
© 2024 Tang, Chen, Yang, Li, Wang, Mo, Zeng,
Ding and Pan. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Original Research
PUBLISHED 08 February 2024
DOI 10.3389/fimmu.2024.1352893

Conclusion: The assessment of angiogenic clusters helps to determine the
prognostic and TME characteristics of LUAD. Angiogenic prognostic models
can be used to assess the prognosis, immunotherapeutic response, and
chemotherapeutic drug sensitivity of LUAD.
KEYWORDS
angiogenesis, tumor microenvironment, immune infiltration, immune therapy,
prognosis, lung adenocarcinoma
1 Introduction
Lung cancer is the most common cause of cancer-related death
(1), and lung adenocarcinoma (LUAD) is its leading pathological type
(2), which accounts for 50% of all lung cancer cases (3).
Tumor heterogeneity is the main cause of drug resistance and
tumor recurrence in LUAD (4), and the complex tumor
microenvironment (TME) is key to LUAD heterogeneity (5).
Chemotherapeutic and immunotherapeutic efficacy exhibit varying
degrees of heterogeneity in patients with LUAD (6), thus hindering
precise assessment of individual patient prognosis. Recent studies
have suggested that the components of TME can determine the
cancer immunophenotype and help guide chemotherapy and
immunotherapy stratification in the future (6–8).
Angiogenesis is defined as the formation of new blood vessels
from pre-existing vessels through a process called germination.
Angiogenesis is important for the phenotypic differentiation of
TME (9). Vascular endothelial growth factor (VEGF) is a critical
driver of tumor neo-angiogenesis, and its expression within TME is
heterogeneous, leading to an immunosuppressive effect (10).
VEGFA exerts angiogenic effects by activating VEGFR2 expressed
on endothelial cells (11). In recent years, anti-angiogenic drugs
targeting the VEGFA pathway have significantly contributed to the
treatment of LUAD (12).
Cancer-associated fibroblasts within TME are involved in
angiogenesis, immune escape, and drug resistance (13). Tumor-
associated macrophages (TAMs) are enriched in TME in most
cancer types. TAMs polarise into the M1 or M2 phenotype
depending on the environment, and M2 macrophages express
anti-inflammatory cytokines (e.g. IL-10, CCL22, and CCL18) and
low levels of IL-12, thereby exerting anti-inflammatory, angiogenic
and pro-tumor effects (14). Chemokines in TME mediate the
recruitment of immune cells to TME and directly affect cancer
and endothelial cells to regulate tumor neo-angiogenesis (15).
Furthermore, angiogenesis modulates metabolism and immunity.
An abnormal vascular system inevitably leads to hypoxia and
acidosis, resulting in the upregulation of tumor factors such as
VEGF and TGF-bin the TME and eventually promoting metastasis
and immunosuppression (16). Therefore, the regulation of
angiogenesis is extremely complex and closely related to the
TME. However, no multi-omics study of LUAD based on
angiogenesis-related genes has analyzed their specific role in the
TME and prognosis.
Employing scRNA-seq, we can analyze RNA profile variations at
a high resolution to comprehend the intricate tumor
microenvironment (TME) (17). Previous LUAD studies utilized
scRNA-seq to explore diverse cell profiles within the
microenvironment. In this study, distinct angiogenic clusters were
identified based on 36 previously reported angiogenesis-related genes.
We revealed heterogeneity of angiogenic activity in the LUAD tumor
microenvironment at the single-cell level. Additionally, to enhance
clinical applicability, an angiogenic scoring system was developed.
This system evaluates LUAD aggressiveness and TME phenotype,
guiding the customization of chemotherapy and immunotherapy
strategies for individualized patient care.
2 Materials and methods
2.1 Pre-processing of bulk RNA-seq data
The gene expression data and clinical information of patients
with LUAD were downloaded from the NCBI GEO (https://
www.ncbi.nlm.nih.gov/geo/)andTCGA(https://
cancergenome.nih.gov/) databases. A total of 884 LUAD samples
from the GSE31210 (N = 226), GSE42127 (N = 133), GSE50081 (N =
127), and GSE72094 (N = 398) datasets were included in this study.
The RNA-seq data (FPKM format, N = 500) and survival information
of patients with LUAD were extracted from the TCGA database and
converted to the transcripts per million (TPM) format. The Combat
algorithm of the R package “SVA”was used to remove batch effects in
samples from the GEO datasets. All data were log2(X+1) normalized
for subsequent analysis. The somatic gene mutation data of patients
with LUAD were downloaded from the UCSC Xena database
(https://xenabrowser.net/datapages/).
2.2 Extraction and manipulation of single-
cell RNA-seq data
Raw scRNA-seq data were downloaded from the GSE127465
dataset for single-cell analysis. The data contains 12 samples from 5
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lung adenocarcinoma patients. In addition, the expression matrix,
cell clustering, and cell type annotation data of the dataset were
downloaded from the TISCH database (17). Samples with UMI
counts of >1000 and >500 genes expressed in each cell were
retained. For subsequent analysis of malignant epithelial LUAD
cells, the number of highly variable genes was set to 2000, and the
resolution was set to 0.6 for cell clustering. The data were
dimensionalized using the “tSNE”method, and differentially
expressed genes among malignant cell clusters were calculated
using the “FindAllMarkers”algorithm.
2.3 Consensus clustering of
angiogenic clusters
We extracted a set of 36 angiogenesis-related genes from
MsigDB (http://www.gsea-msigdb.org/gsea/msigdb/search.jsp) for
this study. Utilizing the R package “ConsensusClusterPlus”,we
conducted consensus clustering analysis on the gene expressions.
The algorithm employed was “KM”, using “euclidean”distance
calculation and a random seed set to “5555555”. The GEO and
TCGA-LUAD cohorts were categorized into two expression
patterns, Cluster1 and Cluster2. Differential gene expression
between the clusters was identified using the R package “limma”.
2.4 ssGSEA, GSVA, and single-cell
functional gene set activity scores
Transcriptomic pathway activity scores were assessed using gene
set variation analysis (GSVA) with the “HALLMARK dataset”.
Enrichment scores were calculated using single-sample gene set
enrichment analysis (ssGSEA) to represent the activity scores of
cancer-related biological pathways and immune microenvironment-
related signatures. Functional activity scores for each cell were
determined using the “SingleCellSignatureScorer”software, relying
on the differential expression of genes between the two expression
clusters (18).
2.5 GO and KEGG enrichment analyses
and GSEA
GO and KEGG functional enrichment analyses of differentially
expressed genes were performed using the R package “clusterProfiler”.
GO analysis included functional enrichment of biological processes
(BP), cellular components (CC), molecular functions (MF), and
other categories.
2.6 Single-cell trajectory analysis
Based on the single-cell data (Seurat objects), single-cell
trajectories were constructed using the R package “Monocle2”,
and genes regulated in a branch-dependent manner were
identified using the branched expression analysis modeling
(BEAM) algorithm (19).
2.7 Cell communication analysis
Based on the human CellChatDB database, cellular
communication among LUAD cells of different trajectory
branches, immune, and stromal cells in TME was analyzed using
the R package “CellChat”. In addition, ligand–receptor pairs
involved in different signaling pathways in tumor, immune, and
stromal cells were identified.
2.8 Identification of Regulon submodules
A list of human transcription factors was downloaded from the
RcisTarget database (https://resources.aertslab.org/cistarget/) and
used to construct a transcription factor regulatory network. The
“pyscenic”algorithm in Python was used to build a gene co-
expression network based on the abovementioned transcription
factors, establish transcription factor–target regulatory
relationships, and identify a regulon (20). In addition, the regulon
activity score (RAS) of cells was evaluated using the “AUCell”
algorithm. The area under the curve (AUC) and connection
specificity index (CSI) were calculated, and the regulon
submodules were defined by hierarchical clustering of regulons
based on CSI.
2.9 Immunohistochemistry
A total of 18 lung adenocarcinoma samples, along with 7
corresponding paracancerous tissues, were collected. Ethical
approval has been obtained from the Medical Ethics Committee
at The Affiliated First Hospital of Soochow University for the
collection of tissue specimens. The tissues were fixed with 4%
paraformaldehyde, dehydrated, and paraffin-embedded, resulting
in 4 mm sections. Tissue sections underwent incubation at 4°C
overnight with primary antibodies targeting SLC2A1 (Sangon,
D160433, 1:200), CD34 (Sangon, D363155, 1:200), and VEGFA
(Sangon, D260788, 1:200) post-deparaffinization, rehydration, and
antigen retrieval. Subsequently, the slides were exposed to an
antirabbit secondary antibody, followed by DAB staining and
hematoxylin counterstaining. Two blinded pathologists
independently assessed the immunohistochemistry (IHC) results.
Tissue sections were scored based on the percentage of positive cells
and staining intensity. Staining intensity was graded as 0 (negative),
1 (weak), 2 (moderate), or 3 (strong), while the expression
proportion of positive cells was scored as 1 (0–25%), 2 (26–50%),
3 (51–75%), or 4 (76–100%). The proportion and intensity scores
were amalgamated to derive a final score. An IHC score of ≥6
denoted high expression, while <6 indicated low expression.
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2.10 Analysis of immunotherapy response
and chemotherapy drug sensitivity
Data regarding the response of patients with LUAD to
immunotherapy were extracted from the GSE126044 (N = 16)
cohort, and immunotherapeutic efficacy was predicted using the
TIDE algorithm (http://tide.dfci.harvard.edu/). Data regarding the
sensitivity of patients to chemotherapeutic drugs were extracted
from the CTRP 2.0 database (https://portals.broadinstitute.org/
ctrp.v2.1/), and AUC data for PRISM analysis were extracted
from the PRISM Repurposing Secondary Screen 19Q4 dataset
(https://depmap.org/portal/download/). The area under the dose-
response curve (AUC) in both datasets was used to measure drug
sensitivity, with lower AUC values indicating higher sensitivity.
Differences in drug sensitivity were analyzed using the Wilcoxon
test and Spearman correlation analysis (log2FC > 0.15, r < –0.4).
Missing AUC values in the dataset were imputed using the K-
nearest neighbors (KNN) algorithm, and chemotherapeutic drugs
with >20% missing data were excluded (20). The expression profile
data of the CCLE cell line (https://portals.broadinstitute.org/ccle/
data) were used as a training set for predicting drug sensitivity.
Drug response in each sample was evaluated using the
pRRophetic package.
2.11 Statistical analysis
Statistical analyses were performed using the R software
(version 4.2). For comparing the data of two datasets, the
significance of normally distributed variables was estimated using
the Student t-test, whereas that of non-normally distributed
variables was estimated using the Wilcoxon test. For comparing
the data of more than two groups, one-way ANOVA was used to
analyze normally distributed data, whereas the Kruskal–Wallis test
was used to analyze non-normally distributed data. The two-sided
Fisher exact test was used for R*C tables containing <5 samples.
Kaplan–Meier survival analysis and Cox proportional hazards
modelwereusedtoanalyzethesignificance of prognostic
features. A multivariate regression model was used to adjust for
confounders. The Benjamini–Hochberg method was used to control
the false discovery rate (FDR) for multiple hypothesis testing, with
all comparisons being two-sided with an alpha level of 0.05 (21) (*,
P < 0.05; **, P < 0.01; ***, P < 0.001).
3 Results
Figure 1 shows the flow chart of this study.
3.1 Identification of angiogenic clusters
for LUAD
We conducted consensus clustering analysis on lung
adenocarcinoma patients using expression data of 36 angiogenesis-
related genes to differentiate angiogenic clusters of LUAD. Two
clusters, namely, Cluster1 and Cluster2, were identified using
LUAD samples in the GEO dataset (Supplementary Figures S1A–
C). The two clusters possess different angiogenic gene expression
patterns and are associated with different prognoses, with Cluster 1
having a worse prognosis (P < 0.001, log-rank test) (Figure 2A).
Principal component analysis revealed that the two clusters were
completely distinguishable based on the expression of angiogenesis-
related genes (Figure 2B). Samples from both clusters were evenly
distributed in the independent GEO cohort, and only Cluster 1
showed a worse prognosis (Supplementary Figures S1D–G).
Consensus clustering was performed in the TCGA-LUAD cohort
using the same method (Supplementary Figures S1H,I), and similar
results were obtained (Figure 2C). The results of multivariate Cox
analysis validated that the angiogenic clusters identified based on
angiogenesis-related genes might serve as independent prognostic
factors for LUAD (Cluster2 versus Cluster1; HR, 0.57; 95% CI, 0.43–
0.76; P < 0.001) (Figure 2D). Next, the GSVA algorithm evaluated
Hallmark gene sets to explore potential biological mechanisms of the
differences between the two clusters. Cluster1 was significantly
enriched in various oncogenic pathways, such as TGF-bsignaling,
epithelial–mesenchymal transition, angiogenesis, hypoxia, and
apoptosis, whereas Cluster2 was mainly involved in the activation
of biological pathways, such as the P53 signaling pathway and fatty
acid metabolism (Figure 2E). These results suggest that angiogenesis
is closely related to the TME of LUAD and is involved in
LUAD development.
3.2 Differences in TME characteristics
between angiogenic clusters
To understand the tumor microenvironmental phenotype
mappedbyangiogenicclusters,theactivityofsignatures
associated with cancer-related pathways was analyzed using the
ssGSEA algorithm. The results indicated that the expression of
multiple signatures was significantly different between the two
clusters. The expression of signature genes associated with
cancer-related pathways including EMT, WNT targeting, cell
cycle, antigen presentation, and immune checkpoints was higher
in Cluster1 than in Cluster2 (P < 0.001) (Figure 2F). Furthermore,
differences in immune and stromal cell regulation between the two
clusters were analyzed. Stromal cells with pro-oncogenic effects
(e.g., MDSCs and CAFs) and regulatory T cells that suppress anti-
tumor immunity were more active in Cluster1. Meanwhile, the
expression of genes associated with immune checkpoint blockade
(ICB) resistance was also high in Cluster1. However, despite the
aggregation of various cancer-promoting stromal and immune cells
in Cluster1, MHC and co-stimulatory molecules were activated,
suggesting that anti-cancer immune responses are also related to
Cluster1. These results indicate that immune cells and pro-cancer
biological pathways play an important role in Cluster 1. Besides,
there are complex chemokine and cytokine regulatory networks in
TME, and we found that there are entirely different regulatory
factor expression levels for different angiogenic expression patterns
based on the ssGSEA enrichment results of the signature of these
tumor microenvironmental regulators. For example, BCR (B cell
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receptor) signaling, TCR (T cell receptor) signaling, natural killer
cell cytotoxicity, interleukin expression, chemokine expression, and
cytokine expression were significantly upregulated in Cluster1,
suggesting that the destabilization of chemokine and cytokine
regulation in Cluster1 leads to a poor prognosis of LUAD.
Furthermore, immune cell infiltration was analyzed in the two
clusters. The infiltration of T helper, TFH (Follicular helper T cell),
DC (Dendritic cells), mast, Tem (Effective Memory T Cell), and
Th17 cells was significantly high in Cluster2, whereas that of
macrophages and neutrophils was significantly high in Cluster1.
These results validated our previous hypothesis, indicating that the
pro-oncogenic immune microenvironment and pathways
predominated in Cluster1, which suggests that elevated
angiogenic activity accompanies the pro-oncogenic TME.
The two angiogenic clusters exhibited distinct tumor
microenvironmental phenotypes. Differentially expressed genes (|
log2fold change| > 1, adj. P < 0.05) between angiogenic Cluster1 and
Cluster2 were identified as angiogenic clusters-related genes
(Figure 2G). Subsequent GO and KEGG functional enrichment
analysis revealed significant enrichment in the extracellular matrix,
cytokine and chemokine production, angiogenesis regulation,
immune response regulation, Wnt signaling pathway, and EMT-
FIGURE 1
The flow chart of this study.
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A
B
D
E
F
G
C
H
FIGURE 2
Angiogenic clusters distinguish tumor microenvironment phenotypes and prognostic characteristics in lung adenocarcinoma. (A) Kaplan-Meier
curves for overall survival (OS) of lung adenocarcinoma patients with different angiogenic cluster in the GEO cohort, Log-rank test P<0.001. (B)
Principal component analysis based on 36 genes related to angiogenesis can well distinguish the two angiogenic clusters. (C) Overall survival (OS)
Kaplan-Meier curves for lung adenocarcinoma patients in the TCGA cohort with different angiogenic cluster, Log-rank test P=0.008. (D) Multivariate
Cox regression analysis based on clinicopathological characteristics of patients to assess the prognostic value of angiogenic cluster in lung
adenocarcinoma. (E) Enrichment scores for the 50 “Hallmark “gene sets in lung adenocarcinoma patients were assessed using the GSVA algorithm
and tested for the significance of differences, with the horizontal axis indicating the t-value of the difference analysis. Entries with |t value| > 1.96 in
this study were statistically significant, and a negative t value indicated that the signaling pathway was actively expressed in Cluster1. (F) The
enrichment scores of Carcinogenic pathways, TME signature, TME regulatory factor, and immune cell signatures were evaluated based on the
ssGSEA algorithm, and displayed with Heatmap and compared the difference in enrichment scores between the two angiogenic clusters. (G)
Significantly differentially expressed genes (DEGs) between the two angiogenic clusters, 72 genes were upregulated and 81 genes were
downregulated in Cluster2. (H) Functional annotation of DEGs using GO and KEGG functional enrichment analysis. The innermost circle represents
the number of enriched genes in the corresponding pathway, and the remaining circle meanings have been labeled in the center of the circle.
Tang et al. 10.3389/fimmu.2024.1352893
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related processes. This validates that the differentially expressed
genes exhibit characteristics of angiogenesis and its mediated TME
(Figure 2H), reflecting differences in angiogenic clusters and their
underlying biological mechanisms.
3.3 Angiogenic heterogeneity among
different cell types and subtypes
To explore the heterogeneity of angiogenic activity among cell
types, angiogenic clusters-related genes were used as the angiogenic
signature, and scored using the “SingleCellSignatureScorer”
algorithm. Firstly, a total of 12 samples in the scRNA-seq dataset
had a good integration effect among samples, with no significant
batch effect, thus allowing for subsequent analysis (Figure 3A).
Through descending and unsupervised clustering, samples were
classified into 13 cell types, encompassing immune, stromal, and
malignant tumor cells (Figure 3B). Angiogenesis scores, reflecting
the degree of biological activity, varied among these cell types.
Notably, fibroblasts, malignant cells, and neutrophils displayed
significantly higher scores than immune cells, indicating more
active angiogenesis (Figures 3C,D).
Furthermore, focusing on the heterogeneity of scores among
malignant tumor cells, the cells were divided into 11 different
subtypes (Figure 3E). Similarly, significant differences in
angiogenesis scores were observed in different subpopulations of
malignant tumor cells (Figure 3F). Altogether, these results suggest
that different cells in TME exhibit different levels of angiogenesis.
Therefore, it is important to investigate the causes of
angiogenic dysregulation.
To examine the important role of angiogenesis in malignant cell
heterogeneity, cellular pseudo-time analysis was performed to
investigate malignant cell differentiation trajectories. The results
revealed three main differentiation states of malignant cells, namely,
State1, State2, and State3 (Figure 3G). Malignant cells in State1 are
the initiating factors of the reverse chronological trajectory, whereas
State2 is at the end of the trajectory. (Figure 3H). The transition of
State1, State2, and State3 with pseudotime can be visualized clearly
through density diagrams and trajectory plots. (Figures 3I,J).
Furthermore, significant differences in angiogenesis scores were
observed among the three cell states (Kruskal–Wallis test; P < 0.001)
(Figure 3K). State3 had the lowest angiogenesis scores (low-score
group), and State2 had the highest scores (high-score group)
(Figure 3L), suggesting that angiogenesis is involved in malignant
cell heterogeneity. In addition, angiogenesis is dysregulated in
LUAD, and its activation is closely related to the differentiation
status of LUAD cells.
3.4 Regulon submodules of different
cell states
Clustering regulons based on the Connection Specialty Index
(CSI) revealed three submodules, M1, M2, and M3 (Figure 4A).
Regulons within the same submodule exhibited tight expression
correlations. Subsequently, regulon activity scores were calculated
for the three cell states, indicating the activation of regulons in each
state. M1, M2, and M3 module regulons were predominantly
activated in State2, State3, and State1, respectively (Figures 4B–D).
The M1 module regulon, associated with high angiogenic scores,
appeared to primarily regulate angiogenic activation (Figure 4E). The
establishment of a regulon-based regulatory network enhances our
understanding of the three cell differentiation states and aids in
identifying markers and therapeutic targets for LUAD.
3.5 Cell communication of malignant cells
with TME
The findings indicate an association between angiogenesis and
the microenvironment of lung adenocarcinoma. Cell communication
pattern recognition predicts how cells, as signal senders or receivers,
coordinate with each other and signaling pathways to drive
intercellular communication. In this study, we analyzed cell
communication within the lung adenocarcinoma TME involving
malignant cells, immune cells, and stromal cells. The results
revealed there were two incoming signal coordination modes and
two outgoing signal coordination modes for intercellular
communication and the signaling pathways coordinated with it
(Supplementary Figure S2A). State1 cells can be signalled via the
TWEAK signalling pathway (TNFSF12–TNFRSF12A,
Supplementary Figure S2B), IGF signalling pathway (IGF2–[ITGA6
+ITGB4], Supplementary Figure S2C), MK signalling pathway
(MDK–[ITGA6+ITGB1], Supplementary Figure S2D), SEMA3
signalling pathway (SEMA3B–[NRP2+PLXNA2], Supplementary
Figure S2E) and PERIOSTIN signalling pathway (POSTN–[ITGAV
+ITGB5], Supplementary Figure S2F) for active communication with
M2 macrophages, endothelial cells, and CD4 T cells. State2 cells can
be signaled through the EGF signaling pathway (HBEGF–EGFR,
Figure 5A), TRAIL signaling pathway (TNFSF10–TNFRSF10B,
Figure 5B), TGF-bsignaling pathway (TGFB3–[TGFBR1+TGF,
Figure 5C), complement signaling pathway (C3–[ITGAX+ITGB2],
Figure 5D), UGRP1 signaling pathway (SCGB3A2–MARCO,
Figure 5E) and WNT signaling pathway (WNT3A–[FZD4+LRP5],
Figure 5F) for active communication with M2 macrophages, mast
cells, and endothelial cells. It is interesting to note that there are
similar results between State1 and State2 cells. However, State3 cells
communicate closely with M2 macrophages, fibroblasts, endothelial
cells, and cDC cells through a signaling pathway that is distinct from
that associated with State1 and State2 cells (Supplementary
Figure S3). Although the cell types that communicate with cells in
the three states are similar, the signaling pathways are different,
indicating that heterogeneity of the angiogenic regulatory
microenvironment is closely related to these signaling pathways.
3.6 Construction of the angiogenic risk
score and discussion of its
clinical relevance
To find all genes that differ between the branches, that is, cell
differentiation trajectories, we used the branched expression analysis
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modeling (BEAM) to find “branch-dependent”genes (Figure 6A).
These genes are associated with cell differentiation trajectories and
also with angiogenic activation. Therefore, we took the intersection of
cell branch-related genes and angiogenesis clusters-related genes,
which are essential for angiogenic clustering and cell differentiation
trajectories in lung adenocarcinoma. Then, to facilitate the
assessment of the individualized prognosis of LUAD and guide
treatment, a prognostic model, namely the angiogenic risk score
(ARS), was developed based on these 60 intersecting genes
(Figure 6B). The model comprised 12 genes identified via
A
B
DEF
G I
H
JK
L
C
FIGURE 3
Analysis of angiogenic scores at the cellular level and trajectory analysis by single-cell sequencing. (A) The integration effect of 12 samples of lung
adenocarcinoma samples appeared to be good with no significant batch effect. (B) Reduced-dimension visualization of tSNE of lung
adenocarcinoma cells, each color represents a cluster, and the cell type represented by each color is labeled on the right. (C) Angiogenesis scores of
cells were assessed based on DEGs between angiogenesis clusters. (D) The Kruskal-Wallis test for heterogeneity of angiogenesis scores between
different cell types. (E) Reduced dimensional clustering of tSNE of malignant cells in lung adenocarcinoma, each color represents a cluster, and the
cell type represented by each color is labeled on the right. (F) Visualization of angiogenesis score of Malignant cells in lung adenocarcinoma. Pseudo
time analysis of Malignant cells based on Monocle2 inference, (G) each color represents one cell State, (H) shows pseudo time analysis changes and
pseudo time start and endpoints. (I) Density diagram showing the process of cell State changes with pseudo-time. (J) The mapping of pseudo time
distribution to high and low angiogenesis scores. (K) Kruskal-Wallis test for comparing significant differences in angiogenesis scores between the
three cell State states. (L) State type proportion statistics of Malignant cells in lung adenocarcinoma and the proportion composition of HighScore
and LowScore groups of different cell States were counted separately.
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univariate Cox regression and Lasso regression analyses: ARS = Exp
(HPGD) * (–0.035) + Exp(IRX2) * (–0.026) + Exp(SFTPB) * (–0.025)
+Exp(CHIA)*(–0.017) + Exp(HOXD1) * (–0.005) + Exp
(HSD17B6) * (–0.004) + Exp(MUC16) * (0.013) + Exp(S100P) *
(0.032) + Exp(C1orf116) * (0.042) + Exp(KRT16) * (0.045) + Exp
(EGLN3) * (0.090) + Exp(SLC2A1) * (0.166) (Figure 6C). The clinical
significance of the prognostic model was assessed, and the low-ARS
group had a significant survival benefit with good clinical efficacy for
predicting 3-year overall survival in the training set, validation set,
TCGA independent validation set, and the whole GEO dataset
(Figure 6D), with AUC values of 0.71, 0.71, 0.68 and 0.70,
respectively (Supplementary Figure S4A). Multivariate Cox
regression analysis integrating the age, sex, pathological stage,
smoking history, and ARSs of patients revealed that ARS was an
independent biomarker for the prognosis of LUAD (HR, 3.12; 95%
CI, 2.36–4.12; P < 0.001, Supplementary Figure S4B).
In addition, a positive correlation was observed between ARS and
cancer-related biological signatures reported by Mariathasan et al,
especially for cell cycle, EMT, and immune checkpoints, which have
been reported to promote proliferation, metastasis, and immune
escape in LUAD (Figure 6E). These results validate that ARS is
associated with a worse prognosis and can be used as an independent
prognostic biomarker. Furthermore, the correlation between ARS
and immune cell infiltration in the immune microenvironment was
analyzed, which revealed that ARS fairly characterized the immune
microenvironment.ARS had a positivecorrelation with Th2 cells (r =
0.5, P < 0.05) and neutrophils (r = 0.14) but a negative correlation
with T cells (r = –0.14), Tcm cells (r = –0.34), Tem cells (r = –0.29),
CD8 T cells (r = –0.32), TFH cells (r = –0.5), DC (r = -0.3),
eosinophils (r = –0.34) and mast cells (r = –0.46) (Figure 6F).
These results suggest that an increasingly strong tumor-suppressive
immune microenvironment is characterized by elevated ARSs. In
addition, various immune cells extensively interact with each other,
reflecting the complexity of TME.
Furthermore, mutated genes in LUAD were identified in the
high- and low-ARS groups. The results showed that both groups
had different somatic mutation patterns. The mutation frequency
of TP53 (61% versus 44%, respectively; OR, 2.029; P < 0.01), TTN
A
B
D
E
C
FIGURE 4
Distinct regulon submodules activation in State1, State2, and State3 cells. (A) The transcription factors of different States of lung adenocarcinoma
Malignant cells can be clustered into three regulon submodules, M1, M2, and M3. (B) Regulon activity score for regulon submodules in three cell
states. (C) Visualization of the tSNE reduced the dimensionality of three cell States. (D) The Regulon activity score has been mapped to each cell. (E)
Regulon activity scores of M1, M2, and M3 regulon submodules in three cell states.
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(54% versus 39%, respectively; OR, 1.84; P < 0.05), ZFHX4 (41%
versus 26%, respectively; OR, 2.021; P < 0.01), XIRP2 (37% versus
19%, respectively; OR, 2.552; P < 0.01), KEAP1 (31% versus 15%,
respectively; OR, 2.598; P < 0.01) and COL11A1 (29% versus 16%,
respectively; OR, 2.037; P < 0.01) was higher in the high-ARS
group, suggesting that angiogenesis relates to the occurrence of
somatic mutations in tumor cells (Figure 6G). Therefore, ARS
constructed based on angiogenesis-related genes can help to assess
TME and genomic somatic mutation patterns in each patient with
LUAD, indicating that different ARSs may predict different
chemotherapeutic and immunotherapeutic effects.
3.7 SLC2A1 promotes angiogenesis in
lung adenocarcinoma
The ARS prognostic model was established based on the lasso
regression algorithm. Among them, SLC2A1 was found to have the
largest Lasso regression coefficient of 0.166 and as a high-risk gene,
which had the greatest impact on the model and drove us to further
validate the role of SLC2A1 on angiogenesis. Consequently, we
collected cancerous and paracancerous tissues from seven pairs of
lung adenocarcinoma patients and performed immunohistochemical
staining for SLC2A1 and VEGFA (Figure 7A), and statistical analyses
showed that the expression of SLC2A1 and VEGFA was significantly
upregulated in lung adenocarcinoma tissues (Figures 7B,C), which
was in agreement with the expression of SLC2A1 in the TCGA public
database (Figure 7D). Meanwhile, we found that the expression level
of SLC2A1 was significantly associated with the prognosis of lung
adenocarcinoma patients, and patients in the high-expression
SLC2A1 group had a significantly lower overall survival rate
(Figure 7E, HR = 1.87, P<0.001). Tumor tissues from 18 patients
with lung adenocarcinoma were collected subsequently, and the
correlation between SLC2A1 expression level and microvessel
density was observed by immunohistochemical staining. Here we
visualized the proliferation of microvessels by immunohistochemical
staining of CD34. The microvessels in the SLC2A1 high-expression
group were shown to be significantly proliferated under high
magnification, and the number of CD34-positive microvessels was
significantly higher at 22.70 ± 10.34 than that in the low-expression
group, which was 4.625 ± 1.506 (Mean ± SD) (Figure 7F), and the
difference was statistically significant (Figure 7G).
Meanwhile, we further verified the role that SLC2A1
mediates VEGFA secretion in lung adenocarcinoma tissues.
We examined the expression levels of SLC2A1 and VEGFA in
the tumor tissues of 18 lung adenocarcinoma patients by
A
B
DEF
C
FIGURE 5
Ligand receptor pairs mediating cell communication between cell state2 and the tumor microenvironment. (A) State2 cells communicate with M2-
type macrophages via HBEGF-EGFR. (B) State2 cells in concert with State1 communicate closely with Mast and Endothelial via TNFSF10-TNFRSF10B.
(C) State2 cells communicate with State1 synergistically via TGFB3-(TGFBR1+TFGBR2), (D) C3-(ITGAX+ITGB2), (E) SCGB3A2-MARCO and M2
macrophages. (F) State2 intercommunicates with Endothelial via WNT3A-(FZD+LRP5).
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immunohistochemical staining, and the IHC results showed that
high expression of SLC2A1 was significantly correlated with the
increased secretion of VEGFA (Figures 7H,I). The chi-square
test showed that more samples in the high-expressing SLC2A1
group overexpressed VEGFA, OR = 13.33, P = 0.0474 (Figure 7J),
suggesting that patients with high expression of SLC2A1 are
more at risk of overexpressing VEGFA, which promotes
angiogenesis in tumors.
A
B
D
EF
G
C
FIGURE 6
Construction of angiogenic prognostic model and its prognostic value assessment. (A) Finding of all genes that differ between the cell branches. The
center of the heatmap is the start of the pseudotime, and to the sides are the dynamics of genes associated with different cell fates or branches. The
columns in the heatmap are pseudotimes and the rows are genes. The cell state branch-related genes can be clustered into four gene clusters
based on co-expression relationships. (B) A total of 60 genes were intersected by cell “branch-dependent”genes and “angiogenesis-clusters”related
genes. (C) Twelve model genes and their coefficients were identified based on univariate Cox regression and Lasso regression analysis. (D) Kaplan-
Meier curves for overall survival (OS) in the high ARS and low ARS groups were evaluated in the training cohort (N = 532), test cohort (N = 352),
external independent validation cohort TCGA cohort (N = 500), and Whole GEO cohort (N = 884), respectively. (E) Correlation of ARS with cancer-
related biological features and (F) the degree of immune cell infiltration using the Spearman analysis. (G) Differences in somatic mutations in the
tumor genome between the high-ARS and low-ARS groups and statistical tests.
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3.8 Prediction of immunotherapeutic and
chemotherapeutic effects and construction
of an individualized nomogram based
on ARS
In recent years, both immunotherapy and chemotherapy have
played an important role in remodeling TME for the treatment of
LUAD. The abovementioned results indicate that ARS is associated
with the TME of LUAD, somatic mutations in LUAD cells, and the
clinical immunotherapeutic and chemotherapeutic effects,
suggesting that ARS can facilitate individualized prediction of the
efficacy of immunotherapy in patients with LUAD to guide the
selection of chemotherapeutic drugs. Furthermore, a majority of
immune checkpoints were differently expressed in two groups
(Figure 8A). High expression of checkpoints is involved in
promoting the immune escape of LUAD cells, and these
checkpoints mediate the immunosuppressive microenvironment,
which may be attributed to the poor prognosis of the high-ARS
A
BDE
F
G
I
H
J
C
FIGURE 7
Immunohistochemical staining validates that SLC2A1 promotes angiogenesis in lung adenocarcinoma. (A) Immunohistochemical staining of SLC2A1
and VEGFA in lung adenocarcinoma tissues and paracarcinoma tissues. (B) The t-test for SLC2A1 IHC score in paired tissues. (C) Differential
expression of SLC2A1 in lung adenocarcinoma in the TCGA database. (D) Overall survival of high and low expression of SLC2A1 in lung
adenocarcinoma in the TCGA database. (E) Differential expression of VEGFA in lung adenocarcinoma in the TCGA database. (F)
Immunohistochemical staining of CD34+ microvessels in high and low SLC2A1 expression groups. (G) The t-test for the number of CD34+
microvessels per high field in high and low SLC2A1 expression groups. (SLC2A1(+), High SLC2A1 expression group; SLC2A1 (–), Low SLC2A1
expression group). (H) Immunohistochemical staining of VEGFA in high and low SLC2A1 expression groups. (I) The t-test for VEGFA IHC score in
high and low SLC2A1 expression groups. (J) Correlation between SLC2A1 and VEGFA by chi-square test.
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group. These results suggest that the two groups respond differently
to immunotherapy. Furthermore, the SD/PD (Stable disease/
Progressive disease) group in the LUAD immunotherapy cohort
(GSE126044) had higher ARSs, leading to a poor response to
immunotherapy (Figure 8B). In addition, the TIDE algorithm was
used to assess immunotherapy response in the GEO and TCGA
cohorts. The response to immunotherapy was poorer in the high-
ARS group than in the low-ARS group, indicating that patients with
AB
DE
FG
I
H
J
C
FIGURE 8
Prediction of immunotherapy effects and sensitive chemotherapeutic agents in the high and low ARS groups. (A) Differential ex pressio n of immune
checkpoints in the high ARS and low ARS groups. (B) ARS differences between samples in the group with and without clinical response to
immunotherapy. The proportion of immunotherapy with clinical response in the High ARS and Low ARS groups in the (C) GEO cohort and (D) TCGA
cohort was predicted based on the TIDE algorithm. GEO cohort: No immunotherapy response in High ARS versus Low ARS (OR =2.297, p<0.001). TCGA
cohort: No immunotherapy response in High ARS versus Low ARS (OR = 3.342, p<0.001). (E) Number of chemotherapy drugs in PRISM database and
CTRP V2 database. (F) Screening of sensitive chemotherapeutic agents based on analysis of variance log2FC and Spearman correlation analysis. (G) The
correlation between the area under the drug dose-response curve (AUC) and ARS in patients with lung adenocarcinoma was calculated from drug
sensitivity data in the PRISM database. (H) The difference between the area under the drug dose-response curve (AUC) between the high ARS and low
ARS groups was calculated based on the PRISM database. (I) The correlation between the area under the drug dose-response curve (AUC) and ARS in
patients with lung adenocarcinoma was calculated from drug sensitivity data in the CTRP V2 database. (J) The difference between the area under the
drug dose-response curve (AUC) between the high ARS and low ARS groups was calculated based on the CTRP V2 database.
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low ARSs can benefit from ICB treatment (GEO cohort
immunotherapy non-response rate: 71.8% versus 52.5%,
respectively; OR, 2.297; P < 0.001) (Figure 8C), (TCGA cohort
immunotherapy non-response rate: 82.8% versus 59.1%; OR, 3.342;
P < 0.001) (Figure 8D).
Given that ARS significantly affects pathways such as drug
metabolism and mediates multiple oncogenic signaling pathways,
sensitive chemotherapeutic agents for LUAD can be identified
based on ARS. To analyze the potential of ARS as a biomarker
for predicting sensitivity to chemotherapeutic agents, the sensitivity
of patients with LUAD to chemotherapeutic agents was evaluated
based on drug sensitivity data (Figure 8E) extracted from the
PRISM (1448 compounds) and CTRP V2 (481 compounds)
databases. The expression data extracted from CCLE were used as
a training cohort. The area under the dose-response curve (AUC)
was used to quantify drug sensitivity, with higher AUC values
representing lower drug sensitivity. Sensitive drugs were screened
using the Wilcoxon test and Spearman correlation analysis (log2FC
> 0.15, r < –0.4, Figure 8F). Based on the CTRP V2 database, 4
chemotherapeutic agents were identified, including paclitaxel, KX2-
391, CR-1-31B, and leptomycin (Figures 8G,H). In addition, 11
chemotherapeutic drugs with high sensitivity were identified based
on the PRISM database using the same screening criteria, including
docetaxel, epothilone-b, ispinesib, paclitaxel, cabazitaxel, litronesib,
irinotecan gemcitabine, vincristine, topotecan, and rubitecan
(Figures 8I,J). Patients with high ARSs may benefit from the
above mentioned chemotherapeutic agents.
Furthermore, the independent prognostic marker ARS was
combined with clinical prognostic characteristics such as age,
gender, pathological stage, and smoking history to construct a
nomogram for clinical prognostic prediction (Figure 9A), which
can better assess the risk factors and guide subsequent treatment
strategies. The calibration curve of the nomogram showed good
performance with a concordance index (C-index) of 0.768
(Figure 9B), and the AUC of the ROC curve for predicting 1-, 3-
and 5-year survival were 0.78, 0.82, and 0.81, respectively (Figure 9C),
indicating that the nomogram had good accuracy in predicting
overall survival. Decision curve analysis (DCA) and time-
dependent C-index revealed that the clinical prediction accuracy of
the nomogram was superior to that of other clinicopathological
features (Figures 9D,E), indicating that the nomogram can be used
in clinical settings in the future. In addition, we validate the accuracy
of the Nomogram in three independent datasets. High and low
Nomogram scores showed significant differences, and notably, the
AUCs of 5-year overall survival for the Nomogram were 0.76, 0.74,
and 0.93, respectively (Figures 9F-H), further confirming the clinical
predictive performance of Nomogram. In conclusion, the assessment
of angiogenesis and the rest of the clinicopathological features can be
integrated to assess the prognosis of lung adenocarcinoma patients
with great accuracy.
4 Discussion
LUAD is a highly heterogeneous malignancy, and several
studies have used single-cell and bulk sequencing studies to
discuss the heterogeneity of the TME of LUAD (22).
Angiogenesis plays a crucial role in promoting tumor growth and
metastasis, and vascular endothelial growth factor (VEGF) and
inflammatory chemokines exert immunomodulatory effects,
which enhance angiogenesis while leading to immunosuppression
(23). Studies have indicated the importance of angiogenesis for the
differentiation of TME phenotypes (9). Clinically, anti-angiogenic
drugs that block VEGF/VEGFR signaling have been successful in
treating LUAD; however, they can induce hypoxia, leading to drug
resistance, thereby exacerbating immunosuppression and
increasing immune checkpoint PD-L1 expression (24). Therefore,
an in-depth understanding of angiogenesis and TME interactions
can help guide combination therapy for LUAD. Meanwhile, it is
crucial to construct prognostic models based on angiogenesis to
individually assess the prognosis and microenvironmental status
of patients.
In this study, two angiogenic clusters showed different tumor
microenvironmental phenotypes and prognostic features. LUAD
microenvironment has been categorized into three phenotypes,
namely, “inflamed”,“immune-desert”, and “immune-excluded”,
which mediate different prognoses and immunotherapeutic
responses (25). The inflamed phenotype demonstrates anti-cancer
immune activation and has a better prognosis. However, angiogenic
Cluster1 in this study was associated with a poor prognosis,
demonstrating the characteristics of the immune-deserted and
immune-excluded phenotypes, which are characterized by
differential activation of oncogenic signaling pathways such as
glycolysis, cell cycle, hypoxia, and epithelial–mesenchymal
transition. Moreover, immune cell infiltration and the expression of
immune-related regulatory factors were downregulated in Cluster1.
Angiogenesis mediates different tumor microenvironmental
phenotypes in other solid tumors as well (9,26).
scRNA-seq allows the analysis of interactions between cell
subpopulations and specific transcriptional regulators at a high
resolution (27). In this study, significant differences were observed
in angiogenic activity among different cell types, which validated the
heterogeneity of angiogenesis. The highest angiogenic activity was
observed in malignant cells, fibroblasts, and neutrophils, which is
consistent with the results of previous studies. Unterleuthner et al.
demonstrated that cancer-associated fibroblasts (CAFs) promote
angiogenesis through the expression of WNT2 (28). Neutrophils
have also been reported to secrete pro-angiogenic factors and drive
immunosuppression to promote tumor growth (29).
In this study, angiogenic activation was significantly
heterogeneous in the malignant cell subpopulation of LUAD;
however, the underlying causes and biological mechanisms
warrant further investigation. Pseudotime trajectory analysis of
malignant LUAD cells revealed the presence of three main cell
differentiation states. Furthermore, angiogenesis activated the three
cell states with specific transcription factors (regulons). Evaluation
of RAS revealed differences in transcription factors regulating the
heterogeneity of angiogenic activation in malignant LUAD cells.
Transcription factors of State2 cells were found to be associated
with angiogenic activation. However, transcription factors of State3
cells mediated lower angiogenic activation, and angiogenic
activation was more complex in State 1 cells than in State2 and
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State3 cells. Altogether, exploring the specific regulon of different
cell states is crucial for a deeper understanding of the differences in
angiogenic activation in LUAD.
The complex cellular communication in TME drives cancer
progression and response to the available therapies (30). In this
study, different cell states, that is, different activation states of
angiogenic pathways, communicated significantly differently with
cells in the TME of LUAD, which further reveals the role of
angiogenesis in the crosstalk in TME. Furthermore, multiple
ligand–receptor pairs associated with malignant, immune, and
stromal cells were identified, some of which have been reported
to play a significant role in lung cancer. For example, the
TNFRSF12A/Fn14 signaling axis activates NF-kB to promote the
survival of LUAD cells (31), and IGF2 promotes neovascularisation
in LUAD (32). However, SEMA3B attenuates tumorigenesis and
angiogenesis (33). Furthermore, a complex relationship was
B
CDE
FGH
A
FIGURE 9
Prognostic value analysis of Nomogram was constructed by combining age, gender, pathological stage, smoking history, and ARS. (A) Construction
of Nomogram with 1-, 3- and 5-year survival rates of 0.963, 0.859, and 0.769 for the example sample, respectively. (B) Calibration curve to assess
the prediction accuracy of Nomogram with a Concordance index (C-index) of 0.768 (se = 0.018). (C) The ROC curves of the Nomogram assessed
their 1-, 3-, and 5-year overall survival with AUC values of 0.78, 0.82, and 0.81, respectively. (D) Decision curve analysis as well as (E)Time-
dependent C-index calculations showed that the Nomogram outperformed any other clinical characteristics in predicting overall survival. (F-H)
Kaplan-Meier and ROC curves for overall survival for the GSE31210, GSE50081, and GSE72094 cohorts.
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observed between State1 and angiogenesis, and several novel
cellular communication modes of State1 cells were identified.
State1 cells were found to communicate closely with fibroblasts
and M2-type macrophages via the POSTIN–(ITGAV+ITGB5) and
MDK–(ITGA6+ITGB1) signaling pathways, respectively; however,
State2 cells promoted tumorigenesis by interacting with
microenvironmental cells through a different communication
mode, such as the HBEGF–EGFR pathway that induces the
proliferation and growth of lung cancer cells (34). State3 cells
were also regulated by different ligand–receptor pairs. Therefore,
angiogenesis mediates intercellular communication in the
LUAD microenvironment.
Previous studies have demonstrated that abnormal angiogenesis
is associated with the function and migration of immune cells (35).
However, anti-angiogenic therapy has been shown to improve the
response to immunotherapy while preventing tumor immune
escape (36). Given the significant role of angiogenesis in the
prognosis of LUAD and TME, an individualized prognostic
model (ARS) based on angiogenesis-related genes was constructed
in this study for assessing the TME and survival of patients with
LUAD. ARS can be considered an independent prognostic factor for
LUAD and can guide individualized treatment strategies. It was
significantly correlated with immune-related pathways, cell cycle,
and drug metabolism and was significantly positively correlated
with the infiltration of Th2 cells and neutrophils. Th2 cells can form
an immunosuppressive microenvironment and promote tumor
immune escape (37). However, ARS had a significant negative
correlation with the infiltration of anti-tumor immune cells such
as CD8+ T cells, with the high and low ARSs characterizing the
immunosuppressive and anti-tumor immune microenvironments,
respectively. Significant differences were observed in mutation
frequencies between the high- and low-ARS groups. TP53
mutations significantly increased the expression of immune
checkpoints and were associated with the significant clinical
benefits of PD-1 inhibitors (38). KEAP1-driven co-mutations in
LUAD are closely associated with having high TMB but not
responding to immunotherapy (39). In this study, significant
differences in mutation frequencies between the high- and low-
ARS groups and their close correlation with immunotherapy
response indicated that ARS can help to individually assess the
immune infiltration status, immunotherapeutic response, and
chemotherapeutic drug sensitivity in patients with LUAD. In
addition, both immunotherapy cohort and TIDE algorithm
predictions suggested that the low-ARS group benefitted
from immunotherapy.
Specific sensitive chemotherapeutic agents were predicted in the
high-ARS group to guide LUAD chemotherapy. Paclitaxel and
docetaxel have been extensively used in the treatment of LUAD
(40,41). Cabazitaxel, paclitaxel (42), and epothilone (43)are
commonly used in chemotherapy for advanced non-small cell
lung cancer; they stabilize microtubules and cause apoptosis of
tumor cells. KX2-391 can reduce cell proliferation and angiogenesis,
thereby inhibiting tumor growth (44). Also, gefitinib has excellent
efficacy in the treatment of LUAD (45). Irinotecan in combination
with gemcitabine and cisplatin can be used as a first-line treatment
for advanced LUAD (46). However, the role of CR-1-31B,
litronesib, and ispinesib in LUAD remains unclear. Although
topotecan, vincristine, and rubitecan are widely used for the
treatment of small cell lung cancer, their efficacy in LUAD
treatment warrants further investigation. In this study, drug
sensitivity analysis revealed that the high-ARS group was more
sensitive to the abovementioned drugs, indicating that patients with
high ARSs may benefit from these chemotherapeutic drugs.
Given that ARS has a good prognostic value, a multifactorial
regression model was constructed, and the accuracy of prognostic
prediction (3-year AUC of 0.82) was significantly improved with
excellent discrimination (47). The accuracy is comparable to our
previously established prognostic models related to sumoylation
and M2 macrophages, and ARS can be combined with them in
prognostic assessments (48,49). Although the role of angiogenesis
in mediating intercellular crosstalk in the TME of LUAD was
examined by analyzing angiogenic pathway activation, the
underlying mechanisms warrant comprehensive and in-depth
investigation. Therefore, more single-cell sequencing studies
should be conducted to refine the exploration of the role of
angiogenesis in mediating the TME of LUAD. However,
alterations in circRNA and miRNA levels are also important
mechanisms (50). Due to the lack of these data, our multi-omics
analysis was limited to the mRNA level, and in the future, more
abundant and comprehensive data for multi-omics analysis will be
needed for further analysis. Finally, the predictive efficiency of the
prognostic model established in this study was high in both training
and validation cohorts; however, more LUAD and immunotherapy
cohorts are required to validate the results to further improve the
accuracy of the prognostic model.
5 Conclusions
In conclusion, the assessment of angiogenic clusters helps to
determine the prognostic and TME characteristics of LUAD.
Heterogeneity in the activation of angiogenesis in LUAD is regulated
by regulon submodules. There are significant differences in the cell
communication patterns in TME between different angiogenic
activation states. We further constructed a highly accurate prognostic
model to assist in the clinical assessment of individualized LUAD
patient prognosis and tumor microenvironment and to facilitate
the assessment of immunotherapy response and sensitive
chemotherapeutic agents.
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found in the article/Supplementary Material.
Ethics statement
The studies involving humans were approved by Medical Ethics
Committee at The Affiliated First Hospital of Soochow University.
Tang et al. 10.3389/fimmu.2024.1352893
Frontiers in Immunology frontiersin.org16

The studies were conducted in accordance with the local legislation
and institutional requirements. Written informed consent for
participation was not required from the participants or the
participants’legal guardians/next of kin in accordance with the
national legislation and institutional requirements.
Author contributions
LT: Conceptualization, Data curation, Formal analysis,
Software, Validation, Visualization, Writing –original draft,
Writing –review & editing. ZC: Conceptualization, Formal
analysis, Investigation, Methodology, Software, Validation,
Visualization, Writing –review & editing, Writing –original
draft. JY: Validation, Visualization, Writing –review & editing.
QL: Data curation, Methodology, Writing –review & editing. SW:
Data curation, Formal analysis, Writing –review & editing. TM:
Data curation, Formal analysis, Writing –review & editing. WZ:
Data curation, Formal analysis, Software, Validation, Writing –
review & editing. HD: Formal analysis, Methodology, Software,
Supervision, Validation, Writing –review & editing. SP:
Conceptualization, Funding acquisition, Supervision, Writing –
review & editing.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. This work
was supported by the Natural Science Foundation for the Youth of
Jiangsu Province (No. BK20200196) and the Research Project of
Gusu Health Talents in Suzhou (No. GSWS2021013).
Acknowledgments
We thank Bullet Edits Limited for the linguistic editing.
Conflict of interest
SP was employed by Suzhou Gene Pharma Co., Ltd.
The remaining authors declare that the research was conducted
in the absence of any commercial or financial relationships that
could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of theiraffiliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/fimmu.2024.1352893/
full#supplementary-material
SUPPLEMENTARY FIGURE 1
Consensus clustering of lung adenocarcinoma based on angiogenesis-
related genes.(A-C)Consensus clustering of lung adenocarcinoma
samples from the GEO cohort based on the expression of angiogenesis-
related genes (K = 2, K values determined from CDF curves). (D-G) Survival
analysis of clustered results in an independent data set was performed to
verify prognostic significance. (H-I) Consensus clustering of TCGA cohort
based on angiogenesis-related genes (K = 2, K values determined from
CDF curves).
SUPPLEMENTARY FIGURE 2
Cells communicate in the tumor microenvironment with different ligand-
receptor pairs. (A) Afferent signaling coordination modes of cell-ligand
receptor pairs can be divided into two types. (B) State1 cells communicate
with M2 macrophages via TNFSF12-TNFRSF12A and (C) IGF2-(ITGA6+ITGB4).
(D) State1 cells communicate with M2 macrophages and CD4 T cells via
MDK-(ITGA6+ITGB1). (E)State1 cells communicate extensively with other
cells of the tumor microenvironment via SEMA3B-(NRP2+PLXNA2). (F)
State1 cells are in close contact with Fibroblasts and Endothelial via
POSTN-(ITGAV+ITGB5).
SUPPLEMENTARY FIGURE 3
Ligand receptor pairs mediating cell communication between cell state3 and
the tumor microenvironment. (A) State3 cells communicate with Endothelial
via CALCA-CALCRL, (B) with Fibroblasts via FGF8-FGFR1, and (C) with M2-
type macrophages and cDC via CGA-FSHR. (D) State3 cells send signals to
M2-type macrophages and various other cells via ARTN-GFRA1. Through (E)
LIF-(LIFR+IL6ST) and (F)VEGFA-VEGFR1, State3 can communicate extensively
with Mast, M2-type macrophages, and Endothelial each other.
SUPPLEMENTARY FIGURE 4
Prognostic performance assessment for ARS risk scores. (A) Receiver
operating characteristic (ROC) curves for overall survival (OS) in the high
ARS and low ARS groups were evaluated in the training cohort (N = 532), test
cohort (N = 352), external independent validation cohort TCGA cohort (N =
500), and Whole GEO cohort (N = 884), respectively. (B) Multivariate Cox
analysis combining age, sex, pathological stages, smoking history, and other
clinical characteristics confirmed the independent prognostic value of ARS in
lung adenocarcinoma (HR, 3.12 (95% CI, 2.36-4.12), P<0.001).
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