Figure 4 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
Stratification of patients based on the median immune risk score. (A) Distribution of risk score, (B) survival overview and (C) mRNA expression levels in the entire The Cancer Genome Atlas cohort. (D) Distribution of risk score, (E) survival overview and (F) mRNA expression levels in the International Cancer Gene Consortium cohort. The dotted lines represent the median risk score cut-off dividing patients into low-and high-risk groups. The red dots and lines represent the patients in the high-risk group. The green dots and lines represent the patients in the low-risk group.
Source publication
Hepatocellular carcinoma (HCC) is one of the most malignant types of cancer, and is associated with high recurrence rates and a poor response to chemotherapy. Immune signatures in the microenvironment of HCC have not been well explored systematically. The aim of the present study was to identify prognostic immune signatures and build a nomogram for...
Contexts in source publication
Context 1
... final model was then used to calculate the IRS of patients for prognostic evaluation in four separate cohorts: The training cohort from TCGA ( Fig. 3A-C), the test cohort from TCGA ( Fig. 3D-F), the entire TCGA cohort (Fig. 4A-C) and the independent cohort from ICGC ( Fig. 4D-F). The median IRSs of the four cohorts were used to stratify patients into high-and low-score ...
Context 2
... final model was then used to calculate the IRS of patients for prognostic evaluation in four separate cohorts: The training cohort from TCGA ( Fig. 3A-C), the test cohort from TCGA ( Fig. 3D-F), the entire TCGA cohort (Fig. 4A-C) and the independent cohort from ICGC ( Fig. 4D-F). The median IRSs of the four cohorts were used to stratify patients into high-and low-score ...
Similar publications
The present study aimed to construct and evaluate a novel experiment-based hypoxia signature to help evaluations of GBM patient status. First, the 426 proteins, which were previously found to be differentially expressed between normal and hypoxia groups in glioblastoma cells with statistical significance, were converted into the corresponding genes...
Background:
Lower-grade gliomas (LGGs) are less aggressive with a long overall survival (OS) time span. Because of individualized genomic features, a prognostic system incorporating molecular signatures can more accurately predict OS.
Methods:
Differential expression analysis between LGGs and normal tissues was performed using the Gene Expressio...
Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer globally. However, the survival rate of lung adenocarcinoma patients remains low. Immune checkpoints and long noncoding RNAs are emerging as vital tools for predicting the immunotherapeutic response and outcomes of patients with lung adenocarcinoma. It is critical to identify ln...
Differentiation states of glioma cells correlated with prognosis and tumor-immune microenvironment (TIME) in patients with gliomas. We aimed to identify differentiation related genes (DRGs) for predicting the prognosis and immunotherapy response in patients with gliomas. We identified three differentiation states and the corresponding DRGs in gliom...
Background
Increasing studies have shown the important clinical role of immune and stromal cells in gastric cancer microenvironment. Based on information of immune and stromal cells in The Cancer Genome Atlas, this study aimed to construct a prognostic risk assessment model for gastric cancer.
Material/Methods
Based on the immune/structural scores...
Citations
... The survival outcomes for these cohorts within the TCGA-LUAD dataset and across five independent GEO datasets (GSE3141, GSE50081, GSE68465, GSE37745, and GSE31210) were analyzed using Kaplan-Meier survival curves, facilitated by the "survival" and "survminer" R packages. The prognostic potency of the derived risk score was corroborated by generating ROC curves and computing the AUC with the "survivalROC" R package (21), thereby quantifying the risk model's predictive precision. ...
Background
The tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD.
Method
Utilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2.
Results
Our study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD’s microenvironment. We underscored pDCs’ role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2’s function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment.
Conclusion
The creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.
... We selected patients diagnosed with ICCA from the Incidence-SEER Research Plus Data, 18 Based on the 1446 eligible patients, we further conducted a subgroup survival analysis based on the patient's cause of death, specifically whether it was due to the cancer, and whether the patient underwent surgical treatment. A total of four nomograms were established and validated to predict the survival status of ICCA patients. ...
... The agreement between predictions and observations was evaluated by the calibration curve using R package rms 17 . Compared to the AJCC 8th TNM staging system as a reference, the time-dependent area under the ROC curves (AUC) using R package survivalROC 18 and decision curve analysis (DCA) using R package rmda 19 were employed to evaluate the clinical utility and net benefit of the nomogram. In addition, based on the scores derived from the nomogram constructed using the training cohort, patients from two cohorts were divided into high-risk and low-risk groups in a 1:1 ratio accordingly. ...
This study aimed to develop and validate prognostic nomograms that can estimate the probability of 1-, 3- and 5-year overall survival (OS) as well as cancer-specific survival (CSS) for Intrahepatic cholangiocarcinoma (ICCA) patients. Clinical data of 1446 patients diagnosed with ICCA between 2010 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed. In both the OS and the CSS group, the training cohort and validation cohort were divided into a 7:3 ratio. Age, sex, AJCC T stage, AJCC N stage, AJCC M stage, surgical status, and tumor grade were selected as independent prognostic risk factors to build the nomograms. To compare the efficacy of predicting 1-, 3-, and 5-year OS and CSS rates of the nomogram with the 8th edition of the American Joint Committee on Cancer (AJCC) staging system, we evaluated the Harrell’s index of concordance (C-index), area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) in both cohorts. The results showed the nomogram for 1-, 3-, and 5-year OS and CSS prediction performed better than the AJCC staging system. In the subgroup analysis for patients could not receive surgery as the primary treatment. We developed two nomograms for predicting the 1-, and 2-year OS and CSS rates following the same analysis procedure. Results indicate that the performance of both nomograms, which contained sex, AJCC T stage, AJCC M stage, chemotherapy, and tumor grade and prognostic factors, was also superior to the AJCC staging system. Meanwhile, four dynamic network-based nomograms were published. The survival analysis showed the survival rate of patients classified as high-risk based on the nomogram score was significantly lower compared to those categorized as low-risk (P < 0.0001). Finally, accurate and convenient nomograms were established to assist clinicians in making more personalized prognosis predictions for ICCA patients.
... The model regression was constructed using the "glmnet" package [38]. The "timeROC" package [39] and "survivalROC" package [40] were performed to plot ROC curves for survival outcomes at different time points. Nomogram based on logistic regression and Cox regression was constructed using the "rms" package [41]. ...
Patients with recurrent or metastatic cervical cancer are in urgent need of novel prognosis assessment or treatment approaches. In this study, a novel prognostic gene signature was discovered by utilizing cuproptosis-related angiogenesis (CuRA) gene scores obtained through weighted gene co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. To enhance its reliability, the gene signature was refined by integrating supplementary clinical variables and subjected to cross-validation. Meanwhile, the activation of the VEGF pathway was inferred from an analysis of cell-to-cell communication, based on the expression of ligands and receptors in cell transcriptomic datasets. High-CuRA patients had less infiltration of CD8 + T cells and reduced expression of most of immune checkpoint genes, which indicated greater difficulty in immunotherapy. Lower IC50 values of imatinib, pazopanib, and sorafenib in the high-CuRA group revealed the potential value of these drugs. Finally, we verified an independent prognostic gene SFT2D1 was highly expressed in cervical cancer and positively correlated with the microvascular density. Knockdown of SFT2D1 significantly inhibited ability of the proliferation, migration, and invasive in cervical cancer cells. CuRA gene signature provided valuable insights into the prediction of prognosis and immune microenvironment of cervical cancer, which could help develop new strategies for individualized precision therapy for cervical cancer patients.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12935-023-03189-x.
... The agreement between predictions and observations was evaluated by the calibration curve using R package rms(He, Zhang et al. 2018). Compared to the AJCC 8th TNM staging system as a reference, the time-dependent area under the ROC curves (AUC) using R package survivalROC (Huang, Chen et al. 2020) and decision curve analysis (DCA) using R package rmda(Van Calster, Wynants et al. 2018) were employed to evaluate the clinical utility and net bene t of the nomogram. In addition, based on the scores derived from the nomogram constructed using the training cohort, patients from two cohorts were divided into high-risk and low-risk groups in a 1:1 ratio accordingly. ...
Background
Intrahepatic cholangiocarcinoma (ICCA) is one of the most malignant tumors of the biliary system. This study aims to develop and validate a prognostic nomogram that can estimate the probability of 1-, 3- and 5-year cancer-specific survival (CSS) for ICCA patients.
Methods
Clinical data of 1387 patients diagnosed with ICCA between 2010 and 2017 from the Surveillance, Epidemiology, and End Results database were analyzed. 970 patients were set as the training cohort to construct the nomogram while the rest 417 patients were set as the validation cohort. To compare the efficacy to predict 1-, 3-, and 5-year CSS of the nomogram with the 8th edition of the American Joint Committee on Cancer (AJCC) staging system, we evaluated the Harrell’s index of concordance (C-index), area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) in both cohorts. Meanwhile, a dynamic network-based nomogram was published.
Results
Age, sex, AJCC T stage, AJCC N stage, AJCC M stage, surgery status, and tumor grade were selected as independent prognostic risk factors to build the nomogram. The C-index in the training and validation cohorts were 0.726 and 0.711 respectively. The AUCs in the training cohort for 1-, 3-, and 5-year CSS prediction were 0.787, 0.828, and 0.843. In the validation cohort, the AUCs were 0.776, 0.817, and 0.782. All of the AUCs of the nomogram for 1-, 3-, and 5-year CSS prediction performed better than the AJCC staging system. Moreover, the DCA analysis demonstrated the net benefit of the nomogram was better. The survival analysis showed the survival rate of patients classified as high-risk based on the nomogram score was significantly lower compared to those categorized as low-risk (p < 0.0001).
Conclusions
An accurate and convenient nomogram was established to assist clinicians in making more personalized prognosis predictions for ICCA patients.
... We analyzed the difference in the recurrence of non-cardioembolic IS patients' high and low risk scores using the "survminer" package in R software [30]. To evaluate the accuracy of the risk model, we constructed ROC curves using the "survival ROC" package in both training and testing sets, respectively [31]. ...
Non-cardioembolic ischemic stroke (IS) is the predominant subtype of IS. This study aimed to construct a nomogram for recurrence risks in patients with non-cardioembolic IS in order to maximize clinical benefits. From April 2015 to December 2019, data from consecutive patients who were diagnosed with non-cardioembolic IS were collected from Lanzhou University Second Hospital. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. Multivariable Cox regression analyses were used to identify the independent risk factors. A nomogram model was constructed using the “rms” package in R software via multifactor Cox regression. The accuracy of the model was evaluated using the receiver operating characteristic (ROC), calibration curve, and decision curve analyses (DCA). A total of 729 non-cardioembolic IS patients were enrolled, including 498 (68.3%) male patients and 231 (31.7%) female patients. Among them, there were 137 patients (18.8%) with recurrence. The patients were randomly divided into training and testing sets. The Kaplan–Meier survival analysis of the training and testing sets consistently revealed that the recurrence rates in the high-risk group were significantly higher than those in the low-risk group (p < 0.01). Moreover, the receiver operating characteristic curve analysis of the risk score demonstrated that the area under the curve was 0.778 and 0.760 in the training and testing sets, respectively. The nomogram comprised independent risk factors, including age, diabetes, platelet–lymphocyte ratio, leukoencephalopathy, neutrophil, monocytes, total protein, platelet, albumin, indirect bilirubin, and high-density lipoprotein. The C-index of the nomogram was 0.752 (95% CI: 0.705~0.799) in the training set and 0.749 (95% CI: 0.663~0.835) in the testing set. The nomogram model can be used as an effective tool for carrying out individualized recurrence predictions for non-cardioembolic IS.
... Moreover, an immune gene expression signature was previously shown to be capable of distinguishing central nervous system metastasis from primary tumors in non-small-cell lung cancer [7]. Immune system-associated genes also exhibit statistically significant association with hepatocellular carcinoma, given their use to monitor the effectiveness of treatment strategies and function as markers for prognostic prediction in patients with hepatocellular carcinoma [8]. A recent comprehensive analysis further identified several immune-related genes as potential biomarkers for accurate prediction of survival in head and neck cancer patients [9]. ...
Background
Gastric cancer (GC) seriously threatens people’s health and life quality worldwide.
Aim
The current study sought to explore prognostic immune genes and their regulatory network in GC.
Methods
First, expression data in GC and normal samples were analyzed based on bioinformatics analysis. Immune-related genes were identified and confirmed with univariate/multivariate Cox analysis and receiver-operating characteristic curve. The upstream transcription factors of immune genes were subsequently predicted, and their regulatory network was constructed. GC and adjacent normal tissues were obtained from 76 patients with GC to determine the expression patterns of immune genes and their correlation with overall prognosis. CD8⁺ T-cell infiltration of patients with high or low risk was detected by means of immunohistochemistry.
Results
Bioinformatics analysis highlighted 3689 differentially expressed genes in GC, including 87 immune genes, 8 of which were significantly associated with patient survival. CGB5 and INHBA were high-risk genes, while TRAJ19 was identified as a low-risk gene, all of which were found to be regulated by 11 different transcription factors. Furthermore, CGB5 and INHBA exhibited negative correlation with the prognosis of GC patients; however, TRAJ19 was positively correlated with GC patient prognosis. The incidence of lymph node metastasis was higher, the pathological stage was advanced and the infiltrated CD8⁺ T cells were fewer in the high-risk GC group.
Conclusions
Overall, our findings identified the key roles of CGB5, INHBA and TRAJ19 in prognosis GC patients, serving as an important gene set for prognostic prediction.
... Using the survival and survminer packages (Wang et al., 2020b) in R, Kaplan-Meier survival analysis was performed to assess overall survival between high-and low-risk groups. To examine the specificity and sensitivity of the prognostic signature, time-dependent receiver operating characteristic (ROC) curves were calculated using the survivalROC package (Heagerty et al., 2000;Huang et al., 2020) in R. ...
Given the important role of SLC family in essential physiological processes including nutrient uptake, ion transport, and waste removal, and that their dysregulation was found in distinct forms of cancer, here we identified a novel gene signature of SLC family for patient risk stratification in osteosarcoma. Gene expression data and relevant clinical materials of osteosarcoma samples were retrieved from The Cancer Genome Atlas (TCGA) database. Prognosis-related SLC genes were identified by performing univariate Cox regression analysis and were utilized to construct a four-SLC gene signature in osteosarcoma. It allowed patients to be classified into high- and low-risk groups, and Kaplan-Meier survival analysis in the training, testing, entire, and external GSE21257 cohorts suggested that the overall survival of patients in high-risk group was consistently worse than that in low-risk group, suggesting the promising accuracy and generalizability of the SLC-based signature in predicting the prognosis of patients with osteosarcoma. Moreover, univariate and multivariate Cox regression analyses indicated that the derived risk score was the only independent prognostic factor for osteosarcoma patients in TCGA and GSE21257 cohorts. Besides, a prognostic nomogram comprising the derived risk score and clinical features including gender and age was developed for clinical decision-making. Functional enrichment analyses of the differentially expressed genes between high- and low-risk group revealed that immune-related biological processes and pathways were significantly enriched. Estimation of tumor immune microenvironment using ESTIMATE algorithm revealed that patients with lower risk score had higher stromal, immune, and ESTIMATE score, and lower tumor purity. ssGSEA analyses indicated that the scores of various immune subpopulations including CD8+ T cells, DCs, and TIL were lower in high-risk group than these in low-risk group in both cohorts. As for the related immune functions, the scores of APC co-inhibition, CCR, check-point, T cell co-stimulation, and Type II IFN response were lower in high-risk group than these in low-risk group in both cohorts. In all, we identified a novel prognostic signature based on four SLC family genes that accurately predicted overall survival in osteosarcoma patients. Furthermore, the signature is linked to differences in immunological status and immune cell infiltrations in the tumor microenvironment.
... More importantly, the immune cells within the tumor microenvironment significantly influence tumorigenesis, and these cells may fulfill antagonizing or promoting functions in tumors (Lei et al., 2020). Another report has indicated that immune system-associated genes have the potential to monitor the effectiveness of therapies and predict the prognosis of patients with liver cancer (Huang et al., 2020). Moreover, the infiltration of human tumors by lymphocytes has been studied, including its potential correlation with the prediction of prognosis (Sokratous et al., 2017). ...
Pancreatic cancer remains to have a high mortality, which is partly due to the lack of effective treatment strategies. In this study, genes with potential associations with immunophenotyping of pancreatic cancer were screened through bioinformatics analysis and the correlation between immune-related genes and the prognosis of pancreatic cancer patients was assessed. Firstly, differentially expressed immune genes were extracted from the pancreatic cancer-related datasets obtained for purposes of this study. The samples were processed by the “Consensus Cluster Plus” R package to determine the number of immune subtypes. Then, the pancreatic cancer immunophenotyping-related gene modules were determined. Differential analysis of immune gene modules was performed, and the function of genes related to pancreatic cancer immune subtypes was identified. The number of immune cells in the samples was calculated, followed by the differential expression analysis of immune cell numbers in each immune subtype of pancreatic cancer. The immune infiltration score was also estimated, and the correlation between the immune infiltration score and the patient prognosis with different immune subtypes was determined. Gene differences between each immune subtype were identified by differential expression analysis, and key immune genes affecting immunophenotyping were obtained. Following the analysis, 426 immune-related genes were identified to have potential involvement in the occurrence and development of pancreatic cancer, of which CD19 may be the most critical gene affecting the immunophenotyping of pancreatic cancer. CD19 played a significant role in the occurrence and development of IS2 and IS3 immune subtypes of pancreatic cancer through its action on B cells and T cells. Moreover, the expression of CD19 was increased in the collected pancreatic cancer tissues. Overall, our findings uncovered the critical role of CD19 in the prognosis of pancreatic cancer patients.
... Subsequently, survival analysis was implemented using the "survival" and "survminer" packages via log rank test, and the 5-year survival rate also was acquired. [59]. Moreover, calibration curves were utilized to evaluate the agreement of predictive and observed probabilities of 1-year, 3-year, and 5-year OS from the nomogram. ...
Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients’ overall survival (OS). The turquoise module (cor=0.67; P
... RF algorithm was calculated by the "randomForestSRC" package (Nasejje et al., 2017). The Kaplan-Meier test and ROC analysis were applied by using the "survival" and "survivalROC" packages (Therneau and Li, 1999;Huang et al., 2020). The best cutoff values were computed by using the "survminer" package (Zeng et al., 2019). ...
BackgroundCD8+ T cells work as a key effector of adaptive immunity and are closely associated with immune response for killing tumor cells. It is crucial to understand the role of tumor-infiltrating CD8+ T cells in uveal melanoma (UM) to predict the prognosis and response to immunotherapy.Materials and Methods
Single-cell transcriptomes of UM with immune-related genes were combined to screen the CD8+ T-cell-associated immune-related genes (CDIRGs) for subsequent analysis. Next, a prognostic gene signature referred to tumor-infiltrating CD8+ T cells was constructed and validated in several UM bulk RNA sequencing datasets. The risk score of UM patients was calculated and classified into high- or low-risk subgroup. The prognostic value of risk score was estimated by using multivariate Cox analysis and Kaplan–Meier survival analysis. Moreover, the potential ability of gene signature for predicting immunotherapy response was further explored.ResultsIn total, 202 CDIRGs were screened out from the single-cell RNA sequencing of GSE139829. Next, a gene signature containing three CDIRGs (IFNGR1, ANXA6, and TANK) was identified, which was considered as an independent prognostic indicator to robustly predict overall survival (OS) and metastasis-free survival (MFS) of UM. In addition, the UM patients were classified into high- and low-risk subgroups with different clinical characteristics, distinct CD8+ T-cell immune infiltration, and immunotherapy response. Gene set enrichment analysis (GSEA) showed that immune pathways such as allograft rejection, inflammatory response, interferon alpha and gamma response, and antigen processing and presentation were all positively activated in low-risk phenotype.Conclusion
Our work gives an inspiration to explain the limited response for the current immune checkpoint inhibitors to UM. Besides, we constructed a novel gene signature to predict prognosis and immunotherapy responses, which may be regarded as a promising therapeutic target.
