Identification of immune-related genes as prognostic factors in bladder cancer

AbstractBladder cancer is one of the most common cancers worldwide. The immune response and immune cell infiltration play crucial roles in tumour progression. Immunotherapy has delivered breakthrough achievements in the past decade in bladder cancer. Differentially expressed genes and immune-related genes (DEIRGs) were identified by using the edgeR package. Gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for functional enrichment analysis of DEIRGs. Survival-associated IRGs were identified by univariate Cox regression analysis. A prognostic model was established by univariate COX regression analysis, and verified by a validation prognostic model based on the GEO database. Patients were divided into high-risk and low-risk groups based on the median risk score value for immune cell infiltration and clinicopathological analyses. A regulatory network of survival-associated IRGs and potential transcription factors was constructed to investigate the potential regulatory mechanisms of survival-associated IRGs. Nomogram and ROC curve to verify the accuracy of the model. Quantitative real-time PCR was performed to validate the expression of relevant key genes in the prognostic model. A total of 259 differentially expressed IRGs were identified in the present study. KEGG pathway analysis of IRGs showed that the “cytokine-cytokine receptor interaction” pathway was the most significantly enriched pathway. Thirteen survival-associated IRGs were selected to establish a prognostic index for bladder cancer. In both TCGA prognostic model and GEO validation model, patients with high riskscore had worse prognosis compared to low riskscore group. A high infiltration level of macrophages was observed in high-risk patients. OGN, ELN, ANXA6, ILK and TGFB3 were identified as hub survival-associated IRGs in the network. EBF1, WWTR1, GATA6, MYH11, and MEF2C were involved in the transcriptional regulation of these survival-associated hub IRGs. The present study identified several survival-associated IRGs of clinical significance and established a prognostic index for bladder cancer outcome evaluation for the first time.

Download Full-text

Identification of Immune-Related Genes Associated With Bladder Cancer Based on Immunological Characteristics and Their Correlation With the Prognosis

Frontiers in Genetics ◽

10.3389/fgene.2021.763590 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhen Kang ◽

Wei Li ◽

Yan-Hong Yu ◽

Meng Che ◽

Mao-Lin Yang ◽

...

Keyword(s):

Bladder Cancer ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

External Validation ◽

Experimental Studies ◽

Training Group ◽

Validation Group ◽

Cox Regression Analysis ◽

Immune Related Genes

Background:To identify the immune-related genes of bladder cancer (BLCA) based on immunological characteristics and explore their correlation with the prognosis. Methods:We downloaded the gene and clinical data of BLCA from the Cancer Genome Atlas (TCGA) as the training group, and obtained immune-related genes from the Immport database. We downloaded GSE31684 and GSE39281 from the Gene Expression Omnibus (GEO) as the external validation group. R (version 4.0.5) and Perl were used to analyze all data. Result:Univariate Cox regression analysis and Lasso regression analysis revealed that 9 prognosis-related immunity genes (PIMGs) of differentially expressed immune genes (DEIGs) were significantly associated with the survival of BLCA patients (p < 0.01), of which 5 genes, including NPR2, PDGFRA, VIM, RBP1, RBP1 and TNC, increased the risk of the prognosis, while the rest, including CD3D, GNLY, LCK, and ZAP70, decreased the risk of the prognosis. Then, we used these genes to establish a prognostic model. We drew receiver operator characteristic (ROC) curves in the training group, and estimated the area under the curve (AUC) of 1-, 3- and 5-year survival for this model, which were 0.688, 0.719, and 0.706, respectively. The accuracy of the prognostic model was verified by the calibration chart. Combining clinical factors, we established a nomogram. The ROC curve in the external validation group showed that the nomogram had a good predictive ability for the survival rate, with a high accuracy, and the AUC values of 1-, 3-, and 5-year survival were 0.744, 0.770, and 0.782, respectively. The calibration chart indicated that the nomogram performed similarly with the ideal model. Conclusion:We had identified nine genes, including PDGFRA, VIM, RBP1, RBP1, TNC, CD3D, GNLY, LCK, and ZAP70, which played important roles in the occurrence and development of BLCA. The prognostic model based on these genes had good accuracy in predicting the OS of patients and might be promising candidates of therapeutic targets. This study may provide a new insight for the diagnosis, treatment and prognosis of BLCA from the perspective of immunology. However, further experimental studies are necessary to reveal the underlying mechanisms by which these genes mediate the progression of BLCA.

Download Full-text

Prognostic Model of Colorectal Cancer Constructed by Eight Immune-Related Genes

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.604252 ◽

2020 ◽

Vol 7 ◽

Author(s):

Shuting Wen ◽

Long He ◽

Zhuotai Zhong ◽

Hong Mi ◽

Fengbin Liu

Keyword(s):

Colorectal Cancer ◽

Prognostic Model ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Immune Cell Infiltration ◽

Cox Regression Analysis ◽

Prognostic Prediction ◽

Immune Related Genes

BackgroundColorectal cancer (CRC) is a common malignant tumor of the digestive tract with a high mortality rate. Growing evidence demonstrates that immune-related genes play a prominent role in the occurrence and development of CRC. The aim of this study was to investigate the prognostic value of immune-related genes in CRC.MethodsGene expression profiles and clinical data of 568 CRC and 44 non-tumorous tissues were obtained from The Cancer Genome Atlas (TCGA) database. First, we performed a differentially expressed gene (DEG) analysis and univariate Cox regression analysis to determine the DEGs associated with overall survival. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were subsequently performed for prognostic immune-related genes. Then, a multivariate Cox regression analysis was performed to establish the immune prognostic model and identify the independent prognostic factors of CRC. Next, in vitro experiments were done to further validate the model. Finally, we analyzed the correlation among immune-related genes, clinical traits, and immune cell infiltration.ResultsIn total, 3,702 DEGs were obtained, and 338 prognostic immune-related genes were identified. Among them, 45 genes were significantly correlated with the prognosis of CRC patients. A TF-mediated network was set up to explore its internal mechanism. GO and KEGG analyses further illustrated that these genes were enriched in immune-and inflammatory-related pathways. Then, a prognostic prediction model composed of eight immune-related genes (SLC10A2, UTS2, FGF2, UCN, IL1RL2, ESM1, ADIPOQ, and VIP) was constructed. The AUC of the ROC curve for 1, 3, 5, and 10 years overall survival (OS) was 0.751, 0.707, 0.680, and 0.729, respectively. The survival analysis suggested that the OS of the high-risk group was significantly poorer than that of the low-risk group. Meanwhile, in vitro assays revealed that ESM1 and SLC10A2 exert opposing roles in colon cancer cell proliferation, validating the accuracy of the model. The correlation analysis indicated that immune cell infiltration was positively related to the model.ConclusionThis study screened prognosis-related immune genes and developed a prognostic prediction model of CRC. These findings may help provide potential novel prognostic biomarkers and therapeutic targets for CRC. At the same time, the understanding of the CRC immune microenvironment status was deepened.

Download Full-text

Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.695001 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhipeng Zhu ◽

Mengyu Song ◽

Wenhao Li ◽

Mengying Li ◽

Sihan Chen ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Enrichment Analysis ◽

Expression Data ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Hepatocellular carcinoma is a common malignant tumor with poor prognosis, poor treatment effect, and lack of effective biomarkers. In this study, bioinformatics analysis of immune-related genes of hepatocellular carcinoma was used to construct a multi-gene combined marker that can predict the prognosis of patients. The RNA expression data of hepatocellular carcinoma were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the IMMPORT database. Differential analysis was performed by Wilcox test to obtain differentially expressed genes. Univariate Cox regression analysis, lasso regression analysis and multivariate Cox regression analysis were performed to establish a prognostic model of immune genes, a total of 5 genes (HDAC1, BIRC5, SPP1, STC2, NR6A1) were identified to construct the models. The expression levels of 5 genes in HCC tissues were significantly different from those in paracancerous tissues. The Kaplan-Meier survival curve showed that the risk score calculated according to the prognostic model was significantly related to the overall survival (OS) of HCC. The receiver operating characteristic (ROC) curve confirmed that the prognostic model had high accuracy. Independent prognostic analysis was performed to prove that the risk value can be used as an independent prognostic factor. Then, the gene expression data of hepatocellular carcinoma in the ICGC database was used as a validation data set for the verification of the above steps. In addition, we used the CIBERSORT software and TIMER database to conduct immune infiltration research, and the results showed that the five genes of the model and the risk score have a certain correlation with the content of immune cells. Moreover, through Gene Set Enrichment Analysis (GSEA) and the construction of protein interaction networks, we found that the p53-mediated signal transduction pathway is a potentially important signal pathway for hepatocellular carcinoma and is positively regulated by certain genes in the prognostic model. In conclusion, this study provides potential targets for predicting the prognosis and treatment of hepatocellular carcinoma patients, and also provides new ideas about the correlation between immune genes and potential pathways of hepatocellular carcinoma.

Download Full-text

An immune-associated gene prognostic index risk model for stomach adenocarcinoma

10.21203/rs.3.rs-841317/v1 ◽

2021 ◽

Author(s):

Weijie Xue ◽

Bingzi Dong ◽

Yixiu Wang ◽

Yuwei Xie ◽

Qingkai Xue ◽

...

Keyword(s):

T Cells ◽

Regression Analysis ◽

Risk Model ◽

Cox Regression ◽

Malignant Tumors ◽

Prognostic Index ◽

Low Risk ◽

Cox Regression Analysis ◽

Stomach Adenocarcinoma ◽

Immune Related Genes

Abstract Background Stomach adenocarcinoma (STAD) is one of the most common malignant tumors worldwide. In this study, we attempt to construct a valid immune-associated gene prognostic index risk model which could predict the survival of HCC patients and the efficacy of immune check point inhibitors (ICIs) treatment. Methods The transcriptome, clinical and gene mutational data were obtained from the TCGA database. And immune-related genes were downloaded from the ImmPort and InnateDB databases. Functional and enrichment analysis was performed to identify the potential molecular function and mechanism of these differentially expressed immune-associated genes. And then candidates genes related to overall survival (OS) of STAD was obtained by weighted gene co-expression network analysis (WGCNA). Next, the immune prognostic risk model was constructed via multivariate Cox regression analysis and verified with GEO STAD cohort. Afterwards, the association between the risk model and the immune characteristics and was estimated. Finally, the correlation between the risk model and efficacy of ICIs therapy. Results A total of 493 immune-related genes were identified to enriched in function associated to immune response as well as in immune and tumor-related pathways. Based on the cox regression analysis, we constructed an immune-associated gene prognostic index (IAGPI) risk model based on 8 genes (RNASE2, CGB5, INHBE, PTGER3, CTLA4, DUSP1, APOA1 and CD36). Patients were divided into two subsets according to risk score. Patients in low risk set had a better OS than those in high. In the low risk set, there were more CD8 T cells, activated memory CD4 T cells, follicular helper T cells and M1 macrophages, while monocytes, M2 macrophages, eosinophils and neutrophils were more plentiful in the high. And patients in the low risk set were more sensitive to ICIs therapy. Conclusion The IAGPI risk model can precisely predict prognosis, reflect tumor immune microenvironment and predict the efficacy of ICIs therapy in STAD patients.

Download Full-text

A Novel Signature to Predict Thyroid Cancer Prognosis and Immune Landscape Using Immune-Related LncRNA Pairs

10.21203/rs.3.rs-1026831/v1 ◽

2021 ◽

Author(s):

BO SONG ◽

Lijun Tian ◽

Fan Zhang ◽

Zheyu Lin ◽

Boshen Gong ◽

...

Keyword(s):

Regression Analysis ◽

Thyroid Cancer ◽

High Risk ◽

Small Molecule ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Cox Regression Analysis

Abstract Background: Thyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. TC is still often overtreated due to a lack of reliable diagnostic biomarkers. Therefore, determining accurate prognostic predictions to stratify TC patients is important.Methods: Raw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff score of the model for the 1-year ROC curve. We evaluated the signature in terms of prognostic independence, predictive value, immune cell infiltration, ICI-related molecules and small-molecule inhibitor efficacy. Results: We identified 30 DEirlncRNA pairs through Lasso regression, and 14 pairs served as the novel predictive signature. The high-risk group had a significantly poorer prognosis than the low-risk group. Cox regression analysis revealed that this immune-related signature can predict prognosis independently and reliably for TC. With the CIBERSORT algorithm, we found an association between the signature and immune cell infiltration. Additionally, several immune checkpoint inhibitor (ICI)-related molecules, such as PD-1 and PD-L1, showed a negative correlation with the high-risk group. We further found that some commonly used small-molecule inhibitors, such as sunitinib, were related to this new signature. Conclusions: We constructed a prognostic immune-related lncRNA signature that can predict TC patient survival without considering the technical bias of different platforms, and this signature also sheds light on TC overall prognosis and novel clinical treatments, such as ICB therapy and small molecular inhibitors.

Download Full-text

Systemic characterization of alternative splicing related to prognosis and immune infiltration in malignant mesothelioma

BMC Cancer ◽

10.1186/s12885-021-08548-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jinzhi Lai ◽

Hainan Yang ◽

Tianwen Xu

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Risk Score ◽

Regulatory Network ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Cox Regression Analysis

Abstract Background Malignant mesothelioma (MM) is a relatively rare and highly lethal tumor with few treatment options. Thus, it is important to identify prognostic markers that can help clinicians diagnose mesothelioma earlier and assess disease activity more accurately. Alternative splicing (AS) events have been recognized as critical signatures for tumor diagnosis and treatment in multiple cancers, including MM. Methods We systematically examined the AS events and clinical information of 83 MM samples from TCGA database. Univariate Cox regression analysis was used to identify AS events associated with overall survival. LASSO analyses followed by multivariate Cox regression analyses were conducted to construct the prognostic signatures and assess the accuracy of these prognostic signatures by receiver operating characteristic (ROC) curve and Kaplan–Meier survival analyses. The ImmuCellAI and ssGSEA algorithms were used to assess the degrees of immune cell infiltration in MM samples. The survival-related splicing regulatory network was established based on the correlation between survival-related AS events and splicing factors (SFs). Results A total of 3976 AS events associated with overall survival were identified by univariate Cox regression analysis, and ES events accounted for the greatest proportion. We constructed prognostic signatures based on survival-related AS events. The prognostic signatures proved to be an efficient predictor with an area under the curve (AUC) greater than 0.9. Additionally, the risk score based on 6 key AS events proved to be an independent prognostic factor, and a nomogram composed of 6 key AS events was established. We found that the risk score was significantly decreased in patients with the epithelioid subtype. In addition, unsupervised clustering clearly showed that the risk score was associated with immune cell infiltration. The abundances of cytotoxic T (Tc) cells, natural killer (NK) cells and T-helper 17 (Th17) cells were higher in the high-risk group, whereas the abundances of induced regulatory T (iTreg) cells were lower in the high-risk group. Finally, we identified 3 SFs (HSPB1, INTS1 and LUC7L2) that were significantly associated with MM patient survival and then constructed a regulatory network between the 3 SFs and survival-related AS to reveal potential regulatory mechanisms in MM. Conclusion Our study provided a prognostic signature based on 6 key events, representing a better effective tumor-specific diagnostic and prognostic marker than the TNM staging system. AS events that are correlated with the immune system may be potential therapeutic targets for MM.

Download Full-text

Development of an Immune-Related LncRNA Prognostic Signature for Glioma

Frontiers in Genetics ◽

10.3389/fgene.2021.678436 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yudong Cao ◽

Hecheng Zhu ◽

Jun Tan ◽

Wen Yin ◽

Quanwei Zhou ◽

...

Keyword(s):

Regression Analysis ◽

Clinical Outcome ◽

Immune Cell ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Infiltration

IntroductionGlioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management.Materials and methodsSingle-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature.ResultsA total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p < 0.05) in ssGSEA except neutrophils (p = 0.43).ConclusionThe study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.

Download Full-text

Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021333 ◽

2021 ◽

Vol 18 (5) ◽

pp. 6709-6723

Author(s):

Xin Yu ◽

◽

Jun Liu ◽

Ruiwen Xie ◽

Mengling Chang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Prognostic Model ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Lung Squamous Cell Carcinoma ◽

Risk Groups ◽

Low Risk ◽

Immune Cell Infiltration ◽

Cox Regression Analysis

<abstract> <sec><title>Objective</title>We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC). </sec> <sec><title>Methods</title>Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups. </sec> <sec><title>Results</title>Seven m6A-related autophagy genes were screened to construct a prognostic model: <italic>CASP4</italic>, <italic>CDKN1A</italic>, <italic>DLC1</italic>, <italic>ITGB1</italic>, <italic>PINK1</italic>, <italic>TP63</italic>, and <italic>EIF4EBP1</italic>. In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups. </sec> <sec><title>Conclusions</title>Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice. </sec> </abstract>

Download Full-text

Identification of Metabolism-Related Genes Influencing Prognosis of Multiple Myeloma Patients

Journal of Healthcare Engineering ◽

10.1155/2021/6574491 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Rui Wang ◽

Wenxuan Bu ◽

Yang Yang

Keyword(s):

Regression Analysis ◽

Candidate Genes ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Cox Regression Analysis ◽

Key Genes

Multiple myeloma (MM) is the second most commonly diagnosed hematological malignancy. Understanding the basic mechanisms of the metabolism in MM may lead to new therapies that benefit patients. We collected the gene expression profile data of GSE39754 and performed differential analysis. Furthermore, identify the candidate genes that affect the prognosis of the differentially expressed genes (DEGs) related to the metabolism. Enrichment analysis is used to identify the biological effects of candidate genes. Perform coexpression analysis on the verified DEGs. In addition, the candidate genes are used to cluster MM into different subtypes through consistent clustering. Use LASSO regression analysis to identify key genes, and use Cox regression analysis to evaluate the prognostic effects of key genes. Evaluation of immune cell infiltration in MM is by CIBERSORT. We identified 2821 DEGs, of which 348 genes were metabolic-related prognostic genes and were considered candidate genes. Enrichment analysis revealed that the candidate genes are mainly related to the proteasome, purine metabolism, and cysteine and methionine metabolism signaling pathways. According to the consensus clustering method, we identified the two subtypes of group 1 and group 2 that affect the prognosis of MM patients. Using the LASSO model, we have identified 10 key genes. The prognosis of the high-risk group identified by Cox regression analysis is worse than that of the low-risk group. Among them, PKLR has a greater impact on the prognosis of MM, and the prognosis of MM patients is poor when the expression is high. In addition, the level of immune cell infiltration in the high-risk group is higher than that in the low-risk group. In the summary, metabolism-related genes significantly affect the prognosis of MM patients through the metabolic process of MM patients. PKLR may be a prognostic risk factor for MM patients.

Download Full-text

Development of a prognostic model based on an immunogenomic landscape analysis of colorectal cancer

Future Oncology ◽

10.2217/fon-2020-0022 ◽

2020 ◽

Author(s):

HJ Li ◽

YL Wang ◽

L Ming ◽

XQ Guo ◽

YL Li ◽

...

Keyword(s):

Colorectal Cancer ◽

Regression Analysis ◽

Prognostic Model ◽

Operating Characteristic ◽

Cox Regression ◽

Area Under The Curve ◽

Patient Needs ◽

Cox Regression Analysis ◽

Immune Related Genes

Screening and therapeutic programs for colorectal cancer (CRC) are invasive or not effective and unable to meet patient needs. Major advances in immunogenomics may change this status but need more exploration. Differentially expressed genes and immune-related genes (IRGs) were identified by computational methods. A prognostic model was established and validated based on survival-related IRGs via stepwise multivariate Cox regression analysis. Nine IRGs were selected and identified as survival-related genes. A 7-gene prognostic model could offer a preliminary and valid determination of risk in CRC patients. The area under the curve of the receiver operating characteristic was 0.672. The 7-gene prognostic model might be used as a novel prognostic tool in CRC patients.

Download Full-text