scholarly journals Development and Validation of a Combined Glycolysis and Immune Prognostic Model for Melanoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Yang ◽  
Yaling Li ◽  
Ruiqun Qi ◽  
Lan Zhang
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Gene Expressions ◽  
Glycolytic Gene ◽  
Prognostic Prediction ◽  
Immune Related Genes ◽  
And Function ◽  
Development And Validation

BackgroundGlycolytic effects and immune microenvironments play important roles in the development of melanoma. However, reliable biomarkers for prognostic prediction of melanoma as based on glycolysis and immune status remain to be identified.MethodsGlycolysis-related genes (GRGs) were obtained from the Molecular Signatures database and immune-related genes (IRGs) were downloaded from the ImmPort dataset. Prognostic GRGs and IRGs in the TCGA (The Cancer Genome Atlas) and GSE65904 datasets were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used for model construction. Glycolysis expression profiles and the infiltration of immune cells were analyzed and compared. Finally, in vitro experiments were performed to assess the expression and function of these CIGI genes.ResultsFour prognostic glycolysis- and immune-related signatures (SEMA4D, IFITM1, KIF20A and GPR87) were identified for use in constructing a comprehensive glycolysis and immune (CIGI) model. CIGI proved to be a stable, predictive method as determined from different datasets and subgroups of patients and served as an independent prognostic factor for melanoma patients. In addition, patients in the high-CIGI group showed increased levels of glycolytic gene expressions and exhibited immune-suppressive features. Finally, SEMA4D and IFITM1 may function as tumor suppressor genes, while KIF20A and GPR87 may function as oncogenes in melanoma as revealed from results of in vitro experiments.ConclusionIn this report we present our findings on the development and validation of a novel prognostic classifier for use in patients with melanoma as based on glycolysis and immune expression profiles.

scholarly journals Three Immune-Associated Subtypes of Diffuse Glioma Differ in Immune Infiltration, Immune Checkpoint Molecules, and Prognosis

2020 ◽  
Vol 10 ◽  
Author(s):  
Quanwei Zhou ◽  
Xuejun Yan ◽  
Weidong Liu ◽  
Wen Yin ◽  
Hongjuan Xu ◽  
...  
Keyword(s):  
Immune Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Checkpoints ◽  
Signal Pathways ◽  
Diffuse Glioma ◽  
Immune Infiltration ◽  
Immune Related Genes

Diffuse glioma is one of the most prevalent malignancies of the brain, with high heterogeneity of tumor-infiltrating immune cells. However, immune-associated subtypes of diffuse glioma have not been determined, nor has the effect of different immune-associated subtypes on disease prognosis and immune infiltration of diffuse glioma patients. We retrieved the expression profiles of immune-related genes from The Cancer Genome Atlas (TCGA) (n = 672) and GSE16011 (n = 268) cohorts and used them to identify subtypes of diffuse glioma via Consensus Cluster Plus analysis. We used the limma, clusterProfiler, ESTIMATE, and survival packages of R for differential analysis, functional enrichment, immune and stromal score evaluation respectively in three subtypes, and performed log-rank tests in immune subtypes of diffuse glioma. The immune-associated features of diffuse glioma in the two cohorts were characterized via bioinformatic analyses of the mRNA expression data of immune-related genes. Three subtypes (C1–3) of diffuse glioma were identified from TCGA data, and were verified using the GSE16011 cohort. We then evaluated their immune characteristics and clinical features. Our mRNA profiling analyses indicated that the different subtypes of diffuse glioma presented differential expression profile of specific genes and signal pathways in the TCGA cohort. Patients with subtype C1, who were mostly diagnosed with grade IV glioma, had poorer outcomes than patients with subtype C2 or C3. Subtype C1 was characterized by a higher degree of immune cell infiltration as estimated by GSVA, and more frequent wildtype IDH1. By contrast, subtype C3 included more grade II and IDH1-mutated glioma, and was associated with more infiltration of CD4+T cells. Most subtype C2 had the features between subtypes C1 and C3. Meanwhile, immune checkpoints and their ligand molecules, including PD1/(PD-L1/PDL2), CTLA4/(CD80/CD86), and B7H3/TLT2, were significantly upregulated in subtype C1 and downregulated in subtype C3. In addition, patients with subtype C1 exhibited more frequent gene mutations. Univariate and multivariate Cox regression analyses revealed that diffuse glioma subtype was an effective, independent, and better prognostic factor. Therefore, we established a novel immune-related classification of diffuse glioma, which provides potential immunotherapy targets for diffuse glioma.

scholarly journals Integrated analysis of lymphocyte infiltration-associated lncRNA for ovarian cancer via TCGA, GTEx and GEO datasets

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8961 ◽  
Author(s):  
Meijing Wu ◽  
Xiaobin Shang ◽  
Yue Sun ◽  
Jing Wu ◽  
Guoyan Liu
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Lymphocyte Infiltration ◽  
Gene Expressions ◽  
Gene Markers ◽  
Cox Regression Analysis ◽  
Cerna Network

Background Abnormal expression of long non-coding RNAs (lncRNA) play a significant role in the incidence and progression of high-grade serous ovarian cancer (HGSOC), which is a leading cause of mortality among gynecologic malignant tumor patients. In this study, our aim is to identify lncRNA-associated competing endogenous RNA (ceRNA ) axes that could define more reliable prognostic parameters of HGSOC, and to investigate the lncRNAs’ potential mechanism of in lymphocyte infiltration. Methods The RNA-seq and miRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database; while for obtaining the differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs), we used edgeR, limma and DESeq2. After validating the RNA, miRNA and gene expressions, using integrated three RNA expression profiles (GSE18520, GSE27651, GSE54388) and miRNA profile (GSE47841) from the Gene Expression Omnibus (GEO) database, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analyses through ClusterProfiler. The prognostic value of these genes was determined with Kaplan–Meier survival analysis and Cox regression analysis. The ceRNA network was constructed using Cytoscape. The correlation between lncRNAs in ceRNA network and immune infiltrating cells was analyzed by using Tumor IMmune Estimation Resource (TIMER), and gene markers of tumor-infiltrating immune cells were identified using Spearman’s correlation after removing the influence of tumor purity. Results A total of 33 DELs (25 upregulated and eight downregulated), 134 DEMs (76 upregulated and 58 downregulated), and 1,612 DEGs (949 upregulated and 663 downregulated) were detected that could be positively correlated with overall survival (OS) of HGSOC. With the 1,612 analyzed genes, we constructed a ceRNA network, which indicated a pre-dominant involvement of the immune-related pathways. Furthermore, our data revealed that LINC00665 influenced the infiltration level of macrophages and dendritic cells (DCs). On the other hand, FTX and LINC00665, which may play their possible roles through the ceRNA axis, demonstrated a potential to inhibit Tregs and prevent T-cell exhaustion. Conclusion We defined several prognostic biomarkers for the incidence and progression of HGSOC and constructed a network for ceRNA axes; among which three were indicated to have a positive correlation with lymphocyte infiltration, namely: FTX-hsa-miR-150-5p-STK11, LINC00665-hsa-miR449b-5p-VAV3 and LINC00665-hsa-miR449b-5p-RRAGD.

scholarly journals Immune-Related Genes: Potential Prognostic Factors and Regulatory Targets for Cervical Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Feng Xie ◽  
Xiaofeng Liu ◽  
Hua Liu ◽  
Min Wei ◽  
Wei Liu
Keyword(s):  
Regression Analysis ◽  
Survival Rate ◽  
Cervical Carcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Prognosis Model ◽  
Immune Related Genes

Background. Advanced cervical carcinoma carries a particularly poor prognosis, and few treatment options exist. It is very important to find a method to evaluate the prognosis and survival rate of cervical carcinoma. The metastasis and invasion of cervical carcinoma are closely related to tumor immune microenvironment (TIME), and immune related genes (IRGs) are involved in the regulation of TIME. However, the role of IRGs in the prognosis of patients with cervical carcinoma remains unclear. Methods. The gene expression profiles of cervical carcinoma were downloaded from The Cancer Genome Atlas (TCGA) database, and IRG information were obtained from the ImmPort database. The IRGs were screened by coexpression analysis and were also performed function enrichment and pathway analyses. A prognosis model was built based on IRGs, and the risk score (RS) was calculated by Cox regression analysis. The accuracy was assessed by receiver operating characteristic (ROC) curve analysis. Besides, the relationship between RS and TIMER-generating immune cell content was performed by immune infiltration analysis. Results. In a total of 2503 differentially expressed genes (DEGs), 204 genes were IRGs, 20 of which were crucially correlated with the survival rate of cervical carcinoma. On the basis of Cox regression analysis, 6 IRGs were included in the prognosis model to calculate the RS. Kaplan-Meier survival and ROC analyses showed that the prognostic function of the model was superior to the current model constructed by clinicopathological risk factors. In addition, these 6 IRG signatures were related to the immune infiltration levels of six immune cells and the overall survival (OS) of cervical carcinoma. Finally, C-terminal Src kinase (CSK) gene is related to tumor metastasis, and Slit guidance ligand 2 (Slit2) is related to tumor clinical stage. Conclusion. The IRGs may contribute to the stratification of prognosis, and CSK/Slit2 may be two suppressor genes for cervical carcinoma.

scholarly journals Development and Verification of a Prostate Cancer Prognostic Signature Based on an Immunogenomic Landscape Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hong Cheng ◽  
Yi Wang ◽  
Chunhui Liu ◽  
Tiange Wu ◽  
Shuqiu Chen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Normal Prostate ◽  
Tissue Samples ◽  
Tumor Stages ◽  
Normal Prostate Tissue ◽  
Immune Related Genes

PurposeProstate cancer (PCa) has a high incidence among older men. Until now, there are no immunological markers available to predict PCa patients’ survival. Therefore, it is necessary to explore the immunological characteristics of PCa.MethodsFirst, we retrieved RNA-seq and clinical data of 499 PCa and 52 normal prostate tissue samples from the Cancer Genome Atlas (TCGA). We identified 193 differentially expressed immune-related genes (IRGs) between PCa and normal prostate tissues. Functional enrichment analyses showed that the immune system can participate in PCa initiation. Then, we constructed a correlation network between transcription factors (TFs) and IRGs. We performed univariate and multivariate Cox regression analyses and identified five key prognostic IRGs (S100A2, NOX1, IGHV7-81, AMH, and AGTR1). Finally, a predictive nomogram was established and verified by the C-index.ResultsWe successfully constructed and validated an immune-related PCa prediction model. The signature could independently predict PCa patients’ survival. Results showed that high-immune-risk patients were correlated with advanced stage. We also validated the S100A2 expression in vitro using PCa and normal prostate tissues. We found that higher S100A2 expressions were related to lower biochemical recurrences. Additionally, higher AMH expressions were related to higher Gleason score, lymph node metastasis and positive rate, and tumor stages, and higher ATGR1 expressions were related to lower PSA value.ConclusionOverall, we detected five IRGs (S100A2, NOX1, IGHV7-81, AMH, and AGTR1) that can be used as independent PCa prognostic factors.

scholarly journals Development and Validation of a Combined Ferroptosis and Immune Prognostic Classifier for Hepatocellular Carcinoma

2020 ◽  
Vol 8 ◽  
Author(s):  
Yang Liu ◽  
Xi Zhang ◽  
Junjun Zhang ◽  
Juan Tan ◽  
Jie Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Somatic Mutation ◽  
Immune Status ◽  
Cox Regression ◽  
Expression Profiles ◽  
Suppressor Cells ◽  
The Cancer Genome Atlas ◽  
Individual Treatment ◽  
The Individual ◽  
Immune Related Genes

BackgroundImmunotherapy and sorafenib exert anti-tumor effects via ferroptosis, but reliable biomarkers for the individual treatment and prognosis prediction of hepatocellular carcinoma (HCC) based on the ferroptosis and immune status remain lacking.MethodsFerroptosis-related genes (FRGs) were identified by downloading data from FerrDb and by searching and reading original articles from PubMed. Immune-related genes (IRGs) were downloaded from ImmPort. Prognostic FRGs and IRGs in the GSE14520 (n = 220) and The Cancer Genome Atlas (TCGA, n = 365) datasets were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used for model construction. Ferroptosis expression profiles, the infiltration of immune cells, and the somatic mutation status were analyzed and compared.ResultsTwenty-seven prognostic ferroptosis- and immune-related signatures were included to construct a comprehensive index of ferroptosis and immune status (CIFI). A subgroup of patients was identified as having a high CIFI value, which was associated with a worse prognosis. This subgroup of patients had significantly up-regulated expressions of many suppressors of ferroptosis and higher fractions of immunosuppressive cells, such as cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs). Notably, somatic mutation analysis indicated that high-CIFI patients had higher levels of tumor heterogeneity and higher mutation frequencies of genes like TP53.ConclusionIn this work, a novel prognostic classifier was developed based on ferroptosis- and IRGs in HCC, and this classifier could be used for prognostic prediction and the selection of patients for immunotherapies and targeted therapies.

scholarly journals Immune-related gene data-based molecular subtyping related to the prognosis of breast cancer patients

Breast Cancer ◽  
2020 ◽  
Author(s):  
Guoyu Mu ◽  
Hong Ji ◽  
Hui He ◽  
Hongjiang Wang
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Tumor Development ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Prognostic Prediction ◽  
Immune Related Genes ◽  
Accuracy And Stability

Abstract Background Breast cancer (BC), which is the most common malignant tumor in females, is associated with increasing morbidity and mortality. Effective treatments include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular-targeted therapy. With the development of molecular biology, immunology and pharmacogenomics, an increasing amount of evidence has shown that the infiltration of immune cells into the tumor microenvironment, coupled with the immune phenotype of tumor cells, will significantly affect tumor development and malignancy. Consequently, immunotherapy has become a promising treatment for BC prevention and as a modality that can influence patient prognosis. Methods In this study, samples collected from The Cancer Genome Atlas (TCGA) and ImmPort databases were analyzed to investigate specific immune-related genes that affect the prognosis of BC patients. In all, 64 immune-related genes related to prognosis were screened, and the 17 most representative genes were finally selected to establish the prognostic prediction model of BC (the RiskScore model) using the Lasso and StepAIC methods. By establishing a training set and a test set, the efficiency, accuracy and stability of the model in predicting and classifying the prognosis of patients were evaluated. Finally, the 17 immune-related genes were functionally annotated, and GO and KEGG signal pathway enrichment analyses were performed. Results We found that these 17 genes were enriched in numerous BC- and immune microenvironment-related pathways. The relationship between the RiskScore and the clinical characteristics of the sample and signaling pathways was also analyzed. Conclusions Our findings indicate that the prognostic prediction model based on the expression profiles of 17 immune-related genes has demonstrated high predictive accuracy and stability in identifying immune features, which can guide clinicians in the diagnosis and prognostic prediction of BC patients with different immunophenotypes.

scholarly journals Construction of a Prognostic Value Model in Papillary Renal Cell Carcinoma by Immune-Related Genes

2020 ◽  
Author(s):  
Leilei Wang ◽  
Weile Gu ◽  
Huijun Ni
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Cox Regression ◽  
Expression Profiles ◽  
Papillary Renal Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Papillary renal cell carcinoma (PRCC) is the second most common type of renal carcinoma following clear cell renal cell carcinoma, and the role of immune-related genes (IRGs) in tumorigenesis and metastasis is evident; its prognostic value in PRCC remains unclear. In this study, we downloaded the gene expression profiles and clinical data of patients with PRCC from The Cancer Genome Atlas (TCGA) database and obtained IRGs from the ImmPort database. A total of 371 differentially expressed IRGs (DEIRGs) were discovered between PRCC and normal kidney tissues. Prognostic DEIRGs (PDEIRGs) were identified by univariate Cox regression analysis. Then, we screened the four most representative PDEIRGs (IL13RA2, CCL19, BIRC5, and INHBE) and used them to construct a risk model to predict the prognosis of patients with PRCC. This model precisely stratified survival outcome and accurately identified mutation burden in PRCC. Thus, our results suggest that these four PDEIRGs are available prognostic predictors for PRCC. They could be used to assess the prognosis and to guide individualized treatments for patients with PRCC.

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

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.

scholarly journals Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiwei Mu ◽  
Yi Tang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
Xiaohong Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Future Research ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Sets ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

Immune Characteristics-Related Typing of Colorectal Cancer and the Establishment of 14-Gene Prognostic Model

2021 ◽  
Author(s):  
Jianxing Ma ◽  
Chen Wang
Keyword(s):  
Colorectal Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Nonnegative Matrix ◽  
Test Group ◽  
Progression Free Survival ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Immune Related Genes

Abstract This study is to establish NMF (nonnegative matrix factorization) typing related to the tumor microenvironment (TME) of colorectal cancer (CRC) and to construct a gene model related to prognosis to be able to more accurately estimate the prognosis of CRC patients. NMF algorithm was used to classify samples merged clinical data of differentially expressed genes (DEGs) of TCGA that are related to the TME shared in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and survival differences between subtype groups were compared. By using createData Partition command, TCGA database samples were randomly divided into train group and test group. Then the univariate Cox analysis, Lasso regression and multivariate Cox regression models were used to obtain risk model formula, which is used to score the samples in the train group, test group and GEO database, and to divide the samples of each group into high-risk and low-risk groups, according to the median score of the train group. After that, the model was validated. Patients with CRC were divided into 2, 3, 5 subtypes respectively. The comparison of patients with overall survival (OS) and progression-free survival (PFS) showed that the method of typing with the rank set to 5 was the most statistically significant (p=0.007, p<0.001, respectively). Moreover, the model constructed containing 14 immune-related genes (PPARGC1A, CXCL11, PCOLCE2, GABRD, TRAF5, FOXD1, NXPH4, ALPK3, KCNJ11, NPR1, F2RL2, CD36, CCNF, DUSP14) can be used as an independent prognostic factor, which is superior to some previous models in terms of patient prognosis. The 5-type typing of CRC patients and the 14 immune-related genes model constructed by us can accurately estimate the prognosis of patients with CRC.

Sign in / Sign up

Export Citation Format

Share Document