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A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer.

ABSTRACT: PURPOSE:The aims of the present study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment. METHODS:High-throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Univariate Cox regression, LASSO, and multivariate Cox regression analyses were used to determine the final variables for construction of the prognostic risk score model. GSE17536 from the GEO database was used as an external validation dataset to evaluate the predictive power of the model. RESULTS:A total of 770 candidate DE-IRGs were obtained, and a prognostic risk score model was constructed by variable screening using the following 12 genes: FGFR4, LGR6, TRBV12-3, NUDT6, MET, PDIA2, ORM1, IGKV3D-20, THRB, WNT5A, FGF18, and CCR8. In the external validation set, the survival prediction C-index was 0.685, and the AUC values were 0.583, 0.731, and 0.837 for 1-, 2- and 3-year OS, respectively. Univariate and multivariate Cox regression analyses demonstrated that the risk score model was an independent prognostic factor for patients with stage IV CRC. High- and low-risk patient groups had significant differences in the expression of checkpoint coding genes (ICGs). CONCLUSION:The prognostic risk score model for stage IV CRC developed in the present study based on immune-related genes has acceptable predictive power, and is closely related to the expression of ICGs.

SUBMITTER: Xu K

PROVIDER: S-EPMC7584813 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer.

Xu Ke K He Jie J Zhang Jie J Liu Tao T Yang Fang F Ren Tao T

Bioscience reports 20201001 10

<h4>Purpose</h4>The aims of the present study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment.<h4>Methods</h4>High-throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Un ...[more]

PMID: 33034614

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Project description: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.

Project description:BackgroundAutophagy related genes (ARGs) regulate lysosomal degradation to induce autophagy, and are involved in the occurrence and development of a variety of cancers. The expression of ARGs in tumor tissues has a great prospect in predicting the survival of patients. The aim of this study was to construct a prognostic risk score model for lung adenocarcinoma (LUAD) based on ARGs.Methods5,786 ARGs were obtained from GeneCards database. Gene expression profiles and clinical data of 395 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. All ARGs expression data were extracted, and The ARGs differentially expressed were identified by R software. Survival analysis of differentially expressed ARGs was performed to screen for ARGs with prognostic value, and functional enrichment analysis was performed. The least absolute selection operator (LASSO) regression and Cox regression model were used to construct a prognostic risk scoring model for ARGs. The receiver operating characteristic (ROC) curve was drawn to obtain the optimal cut-off value of risk score. According to the cut-off value, the patients were divided into high-risk group and low-risk group. The area under curve (AUC) and the Kaplan-Meier survival curve was plotted to evaluate the model performance, which was verified in external data sets. Finally, univariate and multivariate Cox regression analysis was applied to evaluate the independent prognostic value of the model, and its clinical relevance was analyzed.ResultsSurvival analysis, Lasso regression and Cox regression analysis were used to construct a LUAD prognostic risk score model with five ARGs (ADAM12, CAMP, DKK1, STRIP2 and TFAP2A). The survival time of patients with low-risk score in this model was significantly better than that of patients with high-risk score (P<0.001). The model showed good prediction performance for LUAD in both the training set (AUCmax=0.78) and two external validation sets (AUCmax=0.88). Risk score was significantly associated with the prognosis of LUAD patients in univariate and multivariate Cox regression analyses, suggested that risk score could be a potential independent prognostic factor for LUAD. Correlation analysis of clinical characteristic showed that high risk score was closely associated with high T stage, high tumor stage and poor prognosis.ConclusionsWe constructed a LUAD risk score model consisting of five ARGs, which can provide a reference for predicting the prognosis of LUAD patients, and may be used in combination with tumor node metastasis (TNM) staging for prognosis prediction of LUAD patients in the future.

Project description:BackgroundRadiotherapy (RT) is one of the most important treatments for patients with colorectal cancer (CRC). Radioresistance is the crucial cause of poor therapeutic outcomes in colorectal cancer. However, the underlying mechanism of radioresistance in colorectal cancer is still poorly defined. Herein we established a radioresistant colorectal cancer cell line and performed transcriptomics analyses to search for the underlying genes that contribute to radioresistance and investigate its association with the prognosis of CRC patients.MethodsThe radioresistant cell line was developed from the parental HCT116 cell by a stepwise increased dose of irradiation. Differential gene analysis was performed using cellular transcriptome data to identify genes associated with radioresistance, from which extracellular matrix (ECM) and cell adhesion-related genes were screened. Survival data from a CRC cohort in the TCGA database were used for further model gene screening and validation. The correlation between the risk score model and tumor microenvironment, clinical phenotype, drug treatment sensitivity, and tumor mutation status were also investigated.ResultsA total of 493 different expression genes were identified from the radioresistant and wild-type cell line, of which 94 genes were associated with ECM and cell adhesion-related genes. The five model genes TNFRSF13C, CD36, ANGPTL4, LAMB3, and SERPINA1 were identified for CRC radioresistance via screening using the best model. A ROC curve indicated that the AUC of the resulting prognostic model (based on the 5-gene risk score and other clinical parameters, including age, sex, and tumor stages) was 0.79, 0.77, and 0.78 at 1, 2, and 3 years, respectively. The calibration curve showed high agreement between the risk score prediction and actual survival probability. The immune microenvironment, drug treatment sensitivity, and tumor mutation status significantly differed between the high- and low-risk groups.ConclusionsThe risk score model built with five radioresistance genes in this study, including TNFRSF13C, CD36, ANGPTL4, LAMB3, and SERPINA1, showed favorable performance in prognosis prediction after radiotherapy for CRC.

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A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer.

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A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer.

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