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pmid:33034614

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

Ke Xu · Jie He · Jie Zhang · Tao Liu · Fang Yang · Tao Ren

Bioscience reports · 2020 doi:10.1042/BSR20201725

Paper

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Experiment

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Data

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Analysis

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Execution

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title
A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer.
doi
10.1042/BSR20201725
pmid
33034614
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Ke Xu
[1]
Jie He
[2]
Jie Zhang
[3]
Tao Liu
[4]
Fang Yang
[5]
Tao Ren
journal
Bioscience reports
year
2020
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.
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The Kaplan–Meier method was used for survival analysis, and the log-rank test applied to evaluate differences between groups.
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The Kaplan–Meier method was used for survival analysis, and the log-rank test applied to evaluate differences between groups.
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The datasets generated and analyzed during the current study are available in TCGA official website repository (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/).
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The datasets generated and analyzed during the current study are available in TCGA official website repository (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/).
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Metastasis Gene Expression Profile Predicts Recurrence and Death in Colon Cancer Patients (Moffitt Samples)
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The clinical characteristics of IV CRC patients
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Clinical characteristics of patients with stage IV CRC
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The clinical characteristics of IV CRC patients
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GO and KEGG enrichment analysis of candidate IRGs in IV CRC ranked by P value (Top 5)
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GO and KEGG enrichment analysis of candidate IRGs in IV CRC ranked by P value (Top 5)
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Immune-related characteristics of high- and low-risk patients, based on the prognostic risk score model
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The relationship between the expression of IRGs included in this prognostic risk score model and the clinical outcome of immunotherapy (TISIDB)
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clinical treatment. METHODS: High-throughput RNA-sequencing, phenotype, and survival data from patients
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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.\nRESULTS: 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. 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