摘要
Objective: Tumor microenvironment, especially the host immune system, plays a pivotal role in tumor initiation and progression. Profiling of immune signature within tumor might uncover biomarkers for targeted therapies and clinical outcomes. However, systematic analysis of immune-related genes in gastric cancer(GC) has not been reported.Methods: Expressions of a total of 718 immune-related genes were generated in 372 stomach adenocarcinoma(STAD) patients from The Cancer Genome Atlas(TCGA) database using RNA-sequencing data. Integrated bioinformatics analyses were performed to identify prognostic factors as well.Results: Survival analyses revealed 73 genes, which were significantly associated with patient's overall survival(OS). Taken together with clinicopathological parameters, we established a predictive model, containing 10 immune-related genes, which were NRP1, C6, CXCR4, LBP, PNMA1, TLR5, ITGA6, MICB, PBK and TNFRSF18,with powerful efficiency in distinguishing satisfactory or poor survival of STAD patients. Moreover, the top 3 ranked prognostic genes, NRP1, TGFβ2 and MFGE8, were also significantly associated with patient's OS by an independent validation achieved from Kaplan-Meier plotter database.Conclusions: We profiled prognostic immune signature and established prognostic predictive model for GC,which could reflect immune disorders within tumor microenvironment, and also may provide novel predictive and therapeutic targets for GC patients in the near future.
Objective: Tumor microenvironment, especially the host immune system, plays a pivotal role in tumor initiation and progression. Profiling of immune signature within tumor might uncover biomarkers for targeted therapies and clinical outcomes. However, systematic analysis of immune-related genes in gastric cancer(GC) has not been reported.Methods: Expressions of a total of 718 immune-related genes were generated in 372 stomach adenocarcinoma(STAD) patients from The Cancer Genome Atlas(TCGA) database using RNA-sequencing data. Integrated bioinformatics analyses were performed to identify prognostic factors as well.Results: Survival analyses revealed 73 genes, which were significantly associated with patient's overall survival(OS). Taken together with clinicopathological parameters, we established a predictive model, containing 10 immune-related genes, which were NRP1, C6, CXCR4, LBP, PNMA1, TLR5, ITGA6, MICB, PBK and TNFRSF18,with powerful efficiency in distinguishing satisfactory or poor survival of STAD patients. Moreover, the top 3 ranked prognostic genes, NRP1, TGFβ2 and MFGE8, were also significantly associated with patient's OS by an independent validation achieved from Kaplan-Meier plotter database.Conclusions: We profiled prognostic immune signature and established prognostic predictive model for GC,which could reflect immune disorders within tumor microenvironment, and also may provide novel predictive and therapeutic targets for GC patients in the near future.
引文
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