期刊
FRONTIERS IN GENETICS
卷 11, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2020.607009
关键词
colorectal cancer; immunity; prediction model; gene signature; prognosis
资金
- Key Technology Research and Development Program of Zhejiang Province [2017C03017]
- National Natural Science Foundation of China [81672916, 11932017, 81802750]
- Natural Science Foundation of Zhejiang Province [LQ20H180014]
- China Postdoctoral Science Foundation [2019M652117]
Background Despite recent advance in immune therapy, great heterogeneity exists in the outcomes of colorectal cancer (CRC) patients. In this study, we aimed to analyze the immune-related gene (IRG) expression profiles from three independent public databases and develop an effective signature to forecast patient's prognosis. Methods IRGs were collected from the ImmPort database. The CRC dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another two CRC datasets from the Gene Expression Omnibus (GEO). Gene function enrichment analysis was conducted. A prognostic nomogram was built incorporating the IRG signature with clinical risk factors. Results The three datasets had 487, 579, and 224 patients, respectively. A prognostic six-gene-signature (CCL22, LIMK1, MAPKAPK3, FLOT1, GPRC5B, and IL20RB) was developed through feature selection that showed good differentiation between the low- and high-risk groups in the training set (p < 0.001), which was later confirmed in the two validation groups (log-rank p < 0.05). The signature outperformed tumor TNM staging for survival prediction. GO and KEGG functional annotation analysis suggested that the signature was significantly enriched in metabolic processes and regulation of immunity (p < 0.05). When combined with clinical risk factors, the model showed robust prediction capability. Conclusion The immune-related six-gene signature is a reliable prognostic indicator for CRC patients and could provide insight for personalized cancer management.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据