期刊
AMERICAN JOURNAL OF CANCER RESEARCH
卷 13, 期 1, 页码 45-+出版社
E-CENTURY PUBLISHING CORP
关键词
Colon cancer; prediction; prognosis; gene signature; risk model
类别
This study aimed to identify gene signatures that could predict the prognosis of colon cancer patients. A five-gene signature was identified and found to be associated with patient survival. The functional analysis revealed its association with extracellular matrix organization. The study also demonstrated the effect of the signature genes on the behavior of colon cancer cells.
Colon cancer is one of the most common cancers in digestive system, and its prognosis remains unsat-isfactory. Therefore, this study aimed to identify gene signatures that could effectively predict the prognosis of colon cancer patients by examining the data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. LASSO-Cox regression analysis generated a five-gene signature (DCBLD2, RAB11FIP1, CTLA4, HOXC6 and KRT6A) that was associated with patient survival in the TCGA cohort. The prognostic value of this gene signature was further validated in two independent GEO datasets. GO enrichment revealed that the function of this gene signature was mainly associated with extracellular matrix organization, collagen-containing extracellular matrix, and extracellular matrix structural constituent. Moreover, a nomogram was established to facilitate the clini-cal application of this signature. The relationships among the gene signature, mutational landscape and immune infiltration cells were also investigated. Importantly, this gene signature also reliably predicted the overall survival in IMvigor210 anti-PD-L1 cohort. In addition to the bioinformatics study, we also conducted a series of in vitro ex-periments to demonstrate the effect of the signature genes on the proliferation, migration, and invasion of colon cancer cells. Collectively, our data demonstrated that this five-gene signature might serve as a promising prognostic biomarker and shed light on the development of personalized treatment in colon cancer patients.
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