Discover oncology · 2025
Identification of a novel anoikis-related gene signature to predict prognosis and tumor microenvironment in intrahepatic cholangiocarcinoma carcinoma.
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Zhou Xuan, Wei Bai, Wang Yan, Liu Mingjie, Guo Xiangru, Duan Yuting
Аннотация
BACKGROUND: Intrahepatic cholangiocarcinoma is a malignant tumor of hepatobiliary epithelial cells. In recent years, its incidence has gradually increased. It has a very high fatality rate and low survival rate, and the existing predictive factors for intrahepatic cholangiocarcinoma are unclear. The role of anoikis, a form of programmed cell death, in intrahepatic cholangiocarcinoma is not fully understood. This study focuses on identifying and analyzing anoikis-related differentially expressed genes in intrahepatic cholangiocarcinoma, aiming to enhance our understanding of potential treatment strategies and prognosis of intrahepatic cholangiocarcinoma.
METHODS: In our study, we employed a clustering algorithm to classify samples from The Cancer Genome Atlas (TCGA) based on differentially expressed overlapping anoikis-related genes. Subsequently, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazard regression. We validated the model's reliability using external datasets from the International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO). Finally, we used the CIBERSORT algorithm to investigate the correlation between risk scores and immune infiltration.
RESULTS: The results showed that the TCGA cohort could be divided into 2 subgroups, among which subgroup B had a lower survival probability. We identified three prognostic genes (EGF, BNIP3, TDGF1) associated with anorexia. The prognostic risk model effectively predicted overall survival and was validated in ICGC and GEO data sets. Furthermore, there were significant correlations between infiltrating immune cells and prognostic genes and risk scores.
CONCLUSION: We identified subgroups and prognostic genes associated with ICCA dysregulation, which are important for understanding the treatment and prognosis of ICCA.
METHODS: In our study, we employed a clustering algorithm to classify samples from The Cancer Genome Atlas (TCGA) based on differentially expressed overlapping anoikis-related genes. Subsequently, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazard regression. We validated the model's reliability using external datasets from the International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO). Finally, we used the CIBERSORT algorithm to investigate the correlation between risk scores and immune infiltration.
RESULTS: The results showed that the TCGA cohort could be divided into 2 subgroups, among which subgroup B had a lower survival probability. We identified three prognostic genes (EGF, BNIP3, TDGF1) associated with anorexia. The prognostic risk model effectively predicted overall survival and was validated in ICGC and GEO data sets. Furthermore, there were significant correlations between infiltrating immune cells and prognostic genes and risk scores.
CONCLUSION: We identified subgroups and prognostic genes associated with ICCA dysregulation, which are important for understanding the treatment and prognosis of ICCA.