Construction and verification of a prognostic model combining anoikis and immune prognostic signatures for primary liver cancer.
10.12122/j.issn.1673-4254.2025.09.16
- Author:
Ying WANG
1
;
Jing LI
1
;
Yidi WANG
2
;
Mingyu HUA
1
;
Weibin HU
1
;
Xiaozhi ZHANG
1
Author Information
1. Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
2. Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
- Publication Type:Journal Article
- Keywords:
anoikis;
bioinformatics;
immune-related genes;
liver cancer;
prognosis model
- MeSH:
Humans;
Liver Neoplasms/diagnosis*;
Prognosis;
Anoikis;
Nomograms;
Computational Biology;
Tumor Microenvironment;
Semaphorins/metabolism*
- From:
Journal of Southern Medical University
2025;45(9):1967-1979
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:To establish a prognostic model for primary liver cancer (PLC) using bioinformatics methods.
METHODS:Based on the data from 404 patients in the Cancer Genome Atlas (TCGA) database, we constructed a prognostic model integrating the differentially expressed genes, anoikis, and immune-related genes (DAIs) using univariate Cox regression and the LASSO-Cox approach. The predictive ability of the model was evaluated using Kaplan-Meier method and receiver-operating characteristic curves, and a nomogram was developed to facilitate its clinical applications. Gene set enrichment analysis (GSEA) was performed to explore the associated pathways and relationship between the DAIs and the tumor immune microenvironment, and the half-maximal inhibitory concentration (IC50) of liver cancer drugs was calculated using the "pRRophetic" R package. We also detected the expression of SEMA7A in paired tumor and adjacent tissues from liver cancer patients.
RESULTS:We constructed and validated a prognostic model based on 7 DAIs (NR4A3, SEMA7A, IL11, AR, BIRC5, EGF, and SPP1), and obtained consistent results in both the TCGA training cohort and GEO validation cohort (GSE14520), where the patients in the low-risk group were characterized by more favorable clinical outcomes and immune status. By integrating this prognostic signature with clinical information, a composite nomogram was generated. Somatic mutation analysis showed that TTN, TP53, and CTNNB1 mutations accounted for the largest proportion of total mutations, and the patients in the low-risk-low-TMB group had higher survival rate. Drug sensitivity analysis revealed differences in sensitivity to chemotherapeutic agents between high- and low-risk groups and between TP53 mutations and non-mutations. In clinical tissue specimens, SEMA7A expression was significantly higher in liver cancer tissues than in the adjacent tissues.
CONCLUSIONS:We established a new prognostic model based on DAIs for predicting clinical outcomes and therapeutic response of patients with primary liver cancer.
