A logistic regression model for predicting the prognosis of hepatocellular carcinoma based on oxidative stress-related genes
10.3760/cma.j.cn113884-20240507-00134
- VernacularTitle:基于氧化应激相关基因构建预测肝细胞癌预后的logistic回归模型
- Author:
Wenshi OU
1
;
Shangeng WENG
;
Nanping LIN
Author Information
1. 福建医科大学附属第一医院滨海院区国家区域医疗中心，福州 350000
- Publication Type:Journal Article
- Keywords:
Carcinoma, hepatocellular;
Oxidative stress;
Prognostic model
- From:
Chinese Journal of Hepatobiliary Surgery
2024;30(12):908-911
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a logistic regression model to predict the prognosis of patients with hepatocellular carcinoma (HCC) based the oxidative stress-related genes.Methods:The HCC dataset was download from the cancer genome atlas (TCGA) database, and the oxidative stress-related genes were extracted from geneontology unifying biology database. Through the difference analysis and univariate Cox regression analysis of normal liver tissue and HCC tissue, the oxidative stress genes associated with HCC-related death were screened out. The least absolute shrinkage and selection opetator (LASSO) regression analysis were used to select prognostic oxidative stress genes, and the genes screened by LASSO regression were incorporated into the logistic regression model to construct the prognosis prediction model. The receiver operating characteristic (ROC) curve were used to assess the performance accuracy of the predictive model. The international cancer genome consortium (ICGC) database was used for external validation of the model.Results:A total of 147 oxidative stress-related genes were differentially expressed between HCC tissues and adjacent non-cancerous tissues (all P<0.05). Forty-three prognostic genes were identified by univariate Cox regression analysis and included in LASSO regression, and subsequently 11 features with nonzero coefficient were obtained. The 11 oxidative stress-related genes were incorporated into the logistic regression model. The findings revealed that HCC patients exhibiting positive expression of SRXN1, G6PD, MAPK7, STK25, GLRX2, ANKZF1, STC2, APEX1, PRKCD, MT3 and EZH2 genes had a significantly increased risk of postoperative death (all P<0.05). The logistic regression model was constructed as follows: 0.100 575×SRXN1+ 0.002 908×ANKZF1+ 0.010 061×MAPK7+ 0.022 816×STK25+ 0.018 489×GLRX2+ 0.004 291×G6PD+ 0.004 790×STC2+ 0.000 135×APEX1+ 0.007 531×PRKCD+ 0.025 770×MT3+ 0.079 615×EZH2. The logistic regression model demonstrated good predictive efficacy for the 1-, 2-, and 3-year survival of HCC patients after surgery, the values in area under the curve of ROC is 0.798, 0.752, and 0.720, respectively. According to the TCGA cohort, a median risk score model of 4.80 was established, which facilitated the division of ICGC patients into two distinct groups: the high-risk ( n=121) and low-risk ( n=122). The Kaplan-Meier survival curves demonstrated a significantly lower cumulative survival rate in the high-risk group ( P=0.003). Conclusion:The constructed logistic regression model of oxidative stress-related genes can be utilized to predict the outcome of HCC patients, thereby facilitating personalized treatment strategies and serving as a valuable reference for clinical decision-making.
