Identification of 13 kinds programmed cell death in male hepatocellular carcinoma based on WGCNA and machine learning algorithm

Abstract

Liver cancer is considered the sixth most common malignancy worldwide and is the second leading cause of cancer-related fatalities. The incidence rate in males is approximately three to four times greater than that in females. Additionally, male patients often experience higher rates of recurrence and poorer prognoses following a liver cancer diagnosis in comparison to women. Various forms of programmed cell death (PCD) have become significant disease phenotypes, with potential applications as targets for diagnosis and drug research aimed at male liver cancer patients. Transcriptome profiling related to liver cancer was sourced from the TCGA database. A set of 13 PCD-related genes (including pyroptosis, parthanatos, oxeiptosis, netotic cell death, necroptosis, lysosome-dependent cell death, ferroptosis, entotic cell death, disulfidptosis, cuproptosis, autophagy-dependent cell death, apoptosis, and alkaliptosis) was gathered from multiple public databases and literature reviews. Utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, transcriptomic datasets from the TCGA were examined to identify essential death genes within the core PCDs. Through the combination of three machine learning methodologies, we pinpointed three central PCD-related hub genes, specifically CDKN2A, CLTRN, and HGF, and conducted analyses on immune infiltration and ssGSEA. This study highlights three hub genes, identified through the integration of three machine learning algorithms with WGCNA analysis, as promising novel targets for the diagnosis and treatment of male liver cancer.