Analysis of Somatic Mutations in the TCGA-LIHC Whole Exome Sequence to Identify the Neoantigen for Immunotherapy in Hepatocellular Carcinoma

Abstract

There are numerous clinically proven methods for treating cancer worldwide. Immunotherapy has been used to treat cancer with significant success in the current studies. The purpose of this work is to identify somatically altered target gene neoantigens and investigate liver cancer-related immune cell interaction and functional changes for potential immunotherapy in future clinical trials. Clinical patient data from the Cancer Genome Atlas (TCGA) database were used in this investigation. The R maf utility package was used to perform somatic analysis. The 17-mer peptide neoantigens were extracted using an in-house Python software called Peptide.py. Additionally, the epitope analysis was conducted using NetMHCpan4.1 program. Neopeptide immunogenicity was assessed using DeepCNN-Ineo, and tumor immune interaction, association with immune cells, correlation, and survival analysis were assessed using the TIMER web server. Based on somatic mutation analysis, we have identified the top 10 driver genes (TP53, TNN, CTNNB1, MUC16, ALB, PCLO, MUC4, ABCA13, APOB, and RYR2). From the superfamily of 20 HLA (Human leukocyte antigens) allele epitopes, we discovered 5653 neopeptides. Based on T cell receptor face hydrophobic analysis, these neopeptides were subjected to immunogenicity investigation. A mutation linked to tumor growth may have an impact on immune cells. According to this study’s correlation and survival analysis, all driver genes may function as immune targets for liver cancer. These genes are recognized to be immune targets. In the future, immune checkpoint inhibitors may be developed to prolong patient survival times and prevent hepatocellular carcinoma (HCC) through immunotherapy.

Neoantigens in cancer immunotherapy: focusing on alternative splicing

“…Recent studies have demonstrated that peptides originating from tumor-specific mRNA splicing events possess the capability to interact with MHC class I (MHC I) molecules, acting as neoepitopes ( 44 , 45 ). A comprehensive The Cancer Genome Atlas (TCGA) study across various cancers suggested the involvement of alternative splicing programs in neoantigen generation by pinpointing cancer-specific exon-exon junctions and validating the presence of splicing-derived peptides through proteomics databases ( 46 , 47 ). Furthermore, intron retention has been highlighted as a crucial mechanism for neoantigen prediction, as numerous intron-retaining neoantigens have been identified through transcriptome sequencing and mass spectrometry analyses ( 48 ).…”