Exploring Autophagy-Related Gene Expression in Hepatocellular Carcinoma via TCGA, GEPIA2, and HPA Databases: Implications for Prognosis

Abstract

Xiangran Gu,1,* Yuan Chen,2,* Xinyue Hu,1,* Yunhui Li,3 Renlong Zhu,2 Hongxu Li,1 Zhengrong Yuan,1 Yajie Wang2,3 1College of Biological Sciences and Technology, Beijing Forestry University, Beijing, People’s Republic of China; 2Department of Clinical Laboratory, Peking University Ditan Teaching Hospital, Beijing, People’s Republic of China; 3Department of Clinical Laboratory, Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhengrong Yuan, College of Biological Sciences and Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Haidian District, Beijing, 100083, People’s Republic of China, Email [email protected] Yajie Wang, Department of Clinical Laboratory, Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China, Email [email protected]: This study aimed to identify autophagy-related genes (ARGs) with prognostic significance in hepatocellular carcinoma (HCC) using bioinformatics and survival analysis.Materials and Methods: ARGs were sourced from multiple references, including the Human Autophagy Database (HADb), relevant literatures, the Gene Set Enrichment Analysis (GSEA), and a final list was confirmed after eliminating duplicate entries. Differential expression analysis between normal and tumor tissues relied on data from The Cancer Genome Atlas (TCGA). Subsequently, the univariate and multivariate Cox regression analysis, along with the Kaplan-Meier survival analysis, were conducted to identify survival-associated genes. These findings were cross-validated using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database and the Human Protein Atlas (HPA) database. Furthermore, expression levels of randomly selected ARGs were validated in HCC and normal cell lines using Real-time quantitative PCR (RT-qPCR), confirming bioinformatics findings.Results: 41 ARGs were pinpointed. The bioinformatics analysis revealed elevated expression levels of these genes in HCC tissues compared to normal tissues. Notably, mRNA expression levels of ARGs were markedly higher in the tumor tissue samples than in the normal liver tissue samples. This observation was corroborated by data from the GEPIA2 and HPA databases, except for ATG4B and CAPN10. Results from the HPA database aligned with those from the TCGA analysis. GSEA uncovered potential signaling pathways associated with ARGs, including pathways relevant to cancer and autophagy. RT-qPCR analysis further confirmed significant upregulation of mRNA expression levels of randomly selected BAG3, EIF2AK2, KIF5B, and RAB24 in HCC cell lines, consistent with the bioinformatics analysis findings.Conclusion: This study showed that the 41 obtained ARGs, such as ATG16L1, ATG4B, BAG3, KIF5B, MAPK1, RAB24, and SOGA1, these findings suggest that ARGs may serve as prognostic biomarkers for HCC, warranting further validation in clinical cohorts and functional studies.Keywords: hepatocellular carcinoma, bioinformatics, autophagy, prognostic markers, TCGA

Keywords

• Hepatocellular carcinoma
• bioinformatics
• autophagy
• prognostic markers
• TCGA