Identification and Validation of Hub Genes Predicting Prognosis of Hepatocellular Carcinoma

Comprehensive analysis of diverse programmed cell death patterns in the prognosis, tumor microenvironment and drug sensitivity in hepatocellular carcinoma

Treatment failure in patients with liver hepatocellular carcinoma (LIHC) is primarily caused by tumor progression and therapy resistance. Tumor immunity plays a crucial role in regulating the homeostasis of cells through the process of programmed cell death (PCD). However, the expression profile and clinical significance of PCD-related genes in LIHC require further investigation. In this study, we analyzed twelve commonly observed PCD patterns to construct a prognostic model. We collected RNA-seq data, genomics, and clinical information from TCGA-LIHC and GSE14520 cohorts to validate the prognostic gene signature. We discovered 75 PCD-related differentially expressed genes (DEGs) with prognostic significance in LIHC. Using these genes, we constructed a PCD-related score (PCDscore) with an 11-gene signature through LASSO COX regression analysis. Validation in the GSE14520 cohort demonstrated that LIHC patients with high PCDscore had poorer prognoses. Unsupervised clustering based on the 11 model genes revealed 3 molecular subtypes of LIHC with distinct prognoses. By incorporating PCDscore with clinical features, we constructed a highly predictive nomogram. Additionally, PCDscore was correlated with immune checkpoint genes and immune cell infiltration. LIHC patients with high PCDscore exhibited sensitivity to common chemotherapy drugs (such as cisplatin and docetaxel). To summarize, our study developed a novel PCDscore model that comprehensively analyzed different cell death modes, providing an accurate prediction of clinical prognosis and drug sensitivity for LIHC patients.

Construction of novel lncRNA-miRNA-mRNA ceRNA networks associated with prognosis of hepatitis C virus related hepatocellular carcinoma

Background

Hepatitis C virus (HCV) infection contribute to liver fibrosis and cirrhosis, which significantly increases the risk of hepatocellular carcinoma (HCC) development. Previous studies have demonstrated the pivotal role of competitive endogenous RNA (ceRNA) networks in tumorigenesis and cancer progression. Consequently, we herein seek to identify and evaluate the prognostic relevance of a novel ceRNA network associated with HCV-related HCC.

Methods

Differentially expressed genes (DEGs) in GSE140846 dataset from GEO were identified using Network Analyst, and GO, KEGG and Reactome analyses were performed. Furthermore, a protein-protein interaction network was generated, and hub genes were detected. Hub gene expression levels, as well as those of their upstream lncRNAs and miRNAs and associated survival analyses were conducted using appropriate bioinformatics databases. Predicted target relationships were used to establish putative ceRNA networks for HCV-related HCC.

Results

A total of 372 and 360 up- and down-regulated DE-mRNA were identified, which were associated with nuclear division, cell cycle, and ATPase activity. A PPI network containing 704 DE-mRNAs was constructed, and the 6 hub gene with the highest degree of connectivity were selected for subsequent analysis. We discovered that 22 miRNAs and 4 lncRNAs upstream of 11 hub gene were significantly associated with poor prognosis of HCV-related HCC, and used them to constructe a prognostic ceRNA network. Further experiments confirmed the ceRNA-regulatory relationship of BUB1-hsa-miR-193a-3p-MALAT1.

Conclusion

This study provides novel insights into the lncRNA-miRNA-mRNA ceRNA network, and reveals potential lncRNA biomarkers in HCV related HCC.