Identifying Prognostic Significance of RCL1 and Four-Gene Signature as Novel Potential Biomarkers in HCC Patients

Cellular senescence throws new insights into patient classification and pharmacological interventions for clinical management of hepatocellular carcinoma

BACKGROUND

Cellular senescence, a state of stable growth arrest, is intertwined with human cancers. However, characterization of cellular senescence-associated phenotypes in hepatocellular carcinoma (HCC) remains unexplored.

AIM

To address this issue, we delineated cellular senescence landscape across HCC.

METHODS

We enrolled two HCC datasets, TCGA-LIHC and International Cancer Genome Consortium (ICGC). Unsupervised clustering was executed to probe tumor heterogeneity based upon cellular senescence genes. Least absolute shrinkage and selection operator algorithm were utilized to define a cellular senescence-relevant scoring system. TRNP1 expression was measured in HCCs and normal tissues through immunohistochemistry, immunoblotting and quantitative real-time polymerase chain reaction. The influence of TMF-regulated nuclear protein (TRNP)1 on HCC senescence and growth was proven via a series of experiments.

RESULTS

TCGA-LIHC patients were classified as three cellular senescence subtypes, named C1-3. The robustness and reproducibility of these subtypes were proven in the ICGC cohort. C2 had the worst overall survival, C1 the next, and C3 the best. C2 presented the highest levels of immune checkpoints, abundance of immune cells, and immunogenetic indicators. Thus, C2 might possibly respond to immunotherapy. C2 had the lowest somatic mutation rate, while C1 presented the highest copy number variations. A cellular senescence-relevant gene signature was generated, which can predict patient survival, and chemo- or immunotherapeutic response. Experimentally, it was proven that TRNP1 presented the remarkable upregulation in HCCs. TRNP1 knockdown induced apoptosis and senescence of HCC cells and attenuated tumor growth.

CONCLUSION

These findings provide a systematic framework for assessing cellular senescence in HCC, which decode the tumor heterogeneity and tailor the pharmacological interventions to improve clinical management.

Identification of fatty acid metabolism-related clusters and immune infiltration features in hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) is a type of liver cancer which is characterized by inflammation-associated tumor. The unique characteristics of tumor immune microenvironment in HCC contribute to hepatocarcinogenesis. It was also clarified that aberrant fatty acid metabolism (FAM) might accelerate tumor growth and metastasis of HCC. In this study, we aimed to identify fatty acid metabolism-related clusters and establish a novel prognostic risk model in HCC. Gene expression and corresponding clinical data were searched from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) portal. From the TCGA database, by unsupervised clustering method, we determined three FAM clusters and two gene clusters with distinct clinicopathological and immune characteristics. Based on 79 prognostic genes identified from 190 differentially expressed genes (DEGs) among three FAM clusters, five prognostic DEGs (CCDC112, TRNP1, CFL1, CYB5D2, and SLC22A1) were determined to construct risk model by least absolute shrinkage and selection operator (LASSO) and multivariate cox regression analysis. Furthermore, the ICGC dataset was used to validate the model. In conclusion, the prognostic risk model constructed in this study exhibited excellent indicator performance of overall survival, clinical feature, and immune cell infiltration, which has the potential to be an effective biomarker for HCC immunotherapy.

Prognosis-Related Molecular Subtypes and Immune Features Associated with Hepatocellular Carcinoma

Bioinformatics tools were used to identify prognosis-related molecular subtypes and biomarkers of hepatocellular carcinoma (HCC). Differential expression analysis of four datasets identified 3330 overlapping differentially expressed genes (DEGs) in the same direction in all four datasets. Those genes were involved in the cell cycle, FOXO signaling pathway, as well as complement and coagulation cascades. Based on non-negative matrix decomposition, two molecular subtypes of HCC with different prognoses were identified, with subtype C2 showing better overall survival than subtype C1. Cox regression and Kaplan-Meier analysis showed that 217 of the overlapping DEGs were closely associated with HCC prognosis. The subset of those genes showing an area under the curve >0.80 was used to construct random survival forest and least absolute shrinkage and selection operator models, which identified seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14, and FANCI) that may be involved in HCC occurrence and prognosis. Based on the feature genes, risk score and recurrence models were constructed, while a univariate Cox model identified FANCI as a key gene involved mainly in the cell cycle, DNA replication, and mismatch repair. Further analysis showed that FANCI had two mutation sites and that its gene may undergo methylation. Single-sample gene set enrichment analysis showed that Th2 and T helper cells are significantly upregulated in HCC patients compared to controls. Our results identify FANCI as a potential prognostic biomarker for HCC.

Seven Hub Genes Predict the Prognosis of Hepatocellular Carcinoma and the Corresponding Competitive Endogenous RNA Network

Purpose

This study was aimed at identifying hub genes and ceRNA regulatory networks linked to prognosis in hepatocellular carcinoma (HCC) and to identify possible therapeutic targets.

Methods

Differential expression analyses were performed to detect the differentially expressed genes (DEGs) in the four datasets (GSE76427, GSE6764, GSE62232, and TCGA). The intersected DEmRNAs were identified to explore biological significance by enrichment analysis. We built a competitive endogenous RNA (ceRNA) network of lncRNA-miRNA-mRNA. The mRNAs of the ceRNA network were used to perform Cox and Kaplan-Meier analyses to obtain prognosis-related genes, followed by the selection of genes with an area under the curve >0.8 to generate the random survival forest model and obtain feature genes. Furthermore, the feature genes were subjected to least absolute shrinkage and selection operator (LASSO) and univariate Cox analyses were used to identify the hub genes. Finally, the infiltration status of immune cells in the HCC samples was determined.

Results

A total of 1923 intersected DEmRNAs were identified in four datasets and involved in cell cycle and carbon metabolism. ceRNA network was created using 10 lncRNAs, 67 miRNAs, and 1,923 mRNAs. LASSO regression model was performed to identify seven hub genes, SOCS2, MYOM2, FTCD, ADAMTSL2, TMEM106C, LARS, and KPNA2. Among them, TMEM106C, LARS, and KPNA2 had a poor prognosis. KPNA2 was considered a key gene base on LASSO and Cox analyses and involved in the ceRNA network. T helper 2 cells and T helper cells showed a higher degree of infiltration in HCC.

Conclusion

The findings revealed seven hub genes implicated in HCC prognosis and immune infiltration. A corresponding ceRNA network may help reveal their potential regulatory mechanism.