Multi-omics characterization of RNF157 expression patterns in hepatocellular carcinoma and development of an...

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Hepatocellular carcinoma (HCC) is a lethal cancer characterized by tumor heterogeneity and treatment resistance.
• Understanding the molecular drivers of HCC, such as E3 ubiquitin ligase RNF157, is crucial for improving patient outcomes.
• The role of RNF157 in HCC and its interaction with the tumor microenvironment (TME) remain incompletely understood.

Purpose of the Study:

• To characterize RNF157 expression patterns in HCC at the single-cell level.
• To develop a prognostic signature based on RNF157 and associated genes for HCC.
• To investigate the relationship between RNF157 expression and tumor microenvironment (TME) populations in HCC.

Main Methods:

• Integrated multi-omics and single-cell RNA sequencing (scRNA-seq) data analysis from public databases (TCGA, GEO).
• Construction of protein-protein interaction networks using the STRING database.
• Experimental validation using lentiviral shRNAs to target specific cell populations (FAP+ CAFs, CD11b+ TAMs) within the TME.

Main Results:

• Single-cell analysis revealed heterogeneous RNF157 expression across different cell types in HCC, particularly within cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs).
• A prognostic signature developed from RNF157-associated genes demonstrated moderate predictive performance for 1-5 year survival (AUC: 0.65-0.78).
• Experimental depletion of FAP in CAFs and CD11b in TAMs significantly reduced these TME populations, confirming successful manipulation.

Conclusions:

• RNF157 expression patterns in HCC have been characterized at single-cell resolution.
• A prognostic signature associated with RNF157 has been established, offering moderate predictive value.
• Further independent validation is required before the clinical application of this prognostic signature in HCC management.