Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Hepatocellular carcinoma (HCC) is an aggressive cancer with poor prognosis, particularly with microvascular invasion (MVI).
• Accurate identification of patients at high risk for MVI is essential for effective, personalized treatment strategies.
• Current prognostic models for HCC may not fully capture the complexities associated with MVI.

Purpose of the Study:

• To identify malignant cell subtypes and MVI-related genes (MRGs) in HCC using single-cell RNA sequencing and TCGA datasets.
• To develop and validate an optimal 11-gene prognostic model for predicting HCC prognosis in patients with MVI.
• To explore differences in mutation profiles, immune infiltration, and immunotherapy responses between high- and low-risk groups.

Main Methods:

• Integrated single-cell RNA sequencing (GSE242889) and TCGA datasets to identify MVI-associated malignant cell subtypes and MRGs.
• Employed 101 machine learning algorithms to construct an 11-gene prognostic model based on MRGs.
• Validated the model's predictive accuracy and superiority via meta-analysis against existing HCC models.
• Conducted mutation, immune infiltration, immunotherapy response, and drug sensitivity analyses.

Main Results:

• An 11-gene signature (NOP16, YIPF1, HMMR, NDC80, DYNLL1, CDC34, NLN, KHDRBS3, MED8, SLC35G2, RAB3B) was identified as an optimal prognostic marker for HCC.
• The developed model demonstrated high predictive precision and outperformed existing HCC prognostic models.
• Significant differences were observed in mutation patterns, immune cell infiltration, and responses to immunotherapy and targeted therapies between high- and low-risk patient groups.

Conclusions:

• The 11-gene prognostic model provides a robust tool for predicting HCC prognosis, especially in the context of MVI.
• The findings offer novel therapeutic insights by characterizing distinct molecular and immunological landscapes between risk groups.
• This research facilitates personalized treatment strategies and improves outcomes for HCC patients at high risk of MVI.