Tumor Prognostic Risk Model Related to Monocytes/Macrophages in Hepatocellular Carcinoma Based on Machine Learning...

Area of Science:

• Oncology
• Immunology
• Genomics

Background:

• Tumor-associated macrophages (TAMs) play a key role in hepatocellular carcinoma (HCC) progression and invasion.
• Understanding monocyte/macrophage gene expression is crucial for developing prognostic tools in HCC.

Purpose of the Study:

• To explore monocyte/macrophage-associated gene expression profiles in HCC.
• To construct and validate a prognostic model for HCC based on these genes.
• To investigate the model's relationship with drug resistance and immune therapy response.

Main Methods:

• Single-cell RNA sequencing (scRNA-seq) data from HCC tissues were analyzed to identify marker genes.
• A prognostic model was built using the TCGA dataset and validated with Western blot and external datasets.
• Spatial transcriptomics, drug sensitivity, and immune therapy response analyses were performed.

Main Results:

• A prognostic model based on the single gene UQCRH was developed, stratifying HCC patients into high- and low-risk groups.
• High UQCRH expression correlated with reduced overall survival, increased tumor invasion, and lower sensitivity to sorafenib and axitinib.
• The high-risk group exhibited poorer immune therapy outcomes, characterized by APC inhibition and weaker IFN-II response.

Conclusions:

• The UQCRH-based prognostic model effectively predicts survival and treatment outcomes in HCC patients.
• This model identifies high-risk HCC patients with specific drug sensitivities and immune suppression profiles.
• UQCRH expression is linked to oxidative phosphorylation and mitochondrial function in HCC.