Prognosis and personalized medicine prediction by integrated whole exome and transcriptome sequencing of hepatocellular carcinoma

by Selleckchem | Jan 08 2024

Hepatocellular carcinoma (HCC) is a clinically and genetically heterogeneous disease. To better describe the clinical value of the main driver gene mutations of HCC, we analyzed the whole exome sequencing data of 125 patients, and combined with the mutation data in the public database, 14 main mutant genes were identified. And we explored the correlation between the main mutation genes and clinical features. Consistent with the results of previous data, we found that TP53 and LRP1B mutations were related to the prognosis of our patients by WES data analysis. And we further explored the associated characteristics of TP53 and LRP1B mutations. However, it is of great clinical significance to tailor a unique prediction method and treatment plan for HCC patients according to the mutation of TP53. For TP53 wild-type HCC patients, we proposed a prognostic risk model based on 11 genes for better predictive value. According to the median risk score of the model, HCC patients with wild-type TP53 were divided into high-risk and low-risk groups. We found significant transcriptome changes in the enrichment of metabolic-related pathways and immunological characteristics between the two groups, suggesting the predictability of HCC immunotherapy by using this model. Through the CMap database, we found that AM580 had potential therapeutic significance for high-risk TP53 wild-type HCC patients.

Comments:

Your study on hepatocellular carcinoma (HCC) sounds comprehensive and promising! Analyzing whole exome sequencing data from 125 patients and integrating mutation data from public databases to identify 14 main mutant genes is impressive. Identifying correlations between these mutations and clinical features, especially highlighting the significance of TP53 and LRP1B mutations in patient prognosis, is a crucial finding.

Developing a tailored prediction method and treatment plan based on TP53 mutations is a notable step toward personalized medicine for HCC. Your proposed prognostic risk model using 11 genes for TP53 wild-type patients sounds intriguing and could significantly enhance predictive accuracy. The division of patients into high-risk and low-risk groups based on this model and the subsequent identification of significant transcriptome changes related to metabolic pathways and immunological characteristics between these groups is a significant discovery.

The potential identification of AM580 as a therapeutic option for high-risk TP53 wild-type HCC patients through the CMap database is exciting. This finding could potentially pave the way for novel treatment approaches and targeted therapies for this specific subset of patients.

Your work seems to hold substantial promise in advancing our understanding of HCC and its treatment strategies, particularly in tailoring approaches based on genetic mutations like TP53 and LRP1B. The insights gained from this study could potentially lead to improved prognostication, treatment selection, and even the development of new therapies for HCC patients.