Two predictive precision medicine tools for hepatocellular carcinoma

Area of Science:

• Oncology
• Genomics
• Biostatistics

Background:

• Hepatocellular carcinoma (HCC) presents a significant public health challenge due to its poor patient prognosis.
• Existing prognostic models for HCC often lack precision, necessitating improved tools for survival prediction.

Purpose of the Study:

• To develop and validate a novel prognostic nomogram for predicting overall survival in hepatocellular carcinoma (HCC) patients.
• To identify key mRNA biomarkers associated with HCC patient survival.

Main Methods:

• Utilized a large cohort of 348 HCC patients with 24,991 mRNA expression data points.
• Employed the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model to identify prognostic mRNA biomarkers.
• Constructed and validated an "Eight-mRNA prognostic nomogram" using multivariate Cox proportional regression analyses.

Main Results:

• The Eight-mRNA prognostic nomogram was built using expression data for N4BP3, ADRA2B, E2F8, MAPT, PZP, HOXD9, COL15A1, and NDST3.
• The nomogram demonstrated good predictive accuracy with a C-index of 0.765 in the model cohort and 0.715 in the validation cohort.
• Survival analysis confirmed significantly poorer overall survival for HCC patients categorized in the high-risk group compared to the low-risk group.

Conclusions:

• Developed two accessible precision medicine tools for HCC: the Smart Cancer Predictive System and the Gene Survival Analysis Screen System.
• These tools facilitate the prediction of individual mortality risk, thereby enhancing personalized treatment approaches for HCC.
• The predictive tools aim to improve comprehensive, individualized care for patients diagnosed with hepatocellular carcinoma.