Identifying the optimal gene and gene set in hepatocellular carcinoma based on differential expression and...

Area of Science:

• Oncology
• Bioinformatics
• Molecular Biology

Background:

• Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide.
• Identifying specific molecular markers is crucial for improving HCC diagnosis and treatment.
• Existing methods for gene discovery may not fully capture complex gene interactions in cancer.

Purpose of the Study:

• To identify the optimal gene and gene set for hepatocellular carcinoma (HCC) using a novel differential expression and differential co-expression (DEDC) algorithm.
• To establish optimal thresholds for differential expression (DE) and differential co-expression (DC) to categorize genes into distinct functional partitions.
• To validate the identified optimal gene using reverse transcription-polymerase chain reaction (RT-PCR).

Main Methods:

• The DEDC algorithm was employed, involving calculation of DE (absolute t-value) and DC (Z-test).
• Optimal thresholds were determined via Chi-squared (χ2) maximization, dividing genes into four partitions: HDE-HDC, HDE-LDC, LDE-HDC, and LDE-LDC.
• Functional relevance was assessed using mean minimum functional information (FI) gain (Δ*G) to pinpoint the optimal gene set.

Main Results:

• The optimal thresholds for DE and DC were determined to be 1.911 and 1.032, respectively.
• Microtubule-associated protein RP/EB family member 1 (MAPRE1) was identified as the optimal gene.
• The nucleoside metabolic process (GO:0009116) was identified as the optimal gene set with a Δ*G of 18.681.

Conclusions:

• MAPRE1 and the nucleoside metabolic process gene set are potential biomarkers for HCC targeted therapy.
• The DEDC algorithm effectively identifies key genes and pathways in HCC pathogenesis.
• This research offers significant insights into the underlying pathological mechanisms of HCC.