meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data

Motivation: Hepatocellular carcinoma (HCC) is a primary malignancy with a poor prognosis. Recently, multi-omics molecular-level measurement enables HCC diagnosis and prognosis prediction, which is crucial for early intervention of personalized therapy to diminish mortality. Here, we introduce a novel strategy utilizing DNA methylation and RNA expression data to achieve a multi-omics gene pair signature (GPS) for HCC discrimination. Results: The immune genes with negative correlations between expression and promoter methylation are enriched in the highly connected cancer-related pathway network, which are considered as the candidates for HCC detection. After that, we separately construct a methylation GPS (mGPS) and an expression GPS (eGPS), and then assemble them as a meGPS with five gene pairs, in which the significant methylation and expression changes occur between HCC tumor and non-tumor groups. Reliable performance has been validated by independent tissue (age, gender and etiology) and blood datasets. This study proposes a procedure for multi-omics GPS identification and develops a novel HCC signature using both methylome and transcriptome data, suggesting potential molecular targets for the detection and therapy of HCC.

Automated summary

This study introduces a novel strategy using DNA methylation and RNA expression data to discriminate hepatocellular carcinoma (HCC). Immune genes with negative correlations between expression and promoter methylation are identified as candidates for HCC detection. A methylation GPS (mGPS) and an expression GPS (eGPS) are separately constructed and then assembled into a meGPS, which successfully detects and predicts HCC with reliable performance validated by independent datasets. This study provides potential molecular targets for the detection and therapy of HCC.