Identification of the association between HMMR expression and progression of hepatocellular carcinoma via construction of a co-expression network

Oncol Lett. 2020 Sep;20(3):2645-2654. doi: 10.3892/ol.2020.11844. Epub 2020 Jul 9.

Abstract

The aim of the present study was to identify key genes involved in the progression of hepatocellular carcinoma (HCC). According to the theory of the multistep process of hepatocarcinogenesis and weighted gene co-expression network analysis, hub genes associated with the progression of HCC were identified using the gene expression profiles of patients with normal to chronic hepatitis/cirrhosis and dysplastic nodules to HCC. An independent dataset was used to verify the association between hub gene and clinical phenotype. The diagnostic and prognostic value of hub genes regarding HCC were evaluated. Gene set enrichment analysis (GSEA) was performed to explore the function of hub genes. A co-expression gene module positively associated with HCC progression was identified. Combined with a protein-protein interaction (PPI) network, a total of 10 common hub genes common to both the module of interest and the PPI network were selected as hub genes. Hyaluronan mediated motility receptor (HMMR) was selected as the candidate gene and was significantly upregulated in HCC at the mRNA and protein expression levels. HMMR is a promising diagnostic biomarker for HCC, and is also associated with its progression. The expression of HMMR was positively correlated with HCC tumor grade, pathological stage, tumor stage and Ishak score. The expression of HMMR was an independent prognostic factor compared with clinicopathological features. Patients with high expression levels of HMMR exhibited a less favorable prognosis. GSEA identified 6 representative gene sets that were associated with cancer. Overall, HMMR may serve an important role in HCC and may have potential as a biomarker of HCC diagnosis and progression.

Keywords: HMMR; hepatocellular carcinoma; multistep process of hepatocarcinogenesis; weighted gene co-expression network analysis.