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Biomarker Identification for Liver Hepatocellular Carcinoma and Cholangiocarcinoma Based on Gene Regulatory Network Analysis

Author(s):

Qiuyan Huo, Yuying Ma, Yu Yin and Guimin Qin*  

Abstract:


Aims:We aimed to find common and distinct molecular characteristics between LIHC and CHOL based on miRNA-TF-gene FFL.

Background: Liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) are two main histological subtypes of primary liver cancer with a unified molecular landscape, and feed-forward loops (FFLs) have been shown to be relevant in these complex diseases.

Objective: To date, there has been no comparative analysis of the pathogenesis of LIHC and CHOL based on regulatory relationships. Therefore, we investigated the common and distinct regulatory properties of LIHC and CHOL in terms of gene regulatory networks.

Method: Based on identified FFLs and an analysis of pathway enrichment, we constructed pathway-specific co-expression networks and further predicted biomarkers for these cancers by network clustering.

Result: We identified 20 and 36 candidate genes for LIHC and CHOL, respectively. The literature from PubMed supports the reliability of our results.

Conclusion: Our results indicated that the hsa01522-Endocrine resistance pathway was associated with both LIHC and CHOL. Additionally, six genes (SPARC, CTHRC1, COL4A1, EDIL3, LAMA4 and OLFML2B) were predicted to be highly associated with both cancers, of which SPARC was significantly highly ranked.

Other: In addition, we inferred that the Collagen gene family, which appeared more frequently in our overall prediction results, might be closely related to cancer development.

Keywords:

Hepatocellular carcinoma, Cholangiocarcinoma, Feed-forward loops, Gene regulatory network, Transcription factor, ClusterONEHepatocellular carcinoma, ClusterONE

Affiliation:

Xidian University, School of Computer Science and Technology, Xidian University, School of Computer Science and Technology, Xidian University, School of Computer Science and Technology, Xidian University, School of Computer Science and Technology



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