Gene Network Analysis of Hepatocellular Carcinoma Identifies Modules Associated with Disease Progression, Survival, and Chemo Drug Resistance

Abstract

Background

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. HCC transcriptome has been extensively studied; however, the progress in disease mechanisms, prognosis, and treatment is still slow.

Methods

A rank-based module-centric workflow was introduced to analyze important modules associated with HCC development, prognosis, and drug resistance. The currently largest HCC cell line RNA-Seq dataset from the LIMORE database was used to construct the reference modules by weighted gene co-expression network analysis.

Results

Thirteen reference modules were identified with validated reproducibility. These modules were all associated with specific biological functions. Differentially expressed module analysis revealed the crucial modules during HCC development. Modules and hub genes are indicative of patient survival. Modules can differentiate patients in different HCC stages. Furthermore, drug resistance was revealed by drug-module association analysis. Based on differentially expressed modules and hub genes, six candidate drugs were screened. The hub genes of those modules merit further investigation.

Conclusion

We proposed a reference module-based analysis of the HCC transcriptome. The identified modules are associated with HCC development, survival, and drug resistance. M3 and M6 may play important roles during HCV to HCC development. M1, M3, M5, and M7 are associated with HCC survival. High M4, high M9, low M1, and low M3 may be associated with dasatinib, doxorubicin, CD532, and simvastatin resistance. Our analysis provides useful information for HCC diagnosis and treatment.

Keywords:

• HCV
• IC50
• microarray
• module preservation
• WGCNA

Data Sharing Statement

The datasets analyzed in the study are available at the public database NCBI GEO (https://www.ncbi.nlm.nih.gov/geo/), LIMORE (https://www.picb.ac.cn/limore/batch), and LCCL (https://lccl.zucmanlab.com/hcc/download). Codes to generate gene co-expression network is freely available at https://github.com/yhlaile/HCCnetwork.

Ethics Approval and Consent to Participate

This study contained no data from human participants or animals performed by any of the authors, so the need for ethical approval of it was waived by the Ethics Committee of Ningbo Medical Treatment Center Lihuili Hospital.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

This research was funded by the Zhejiang Provincial Natural Science Foundation of China (No. LGF19H030006), Ningbo Clinical Medicine Research Center Project (No. 2019A21003), and Science and Technology Project of Ningbo (No. 2019C50100).