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Reference Module-Based Analysis of Ovarian Cancer Transcriptome Identifies Important Modules and Potential Drugs

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Abstract

Ovarian cancer (OVC) is often diagnosed at the advanced stage resulting in a poor overall outcome for the patient. The disease mechanisms, prognosis, and treatment require imperative elucidation. A rank-based module-centric framework was proposed to analyze the key modules related to the development, prognosis, and treatment of OVC. The ovarian cancer cell line microarray dataset GSE43765 from the Gene Expression Omnibus database was used to construct the reference modules by weighted gene correlation network analysis. Twenty-three reference modules were tested for stability and functionally annotated. Furthermore, to demonstrate the utility of reference modules, two more OVC datasets were collected, and their gene expression profiles were projected to the reference modules to generate a module-level expression. An epithelial-mesenchymal transition module was activated in OVC compared to the normal epithelium, and a pluripotency module was activated in ovarian cancer stroma compared to ovarian cancer epithelium. Seven differentially expressed modules were identified in OVC compared to the normal ovarian epithelium, with five up-regulated, and two down-regulated. One module was identified to be predictive of patient overall survival. Four modules were enriched with SNP signals. Based on differentially expressed modules and hub genes, five candidate drugs were screened. The hub genes of those modules merit further investigation. We firstly propose the reference module-based analysis of OVC. The utility of the analysis framework can be extended to transcriptome data of other kinds of diseases.

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Data Availability

The datasets analyzed in the study are available at the public database NCBI GEO (https://www.ncbi.nlm.nih.gov/geo/). The code used to generate the main result has been made available on GitHub (https://github.com/mrliuw/RefNet).

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Funding

This work was supported by the Department of Science and Technology of Fuzhou City under Grant Nos. 2018-S-105-4 and 2017-S-129-3; the Construction Project for Clinical Medicine Center in Fujian Province under Grant No. 201610192; and the Natural Science Foundation of Fujian Province under Grant No. 2019D004.

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  1. Xuedan Lai and Peihong Lin have contributed equally to this work.

Authors and Affiliations

  1. Department of Gynaecology and Obstetrics, Fuzhou First Hospital Affiliated to Fujian Medical University, Fuzhou, 350009, People’s Republic of China

    Xuedan Lai, Peihong Lin, Jianwen Ye & Zhou Lin

  2. Department of Bioinformatics, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People’s Republic of China

    Wei Liu & Shiqiang Lin

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  1. Xuedan Lai
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  2. Peihong Lin
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  4. Wei Liu
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  6. Zhou Lin
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Contributions

ZL, XL, and PL designed the study; JY, WL, and SL collected and analyzed the data; XL and PL analyzed the results, plotted the figures, and drafted the manuscript; All authors read and approved the manuscript.

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Correspondence to Zhou Lin.

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Lai, X., Lin, P., Ye, J. et al. Reference Module-Based Analysis of Ovarian Cancer Transcriptome Identifies Important Modules and Potential Drugs. Biochem Genet 60, 433–451 (2022). https://doi.org/10.1007/s10528-021-10101-7

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