Bioinformatics analysis identifies potential chemoresistance‑associated genes across multiple types of cancer

  • Authors:
    • Jingsheng Yuan
    • Lulu Tan
    • Zhijie Yin
    • Kaixiong Tao
    • Guobing Wang
    • Wenjia Shi
    • Jinbo Gao
  • View Affiliations

  • Published online on: June 27, 2019     https://doi.org/10.3892/ol.2019.10533
  • Pages: 2576-2583
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Abstract

Despite the fact that studies have revealed mechanisms underlying tumor chemoresistance, the functions of numerous potential chemoresistance‑associated genes have yet to be elucidated. A bioinformatics analysis was conducted to screen differentially expressed genes (DEGs) across four types of chemoresistant tumors and functional enrichment analysis was used to examine the biological significance of these genes. Furthermore, a gene network was constructed using weighted gene co‑expression network analysis to identify hub genes. A total of 6,015, 2,074, 2,141 and 954 differentially expressed genes were identified in estrogen receptor‑negative breast cancer, ovarian cancer, rectal cancer and gastric cancer, respectively; however, only five of these DEGs were dysregulated in all four types of cancer. Functional enrichment analysis of the DEGs suggested that genomic stability and immune response are crucial determinants of tumor chemoresistance. In addition, 14, 8, 6 and 1 co‑expressed gene modules were identified in estrogen receptor‑negative breast cancer, ovarian cancer, rectal cancer and gastric cancer, respectively, and protein‑protein interaction networks were created. The analysis identified only calcium‑calmodulin‑dependent protein kinase kinase 2, erythropoietin receptor, mitochondrial poly(A) RNA polymerase, α‑parvin, and zinc finger and BTB domain‑containing protein 44 to be dysregulated in all four cancer types, indicating varying mechanisms of chemoresistance in different tumor types. Furthermore, our analysis suggests that type I collagen α1, fibroblast growth factor 14 and major histocompatibility complex, class II, DR β1 potentially serve key roles in the development of chemoresistance. In conclusion, the present study proposes a simple and effective strategy for identifying genes involved in chemoresistance and predicting their potential functional roles, which may guide subsequent experimental studies.

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September 2019
Volume 18 Issue 3

Print ISSN: 1792-1074
Online ISSN:1792-1082

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APA
Yuan, J., Tan, L., Yin, Z., Tao, K., Wang, G., Shi, W., & Gao, J. (2019). Bioinformatics analysis identifies potential chemoresistance‑associated genes across multiple types of cancer. Oncology Letters, 18, 2576-2583. https://doi.org/10.3892/ol.2019.10533
MLA
Yuan, J., Tan, L., Yin, Z., Tao, K., Wang, G., Shi, W., Gao, J."Bioinformatics analysis identifies potential chemoresistance‑associated genes across multiple types of cancer". Oncology Letters 18.3 (2019): 2576-2583.
Chicago
Yuan, J., Tan, L., Yin, Z., Tao, K., Wang, G., Shi, W., Gao, J."Bioinformatics analysis identifies potential chemoresistance‑associated genes across multiple types of cancer". Oncology Letters 18, no. 3 (2019): 2576-2583. https://doi.org/10.3892/ol.2019.10533