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. 2022 Jun 8;12:9417. doi: 10.1038/s41598-022-13719-8

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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The workflow diagram of the proposed method. This study applies a gene co-expression network and a drug–gene regulatory network analysis to reposition candidate drugs for NSCLC treatment. (a,b) At first, a transcriptome profile for normal and NSCLC samples was downloaded from the GEO database with the accession number GSE21933. (c,d) Then, a gene co-expression network was reconstructed for the differentially expressed genes (p_value < 0.01) of normal and NSCLC groups using the WGCNA package in the R programming environment, and two significant gene modules (purple and magenta) were extracted from the NSCLC co-expression network. (e) Next, a list of transcription factor genes, which regulate purple and magenta modules' genes, were obtained from the Trrust V2.0 ⁴⁰ online database. (f,g) Subsequently, two drug–gene interaction networks, named drug-TG (target gene) and drug-TF (transcription factor gene), were drawn using the DGIdb V4.0⁴² online database. (e) Finally, 18 candidate drugs are proposed for NSCLC treatment.