Prediction of drug candidates for the treatment of advanced lung adenocarcinoma based on network target convergence algorithm(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2024年第4期
- Page:
- 504-511
- Research Field:
- 医学生物信息
- Publishing date:
Info
- Title:
- Prediction of drug candidates for the treatment of advanced lung adenocarcinoma based on network target convergence algorithm
- Author(s):
- LIU Xi; GUAN Shuang; YU Chengcheng; WANG Zhong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Keywords:
- Keywords: advanced lung adenocarcinoma prognostic gene complex network target convergence anticancer agent
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2024.04.016
- Abstract:
- Abstract: Objective To identify the convergent gene sets in the regulatory network of advanced lung adenocarcinoma, and predict drug candidates for the treatment of advanced lung adenocarcinoma using connectivity map (CMap). Methods The TCGA database was used to search for the transcriptome and clinical data of lung adenocarcinoma, and R4.0.3 software to screen the differential genes of early- and advanced-stage patients, and Kaplan-Meier and log-rank tests to identify prognostic genes. The enrichment analysis of prognostic genes was carried out in DAVID and KEGG databases. The differential prognostic gene regulatory network was constructed based on the background network, and the collective influence algorithm was employed to calculate the convergent gene set which was then imported into CMap to obtain drug candidates for the treatment of advanced lung adenocarcinoma. Further investigation and analysis were conducted on the drug candidates. Results A total of 3 409 differentially expressed genes were obtained, with 1 981 genes significantly associated with survival. Enrichment analysis showed that prognostic genes were mainly related to biological processes such as cell division, chromosome segregation, mitotic cell cycle, DNA replication, B-cell activation, T-cell activation, etc. The collective influence method identified 96 convergent prognostic genes in advanced lung adenocarcinoma. The top 20 candidate compounds were obtained through CMap linkage map calculation, of which thapsigargin and nutlin-3 had been proven to have potential therapeutic effects on advanced lung adenocarcinoma through literature review. Conclusion The study leverages bioinformatics, network target convergence algorithm and CMap database to explore drugs with therapeutic effects on advanced lung adenocarcinoma, which opens up new ways and ideas for discovering candidate therapeutic targets and drugs for diseases.
Last Update: 2024-04-25