2. Academic Validation
  3. Identification of potential hub genes and drugs in septic kidney injury: a bioinformatic analysis with preliminary experimental validation

Identification of potential hub genes and drugs in septic kidney injury: a bioinformatic analysis with preliminary experimental validation

  • Front Med (Lausanne). 2025 Mar 17:12:1502189. doi: 10.3389/fmed.2025.1502189.
Shujun Sun # 1 2 3 4,  Yuanyuan Ding # 1 2 3,  Dong Yang # 2 3 4,  Jiwei Shen 1 2 3,  Tianhao Zhang 1 2 3,  Guobin Song 1 2 3,  Xiangdong Chen 1 2 3,  Yun Lin 1 2 3,  Rui Chen 1 2 3 5
Affiliations

Affiliations

  • 1 Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • 2 Institute of Anesthesia and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • 3 Key Laboratory of Anesthesiology and Resuscitation (Huazhong University of Science and Technology), Ministry of Education, Wuhan, China.
  • 4 Department of Pain, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • 5 Department of Anesthesiology, Zhejiang Hospital, Hangzhou, China.
  • # Contributed equally.
PMID: 40166075 DOI: 10.3389/fmed.2025.1502189
Abstract

Background: Sepsis-associated kidney injury (SAKI) is a prevalent complication in intensive care unit (ICU) patients with sepsis. Diagnosis currently relies on clinical assessment, urine output, and serum creatinine levels, yet effective clinical treatments remain scarce. Our objectives are to explore prospective, targeted medications for the treatment of septic kidney injury and to employ bioinformatics to identify key genes and pathways that may be implicated in the pathogenesis of SAKI.

Methods: We utilized the GEO database for differential gene screening. Related genes of septic kidney injury were identified through Pubmed2Ensembl, followed by annotation and visualization of gene ontology biological processes and KEGG pathways using DAVID. Protein-protein interactions were analyzed with the STRING database, and hub genes were identified using Cytoscape software. Candidate genes were further validated through Metascape. The CTD database was employed to uncover the relationship between hub genes and acute kidney injury (AKI). CIBERSORT was applied to evaluate the infiltration of immune cells and their association with hub genes. Hub genes were experimentally verified through qPCR detection. Lastly, the Drug-Gene Interaction Database (DGIdb) was utilized to identify drug-gene interactions.

Results: Six genes, including TNF, CXCL8, IL-6, IL-1β, IL-2, and IL-10, were associated with three major signaling pathways: the COVID-19 adverse outcome pathway, an overview of pro-inflammatory and pro-fibrotic mediators, and the interleukin-10 signaling pathway. Additionally, 12 targeted drugs were identified as potential therapeutic agents.

Keywords

drug discovery; immune cell infiltration; mRNA-miRNA co-expression networks; sepsis-associated kidney injury; text mining.

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