# Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury

## TL;DR

This study identifies key genes and potential drugs for sepsis-related lung injury using bioinformatics and molecular docking.

## Contribution

The study combines transcriptomic analysis, machine learning, and molecular docking to discover novel hub genes and drug candidates for sepsis-associated acute lung injury.

## Key findings

- Six hub genes (PGM3, GDF15, GART, GFOD2, E2F2, ATP1B2) were identified and validated in sepsis-associated acute lung injury.
- Celastrol and Thiostrepton showed strong binding to the hub proteins, suggesting potential therapeutic use.
- The hub genes are linked to inflammation, immune regulation, oxidative stress, and tissue remodeling pathways.

## Abstract

Sepsis-associated acute lung injury (SA-ALI) is a severe complication of sepsis with high mortality. This study aimed to identify key diagnostic genes and potential therapeutic drugs for SA-ALI.

Transcriptomic data from GSE10474 and GSE32707 were integrated for differential expression and WGCNA analysis. Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.

Six hub genes (PGM3, GDF15, GART, GFOD2, E2F2, ATP1B2) were identified and validated with elevated expression in SA-ALI. These genes were enriched in inflammation, immune regulation, oxidative stress, and tissue remodeling pathways, and showed significant correlations with specific immune cell subsets. Five candidate small molecules were predicted; molecular docking revealed Celastrol had the strongest binding to all six proteins, particularly GDF15 (-9.988 kcal/mol), while Thiostrepton showed strong binding to PGM3, GFOD2, and GDF15.

Six diagnostic hub genes and two priority candidate drugs, Celastrol and Thiostrepton, were identified for SA-ALI, providing potential biomarkers and therapeutic targets.