Analysis of hub genes and signalling pathways in lipomas by integrated bioinformatics

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AI Med cover, Vol. 1, No. 3 (2025)
PDF Supplementary Figure S
Submitted: 2025-06-05
Accepted: 2025-08-01
Published: 2025-08-02
DOI: https://doi.org/10.71423/aimed.20250802
Keywords:
Bioinformatics, Oncology, lipoma, WGCNA, Hub gene

Main Article Content

Fei Wang
Shanghai Medical College, Fudan University, Shanghai, China
Yuyang Xia
Institute of Medicine, Sechenov First Moscow State Medical University, Moscow, Russian Federation
Yuxin Jia
Brown School, Washington University in St.Louis, MO, USA
Zhuyuan Zhang
College of Basic Medical Sciences, Hebei Medical University, Shijiazhuang, China
Yu Deng
Cardiology Department, Sanyuan County Traditional Chinese Medicine Hospital, Xianyang, Shanxi, China
YuJing Wu
School of Clinical Medicine, Jining Medical University, Jining, China
Yating Zhang
College of Information Engineering, Wuhan University of Technology, Wuhan, China

Abstract

Background: Lipomas are the most common benign tumours, but some deep lipomas are technically difficult to remove surgically. Early diagnosis and treatment of lipomas can be facilitated by early genetic biomarkers; however, the key genes and signalling pathways that influence lipoma development are not well understood. The aim of this study was to identify hub genes and signalling pathways associated with the development of lipomas. Methods: A dataset of human lipomas (GSE141027) was first downloaded from Gene Expression Omnibus, differential genes (DEGs) for expression profiles were analysed in R software via the edgeR package, and a protein‒protein interaction network was constructed. Based on preliminary data, further modular analysis, neighbour node analysis and Hubba analysis were performed using Cystoscope to identify intersecting genes and display them in a Venn diagram to obtain key hub genes. Enrichment analysis was then carried out using the ClueGO plugin in Cytoscape (v3.9). In addition, weighted gene coexpression network analysis (WGCNA) was used to identify coexpression modules positively and negatively associated with the clinicopathological features of lipoma in the whole dataset, and enrichment analysis was performed on the module genes to obtain the signalling pathways associated with the clinicopathological features of lipoma by intersecting with the signalling pathway enrichment of DEGs. All data were then used for GSEA enrichment to further validate the signalling pathways related to the clinicopathological features of lipoma. Results: A total of 418 DEGs were identified, of which 176 were upregulated and 242 downregulated. Seventeen hub genes were identified by MCODE and hubba plug-in and collateral node analysis, including CKM, ATP2A1, MYLPF, TNNI2, MYL1, ACTN3, ACTN2, ACTG2, MYH11, NEB, MYBPC2, MYOZ1, MYH2, MYBPC1, TNNC2, ACTA1 and TCAP. TCAP. The enrichment functions and signalling pathways of the DEGs were subsequently analysed by the ClueGO plugin. A Venn diagram revealed the 15 most clinically relevant modular gene-enriched signalling pathways for lipoma (including the calcium signalling pathway and ECM-receptor interaction). In addition, 9 key signalling pathways associated with lipoma were identified using GSEA. Conclusion: This study analysed hub genes and signalling pathways of lipoma by bioinformatics to provide potential targets and signalling pathways for early diagnosis and treatment.
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Article Details

Issue

Vol. 1 No. 3 (2025)

Section

Computer Science and Artificial Intelligence
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Wang, F., Xia, Y., Jia, Y., Zhang, Z., Deng, Y., Wu, Y., & Zhang, Y. (2025). Analysis of hub genes and signalling pathways in lipomas by integrated bioinformatics. AI Med, 1(3). https://doi.org/10.71423/aimed.20250802

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