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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1486251
This article is part of the Research Topic PANoptosis and its role in T cell-based immunotherapy View all 3 articles

Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation

Provisionally accepted
Han Chen Han Chen Enguang Chen Enguang Chen Ting Cao Ting Cao Xuan Wang Xuan Wang Min Lin Min Lin Feifan Feng Feifan Feng Yu Xu Yu Xu *
  • Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

The final, formatted version of the article will be published soon.

    Background: Diabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therapeutic targets. This study aims to identify differentially expressed PANoptosis-related genes (DE-PRGs) in DR, offering insights into the disease's pathogenesis and potential diagnostic tools. Methods: DR datasets were obtained from the Gene Expression Omnibus (GEO) database, while PANoptosis-related genes were sourced from the GeneCards database. Differentially expressed genes (DEGs) were identified using the DESeq2 package, followed by functional enrichment analysis through DAVID and Metascape tools. Three machine learning algorithms-LASSO regression, Random Forest, and SVM-RFE-were employed to identify hub genes. A diagnostic nomogram was constructed and its performance assessed via ROC analysis. The CIBERSORT algorithm analyzed immune cell infiltration. Hub genes were validated through RT-qPCR, Western blotting, immunohistochemistry, and publicly available datasets. Additionally, the impact of FASN and PLSCR3 knockdown on HUVECs behavior was validated through in vitro experiments. Results: Differential expression analysis identified 1,418 DEGs in the GSE221521 dataset, with 39 overlapping DE-PRGs (29 upregulated, 10 downregulated). Functional enrichment indicated that DE-PRGs are involved in apoptosis, signal transduction, and inflammatory responses, with key pathways such as MAPK and TNF signaling. Machine learning algorithms identified six PANoptosis-related hub genes (BEX2, CASP2, CD36, FASN, OSMR, and PLSCR3) as potential biomarkers. A diagnostic nomogram based on these hub genes showed high diagnostic accuracy. Immune cell infiltration analysis revealed significant differences in immune cell patterns between control and DR groups, especially in Activated CD4 Memory T Cells and Monocytes. Validation confirmed the diagnostic efficiency and expression patterns of the PANoptosis-related hub genes, supported by in vitro and the GSE60436 dataset analysis. Furthermore, experiments demonstrated that knocking down FASN and PLSCR3 impacted HUVECs behavior. Conclusion: This study provides valuable insights into the molecular mechanisms of DR, particularly highlighting PANoptosis-related pathways, and identifies potential biomarkers and therapeutic targets for the disease.

    Keywords: Diabetic Retinopathy, PANoptosis, machine learning, Bioinformatics analysis, Differentially expressed genes, biomarkers

    Received: 25 Aug 2024; Accepted: 13 Nov 2024.

    Copyright: © 2024 Chen, Chen, Cao, Wang, Lin, Feng and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yu Xu, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.