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Uncovering the role of transient receptor potential channels in pterygium: a machine learning approach

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Abstract

Objectives

We aimed at identifying the role of transient receptor potential (TRP) channels in pterygium.

Methods

Based on microarray data GSE83627 and GSE2513, differentially expressed genes (DEGs) were screened and 20 hub genes were selected. After gene correlation analysis, 5 TRP-related genes were obtained and functional analyses of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. Multifactor regulatory network including mRNA, microRNAs (miRNAs) and transcription factors (TFs) was constructed. The 5 gene TRP signature for pterygium was validated by multiple machine learning (ML) programs including support vector classifiers (SVC), random forest (RF), and k-nearest neighbors (KNN). Additionally, we outlined the immune microenvironment and analyzed the candidate drugs. Finally, in vitro experiments were performed using human conjunctival epithelial cells (CjECs) to confirm the bioinformatics results.

Results

Five TRP-related genes (MCOLN1, MCOLN3, TRPM3, TRPM6, and TRPM8) were validated by ML algorithms. Functional analyses revealed the participation of lysosome and TRP-regulated inflammatory pathways. A comprehensive immune infiltration landscape and TFs-miRNAs-mRNAs network was studied, which indicated several therapeutic targets (LEF1 and hsa-miR-455-3p). Through correlation analysis, MCOLN3 was proposed as the most promising immune-related biomarker. In vitro experiments further verified the reliability of our in silico results and demonstrated that the 5 TRP-related genes could influence the proliferation and proinflammatory signaling in conjunctival tissue contributing to the pathogenesis of pterygium.

Conclusions

Our study suggested that TRP channels played an essential role in the pathogenesis of pterygium. The identified pivotal biomarkers (especially MCOLN3) and pathways provide novel directions for future mechanistic and therapeutic studies for pterygium.

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Data availability

All data can be obtained from the corresponding authors under reasonable request.

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Acknowledgements

We would like to express our sincere gratitude to the GEO database for sharing the transcriptome data. We also thank the Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology for providing the research platform.

Funding

This work was supported by the National Natural Science Foundation of China (No.81770888 and 82271041).

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Author notes

  1. Yuchen Cai and Tianyi Zhou have contributed equally to this work.

Authors and Affiliations

  1. Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhi-Zao-Ju Road, Huangpu District, Shanghai, 200011, China

    Yuchen Cai, Tianyi Zhou, Jin Chen & Yao Fu

  2. Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

    Yuchen Cai, Tianyi Zhou, Jin Chen & Yao Fu

  3. Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhi-Zao-Ju Road, Huangpu District, Shanghai, 200011, China

    Xueyao Cai

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  1. Yuchen Cai
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Contributions

YC: data interpretation, in vitro experiment, and manuscript writing. TZ: machine learning and in vitro experiment. JC: manuscript revision. XC: study design, data collection and interpretation. YF: manuscript revision and study supervision. All authors have approved the final manuscript.

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Correspondence to Xueyao Cai or Yao Fu.

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Cai, Y., Zhou, T., Chen, J. et al. Uncovering the role of transient receptor potential channels in pterygium: a machine learning approach. Inflamm. Res. 72, 589–602 (2023). https://doi.org/10.1007/s00011-023-01693-4

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