Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning.

Background: Pterygium is a complex ocular surface disease characterized by the abnormal proliferation and growth of conjunctival and fibrovascular tissues at the corneal-scleral margin. Understanding the underlying molecular mechanisms of pterygium is crucial for developing effective diagnostic and therapeutic strategies.

Methods: To elucidate the molecular mechanisms of pterygium, we conducted a differential gene expression analysis between pterygium and normal conjunctival tissues using high-throughput RNA sequencing. We identified differentially expressed genes (DEGs) with statistical significance (adjust < 0.05, |logFC| > 1). Enrichment analyses were performed to assess the biological processes and signaling pathways associated with these DEGs. Additionally, we utilized weighted correlation network analysis (WGCNA) to select module genes and applied Random Forest (RF) and Support Vector Machine (SVM) algorithms to identify pivotal feature genes influencing pterygium progression. The diagnostic potential of these genes was validated using external datasets (GSE2513 and GSE51995). Immune cell infiltration analysis was conducted using CIBERSORT to compare immune cell populations between pterygium and normal conjunctival tissues. Quantitative PCR (qPCR) was used to confirm the expression levels of the identified feature genes. Furthermore, we identified key miRNAs and candidate drugs targeting these feature genes.

Results: A total of 718 DEGs were identified in pterygium tissues compared to normal conjunctival tissues, with 254 genes showing upregulated expression and 464 genes exhibiting downregulated expression. Enrichment analyses revealed that these DEGs were significantly associated with inflammatory processes and key signaling pathways, notably leukocyte migration and IL-17 signaling. Using WGCNA, RF, and SVM, we identified KRT10 and NGEF as pivotal feature genes influencing pterygium progression. The diagnostic potential of these genes was validated using external datasets. Immune cell infiltration analysis demonstrated significant differences in immune cell populations between pterygium and normal conjunctival tissues, with an increased presence of M1 macrophages and resting dendritic cells in pterygium samples. qPCR analysis confirmed the elevated expression of KRT10 and NGEF in pterygium tissues.

Conclusion: Our findings emphasize the importance of gene expression profiling in unraveling the pathogenesis of pterygium. The identification of pivotal feature gene KRT10 and NGEF provide valuable insights into the molecular mechanisms underlying pterygium progression.