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Background <p><i>SELPLG</i>, which encodes P-selectin glycoprotein ligand-1, has emerged as a potential oncological target. A comprehensive understanding of its expression patterns across various cancer types and stages is crucial for elucidating its prognostic, therapeutic, and immunological roles.</p> Methods <p>We conducted an extensive bioinformatics analysis using multiple computational tools. TIMER2 was employed to quantify <i>SELPLG</i> mRNA expression in both tumor and normal tissues across diverse cancer types. Expression differences were further analyzed across clinical TNM stages and T stages. To investigate <i>SELPLG</i> expression at cellular and subcellular levels, we integrated genetic localization and single-cell sequencing data. A pan-cancer mutational landscape analysis was performed using Sangerbox 3.0. The prognostic significance of <i>SELPLG</i> expression was assessed using Cox proportional hazards regression models. Gene set enrichment analysis (GSEA) identified <i>SELPLG</i>-associated signaling pathways, while correlation analyses examined its relationship with immune cell infiltration.</p> Results <p><i>SELPLG</i> was significantly upregulated in multiple tumor types, including breast (BRCA), cholangiocarcinoma (CHOL), esophageal (ESCA), head and neck (HNSC), kidney chromophobe (KICH), kidney renal papillary carcinoma (KIRP), stomach (STAD), and thyroid (THCA) cancers. Conversely, it was downregulated in lung adenocarcinoma (LUAD), colon (COAD), lung squamous cell carcinoma (LUSC), and bladder (BLCA) cancers. Differential expression analyses reinforced these findings across various cancer types and stages. Genetic localization studies revealed predominant <i>SELPLG</i> expression in lymphoid and myeloid cells, while single-cell sequencing data indicated enrichment in immune cell populations. The mutational landscape analysis identified frequent missense mutations across cancers. Prognostic analyses confirmed significant associations between <i>SELPLG</i> expression and patient outcomes. GSEA indicated <i>SELPLG</i>’s involvement in immune-related pathways, and correlation analyses established a positive association between <i>SELPLG</i> expression and immunomodulatory factors.</p> Conclusions <p>This comprehensive study supports <i>SELPLG</i> as a promising prognostic, therapeutic, and immunological biomarker in cancer. Our findings underscore its role in tumor progression, immune response modulation, and potential as a therapeutic target.</p>

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Comprehensive analysis of SELPLG as a potential immunotherapy target and prognostic biomarker in oncology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月12日

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  • Ranran Shi,
  • Ling Ran,
  • Beibei Li,
  • Yuan Tian,
  • Wei Guo,
  • Shaoju Jin,
  • Wenshan Zhao

摘要

Background

SELPLG, which encodes P-selectin glycoprotein ligand-1, has emerged as a potential oncological target. A comprehensive understanding of its expression patterns across various cancer types and stages is crucial for elucidating its prognostic, therapeutic, and immunological roles.

Methods

We conducted an extensive bioinformatics analysis using multiple computational tools. TIMER2 was employed to quantify SELPLG mRNA expression in both tumor and normal tissues across diverse cancer types. Expression differences were further analyzed across clinical TNM stages and T stages. To investigate SELPLG expression at cellular and subcellular levels, we integrated genetic localization and single-cell sequencing data. A pan-cancer mutational landscape analysis was performed using Sangerbox 3.0. The prognostic significance of SELPLG expression was assessed using Cox proportional hazards regression models. Gene set enrichment analysis (GSEA) identified SELPLG-associated signaling pathways, while correlation analyses examined its relationship with immune cell infiltration.

Results

SELPLG was significantly upregulated in multiple tumor types, including breast (BRCA), cholangiocarcinoma (CHOL), esophageal (ESCA), head and neck (HNSC), kidney chromophobe (KICH), kidney renal papillary carcinoma (KIRP), stomach (STAD), and thyroid (THCA) cancers. Conversely, it was downregulated in lung adenocarcinoma (LUAD), colon (COAD), lung squamous cell carcinoma (LUSC), and bladder (BLCA) cancers. Differential expression analyses reinforced these findings across various cancer types and stages. Genetic localization studies revealed predominant SELPLG expression in lymphoid and myeloid cells, while single-cell sequencing data indicated enrichment in immune cell populations. The mutational landscape analysis identified frequent missense mutations across cancers. Prognostic analyses confirmed significant associations between SELPLG expression and patient outcomes. GSEA indicated SELPLG’s involvement in immune-related pathways, and correlation analyses established a positive association between SELPLG expression and immunomodulatory factors.

Conclusions

This comprehensive study supports SELPLG as a promising prognostic, therapeutic, and immunological biomarker in cancer. Our findings underscore its role in tumor progression, immune response modulation, and potential as a therapeutic target.