Discover Oncology (Sep 2024)

Comprehensive analysis to identify IL7R as a immunotherapy biomarker from pan-cancer analysis to in vitro validation

  • Jiafeng Liang,
  • Lucheng Zhu,
  • Jiawei Li,
  • Kan Wu,
  • Minna Zhang,
  • Shenglin Ma,
  • Xueqin Chen,
  • Bing Xia

DOI
https://doi.org/10.1007/s12672-024-01357-7
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 17

Abstract

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Abstract Background Immunotherapy faces a major challenge in treatment resistance, highlighting the need for efficacy biomarkers identification. The tumor microenvironment (TME) significantly influences treatment outcomes, necessitating molecular TME exploration to address immunotherapy resistance. Methods The study initially pinpointed IL7R as a pivotal TME gene and then examined its impact on TME’s CD8 + T cells at the single-cell level. Bulk-RNA analysis investigated IL7R function, immune cell infiltration related to IL7R in TCGA pan-cancer samples with its expression verified in clinical samples through immunohistochemistry. Genome instability and immune-related molecular expression associated with IL7R were also assessed. Furthermore, the clinical efficacy of IL7R was evaluated in various immunotherapy treatment cohorts. Results Our single-cell analyses and cell-cased experiment revealed that T cells with high IL7R expression tended to be non-terminal and correlated with favorable immunotherapy responses. High IL7R expression corresponded to increased immune and stromal cell signiture, immune pathway enrichment, and an immune-inflamed environment in Bulk-RNA analysis and immunohistochemistry verification. These patients exhibited higher proportions of memory T cells and M1 cells within the TME, along with frequent genome instability and immune molecular upregulation. While IL7R had varied prognostic impact across the TCGA dataset, patients with high IL7R expression showed extended survival under immunotherapy. Conclusion IL7R plays a critical role in shaping TME diversity across cancer types and holds promise as a relevant biomarker for predicting immunotherapy benefits.

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