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Background <p>Gene-wise intratumor heterogeneity (ITH), defined as spatial variability in the expression of individual genes across tumor regions, remains incompletely characterized in non-small cell lung cancer (NSCLC).&#xa0;Identifying low-ITH genes as predictive biomarkers offers a promising strategy to enhance the reliability of immunotherapy outcome prediction.</p> Methods <p>We profiled gene-wise ITH using multi-region scRNA-seq data and a computational framework combining variance and clustering metrics. Prognostic low-ITH genes were screened via immunotherapy RNA-seq datasets and six machine learning algorithms to construct the LITHrisk score. Transcriptomic profiling and in vitro experiments elucidated the biological and functional relevance of key score components.</p> Results <p>The gene-wise ITH landscape in NSCLC was delineated, with several critical immune checkpoint genes, including PD-L1, LAG3, and TIM-3, being identified as low-ITH genes. A low-ITH-derived signature, the LITHrisk score (PD-L1, CDK11B, CXCL13, RPS4Y1, AKR1B10, CRLF1), effectively predicted immunotherapy prognosis and response in NSCLC and other cancers. Tumors with a low LITHrisk score were characterized by enrichment of immune response-related pathways and heightened infiltration of immune cells, such as T cells, B cells, and myeloid cells. Furthermore, we found that component genes of the score were linked to key tumor characteristics: AKR1B10 was associated with tumor immune interactions, functioning via SPP1 regulation to suppress anti-tumor immunity, while CRLF1 was implicated in promoting a fibroblast-enriched microenvironment and modulating tumor cell motility.</p> Conclusion <p>Our study provides a practical transcriptome-based tool for advancing precision immunotherapy and nominates potential therapeutic targets for enhancing treatment efficacy.</p>

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Single-cell and multi-omics integration delineates the landscape of gene-wise intratumor heterogeneity and identifies prognostic biomarkers for immunotherapy in non-small cell lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月20日

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  • Jianye Yuan,
  • Zelin Weng,
  • Zhenguo Li,
  • Rui Chen,
  • Chao Cheng,
  • Weixiong Yang,
  • Xiuying Xie,
  • Chang Luo,
  • Tao Wang,
  • Shuishen Zhang,
  • Zihui Tan

摘要

Background

Gene-wise intratumor heterogeneity (ITH), defined as spatial variability in the expression of individual genes across tumor regions, remains incompletely characterized in non-small cell lung cancer (NSCLC). Identifying low-ITH genes as predictive biomarkers offers a promising strategy to enhance the reliability of immunotherapy outcome prediction.

Methods

We profiled gene-wise ITH using multi-region scRNA-seq data and a computational framework combining variance and clustering metrics. Prognostic low-ITH genes were screened via immunotherapy RNA-seq datasets and six machine learning algorithms to construct the LITHrisk score. Transcriptomic profiling and in vitro experiments elucidated the biological and functional relevance of key score components.

Results

The gene-wise ITH landscape in NSCLC was delineated, with several critical immune checkpoint genes, including PD-L1, LAG3, and TIM-3, being identified as low-ITH genes. A low-ITH-derived signature, the LITHrisk score (PD-L1, CDK11B, CXCL13, RPS4Y1, AKR1B10, CRLF1), effectively predicted immunotherapy prognosis and response in NSCLC and other cancers. Tumors with a low LITHrisk score were characterized by enrichment of immune response-related pathways and heightened infiltration of immune cells, such as T cells, B cells, and myeloid cells. Furthermore, we found that component genes of the score were linked to key tumor characteristics: AKR1B10 was associated with tumor immune interactions, functioning via SPP1 regulation to suppress anti-tumor immunity, while CRLF1 was implicated in promoting a fibroblast-enriched microenvironment and modulating tumor cell motility.

Conclusion

Our study provides a practical transcriptome-based tool for advancing precision immunotherapy and nominates potential therapeutic targets for enhancing treatment efficacy.