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Background <p>Melanoma, a type of skin cancer that can spread to other parts of the body, currently lacks highly precise individualized treatment options.</p> Methods <p>We performed multi-omics integration on The Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) cohort to identify melanoma molecular subtypes. The identified genes were validated in independent meta cohorts from GEO, followed by transcriptome-wide association study (TWAS) validation using Genotype-Tissue Expression (GTEx) and UK Biobank datasets. Additionally, we analyzed machine learning-driven signature (CMLS) development, tumor microenvironment characteristics, immunotherapy response, and potential therapeutic targets. Finally, single-cell and spatial transcriptomics provided further biological insights and the pathomechanisms.</p> Result <p>Our study identified two distinct molecular subtypes of SKCM using multimodal data integration with the MOVICS package: Cancer Subtype 1 (CS1) and CS2. CS2 showed a better prognosis and was enriched in immune-suppressive pathways such as WNT–β signaling, while CS1 exhibited higher activation of the PI3K pathway and DNA repair mechanisms, along with greater tumor invasiveness. TWAS analysis results combined the findings from TCGA-SKCM and the meta-cohort, identifying six significant prognostic-related genes (SPRGs). The CMLS prognostic model, based on SPRGs (<i>CAP2</i>, <i>SELL</i>, <i>and LAPTM5</i> as risk factors and <i>GZMA</i>, <i>FCER1G</i>, and <i>LYZ</i> as protective factors), stratified patients into high-group (poorer survival) and low-risk groups. Single-cell and spatial transcriptomic analyses further validated CMLS prognostic results, highlighting distinct tumor microenvironment interactions and progression trajectories.</p> Conclusion <p>Identifications of molecular subtypes and CMLS represent a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice foundation for personalized therapies.</p>

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A cross-scale multimodal framework identifies clinically actionable immunotherapy biomarkers in melanoma through bulk to single-cell and spatial transcriptomics integration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月16日

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  • Wuda Huoshen,
  • Kun Yuan,
  • Junkai Xiong,
  • Yilong Lin,
  • Wenjie Yu,
  • Yun Xie,
  • Qian Yuan,
  • Xinyue Zhang,
  • Changqing Dong,
  • Chen Sun,
  • Sha Yi

摘要

Background

Melanoma, a type of skin cancer that can spread to other parts of the body, currently lacks highly precise individualized treatment options.

Methods

We performed multi-omics integration on The Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) cohort to identify melanoma molecular subtypes. The identified genes were validated in independent meta cohorts from GEO, followed by transcriptome-wide association study (TWAS) validation using Genotype-Tissue Expression (GTEx) and UK Biobank datasets. Additionally, we analyzed machine learning-driven signature (CMLS) development, tumor microenvironment characteristics, immunotherapy response, and potential therapeutic targets. Finally, single-cell and spatial transcriptomics provided further biological insights and the pathomechanisms.

Result

Our study identified two distinct molecular subtypes of SKCM using multimodal data integration with the MOVICS package: Cancer Subtype 1 (CS1) and CS2. CS2 showed a better prognosis and was enriched in immune-suppressive pathways such as WNT–β signaling, while CS1 exhibited higher activation of the PI3K pathway and DNA repair mechanisms, along with greater tumor invasiveness. TWAS analysis results combined the findings from TCGA-SKCM and the meta-cohort, identifying six significant prognostic-related genes (SPRGs). The CMLS prognostic model, based on SPRGs (CAP2, SELL, and LAPTM5 as risk factors and GZMA, FCER1G, and LYZ as protective factors), stratified patients into high-group (poorer survival) and low-risk groups. Single-cell and spatial transcriptomic analyses further validated CMLS prognostic results, highlighting distinct tumor microenvironment interactions and progression trajectories.

Conclusion

Identifications of molecular subtypes and CMLS represent a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice foundation for personalized therapies.