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Machine learning-derived immunosenescence index for predicting outcome and drug sensitivity in patients with skin cutaneous melanoma

Genes & Immunity volume 25, pages 219–231 (2024)Cite this article

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Abstract

The functions of immunosenescence are closely related to skin cutaneous melanoma (SKCM). The aim of this study is to uncover the characteristics of immunosenescence index (ISI) to identify novel biomarkers and potential targets for treatment. Firstly, integrated bioinformatics analysis was carried out to identify risk prognostic genes, and their expression and prognostic value were evaluated. Then, we used the computational algorithm to estimate ISI. Finally, the distribution characteristics and clinical significance of ISI in SKCM by using multi-omics analysis. Patients with a lower ISI had a favorable survival rate, lower chromosomal instability, lower somatic copy-number alterations, lower somatic mutations, higher immune infiltration, and sensitive to immunotherapy. The ISI exhibited robust, which was validated in multiple datasets. Besides, the ISI is more effective than other published signatures in predicting survival outcomes for patients with SKCM. Single-cell analysis revealed higher ISI was specifically expressed in monocytes, and correlates with the differentiation fate of monocytes in SKCM. Besides, individuals exhibiting elevated ISI levels could potentially receive advantages from chemotherapy, and promising compounds with the potential to target high ISI were recognized. The ISI model is a valuable tool in categorizing SKCM patients based on their prognosis, gene mutation signatures, and response to immunotherapy.

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Fig. 1
Fig. 2: Identification of prognostic risk immunosenescence related genes in TCGA-SKCM.
Fig. 3: An ISI was developed and validated using multiple machine learning algorithms.
Fig. 4: Comparison between the ISI signature and other SKCM related signatures.
Fig. 5: Immune landscape of ISI.
Fig. 6: Predictive value of the ISI in immunotherapy response and drug response.
Fig. 7: Genomic heterogeneity in high/low ISI groups.
Fig. 8: Single-cell analysis of ISI.
Fig. 9: Validation in a clinical in-house cohort.
Fig. 10: Prognostic value of ISI in pan-cancer.

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Data availability

All data used in this work can be acquired from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/), the Cancer Genome Atlas (TCGA) datasets (https://xenabrowser.net/).

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Author notes

  1. These authors contributed equally: Linyu Zhu, Lvya Zhang, Junhua Qi.

Authors and Affiliations

  1. Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China

    Linyu Zhu, Gang Nie & Shaolong Leng

  2. Traditional Chinese Medicine department, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, Guangdong, China

    Lvya Zhang & Zhiyu Ye

  3. Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China

    Junhua Qi

  4. Department of Clinical Laboratory, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China

    Junhua Qi

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Contributions

Conceptualization: LYZ, YSW and SLL. Data curation: LYZ and YSW. Formal analysis: LYZ. Methodology: LYZ and YSW. Validation: YSW and SLL. Visualization: LYZ. Supervision: LYZ and SLL. Writing—original draft. SLL: writing—review and editing: YSW and SLL.

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Correspondence to Zhiyu Ye, Gang Nie or Shaolong Leng.

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The study was approved by the ethical committee in the Seventh Affiliated Hospital of Sun Yat-sen University, and informed consent was obtained from all subjects.

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Zhu, L., Zhang, L., Qi, J. et al. Machine learning-derived immunosenescence index for predicting outcome and drug sensitivity in patients with skin cutaneous melanoma. Genes Immun 25, 219–231 (2024). https://doi.org/10.1038/s41435-024-00278-3

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