Machine learning-derived identification of tumor-infiltrating immune cell-related signature for improving prognosis and immunotherapy responses in patients with skin cutaneous melanoma

Cancer Cell International

Research

Gang Nie¹  Yunsheng Xu¹  Shaolong Leng¹  Lvya Zhang¹  Linyu Zhu¹  Changhong Yi² 

[1] Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China;Department of Interventional Radiology, Cancer Hospital of Shantou University Medical College, Shantou, China;

关键词: Skin cutaneous melanoma;    Machine learning;    Tumor microenvironment;    Immunotherapy;   

DOI  :  10.1186/s12935-023-03048-9

 received in 2023-06-29, accepted in 2023-08-31,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundImmunoblockade therapy based on the PD-1 checkpoint has greatly improved the survival rate of patients with skin cutaneous melanoma (SKCM). However, existing anti-PD-1 therapeutic efficacy prediction markers often exhibit a poor situation of poor reliability in identifying potential beneficiary patients in clinical applications, and an ideal biomarker for precision medicine is urgently needed.Methods10 multicenter cohorts including 4 SKCM cohorts and 6 immunotherapy cohorts were selected. Through the analysis of WGCNA, survival analysis, consensus clustering, we screened 36 prognostic genes. Then, ten machine learning algorithms were used to construct a machine learning-derived immune signature (MLDIS). Finally, the independent data sets (GSE22153, GSE54467, GSE59455, and in-house cohort) were used as the verification set, and the ROC index standard was used to evaluate the model.ResultsBased on computing framework, we found that patients with high MLDIS had poor overall survival and has good prediction performance in all cohorts and in-house cohort. It is worth noting that MLDIS performs better in each data set than almost all models which from 51 prognostic signatures for SKCM. Meanwhile, high MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells in the tumor microenvironment. Additionally, patients suffering from SKCM with high MLDIS were more sensitive to immunotherapy.ConclusionsOur study identified that MLDIS could provide new insights into the prognosis of SKCM and predict the immunotherapy response in patients with SKCM.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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