Immune checkpoint inhibitors (ICI) have been widely used in melanoma, but to identify melanoma patients with survival benefit from ICI is still a challenge. There is an urgent need for prognostic signatures improving the prediction of immunotherapy responses of cancer patients.
We used data from EMBL-EBI database and analyzed RNA-seq information and clinical profiles in melanoma. Weighted gene co-expression network analysis (WGCNA) was used to identified the key module, then nonnegative matrix factorization (NMF) was conducted to cluster patients into two different cluster and compared them regarding overall survival (OS) and progression-free survival (PFS). Subsequently, the differentially expressed genes (DEGs) between different clusters were identified and their function and pathway annotation were performed.
91 melanoma biopsies with complete survival information were included in our analyses and we first identified the key module (magenta) by WGCNA, then identified nine prognostic lncRNAs (ENSG00000258869, ENSG00000179840, ENSG00000206344, ENSG00000226777, ENSG00000205018, ENSG00000204261, ENSG00000163597, ENSG00000197536, and ENSG00000263069) signature that predicted for OS and PFS in patients treated with ICI by NMF. Finally, enrichment analysis showed that the functions of DEGs between two consensus clusters were mainly related to the immune process and treatment.
the nine lncRNAs signature is a novel effective predictor for OS and PFS in melanoma patients treated with ICI.
The authors.
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All authors have declared no conflicts of interest.