Necroptosis-associated classification combined with tumor microenvironment characteristic analysis of cutaneous melanoma

Abstract

Necroptosis is a mode of programmed cell death that overcomes apoptotic resistance. The accurate prognosis of cutaneous melanoma is complicated to predict due to tumor heterogeneity. Necroptosis contributes to the regulation of oncogenesis and cancer immunity. We comprehensively investigated different necroptosis patterns by the non-negative matrix factorization (NMF) clustering analysis and explored the relationships among necroptosis patterns, infiltered immune cells, and tumor microenvironment (TME) scores. Two different necroptosis patterns were identified, and the two clusters could predict prognosis and immune landscape. A four-gene signature was successfully constructed and validated its predictive capability of overall survival (OS) in cutaneous melanoma patients. The prognostic value of the signature was further enhanced by incorporating other independent prognostic factors such as age and clinicopathological stages in a nomogram-based prediction model. Patients with lower risk scores tended to have better OS, higher TME score, immune checkpoints, immunophenoscore (IPS), and lower Tumor Immune Dysfunction and Exclusion (TIDE), which indicated better responses to immunotherapy. In addition, the pigmentation score of the high-risk group was visibly higher than those of the low-risk group. In conclusion, the necroptosis-related signature indicated favorable predictive performance in cutaneous melanoma patients, which provides guidance for immunotherapy and provide novel insights into precision medicine.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

“…The year of publication of the 36 included studies was primarily between 2019 and 2022. Of the 36 studies, 18 were conducted in China ( 16 – 33 ) and 5 were conducted in Germany ( 34 – 38 ). The primary outcome measures were treatment response, OS, and PFS.…”

“…In terms of modeling variables, there were 17 clinical features-based models, 27 radiomics-based risk models, and 31 genomics-based models. The specific characteristics of the included studies ( 16 – 50 ) are summarized in Table 1 .…”

“…China(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33) and 5 were conducted in Germany(34)(35)(36)(37)(38). The primary outcome measures were treatment response, OS, and PFS.…”