Skin cutaneous melanoma (SKCM) is a type of skin cancer that has been increasing in incidence in recent years. The development of SKCM involves a complex interplay between genetic alterations and environmental factors, mainly sunlight exposure. Anoikis is a cellular program that ensures normal tissue development and homeostasis by inducing apoptosis when cells lose contact with their extracellular matrix (ECM) substrate. Cancer cells that are resistant to anoikis tend to form metastases in distant organs. To understand the role of anoikis-related genes (ARGs) in SKCM, a team of researchers conducted a comprehensive analysis using transcriptomic and genomics sequencing databases.

Study identifies potential prognostic biomarkers for skin cancer and predicts response to immunotherapy

They identified differentially expressed ARGs and classified SKCM into three clusters with diverse prognosis and immune cell infiltration in the tumor microenvironment (TME). The team constructed and validated a prognostic signature (ARGscore) in an independent testing cohort. They also investigated the differences in immune cell infiltration, gene mutation, microsatellite instability (MSI), and immunotherapy efficacy between low and high ARGscore groups.

To increase the clinical utility of the signature, the team established a prognostic nomogram integrating ARG_score and clinical traits. They concluded that ARGs play a crucial role in the tumor immune microenvironment (TIME) and in predicting patient clinical outcomes and immunotherapy efficacy. The study suggests that ARGs are potential prognostic biomarkers and can predict the response to immunotherapy in SKCM patients.

ARGs as Potential Prognostic Biomarkers for SKCM and Predictors of Immunotherapy Efficacy

The study analyzed gene expression data from 471 SKCM samples and identified 100 ARGs. The team used non-negative matrix factorization clustering analysis to divide the samples into three clusters with different overall survival rates. They further identified 13 differentially expressed ARGs to develop the ARGscore, which was significantly associated with survival in both the training and testing cohorts. The study also analyzed the tumor immune microenvironment (TIME) and found that samples with a low ARGscore had higher immune cell infiltration, while samples with a high ARG_score had a higher proportion of M0 and M2 macrophages.

The study aimed to investigate the role of ARGs in the heterogeneity of SKCM and their prognostic and predictive values. The team established a prognostic scoring system using ARGs, which could predict overall survival in SKCM patients independently of other clinical features. The study also found that ARGs were associated with immune cell infiltration and TME scores, suggesting that they could serve as a potential biomarker to predict patient responses to immunotherapy.