ArticleHeliyon2023
Identification of anoikis-related subtypes and development of risk stratification system in skin cutaneous melanoma.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Anoikis, a form of apoptosis induced by cell detachment, plays a key role in cancer metastasis. However, the potential roles of anoikis-related genes (ARGs) in assessing the prognosis of skin cutaneous melanoma (SKCM) and the tumor microenvironment (TME) remain unclear. Methods: The data from TCGA corresponding to transcriptomic expression patterns for patients with SKCM were downloaded and utilized to screen distinct molecular subtypes by a non-negative matrix factorization algorithm. The prognostic signature was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and was validated in SKCM patients from the GEO cohort. Moreover, the relationship of the ARG_score with prognosis, tumor-infiltrating immune cells, gene mutation, microsatellite instability (MSI), and immunotherapy efficacy. Results: We screened 100 anoikis-related differentially expressed genes between SKCM tissues and normal skin tissues, which could divide all patients into three different subtypes with significantly distinct prognosis and immune cell infiltration. Then, an anoikis-related signature was developed based on subtype-related DEGs, which could classify all SKCM patients into low and high ARG_score groups with differing overall survival (OS) rates. ARG_score was confirmed to be a strong independent prognostic indicator for SKCM patients. By combining ARG_score with clinicopathological features, a nomogram was constructed, which could accurately predict the individual OS of patients with SKCM. Moreover, low ARG_score patients presented with higher levels of immune cell infiltration, TME score, higher tumor mutation burden, and better immunotherapy responses. Conclusions: Our comprehensive analysis of ARGs in SKCM provides important insights into the immunological microenvironment within the tumor of SKCM patients and helps to forecast prognosis and the response to immunotherapy in SKCM patients, thereby making it easier to tailor more effective treatment strategies to individual patients.
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