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Identification and Validation of Ferroptosis-related LncRNA Signature As a Prognostic Model for Skin Cutaneous Melanoma

Overview
Journal Front Immunol
Date 2022 Oct 17
PMID 36248853
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Abstract

Background: Melanoma is a type of skin cancer, which originates from the malignant transformation of epidermal melanocytes, with extremely high lethality. Ferroptosis has been documented to be highly related to cancer pathogenesis and the effect of immunotherapy. In addition, the dysregulation of lncRNAs is greatly implicated in melanoma progression and ferroptosis regulation. However, the significance of ferroptosis-related lncRNA in melanoma treatment and the prognosis of melanoma patients remains elusive.

Methods: Least Absolute Shrinkage Selection Operator (LASSO) regression analysis in the TCGA SKCM database, a cutaneous melanoma risk model was established based on differentially-expressed ferroptosis-related lncRNAs (DEfrlncRNAs). The nomogram, receiver operating characteristic (ROC) curves, and calibration plots were conducted to examine the predictive performance of this model. Sequentially, we continued to analyze the differences between the high- and low-risk groups, in terms of clinical characteristics, immune cell infiltration, immune-related functions, and chemotherapy drug sensitivity. Moreover, the expressions of DEfrlncRNAs, PD-L1, and CD8 were also examined by qRT-PCR and immunohistochemical staining in melanoma tissues to further confirm the potential clinical implication of DEfrlncRNAs in melanoma immunotherapy.

Results: 16 DEfrlncRNAs were identified, and a representative risk score for patient survival was constructed based on these 16 genes. The risk score was found to be an independent prognostic factor for the survival of melanoma patients. In addition, the low-risk group of patients had higher immune cell infiltration in the melanoma lesions, higher sensitivity to chemotherapeutic agents, and a better survival prognosis. Besides, the high expression of the identified 5 DEfrlncRNA in the low-risk group might suggest a higher possibility to benefit from immune checkpoint blockade therapy in the treatment of melanoma.

Conclusion: The DEfrlncRNA risk prediction model related to ferroptosis genes can independently predict the prognosis of patients with melanoma and provide a basis for evaluating the response of clinical treatment in melanoma.

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References
1.
Perez M, Magtanong L, Dixon S, Watts J . Dietary Lipids Induce Ferroptosis in Caenorhabditiselegans and Human Cancer Cells. Dev Cell. 2020; 54(4):447-454.e4. PMC: 7483868. DOI: 10.1016/j.devcel.2020.06.019. View

2.
Friedman J, Hastie T, Tibshirani R . Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010; 33(1):1-22. PMC: 2929880. View

3.
Gutzmer R, Stroyakovskiy D, Gogas H, Robert C, Lewis K, Protsenko S . Atezolizumab, vemurafenib, and cobimetinib as first-line treatment for unresectable advanced BRAF mutation-positive melanoma (IMspire150): primary analysis of the randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2020; 395(10240):1835-1844. DOI: 10.1016/S0140-6736(20)30934-X. View

4.
Liu R, Yang F, Yin J, Liu Y, Zhang W, Zhou H . Influence of Tumor Immune Infiltration on Immune Checkpoint Inhibitor Therapeutic Efficacy: A Computational Retrospective Study. Front Immunol. 2021; 12:685370. PMC: 8248490. DOI: 10.3389/fimmu.2021.685370. View

5.
Zhang F, Li F, Lu G, Nie W, Zhang L, Lv Y . Engineering Magnetosomes for Ferroptosis/Immunomodulation Synergism in Cancer. ACS Nano. 2019; 13(5):5662-5673. DOI: 10.1021/acsnano.9b00892. View