Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Cancer
Published in: BMC Cancer 1/2023

Open Access 01-12-2023 | Melanoma | Research

Necroptosis-related LncRNAs in skin cutaneous melanoma: evaluating prognosis, predicting immunity, and guiding therapy

Authors: Jianlan Liu, Binlin Luo, Pengpeng Zhang, Keyu Jiang, Zuoqiong Hou, Xiaojian Cao, Jian Tang

Published in: BMC Cancer | Issue 1/2023

Login to get access

Abstract

Background

An increasing amount of research has speculated that necroptosis could be a therapeutic strategy for treating cancer. However, understanding the prognostic value of the necroptosis-related long non-coding RNAs (NRLs) in skin cutaneous melanoma (SKCM, hereafter referred to as melanoma) remains poor and needs to be developed. Our research aims to construct a model based on NRLs for the prognosis of patients with melanoma.

Methods

We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) database and retrieved 86 necroptosis-related genes from the GeneCards database. The lncRNAs associated with necroptosis were identified via the Pearson correlation coefficient, and the prognostic model of melanoma was constructed using LASSO regression. Next, we employed multiple approaches to verify the accuracy of the model. Melanoma patients were categorized into two groups (high-risk and low-risk) according to the results of LASSO regression. The relationships between the risk score and survival status, clinicopathological correlation, functional enrichment, immune infiltration, somatic mutation, and drug sensitivity were further investigated. Finally, the functions of AL162457.2 on melanoma proliferation, invasion, and migration were validated by in vitro experiments.

Results

The prognostic model consists of seven NRLs (EBLN3P, AC093010.2, LINC01871, IRF2-DT, AL162457.2, AC242842.1, HLA-DQB1-AS1) and shows high diagnostic efficiency. Overall survival in the high-risk group was significantly lower than in the low-risk group, and risk scores could be used to predict melanoma survival outcomes independently. Significant differences were evident between risk groups regarding the expression of immune checkpoint genes, immune infiltration, immunotherapeutic response and drug sensitivity analysis. A series of functional cell assays indicated that silencing AL162457.2 significantly inhibited cell proliferation, invasion, and migration in A375 cells.

Conclusion

Our prognostic model can independently predict the survival of melanoma patients while providing a basis for the subsequent investigation of necroptosis in melanoma and a new perspective on the clinical diagnosis and treatment of melanoma.
Appendix
Available only for authorised users
Literature
1.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. Cancer J Clin. 2021;71(3):209–49.CrossRef
2.
3.
4.
Tang R, Xu J, Zhang B, Liu J, Liang C, Hua J, Meng Q, Yu X, Shi S. Ferroptosis, necroptosis, and pyroptosis in anticancer immunity. J Hematol Oncol. 2020;13(1):110.CrossRefPubMedPubMedCentral
5.
Hartman ML. Non-apoptotic cell Death Signaling Pathways in Melanoma. Int J Mol Sci 2020, 21(8).
6.
Gong Y, Fan Z, Luo G, Yang C, Huang Q, Fan K, Cheng H, Jin K, Ni Q, Yu X, et al. The role of necroptosis in cancer biology and therapy. Mol Cancer. 2019;18(1):100.CrossRefPubMedPubMedCentral
7.
Geserick P, Wang J, Schilling R, Horn S, Harris PA, Bertin J, Gough PJ, Feoktistova M, Leverkus M. Absence of RIPK3 predicts necroptosis resistance in malignant melanoma. Cell Death Dis. 2015;6(9):e1884.CrossRefPubMedPubMedCentral
8.
Khan MR, Wellinger RJ, Laurent B. Exploring the alternative splicing of long noncoding RNAs. Trends Genet. 2021;37(8):695–8.CrossRefPubMed
9.
Reggiardo RE, Maroli SV, Kim DH. LncRNA biomarkers of inflammation and Cancer. Adv Exp Med Biol. 2022;1363:121–45.CrossRefPubMed
10.
He J, Zhu S, Liang X, Zhang Q, Luo X, Liu C, Song L. LncRNA as a multifunctional regulator in cancer multi-drug resistance. Mol Biol Rep. 2021;48(8):1–15.CrossRefPubMed
11.
Zhang L, Liu C, Zhang X, Wang C, Liu D. Breast cancer prognosis and immunological characteristics are predicted using the m6A/m5C/m1A/m7G-related long noncoding RNA signature. Funct Integr Genomics. 2023;23(2):117.CrossRefPubMed
12.
Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor infiltrating Immune cells with CIBERSORT. Methods in molecular biology (Clifton NJ). 2018;1711:243–59.CrossRef
13.
Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW, Levine DA, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.CrossRefPubMed
14.
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28(11):1747–56.CrossRefPubMedPubMedCentral
15.
16.
17.
Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587–d592.CrossRefPubMed
18.
Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pagès C, Tosolini M, Camus M, Berger A, Wind P, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Sci (New York NY). 2006;313(5795):1960–4.CrossRef
19.
Ruffo E, Wu RC, Bruno TC, Workman CJ, Vignali DAA. Lymphocyte-activation gene 3 (LAG3): the next immune checkpoint receptor. Semin Immunol. 2019;42:101305.CrossRefPubMedPubMedCentral
20.
Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype Relationships and Predictors of response to checkpoint blockade. Cell Rep. 2017;18(1):248–62.CrossRefPubMed
21.
Yoon JH, Abdelmohsen K, Srikantan S, Yang X, Martindale JL, De S, Huarte M, Zhan M, Becker KG, Gorospe M. LincRNA-p21 suppresses target mRNA translation. Mol Cell. 2012;47(4):648–55.CrossRefPubMedPubMedCentral
22.
Strilic B, Yang L, Albarrán-Juárez J, Wachsmuth L, Han K, Müller UC, Pasparakis M, Offermanns S. Tumour-cell-induced endothelial cell necroptosis via death receptor 6 promotes metastasis. Nature. 2016;536(7615):215–8.CrossRefPubMed
23.
Rinn JL, Chang HY. Long noncoding RNAs: Molecular Modalities to Organismal functions. Annu Rev Biochem. 2020;89:283–308.CrossRefPubMed
24.
Iyer MK, Niknafs YS, Malik R, Singhal U, Sahu A, Hosono Y, Barrette TR, Prensner JR, Evans JR, Zhao S, et al. The landscape of long noncoding RNAs in the human transcriptome. Nat Genet. 2015;47(3):199–208.CrossRefPubMedPubMedCentral
25.
26.
Hu X, Li J, Fu M, Zhao X, Wang W. The JAK/STAT signaling pathway: from bench to clinic. Signal Transduct Target therapy. 2021;6(1):402.CrossRef
27.
Kidd LC, Rogers EN, Yeyeodu ST, Jones DZ, Kimbro KS. Contribution of toll-like receptor signaling pathways to breast tumorigenesis and treatment. Breast cancer (Dove Medical Press). 2013;5:43–51.PubMed
28.
Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, Walsh LA, Postow MA, Wong P, Ho TS, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371(23):2189–99.CrossRefPubMedPubMedCentral
29.
Sha D, Jin Z, Budczies J, Kluck K, Stenzinger A, Sinicrope FA. Tumor Mutational Burden as a predictive biomarker in solid tumors. Cancer Discov. 2020;10(12):1808–25.CrossRefPubMedPubMedCentral
30.
Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM, Miller V, Stephens PJ, Daniels GA, Kurzrock R. Tumor Mutational Burden as an independent predictor of response to Immunotherapy in Diverse Cancers. Mol Cancer Ther. 2017;16(11):2598–608.CrossRefPubMedPubMedCentral
31.
Callahan MK, Postow MA, Wolchok JD. Targeting T cell co-receptors for Cancer Therapy. Immunity. 2016;44(5):1069–78.CrossRefPubMed
32.
Ashton TM, McKenna WG, Kunz-Schughart LA, Higgins GS. Oxidative phosphorylation as an emerging target in Cancer Therapy. Clin cancer research: official J Am Association Cancer Res. 2018;24(11):2482–90.CrossRef
33.
Gao SP, Sun HF, Jiang HL, Li LD, Hu X, Xu XE, Jin W. Loss of TIM50 suppresses proliferation and induces apoptosis in breast cancer. Tumour Biol. 2016;37(1):1279–87.CrossRefPubMed
34.
Sun B, Wang J, Zhu YF, Li ZY, Xiang JB, Chen ZY, He ZG, Gu XD. Prognostic value of TIMM50 expression in colorectal cancer. Arch Med Sci. 2023;19(3):626–32.PubMed
35.
Shi R, Li H, Wei S, Yu Z, Zhang J, Zhang Q, Zhou T, Yao Y, Zhang Q, Zhang T, et al. Lactate metabolism-related genes to predict the clinical outcome and molecular characteristics of endometrial cancer. BMC Cancer. 2023;23(1):491.CrossRefPubMedPubMedCentral
36.
Zhang X, Han S, Zhou H, Cai L, Li J, Liu N, Liu Y, Wang L, Fan C, Li A, et al. TIMM50 promotes tumor progression via ERK signaling and predicts poor prognosis of non-small cell lung cancer patients. Mol Carcinog. 2019;58(5):767–76.CrossRefPubMed
Metadata
Title
Necroptosis-related LncRNAs in skin cutaneous melanoma: evaluating prognosis, predicting immunity, and guiding therapy
Authors
Jianlan Liu
Binlin Luo
Pengpeng Zhang
Keyu Jiang
Zuoqiong Hou
Xiaojian Cao
Jian Tang
Publication date
01-12-2023
Publisher
BioMed Central
Keywords
Melanoma
Melanoma
Published in
BMC Cancer / Issue 1/2023
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-023-11246-x

Other articles of this Issue 1/2023

BMC Cancer 1/2023 Go to the issue

Research

Development and validation of a nomogram for predicting overall survival in patients with early-onset endometrial cancer

Research

A nomogram prediction model for the TP53mut subtype in endometrial cancer based on preoperative noninvasive parameters

Research

Lactate metabolism-related genes to predict the clinical outcome and molecular characteristics of endometrial cancer

Research

Association of novel and conventional obesity indices with colorectal cancer risk in older Chinese: a 14-year follow-up of the Guangzhou Biobank Cohort Study

Research

Prognostic impact of severe neutropenia in colorectal cancer patients treated with TAS-102 and bevacizumab, addressing immortal-time bias

Study protocol

Neoadjuvant chemoradiotherapy plus tislelizumab followed by surgery for esophageal carcinoma (CRISEC study): the protocol of a prospective, single-arm, phase II trial
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits