ArticleMedicine2022
The new horizon of biomarker in melanoma patients: A study based on autophagy-related long non-coding RNA.
Article in Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Survival expectations in melanoma patients: a molecular prognostic model associated with aging.Discover oncology · 2025Article
- T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes.Non-coding RNA · 2023Article
- Tumor necrosis factor-related lncRNAs predict prognosis and immunotherapy response for patients with lung adenocarcinoma.Journal of thoracic disease · 2023Article
- Machine learning-based signature of necrosis-associated lncRNAs for prognostic and immunotherapy response prediction in cutaneous melanoma and tumor immune landscape characterization.Frontiers in endocrinology · 2023Article
- A novel endoplasmic reticulum stress-related lncRNA prognostic risk model for cutaneous melanoma.Journal of cancer research and clinical oncology · 2022Article
- In Pancreatic Adenocarcinoma Alpha-Synuclein Increases and Marks Peri-Neural Infiltration.International journal of molecular sciences · 2022Article
- HLA-DQB1-AS1 Promotes Cell Proliferation, Inhibits Apoptosis, and Binds with ZRANB2 Protein in Hepatocellular Carcinoma.Journal of oncology · 2022Article
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7 authors.
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Abstract
abstractAutophagy-related long non-coding RNAs (arlncRNAs) play a crucial role in the pathogenesis and development of the tumor. However, there is a lack of systematic analysis of arlncRNAs in melanoma patients.Melanoma data for analysis were obtained from The Cancer Genome Atlas (TCGA) database. By establishing a co-expression network of autophagy-related mRNAs-lncRNAs, we identified arlncRNAs in melanoma patients. We evaluated the prognostic value of arlncRNAs by univariate and multivariate Cox analysis and constructed an arlncRNAs risk model. Patients were divided into high- and low-risk groups based on the arlncRNAs risk score. This model was evaluated by Kaplan-Meier (K-M) analysis, univariate-multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve analysis. Characteristics of autophagy genes and co-expressive tendency were analyzed by principal component analysis and Gene Set Enrichment Analysis (GSEA) functional annotation.Nine arlncRNAs (USP30-AS1, LINC00665, PCED1B-AS1, LINC00324, LINC01871, ZEB1-AS1, LINC01527, AC018553.1, and HLA-DQB1-AS1) were identified to be related to the prognosis of melanoma patients. Otherwise, the 9 arlncRNAs constituted an arlncRNAs prognostic risk model. K-M analysis and ROC curve analysis showed that the arlncRNAs risk model has good discrimination. Univariate and multivariate Cox regression analysis showed that arlncRNAs risk model was an independent prognostic factor in melanoma patients. Principal component analysis and GSEA functional annotation showed different autophagy and carcinogenic status in the high- and low-risk groups.This novel arlncRNAs risk model plays an essential role in predicting of the prognosis of melanoma patients. The model reveals new prognosis-related biomarkers for autophagy, promotes precision medicine, and provides a lurking target for melanoma's autophagy-related treatment.
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