ArticleFrontiers in oncology2022
An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Article
- An overview on the interaction between non-coding RNAs and CTLA-4 gene in human diseases.Medical oncology (Northwood, London, England) · 2024Review
- A novel arginine methylation-associated lncRNA signature effectively predicts prognosis in breast cancer patients.Frontiers in oncology · 2024Article
- Single-cell analysis of matrisome-related genes in breast invasive carcinoma: new avenues for molecular subtyping and risk estimation.Frontiers in immunology · 2024Article
- Insights from a Computational-Based Approach for Analyzing Autophagy Genes across Human Cancers.Genes · 2023Review
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
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Abstract
Introduction: Breast cancer (BRCA) is the most common malignancy among women worldwide. It was widely accepted that autophagy and the tumor immune microenvironment play an important role in the biological process of BRCA. Long non-coding RNAs (lncRNAs), as vital regulatory molecules, are involved in the occurrence and development of BRCA. The aim of this study was to assess the prognosis of BRCA by constructing an autophagy-related lncRNA (ARlncRNA) prognostic model and to provide individualized guidance for the treatment of BRCA. Methods: The clinical data and transcriptome data of patients with BRCA were acquired from the Cancer Genome Atlas database (TCGA), and autophagy-related genes were obtained from the human autophagy database (HADb). ARlncRNAs were identified by conducting co‑expression analysis. Univariate and multivariate Cox regression analysis were performed to construct an ARlncRNA prognostic model. The prognostic model was evaluated by Kaplan-Meier survival analysis, plotting risk curve, Independent prognostic analysis, clinical correlation analysis and plotting ROC curves. Finally, the tumor immune microenvironment of the prognostic model was studied. Results: 10 ARlncRNAs( Discussion: The ARlncRNA prognostic model showed good performance in predicting the prognosis of patients with BRCA and is of great significance to guide the individualized treatment of these patients.
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