Identification of N6-Methyladenosine-Associated lncRNAs and Analysis of Prognostic Signature in Breast Cancer.

IF 2.1 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Biochemical Genetics Pub Date : 2024-07-23 DOI:10.1007/s10528-024-10889-0
Yun Gu, Min Xu, Wangfei Wu, Zhifang Ma, Weiguang Liu
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Abstract

Breast cancer represents the predominant malignant neoplasm in women, posing significant threats to both life and health. N6-methyladenosine (m6A) methylation, the most prevalent RNA modification, plays a crucial role in cancer development. This study aims to delineate the prognostic implications of m6A-associated long non-coding RNAs (m6AlncRNAs) and identify potential m6AlncRNA candidates as novel therapeutic targets for breast cancer. Through univariate Cox, Least Absolute Shrinkage and Selection Operator and multiple Cox regression analysis, m6AlncRNA was analyzed and a risk-prognosis model was constructed. Kaplan-Meier analysis, principal component analysis and nomogram were used to evaluate the risk model. Finally, we screened candidate lncRNAs and validated them in breast cancer cell lines. m6AlncRNAs were stratified into three subtypes, and their associations with survival outcomes and immune infiltrating capacities were systematically analyzed. Subsequently, breast cancer patients were stratified into high and low-risk groups based on median risk scores, revealing distinct clinical characteristics, tumor immunoinvasive profiles, tumor mutation burden, and survival probabilities. Additionally, a prognostic model was established, highlighting three promising candidate lncRNAs: ECE1-AS1, NDUFA6-DT, and COL4A2-AS1. This study investigated the prognostic implications of m6A-associated long non-coding RNAs (m6AlncRNAs) and developed a prognostic risk model to identify three potential m6AlncRNA candidates. These findings provide valuable insights into the potential application of these m6AlncRNAs in guiding immunotherapeutic strategies for breast cancer.

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N6-甲基腺苷相关lncRNA的鉴定及乳腺癌预后特征的分析
乳腺癌是女性最主要的恶性肿瘤,对生命和健康都构成重大威胁。N6-甲基腺苷(m6A)甲基化是最常见的 RNA 修饰,在癌症发展中起着至关重要的作用。本研究旨在阐明与m6A相关的长非编码RNA(m6AlncRNA)对预后的影响,并确定潜在的m6AlncRNA候选者作为乳腺癌的新型治疗靶点。通过单变量Cox、最小绝对缩减和选择操作器以及多元Cox回归分析,对m6AlncRNA进行了分析,并构建了风险-预后模型。采用卡普兰-梅耶分析、主成分分析和提名图来评估风险模型。最后,我们筛选了候选的lncRNA,并在乳腺癌细胞系中进行了验证。将m6AlncRNA分为三个亚型,并系统分析了它们与生存结果和免疫浸润能力的关系。随后,根据中位风险评分将乳腺癌患者分为高危和低危两组,揭示了不同的临床特征、肿瘤免疫浸润特征、肿瘤突变负荷和生存概率。此外,还建立了一个预后模型,突出了三个有希望的候选lncRNA:ECE1-AS1、NDUFA6-DT和COL4A2-AS1。这项研究调查了m6A相关长非编码RNA(m6AlncRNA)对预后的影响,并建立了一个预后风险模型,以确定三个潜在的m6AlncRNA候选者。这些发现为这些 m6AlncRNAs 在指导乳腺癌免疫治疗策略方面的潜在应用提供了宝贵的见解。
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来源期刊
Biochemical Genetics
Biochemical Genetics 生物-生化与分子生物学
CiteScore
3.90
自引率
0.00%
发文量
133
审稿时长
4.8 months
期刊介绍: Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses. Although studies focusing on particular regions and target organisms are welcome, it is not the journal’s goal to publish essentially descriptive studies that provide results with narrow applicability, or are based on very small samples or pseudoreplication. Rather, Biochemical Genetics welcomes review articles that go beyond summarizing previous publications and create added value through the systematic analysis and critique of the current state of knowledge or by conducting meta-analyses. Methodological articles are also within the scope of Biological Genetics, particularly when new laboratory techniques or computational approaches are fully described and thoroughly compared with the existing benchmark methods. Biochemical Genetics welcomes articles on the following topics: Genomics; Proteomics; Population genetics; Phylogenetics; Metagenomics; Microbial genetics; Genetics and evolution of wild and cultivated plants; Animal genetics and evolution; Human genetics and evolution; Genetic disorders; Genetic markers of diseases; Gene technology and therapy; Experimental and analytical methods; Statistical and computational methods.
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