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Abstract：
BACKGROUND: Long noncoding RNAs (lncRNAs) and N6-methyladenosine (m6A) modification of RNA play pivotal roles in tumorigenesis and cancer progression. However, knowledge regarding the expression patterns of m6A-related lncRNAs and their corresponding m6A regulators in prostate cancer (PCa) is limited. This study aimed to delineate the landscape of m6A-related lncRNAs, develop a predictive model, and identify the critical m6A regulators of prognostic lncRNAs in PCa.
METHODS: Clinical and transcriptome data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-related lncRNAs were subsequently identified through Pearson correlation and univariate Cox regression analyses. The prognostic lncRNAs were clustered into two groups by consensus clustering analysis, and a risk signature model was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis of the lncRNAs. This model was evaluated using survival, clinicopathological, and immunological analyses. Furthermore, based on the constructed lncRNA-m6A regulatory network and RT-qPCR results, RBM15 was identified as a critical regulator of m6A-related lncRNAs. The biological roles of RBM15 in PCa were explored through bioinformatics analysis and biological experiments.
RESULTS: Thirty-four prognostic m6A-related lncRNAs were identified and categorized into two clusters with different expression patterns and survival outcomes in PCa patients. Seven m6A lncRNAs (AC105345.1, AL354989.1, AC138028.4, AC022211.1, AC020558.2, AC004076.2, and LINC02666) were selected to construct a risk signature with robust predictive ability for overall survival and were correlated with clinicopathological characteristics and the immune microenvironment of PCa patients. Among them, LINC02666 and AC022211.1 were regulated by RBM15. In addition, RBM15 expression correlated with PCa progression, survival, and the immune response. Patients with elevated RBM15 expression were more susceptible to the drug AMG-232. Moreover, silencing RBM15 decreased the viability of PCa cells and promoted apoptosis.
CONCLUSIONS: RBM15 is involved in the regulation of prognostic lncRNAs in the risk signature and has a robust predictive ability for PCa, making it a promising biomarker in PCa.
METHODS: Clinical and transcriptome data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-related lncRNAs were subsequently identified through Pearson correlation and univariate Cox regression analyses. The prognostic lncRNAs were clustered into two groups by consensus clustering analysis, and a risk signature model was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis of the lncRNAs. This model was evaluated using survival, clinicopathological, and immunological analyses. Furthermore, based on the constructed lncRNA-m6A regulatory network and RT-qPCR results, RBM15 was identified as a critical regulator of m6A-related lncRNAs. The biological roles of RBM15 in PCa were explored through bioinformatics analysis and biological experiments.
RESULTS: Thirty-four prognostic m6A-related lncRNAs were identified and categorized into two clusters with different expression patterns and survival outcomes in PCa patients. Seven m6A lncRNAs (AC105345.1, AL354989.1, AC138028.4, AC022211.1, AC020558.2, AC004076.2, and LINC02666) were selected to construct a risk signature with robust predictive ability for overall survival and were correlated with clinicopathological characteristics and the immune microenvironment of PCa patients. Among them, LINC02666 and AC022211.1 were regulated by RBM15. In addition, RBM15 expression correlated with PCa progression, survival, and the immune response. Patients with elevated RBM15 expression were more susceptible to the drug AMG-232. Moreover, silencing RBM15 decreased the viability of PCa cells and promoted apoptosis.
CONCLUSIONS: RBM15 is involved in the regulation of prognostic lncRNAs in the risk signature and has a robust predictive ability for PCa, making it a promising biomarker in PCa.
摘要：
背景：长链非编码RNA（lncRNA）和RNA的N6-甲基腺苷（m6A）修饰在肿瘤发生和癌症进展中起关键作用。然而,关于m6A相关lncRNAs及其相应m6A调节因子在前列腺癌(PCa)中的表达模式的知识有限.这项研究旨在描绘m6A相关lncRNAs的景观，建立一个预测模型，并鉴定PCa中预后lncRNAs的关键m6A调节因子。
方法：从癌症基因组图谱（TCGA）数据库下载PCa患者的临床和转录组数据。随后通过Pearson相关性和单变量Cox回归分析鉴定了与m6A相关的lncRNAs。通过共识聚类分析将预后lncRNAs分为两组，并使用lncRNAs的最小绝对收缩和选择算子(LASSO)回归分析构建风险特征模型。这个模型是用生存率来评估的，临床病理,和免疫学分析。此外，基于构建的lncRNA-m6A调控网络和RT-qPCR结果，RBM15被鉴定为m6A相关lncRNAs的关键调节因子。通过生物信息学分析和生物学实验，探讨RBM15在PCa中的生物学作用。
结果：在PCa患者中鉴定出34个预后m6A相关lncRNAs，并将其分类为两个具有不同表达模式和生存结果的簇。选择7个m6AlncRNAs(AC105345.1,AL354989.1,AC138028.4,AC022211.1,AC020558.2,AC004076.2和LINC02666)来构建具有对总生存的稳健预测能力的风险特征,并且与PCa患者的临床病理特征和免疫微环境相关。其中,LINC02666和AC022211.1受RBM15调控。此外,RBM15表达与PCa进展相关，生存,和免疫反应。RBM15表达升高的患者对药物AMG-232更敏感。此外，沉默RBM15可降低PCa细胞的活力，促进细胞凋亡。
结论：RBM15参与风险特征中预后lncRNAs的调节，并且对PCa具有强大的预测能力，使其成为PCa中一个有前途的生物标志物。
方法：从癌症基因组图谱（TCGA）数据库下载PCa患者的临床和转录组数据。随后通过Pearson相关性和单变量Cox回归分析鉴定了与m6A相关的lncRNAs。通过共识聚类分析将预后lncRNAs分为两组，并使用lncRNAs的最小绝对收缩和选择算子(LASSO)回归分析构建风险特征模型。这个模型是用生存率来评估的，临床病理,和免疫学分析。此外，基于构建的lncRNA-m6A调控网络和RT-qPCR结果，RBM15被鉴定为m6A相关lncRNAs的关键调节因子。通过生物信息学分析和生物学实验，探讨RBM15在PCa中的生物学作用。
结果：在PCa患者中鉴定出34个预后m6A相关lncRNAs，并将其分类为两个具有不同表达模式和生存结果的簇。选择7个m6AlncRNAs(AC105345.1,AL354989.1,AC138028.4,AC022211.1,AC020558.2,AC004076.2和LINC02666)来构建具有对总生存的稳健预测能力的风险特征,并且与PCa患者的临床病理特征和免疫微环境相关。其中,LINC02666和AC022211.1受RBM15调控。此外,RBM15表达与PCa进展相关，生存,和免疫反应。RBM15表达升高的患者对药物AMG-232更敏感。此外，沉默RBM15可降低PCa细胞的活力，促进细胞凋亡。
结论：RBM15参与风险特征中预后lncRNAs的调节，并且对PCa具有强大的预测能力，使其成为PCa中一个有前途的生物标志物。
