Xu Feng, Huang Xiaoling, Li Yangyi, Chen Yongsong, Lin Ling
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China.
Clinical Research Center, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China.
Mol Ther Nucleic Acids. 2021 Apr 9;24:780-791. doi: 10.1016/j.omtn.2021.04.003. eCollection 2021 Jun 4.
Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer worldwide. However, the survival rate of LUAD patients remains low. N6-methyladenosine (mA) and long noncoding RNAs (lncRNAs) play vital roles in the prognostic value and the immunotherapeutic response of LUAD. Thus, discerning lncRNAs associated with mA in LUAD patients is critical. In this study, mA-related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were conducted to construct an mA-related lncRNA model. Kaplan-Meier analysis, principal-component analysis (PCA), functional enrichment annotation, and nomogram were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed. The risk model comprising 12 mA-related lncRNAs was identified as an independent predictor of prognoses. By regrouping the patients with this model, we can distinguish between them more effectively in terms of the immunotherapeutic response. Finally, candidate compounds aimed at LUAD subtype differentiation were identified. This risk model based on the mA-based lncRNAs may be promising for the clinical prediction of prognoses and immunotherapeutic responses in LUAD patients.
肺腺癌(LUAD)是全球最常见的肺癌亚型。然而,LUAD患者的生存率仍然很低。N6-甲基腺苷(m6A)和长链非编码RNA(lncRNA)在LUAD的预后价值和免疫治疗反应中起着至关重要的作用。因此,识别LUAD患者中与m6A相关的lncRNA至关重要。在本研究中,通过共表达分析并获得了与m6A相关的lncRNA。进行单因素、最小绝对收缩和选择算子(LASSO)以及多因素Cox回归分析以构建与m6A相关的lncRNA模型。采用Kaplan-Meier分析、主成分分析(PCA)、功能富集注释和列线图对风险模型进行分析。最后,还讨论了针对该模型的潜在免疫治疗特征和药物敏感性预测。由12个与m6A相关的lncRNA组成的风险模型被确定为预后的独立预测因子。通过用该模型对患者进行重新分组,我们可以在免疫治疗反应方面更有效地区分他们。最后,确定了针对LUAD亚型分化的候选化合物。这种基于m6A的lncRNA的风险模型可能对LUAD患者的预后和免疫治疗反应的临床预测具有前景。