Department of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
J Cell Mol Med. 2024 Apr;28(8):e18282. doi: 10.1111/jcmm.18282.
Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.
研究表明，m6A、m5C 和 m1A 修饰与不同类型肿瘤的发展之间存在关联。然而，目前尚不清楚这些修饰是否与 LUAD 的预后有关。TCGA-LUAD 数据集被用于特征训练，而验证队列则通过合并公开可获得的 GEO 数据集（包括 GSE29013、GSE30219、GSE31210、GSE37745 和 GSE50081）创建。该研究集中在 33 个受 m6A、m5C 或 m1A（mRG）调控的基因上，这些基因用于形成 mRGs 簇和 mRG 差异表达基因簇（mRG-DEG 簇）。我们随后的 LASSO 回归分析使用在 mRG-DEG 簇中表现出差异表达且具有预后价值的 lncRNA 训练 m6A/m5C/m1A 相关 lncRNA（mRLncSig）特征。该模型的准确性通过 Kaplan-Meier 分析、Cox 回归、ROC 分析、tAUC 评估、PCA 检查和列线图预测器验证进行了验证。在评估签名的免疫治疗潜力时，我们通过各种分析使用了多个生物信息学算法和概念。其中包括七种新开发的免疫信息学算法，以及对 TMB、TIDE 和免疫检查点的评估。此外，我们还鉴定和验证了针对 LUAD 中高危 mRLncSig 的有前途的药物。为了验证 mRLncSig 在真实世界中的表达模式，我们对人类 LUAD 组织进行了实时 PCR。还评估了特征在泛癌环境中的性能。该研究创建了一个由 10 个 lncRNA 组成的特征 mRLncSig，该特征在验证队列中具有预后能力。实时 PCR 用于验证特征中每个基因在现实世界中的实际表现。我们的免疫治疗分析揭示了 mRLncSig 与免疫状态之间的关联。mRLncSig 与几个检查点密切相关，例如 IL10、IL2、CD40LG、SELP、BTLA 和 CD28，它们可能是 LUAD 的合适免疫治疗靶点。在高危患者中，我们的研究确定了 12 种候选药物，并验证了吉西他滨是最有效的一种，它可以靶向我们的特征，并对治疗 LUAD 有效。此外，我们发现 mRLncSig 中的一些 lncRNA 在某些癌症类型中可能发挥关键作用，因此，在未来的研究中可能需要进一步关注。根据这项研究的结果，使用 mRLncSig 有可能有助于预测 LUAD 的预后，并可能成为免疫治疗的潜在靶点。此外，我们的特征可能有助于更有效地识别靶点和治疗药物。
