【佳学基因检测】m1A/m5C/m6A 相关 lncRNA 特征在骨肉瘤中的预后价值和免疫景观

靶向药一旦停药会怎样省钱要点

查重分析《肿瘤治疗效果与基因检测结果的相关性》《Eur Rev Med Pharmacol Sci》在. 2022 Aug;26(16):5868-5883.发表了一篇题目为《m1A/m5C/m6A 相关 lncRNA 特征在骨肉瘤中的预后价值和免疫景观》肿瘤靶向药物治疗基因检测临床研究文章。该研究由Z-Y Wu, Z-Y Shi等完成。促进了肿瘤的精准治疗与个性化用药的发展，进一步强调了基因信息检测与分析的重要性。

肿瘤靶向药物及精准治疗临床研究内容关键词：

肿瘤靶向治疗基因检测临床应用结果

目的：RNA甲基化修饰主要包括N1-甲基腺苷（m1A）、5-甲基胞嘧啶（m5C）和N6-甲基腺苷（m6A），广泛存在于骨肉瘤中并参与癌症的生物学过程。然而，目前还没有关于骨肉瘤与 m1A/m5C/m6A 相关长非编码 RNA (lncRNAs) 之间关系的研究。患者和方法：这里，来自 Therapeutical Applicable Research to Generate Effective Treatments (TARGET) 的骨肉瘤表达数据) 数据库被检索以识别与骨肉瘤患者的总生存期 (总生存期) 相关的 ER 相关 lncRNA。然后，应用 Lasso 惩罚 Cox 回归分析来构建 lncRNAs 风险特征。同时，根据已识别的 m1A/m5C/m6A 相关 lncRNA，将患者分为两个集群。进一步评估了已识别特征和簇的预后价值和免疫景观。结果：两个 m1A/m5C/m6A 相关的 lncRNA 被纳入我们的风险特征。功能分析表明，预后模型与患者生存、癌症转移和生长相关。同时，特征模型与免疫细胞、免疫微环境以及几个免疫检查点基因的浸润显着相关。对于 lncRNAs 簇也检测到了类似的结果，它们与免疫浸润、癌症微环境和免疫相关基因显着相关，有助于预测患者的预后。此外，我们的风险特征和集群可能有助于指导免疫治疗药物在骨肉瘤患者中的应用。最后，建立了基于风险评分的列线图。结论：总体而言，生成了基于两个 m1A/m5C/m6A 相关 lncRNA 的风险特征，并为骨肉瘤患者的预后和免疫状况提供了预测价值。该特征可进一步用于开发新的骨肉瘤治疗策略。

肿瘤发生与复发转移国际数据库描述：

Objective: RNA methylation modifications, mainly including N1-methyladenosine (m1A), 5-methylcytosine (m5C), and N6-methyladenosine (m6A), are widely existed in osteosarcoma and involved in the biological processes of cancers. However, there is still no study regarding the relationship between osteosarcoma and m1A/m5C/m6A-associated long non-coding RNAs (lncRNAs).Patients and methods: Here, expression data of osteosarcoma from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were retrieved to identify ER-related lncRNAs associated with the overall survival (OS) of osteosarcoma patients. Then, Lasso penalized Cox regression analysis was applied to construct a lncRNAs risk signature. Meanwhile, patients were stratified into two clusters based on the identified m1A/m5C/m6A-associated lncRNAs. The prognostic value and immune landscape of the identified signature and clusters were further evaluated.Results: Two m1A/m5C/m6A-associated lncRNAs were incorporated into our risk signature. The functional analyses indicated that the prognostic model was correlated with patient survival, and cancer metastasis and growth. Meanwhile, the signature model was significantly associated with the infiltration of immune cells, immune microenvironment, as well as several immune checkpoint genes. Similar results were detected for the lncRNAs clusters, which were significantly correlated with immune infiltration, cancer microenvironment, and immune-associated genes, and contributed to predicting the prognosis of patients. Moreover, our risk signature and clusters might help guide the application of immunotherapeutic drugs for osteosarcoma patients. Finally, a nomogram based on the risk score was established.Conclusions: Overall, a risk signature based on two m1A/m5C/m6A-associated lncRNAs was generated and presented predictive value for the prognosis and immune landscapes of osteosarcoma patients. This signature can be further used in the development of novel therapeutic strategies for osteosarcoma.