A novel 8-genome instability-associated lncRNAs signature predicting prognosis and drug sensitivity in gastric cancer

IF 3 3区 医学 Q3 IMMUNOLOGY
C. Yi, Xiulan Zhang, Xia Chen, Birun Huang, Jing Song, Minghui Ma, Xiaolu Yuan, Chaohao Zhang
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引用次数: 1

Abstract

Background Genome instability lncRNA (GILnc) is prevalently related with gastric cancer (GC) pathophysiology. However, the study on the relationship GILnc and prognosis and drug sensitivity of GC remains scarce. Method We extracted expression data of 375 GC patients from TCGA cohort and 205 GC patients from GSE26942 cohort. Then, lncRNA was separated from expression data, and systematically characterized the 8 marker lncRNAs using the LASSO method. Next, we constructed a GILnc model (GILnc score) to quantify the GILnc index of each GC patient. Finally, we analyzed the relationship between GILnc score and clinical traits including survival outcomes, TP53, and drug sensitivity of GC. Results Based on a computational frame, 205 GILncs in GC has been identified. Then, a 8 GILncs was successfully established to predict overall survival in GC patients based on LASSO analysis, divided GC samples into high GILnc score and low GILnc score groups with significantly different outcome and was validated in multiple independent patient cohorts. Furthermore, GILnc model is better than the prediction performance of two recently published lncRNA signatures, and the high GILnc score group was more sensitive to mitomycin. Besides, the GILnc score has greater prognostic significance than TP53 mutation status alone and is capable of identifying intermediate subtype group existing with partial TP53 functionality in TP53 wild-type patients. Finally, GILnc signature as verified in GSE26942. Conclusion We applied bioinformatics approaches to suggest that a 8 GILnc signature could serve as prognostic biomarkers, and provide a novel direction to explore the pathogenesis of GC.
一种新的8基因组不稳定性相关lncRNAs特征预测胃癌预后和药物敏感性
背景基因组不稳定性lncRNA(GILnc)与癌症(GC)病理生理学普遍相关。然而,关于GILnc与GC的预后和药物敏感性的关系的研究仍然很少。方法我们从TCGA队列中提取375例GC患者和GSE26942队列中提取205例GC患者的表达数据。然后,从表达数据中分离出lncRNA,并使用LASSO方法对8个标志物lncRNA进行系统表征。接下来,我们构建了一个GILnc模型(GILnc评分)来量化每个GC患者的GILnc指数。最后,我们分析了GILnc评分与临床特征之间的关系,包括生存结果、TP53和GC的药物敏感性。结果基于一个计算框架,已识别出205个GC中的GILncs。然后,基于LASSO分析,成功建立了8个GILnc来预测GC患者的总生存率,将GC样本分为结果显著不同的高GILnc评分组和低GILnc分数组,并在多个独立患者队列中进行了验证。此外,GILnc模型的预测性能优于最近发表的两个lncRNA特征的预测性能,并且高GILnc评分组对丝裂霉素更敏感。此外,GILnc评分比单独的TP53突变状态具有更大的预后意义,并且能够识别TP53野生型患者中存在部分TP53功能的中间亚型组。最后,在GSE26942中验证了GILnc签名。结论我们应用生物信息学方法表明,8 GILnc信号可以作为预后的生物标志物,并为探索GC的发病机制提供了新的方向。
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来源期刊
CiteScore
4.00
自引率
0.00%
发文量
88
审稿时长
15 weeks
期刊介绍: International Journal of Immunopathology and Pharmacology is an Open Access peer-reviewed journal publishing original papers describing research in the fields of immunology, pathology and pharmacology. The intention is that the journal should reflect both the experimental and clinical aspects of immunology as well as advances in the understanding of the pathology and pharmacology of the immune system.
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