Data_Sheet_1_Genomics Score Based on Genome-Wide Network Analysis for Prediction of Survival in Gastric Cancer: A Novel Prognostic Signature.docx

PurposeGastric cancer (GC) is a product of multiple genetic abnormalities, including genetic and epigenetic modifications. This study aimed to integrate various biomolecules, such as miRNAs, mRNA, and DNA methylation, into a genome-wide network and develop a nomogram for predicting the overall survival (OS) of GC.
Materials and MethodsA total of 329 GC cases, as a training cohort with a random of 150 examples included as a validation cohort, were screened from The Cancer Genome Atlas database. A genome-wide network was constructed based on a combination of univariate Cox regression and least absolute shrinkage and selection operator analyses, and a nomogram was established to predict 1-, 3-, and 5-year OS in the training cohort. The nomogram was then assessed in terms of calibration, discrimination, and clinical usefulness in the validation cohort. Afterward, in order to confirm the superiority of the whole gene network model and further reduce the biomarkers for the improvement of clinical usefulness, we also constructed eight other models according to the different combinations of miRNAs, mRNA, and DNA methylation sites and made corresponding comparisons. Finally, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were also performed to describe the function of this genome-wide network.
ResultsA multivariate analysis revealed a novel prognostic factor, a genomics score (GS) comprising seven miRNAs, eight mRNA, and 19 DNA methylation sites. In the validation cohort, comparing to patients with low GS, high-GS patients (HR, 12.886; P < 0.001) were significantly associated with increased all-cause mortality. Furthermore, after stratification of the TNM stage (I, II, III, and IV), there were significant differences revealed in the survival rates between the high-GS and low-GS groups as well (P < 0.001). The 1-, 3-, and 5-year C-index of whole genomics-based nomogram were 0.868, 0.895, and 0.928, respectively. The other models have comparable or relatively poor comprehensive performance, while they had fewer biomarkers. Besides that, DAVID 6.8 further revealed multiple molecules and pathways related to the genome-wide network, such as cytomembranes, cell cycle, and adipocytokine signaling.
ConclusionWe successfully developed a GS based on genome-wide network, which may represent a novel prognostic factor for GC. A combination of GS and TNM staging provides additional precision in stratifying patients with different OS prognoses, constituting a more comprehensive sub-typing system. This could potentially play an important role in future clinical practice.

研究目的：胃癌（Gastric cancer, GC）是多基因异常共同作用的产物，涵盖遗传与表观遗传修饰。本研究旨在整合miRNA、mRNA、DNA甲基化等多种生物分子，构建全基因组网络，并开发列线图（nomogram）以预测胃癌患者的总生存期（overall survival, OS）。
材料与方法：本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中筛选得到329例胃癌病例作为训练队列，并随机抽取其中150例作为验证队列。基于单变量Cox回归与最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）分析的联合结果，构建全基因组网络，并在训练队列中建立用于预测1年、3年、5年总生存期的列线图。随后在验证队列中对该列线图的校准度、区分度及临床实用性进行评估。为验证全基因网络模型的优越性并进一步精简生物标志物以提升临床实用性，本研究还根据miRNA、mRNA与DNA甲基化位点的不同组合构建了另外8种模型，并开展了相应的对比分析。最后，本研究还通过基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析，阐释该全基因组网络的功能特征。
结果：多变量分析显示，一种包含7个miRNA、8个mRNA及19个DNA甲基化位点的基因组评分（genomics score, GS）为全新的预后危险因素。在验证队列中，与基因组评分较低的患者相比，高基因组评分患者的全因死亡率显著升高（HR=12.886, P<0.001）。此外，在对TNM分期（I、II、III、IV期）进行分层分析后，高、低基因组评分组患者的生存率仍存在显著差异（P<0.001）。基于全基因组分析构建的列线图，其1年、3年、5年的一致性指数（C-index）分别为0.868、0.895与0.928。其余8种模型的综合性能与之相比，或存在可比性或相对较差，但它们所包含的生物标志物数量更少。此外，DAVID 6.8数据库进一步揭示了该全基因组网络相关的多种分子与通路，如细胞膜、细胞周期及脂肪细胞因子信号通路。
结论：本研究成功构建了基于全基因组网络的基因组评分，该评分可作为胃癌潜在的新型预后危险因素。将基因组评分与TNM分期相结合，能够更精准地对不同总生存期预后的胃癌患者进行分层，构建更为完善的分型系统，有望在未来的临床实践中发挥重要作用。