背景：在全球范围内，胃癌（GC）是第五大最常见的肿瘤。有必要识别新的分子亚型以指导患者选择特定的目标治疗益处。
方法：多组学数据，包括转录组学RNA测序(mRNA，LncRNA,miRNA),DNA甲基化，TCGA-STAD队列中的基因突变用于聚类。使用R中的“MOVICS”软件包执行了十种经典聚类算法来识别具有不同分子特征的患者。使用单样本基因集富集分析评估了激活的信号通路。基因突变的差异分布，拷贝数更改，和肿瘤突变负荷进行了比较，我们还评估了对免疫治疗和化疗的潜在反应.
结果：通过十种具有共识集合的聚类算法识别了两种分子亚型（CS1和CS2）。CS1组患者的平均总生存时间较短（28.5vs.68.9个月，P=0.016),和无进展生存期（19.0vs.63.9个月，与CS2组相比，P=0.008）。细胞外相关生物过程激活在CS1组中较高，而CS2组显示细胞周期相关通路的激活增强。在CS2组中观察到明显更高的总突变数量和新抗原，以及TTN中的特定突变，MUC16和ARID1A。在CS2组中也观察到更高的免疫细胞浸润，反映了潜在的免疫治疗益处。此外，CS2组也可以对5-氟尿嘧啶产生反应，顺铂,和紫杉醇。CS1组和CS2组之间的临床结果的相似差异在外部队列中得到了成功验证。GSE62254,GSE26253,GSE15459,和GSE84437.
结论：通过十种聚类算法对五组数据进行综合分析，这些发现提供了对GC亚型的新见解。这些可以基于特定的分子特征提供潜在的临床治疗靶标。
BACKGROUND: Globally, gastric cancer (GC) is the fifth most common tumor. It is necessary to identify novel molecular subtypes to guide patient selection for specific target therapeutic benefits.
METHODS: Multi-omics data, including transcriptomics RNA-sequencing (mRNA, LncRNA, miRNA), DNA methylation, and gene mutations in the TCGA-STAD cohort were used for the clustering. Ten classical clustering algorithms were executed to recognize patients with different molecular features using the \"MOVICS\" package in R. The activated signaling pathways were evaluated using the single-sample gene set enrichment analysis. The differential distribution of gene mutations, copy number alterations, and tumor mutation burden was compared, and potential responses to immunotherapy and chemotherapy were also assessed.
RESULTS: Two molecular subtypes (CS1 and CS2) were recognized by ten clustering algorithms with
consensus ensembles. Patients in the CS1 group had a shorter average overall survival time (28.5 vs. 68.9 months, P = 0.016), and progression-free survival (19.0 vs. 63.9 months, P = 0.008) as compared to those in the CS2 group. Extracellular associated biological process activation was higher in the CS1 group, while the CS2 group displayed the enhanced activation of cell cycle-associated pathways. Significantly higher total mutation numbers and neoantigens were observed in the CS2 group, along with specific mutations in TTN, MUC16, and ARID1A. Higher infiltration of immunocytes was also observed in the CS2 group, reflective of the potential immunotherapeutic benefits. Moreover, the CS2 group could also respond to 5-fluorouracil, cisplatin, and paclitaxel. The similar diversity in clinical outcomes between CS1 and CS2 groups was successfully validated in the external cohorts, GSE62254, GSE26253, GSE15459, and GSE84437.
CONCLUSIONS: The findings provided novel insights into the GC subtypes through integrative analysis of five -omics data by ten clustering algorithms. These could provide potential clinical therapeutic targets based on the specific molecular features.
