Stomach adenocarcinoma (STAD) is a highly heterogeneous disease. Due to the lack of effective molecular markers and personalized treatment, the prognosis of gastric cancer patients is still very poor. The ABSOLUTE algorithm and cancer cell fraction were used to evaluate the clonal and subclonal status of 349 TCGA (The Cancer Genome Cancer Atlas)-STAD patients. Non-negative matrix factorization was used to identify the mutation characteristics of the samples. Univariate Cox regression analysis was used to determine the relationship between clonal/subclonal events and prognosis, and the Spearman correlation was used to evaluate the relationship of clonal/subclonal events to tumor mutation burden (TMB) and neoantigens. The evolution pattern of STAD demonstrated great tumor heterogeneity. TP53, USH2A, and GLI3 appeared earliest in STAD and may drive STAD. CTNNB1, LRP1B, and ERBB4 appeared the latest in STAD, and may be related to STAD’s progress. Univariate Cox regression analysis identified four early genes, eight intermediate genes, and seven late genes significantly associated with overall survival. The number of subclonal events in the T stage was significantly different. The N stage, gender, and histological type were significantly different for clonal events, and there was a significant correlation between clonal/subclonal events and TMB/neoantigens. Our results highlight the importance of systematic evaluation of evolutionary models in the clinical management of STAD and personalized gastric cancer treatment.
Background: Gastric cancer is a highly heterogeneous disease. Due to the lack of effective molecular markers and personalized treatment, the prognosis of patients with gastric cancer is still rather poor. This paper attempts to explore the genomic instability and intratumoral heterogeneity of gastric cancer through bioinformatics analysis. Methods: According to the RNA-Seq data, copy number variation data and clinical follow-up information data of TCGA, brunet algorithm in NMF was used to identify SNV signature. ABSOLUTE algorithm was used to screen and identify CNV signature. The relationship between clonal state and survival of genes was analyzed with Kaplan-Meier method. The Spearman method was used to evaluate the correlation between clonal/sub-clonal events and TMB and neoantigen. Results: Mutation analysis of SNV mutation and CNA data in the TCGA gastric cancer dataset divided mutation of TCGA gastric cancer samples into three signatures with significant differences. Furthermore, the clonal/sub-clonal events of SCNA and SNV were analyzed to identify the clonal/sub-clonal status of mutations in STAD samples. It was further found that the number of mutations in TP53, TTN and MUC16 genes was the highest (> 20%) among the samples. The number of CNV (Gain) in MIEN1, GRB7 and PNMT genes was the highest (> 10%) among the samples. A series of early gastric cancer genes, such as TP53, USH2A and GLI3 as well as advanced genes such as CTNNB1, LRP1B and ERBB4 were further identified. Ultimately, the clonal/sub-clonal status of 5 early genes, 12 intermediate genes and 8 advanced genes was significantly correlated with the overall survival rate of patients. In addition, there was a significant correlation between clonal/sub-clonal events and TMB/Neoantigens. Conclusion: In this paper, the relationship between intratumoral heterogeneity and genomic instability was evaluated based on developmental data of gastric cancer cloning system. A series of molecular features determined by screening can be used as a marker of potential gastric cancer heterogeneity, which is of great significance to the personalized treatment of gastric cancer.
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