To investigate the prognostic factors of stage Ⅰ-Ⅱ gastric cancer based on P53 mutation and tumor budding

Prognostic significance of tumour budding in noncolorectal gastrointestinal tract and pancreatobiliary tract: a systematic review and meta-analysis

Tumour budding shows promise as a prognostic factor in various cancers, but its widespread application is hindered by the lack of large, validated studies and standardized criteria. This meta-analysis aims to review and examine the prognostic role of tumour budding specifically in noncolorectal gastrointestinal and pancreatobiliary tract cancers, broadening our perspective on its clinical relevance. The literature review was conducted through PubMed, Embase, and Web of Science from inception till 20 February 2023. Pooled odds ratio (OR) and hazard ratio (HR) with 95% confidence interval (CI) were calculated to assess the relation between tumour budding and clinicopathologic features, as well as overall survival. Each study was evaluated using the Newcastle-Ottawa Scale and both heterogeneity and publication bias were analysed. In this meta-analysis of 57 studies across various cancer types, multivariate HR revealed worse overall survival in oesophageal squamous cell carcinoma (HR 3.34 [95% CI 2.21-5.04]), gastric adenocarcinoma (2.03 [1.38-2.99]), pancreatic ductal adenocarcinoma (2.56 [2.02-3.25]), and biliary tract adenocarcinoma (3.11 [2.46-3.93]) with high-grade tumour budding. Additionally, high-grade tumour budding consistently correlated with adverse clinicopathological features, including lymph node metastasis, lymphovascular invasion, and distant metastasis without any observed inverse association. High heterogeneity was noted. Our study suggests that tumour budding is a valuable prognostic marker in various cancers. Nonetheless, standardized criteria tailored to specific organ types are necessary to enhance its clinical utility.

Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC

BACKGROUND

Prognostic models based on multiomics data may provide better predictive capability than those established at the single-omics level. Here we aimed to establish a prognostic model for resectable gastric cancer (GC) with multiomics information involving mutational, copy number, transcriptional, methylation, and clinicopathological alterations.

PATIENTS AND METHODS

The mutational, copy number, transcriptional, methylation data of 268, 265, 226, and 252 patients with stages I-III GC were downloaded from the TCGA database, respectively. Alterations from all omics were characterized, and prognostic models were established at the individual omics level and optimized at the multiomics level. All models were validated with a cohort of 99 patients with stages I-III GC.

RESULTS

TTN, TP53, and MUC16 were among the genes with the highest mutational frequency, while UBR5, ZFHX4, PREX2, and ARID1A exhibited the most prominent copy number variations (CNVs). Upregulated COL10A1, CST1, and HOXC10 and downregulated GAST represented the biggest transcriptional alterations. Aberrant methylation of some well-known genes was revealed, including CLDN18, NDRG4, and SDC2. Many alterations were found to predict the patient prognosis by univariate analysis, while four mutant genes, two CNVs, five transcriptionally altered genes, and seven aberrantly methylated genes were identified as independent risk factors in multivariate analysis. Prognostic models at the single-omics level were established with these alterations, and optimized combination of selected alterations with clinicopathological factors was used to establish a final multiomics model. All single-omics models and the final multiomics model were validated by an independent cohort. The optimal area under the curve (AUC) was 0.73, 0.71, 0.71, and 0.85 for mutational, CNV, transcriptional, and methylation models, respectively. The final multiomics model significantly increased the AUC to 0.92 (P < 0.05).

CONCLUSIONS

Multiomics model exhibited significantly better capability in predicting the prognosis of resectable GC than single-omics models.