Abstract
This study conducts a systematic review through meta-analysis, comparing the composition and diversity of the gut microbiome in patients with esophageal cancer and healthy individuals, and explores the relationship between risk factors and related factors of esophageal cancer. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we comprehensively searched the databases of PubMed, Web of Science, Embase, Cochrane Library. In addition, we applied the R programming language version 4.0.3 and Stata 15.1 software for data analysis. We also implemented the Newcastle-Ottawa Scale (NOS), funnel plot analysis, Egger's test, and Begg's test to assess the risk of bias. In this study, a total of 328 studies were identified through the literature search. Among them, 117 duplicate studies were removed, and 202 studies were excluded based on inclusion and exclusion criteria. Finally, 9 studies were included in the analysis, involving a total of 216 patients with esophageal carcinoma and 352 healthy controls. Four studies provided Chao1 index for quantitative consolidation (ES = 637.41, 95% CI: 549.16 to 725.66, p = 0.000, I2 = 98.2%). Two studies [27, 29] reported ACE index (ES = 438.89, 95% CI: 362.42 to 515.35, p = 0.000, I2 = 97%). Seven studies [26,27,29,30,32] reported the Shannon index for quantitative consolidation (ES = 4.38, 95% CI: 3.95 to 4.81, p = 0.000, I2 = 99%). At the phylum level, the abundance of Bacteroidetes(ES = 37.8, 95% CI: 25.75 to 49.85, p = 0.000, I2 = 87.2%) and Proteobacteria(ES = 7.48, 95% CI: 5.02 to 8.85, p = 0.04, I2 = 2.4%) have statistical difference between ESCC and HC. There was no significant difference between ESCC and HC in the abundance of genera(p>0.05). This observational meta-analysis revealed that changes in the GM were correlated with esophageal carcinoma, and variations in some advantageous GM might involve regional differences. Additionally, the study aims to facilitate early diagnosis of esophageal cancer and improve screening and diagnostic efficiency.
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