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Updated: Aug 2, 2025

Modeling Oral-Esophageal Squamous Cell Carcinoma in 3D Organoids
Published on: December 23, 2022
Gut microbiome can predict chemoradiotherapy efficacy in patients with esophageal squamous cell carcinoma
Takuma Sasaki1, Yasunori Matsumoto2, Kentaro Murakami1
1Department of Frontier Surgery, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan.
Purpose:
The gut microbiome plays an important role in cancer pathogenesis and therapy. Some studies have reported that specific bacteria in tumor tissues may contribute to the prognosis and treatment of esophageal squamous cell carcinoma (ESCC). However, there is limited evidence that the gut microbiome is associated with ESCC. This study assessed the utility of the gut microbiome as a predictive marker of the therapeutic effect in patients with ESCC undergoing chemo-radiotherapy (CRT).
Patients And Methods:
Fecal samples were collected from 51 patients with ESCC who had never undergone treatment between April 2021 and May 2022 in the Department of Frontier Surgery, Chiba University. The gut microbiome was analyzed using 16S metagenomics sequencing. The association between the gut microbiome composition and stage according to the TNM classification (American Joint Committee on Cancer 7.0) and CRT response according to the RECIST criteria was evaluated.
Results:
The relative abundance of Fusobacteriaceae was enriched in cStage III-IVb group. Among the 27 patients who received CRT, the relative abundance of Lactobacillaceae was enriched in those with a partial and complete response. Lactobacillaceae also did not correlate with any clinical data, but the high Lactobacillales group had a higher LMR (P = 0.032) and lower PLR (P = 0.045) than in the low Lactobacillales group.
Conclusions:
In conclusion, we found that the relative abundance of Lactobacillaceae was enriched in patients with a partial or complete response among CRT those with ESCC, thus suggesting that the relative abundance of Lactobacillaceae can predict the effect of CRT.
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