Unveiling the Power of Gut Microbiome in Predicting Neoadjuvant Immunochemotherapy Responses in Esophageal Squamous Cell Carcinoma

被引:1
|
作者
Liu, Le [1 ,2 ]
Liang, Liping [3 ]
Luo, Yingjie [4 ]
Han, Jimin [5 ]
Lu, Di [4 ]
Cai, Ruijun [4 ]
Sethi, Gautam [6 ]
Mai, Shijie [4 ]
机构
[1] Southern Med Univ, Shenzhen Hosp, Integrated Clin Microecol Ctr, Shenzhen, Peoples R China
[2] Southern Med Univ, Zhujiang Hosp, Dept Gastroenterol, Guangzhou, Peoples R China
[3] South China Univ Technol, Guangzhou Peoples Hosp 1, Guangzhou Key Lab Digest Dis, Sch Med,Guangzhou Digest Dis Ctr, Guangzhou, Peoples R China
[4] Southern Med Univ, Nanfang Hosp, Dept Thorac Surg, Guangzhou, Peoples R China
[5] Tsinghua Univ, Sch Life Sci, Beijing, Peoples R China
[6] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Pharmacol, Singapore, Singapore
关键词
FUSOBACTERIUM-NUCLEATUM; CHEMORADIOTHERAPY; IMMUNOTHERAPY; CANCER;
D O I
10.34133/research.0529
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The role of the gut microbiome in enhancing the efficacy of anticancer treatments like chemotherapy and radiotherapy is well acknowledged. However, there is limited empirical evidence on its predictive capabilities for neoadjuvant immunochemotherapy (NICT) responses in esophageal squamous cell carcinoma (ESCC). Our study fills this gap by comprehensively analyzing the gut microbiome's influence on NICT outcomes. We analyzed 16S rRNA gene sequences from 136 fecal samples from 68 ESCC patients before and after NICT, along with 19 samples from healthy controls. After NICT, marked microbiome composition changes were noted, including a decrease in ESCC-associated pathogens and an increase in beneficial microbes such as Limosilactobacillus, Lacticaseibacillus, and Staphylococcus. Baseline microbiota profiles effectively differentiated responders from nonresponders, with responders showing higher levels of short-chain fatty acid (SCFA)-producing bacteria such as Faecalibacterium, Eubacterium_eligens_group, Anaerostipes, and Odoribacter, and nonresponders showing increases in Veillonella, Campylobacter, Atopobium, and Trichococcus. We then divided our patient cohort into training and test sets at a 4:1 ratio and utilized the XGBoost-RFE algorithm to identify 7 key microbial biomarkers-Faecalibacterium, Subdoligranulum, Veillonella, Hungatella, Odoribacter, Butyricicoccus, and HT002. A predictive model was developed using LightGBM, which achieved an area under the receiver operating characteristic curve (AUC) of 86.8% [95% confidence interval (CI), 73.8% to 99.4%] in the training set, 76.8% (95% CI, 41.2% to 99.7%) in the validation set, and 76.5% (95% CI, 50.4% to 100%) in the testing set. Our findings underscore the gut microbiome as a novel source of biomarkers for predicting NICT responses in ESCC, highlighting its potential to enhance personalized treatment strategies and advance the integration of microbiome profiling into clinical practice for modulating cancer treatment responses.
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收藏
页数:15
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