Intratumoral and fecal microbiota reveals microbial markers associated with gastric carcinogenesis

被引:7
作者
Wang, Yiwen [1 ,2 ]
Wang, Yue [1 ,2 ]
Han, Wenjie [1 ,2 ]
Han, Mengzhen [1 ,2 ]
Liu, Xiaolin [3 ]
Dai, Jianying [4 ]
Dong, Yuesheng [4 ]
Sun, Tao [1 ,5 ,6 ]
Xu, Junnan [1 ,2 ,6 ]
机构
[1] China Med Univ, Liaoning Canc Hosp, Canc Hosp, Dept Breast Med 1, Shenyang, Peoples R China
[2] China Med Univ, Liaoning Canc Hosp, Dept Pharmacol, Canc Hosp, Shenyang, Peoples R China
[3] Kanghui Biotechnol Co Ltd, Dept Bioinformat, Shenyang, Peoples R China
[4] Dalian Univ Technol, Sch Bioengn, Dalian, Liaoning, Peoples R China
[5] Dept Med Oncol, Key Lab Liaoning Breast Canc Res, Shenyang, Liaoning, Peoples R China
[6] Dalian Univ Technol, Liaoning Canc Hosp, Dept Breast Med, Canc Hosp, Shenyang, Peoples R China
关键词
gastric cancer; intratumoral microbiota; fecal microbiota; microbial marker; non-invasive prediction; HELICOBACTER-PYLORI; INTESTINAL MICROBIOTA; CANCER; IDENTIFICATION; COLONIZATION; RISK;
D O I
10.3389/fcimb.2024.1397466
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Background The relationship between dysbiosis of the gastrointestinal microbiota and gastric cancer (GC) has been extensively studied. However, microbiota alterations in GC patients vary widely across studies, and reproducible diagnostic biomarkers for early GC are still lacking in multiple populations. Thus, this study aimed to characterize the gastrointestinal microbial communities involved in gastric carcinogenesis through a meta-analysis of multiple published and open datasets.Methods We analyzed 16S rRNA sequencing data from 1,642 gastric biopsy samples and 394 stool samples across 11 independent studies. VSEARCH, QIIME and R packages such as vegan, phyloseq, cooccur, and random forest were used for data processing and analysis. PICRUSt software was employed to predict functions.Results The alpha-diversity results indicated significant differences in the intratumoral microbiota of cancer patients compared to non-cancer patients, while no significant differences were observed in the fecal microbiota. Network analysis showed that the positive correlation with GC-enriched bacteria increased, and the positive correlation with GC-depleted bacteria decreased compared to healthy individuals. Functional analyses indicated that pathways related to carbohydrate metabolism were significantly enriched in GC, while biosynthesis of unsaturated fatty acids was diminished. Additionally, we investigated non-Helicobacter pylori (HP) commensals, which are crucial in both HP-negative and HP-positive GC. Random forest models, constructed using specific taxa associated with GC identified from the LEfSe analysis, revealed that the combination of Lactobacillus and Streptococcus included alone could effectively discriminate between GC patients and healthy individuals in fecal samples (area under the curve (AUC) = 0.7949). This finding was also validated in an independent cohort (AUC = 0.7712).Conclusions This study examined the intratumoral and fecal microbiota of GC patients from a dual microecological perspective and identified Lactobacillus, Streptococcus, Roseburia, Faecalibacterium and Phascolarctobacterium as intratumoral and intestinal-specific co-differential bacteria. Furthermore, it confirmed the validity of the combination of Lactobacillus and Streptococcus as GC-specific microbial markers across multiple populations, which may aid in the early non-invasive diagnosis of GC.
引用
收藏
页数:15
相关论文
共 62 条
[1]   Antibiotic-Induced Changes in the Intestinal Microbiota and Disease [J].
Becattini, Simone ;
Taur, Ying ;
Pamer, Eric G. .
TRENDS IN MOLECULAR MEDICINE, 2016, 22 (06) :458-478
[2]   Dysbiosis of the microbiome in gastric carcinogenesis [J].
Castano-Rodriguez, Natalia ;
Goh, Khean-Lee ;
Fock, Kwong Ming ;
Mitchell, Hazel M. ;
Kaakoush, Nadeem O. .
SCIENTIFIC REPORTS, 2017, 7
[3]   Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data [J].
Chang, Fang ;
He, Shishi ;
Dang, Chenyuan .
JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2022, (183)
[4]   Interplay between Dysbiosis of Gut Microbiome, Lipid Metabolism, and Tumorigenesis: Can Gut Dysbiosis Stand as a Prognostic Marker in Cancer? [J].
Chattopadhyay, Indranil ;
Gundamaraju, Rohit ;
Jha, Niraj Kumar ;
Gupta, Piyush Kumar ;
Dey, Abhijit ;
Mandal, Chandi C. ;
Ford, Bridget M. .
DISEASE MARKERS, 2022, 2022
[5]   Characteristics of gastric cancer gut microbiome according to tumor stage and age segmentation [J].
Chen, Changchang ;
Du, Yaoqiang ;
Liu, Yanxin ;
Shi, Yongkang ;
Niu, Yaofang ;
Jin, Gulei ;
Shen, Jian ;
Lyu, Jianxin ;
Lin, Lijun .
APPLIED MICROBIOLOGY AND BIOTECHNOLOGY, 2022, 106 (19-20) :6671-6687
[6]   Mucosa-Associated Microbiota in Gastric Cancer Tissues Compared With Non-cancer Tissues [J].
Chen, Xiao-Hui ;
Wang, Ang ;
Chu, Ai-Ning ;
Gong, Yue-Hua ;
Yuan, Yuan .
FRONTIERS IN MICROBIOLOGY, 2019, 10
[7]   Risk of gastric cancer development after eradication of Helicobacter pylori [J].
Cheung, Ka-Shing ;
Leung, Wai K. .
WORLD JOURNAL OF GASTROINTESTINAL ONCOLOGY, 2018, 10 (05) :115-123
[8]   Mucosal microbiome dysbiosis in gastric carcinogenesis [J].
Coker, Olabisi Oluwabukola ;
Dai, Zhenwei ;
Nie, Yongzhan ;
Zhao, Guijun ;
Cao, Lei ;
Nakatsu, Geicho ;
Wu, William K. K. ;
Wong, Sunny Hei ;
Chen, Zigui ;
Sung, Joseph J. Y. ;
Yu, Jun .
GUT, 2018, 67 (06) :1024-1032
[9]   Differences in Gastric Mucosal Microbiota Profiling in Patients with Chronic Gastritis, Intestinal Metaplasia, and Gastric Cancer Using Pyrosequencing Methods [J].
Eun, Chang Soo ;
Kim, Byung Kwon ;
Han, Dong Soo ;
Kim, Seon Young ;
Kim, Kyung Mo ;
Choi, Bo Youl ;
Song, Kyu Sang ;
Kim, Yong Sung ;
Kim, Jihyun F. .
HELICOBACTER, 2014, 19 (06) :407-416
[10]   Gastric microbial community profiling reveals a dysbiotic cancer-associated microbiota [J].
Ferreira, Rui M. ;
Pereira-Marques, Joana ;
Pinto-Ribeiro, Ines ;
Costa, Jose L. ;
Carneiro, Fatima ;
Machado, Jose C. ;
Figueiredo, Ceu .
GUT, 2018, 67 (02) :226-236