Gut microbiome development along the colorectal adenoma–carcinoma sequence
By Q. Feng, Suisha Liang, Huijue Jia, A. Stadlmayr, Longqing Tang, Zhou Lan, Dongya Zhang, Huihua Xia, Xiaoying Xu, Zhuye Jie and 16 more. Nature Communications, 2015
1,299 citations recorded by the source corpus.
Colorectal cancer, a commonly diagnosed cancer in the elderly, often develops slowly from benign polyps called adenoma. The gut microbiota is believed to be directly involved in colorectal carcinogenesis. The identity and functional capacity of the adenoma- or carcinoma-related gut microbe(s), however, have not been surveyed in a comprehensive manner. Here we perform a metagenome-wide association study (MGWAS) on stools from advanced adenoma and carcinoma patients and from healthy subjects, revealing microbial genes, strains and functions enriched in each group. An analysis of potential risk factors indicates that high intake of red meat relative to fruits and vegetables appears to associate with outgrowth of bacteria that might contribute to a more hostile gut environment. These findings suggest that faecal microbiome-based strategies may be useful for early diagnosis and treatment of colorectal adenoma or carcinoma. The gut microbiota is involved in the development of colorectal cancer. Here, the authors analyse the faecal microbiomes of healthy subjects and of patients with colorectal cancer or benign adenoma, revealing microbial genes, strains and functions enriched in each group.
Subfields: Molecular Biology, Oncology, Physiology
View Paper: https://www.semanticscholar.org/paper/fec3808ad390f96468e039585e4642effa95fd63
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In Molecular Biology
Citations in the corpus
- Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma (2018, background): “…our cohort, but likely suggested the overgrowth of various harmful bacteria or archaea in patients with HCC. Gut microbial alteration in chronic diseases including T2D, 17 inflammatory bowel diseases, 37 38 colorectal adenoma–carcinoma sequence 32 and liver cirrhosis 23 is unique for each disease.”
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- Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, neutral): “) (Feng et al., 2015; Purcell et al., 2017).”
- Geography, Ethnicity or Subsistence-Specific Variations in Human Microbiome Composition and Diversity (2017, background): “For instance, Bacteroides are dominant in people from USA (38%), China (39%), Spain (23%), andDenmark (20%) compared toMalawi (3%), Peru (1%), Russia (8%), Venezuela (3%), and Sweden (8%), while Prevotella is enriched in Malawi (32%), Peru (14%), and Venezuela (24%) as compared to USA (4%) and Canad”