&lt;p&gt;Comparison of Gut Microbiome in Human Colorectal Cancer in Paired Tumor and Adjacent Normal Tissues&lt;/p&gt;

Sheng, Qinsong; He, Kangxin; Li, Jian‐Jiong; Zhong, Zi-Feng; Wang, Fei-Xia; Pan, Lelin; Lin, Jie

doi:10.2147/ott.s218004

Cited by 29 publications

(42 citation statements)

References 37 publications

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“…Fn has been recently identified as a pathogenic bacteria in CRC (38). Additionally, the present study reported that the levels of Fn were increased in tumor tissues compared with in adjacent normal tissues of the same patients, supporting the authors' previous study (39), which revealed its role in promoting the initiation and development of CRC. Future investigations with a larger number of patients are required to determine the effects of Fn .…”

Section: Discussionsupporting

confidence: 92%

Characteristics of fecal gut microbiota in patients with colorectal cancer at different stages and different sites

Sheng

Cheng

et al. 2019

Oncol Lett

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Numerous studies have revealed that the gut microbiota serves an important role in the pathogenesis of colorectal cancer (CRC). The present study aimed to investigate the populations present in the gut microbiota in patients with CRC of different stages and at different sites. Fecal samples were obtained from 67 CRC patients and 30 healthy controls, which were analyzed by sequencing the V3-V4 region of the 16S rRNA gene. Increased diversity of the fecal gut microbiota in patients with CRC was reported compared with the healthy controls. In the present study, at the genus level, the relative abundances of Prevotella, Collinsella and Peptostreptococcus in the gut microbiota of CRC patients were substantially increased compared with healthy controls, while the relative abundance of Escherichia-Shigella was significantly lower. In addition, differences in the fecal gut microbiota were also compared between patients with stage I–IV CRC and healthy controls. The results revealed that the abundances of the genera Peptostreptococcus, Collinsella and Ruminococcus were significantly increased in patients with CRC stage I compared with the healthy controls, while Alistipes was enriched in patients with stage III CRC compared with patients with stage IV. Furthermore, the present study reported that the genera Veillonella and Coprobacter were more abundant in the proximal segments than in the distal segments of the colon. In conclusion, despite the low number of samples employed in the present study, a signature of genera indicating dysbiosis of the gut microbiota of patients with stage I–IV CRC patients was proposed, which may provide insight into the mechanisms underlying the progression of CRC. These findings are also valuable for developing novel fecal diagnostic methods and therapeutic strategies for the treatment of CRC.

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Section: Discussionsupporting

confidence: 92%

Characteristics of fecal gut microbiota in patients with colorectal cancer at different stages and different sites

Sheng

Cheng

et al. 2019

Oncol Lett

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“…These results are further supported by a number of recent studies [39][40][41][42][43][44][45][46][47]. For example, Sheng and coworkers [39] found the pathway of porphyrin and chlorophyll metabolism to be significantly altered in CRC patients, the perturbation is also validated in literature [40] by the genes from the pathway. Purines are basic components of nucleotides in cell proliferation, thus impaired purine metabolism has been associated with the progression of cancer [41,42].…”

Section: ) Key Pathways Identified By Differential Msan Networksupporting

confidence: 55%

A Novel Network Modelling for Metabolite Set Analysis: A Case Study on CRC Metabolomics

Liu

Deng

et al. 2020

IEEE Access

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In metabolomics, pathway analysis normally refers to analysis of a pre-defined sets of metabolites (metabolite set) associated to the metabolic pathways. The metabolite set analysis is useful to facilitate biological interpretation of metabolomics data. The currently available methods may be divided into three generations: over-representation analysis, functional class scoring, and network topology analysis. Among the three generations of tools, the network topology methods have been shown to have lower false discovery rates and better biological interpretability than the other two earlier generations of tools. However, most of the current network topology methods focus the analysis only at the metabolite-level network. The interaction between pathways are not taken into consideration. To address this issue, we propose a new metabolite sets association network (MSAN) modelling scheme. In the developed method, the metabolite sets are defined based on the KEGG databases. By using the metabolite sets as vertexes, the MSAN network evaluated the relationships between pairs of metabolite sets based on their mutual information. The impact of a single metabolite set on the overall network was evaluated by the MSAN network, which may help to uncover differential metabolite sets relevant to the underlying biology mechanism of the study. A metabolomic dataset from a published colorectal cancer (CRC) study is used to evaluate the performance of MSAN network to identify perturbed metabolite sets in colorectal cancer patients. The current results are compared to that of two commonly used methods, NetGSA and MetaboAnalyst, which are based on the metabolite-level network approach. The current method highlights a number of metabolites sets consistent with recent published CRC reports. Taken together, the proposed method may provide an alternative tool for the identification of dysregulated pathways and facilitate biological interpretation of metabolomics data. INDEX TERMS Metabolite sets association network (MSAN), colorectal cancer (CRC) metabolomics, mutual information, pathway analysis

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“…The link between dysbiosis and colorectal cancer (CRC) is the most extensively studied. One major indication of association between microbiota alteration and cancer was the observation that the bacterial composition and diversity of tumours was significantly different than that of adjacent normal tissue [50][51][52][53][54][55]. Intriguingly, tumour-associated bacteria included strains known to impair anticancer immune responses and induce a chronic inflammatory state, such as Fusobacterium nucleatum [56] and Bacteroides fragilis [57,58], respectively.…”

Section: Dysbiosis Impairs Immune Function and Promotes Cancer Develomentioning

confidence: 99%

Modulation of Anti-Tumour Immune Responses by Probiotic Bacteria

Aindelis

Chlichlia

2020

Vaccines

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There is a growing amount of evidence to support the beneficial role of a balanced intestinal microbiota, or distinct members thereof, in the manifestation and progression of malignant tumours, not only in the gastrointestinal tract but also in distant tissues as well. Intriguingly, bacterial species have been demonstrated to be indispensable modulatory agents of widely-used immunotherapeutic or chemotherapeutic regiments. However, the exact contribution of commensal bacteria to immunity, as well as to neoplasia formation and response to treatment, has not been fully elucidated, and most of the current knowledge acquired from animal models has yet to be translated to human subjects. Here, recent advances in understanding the interaction of gut microbes with the immune system and the modulation of protective immune responses to cancer, either naturally or in the context of widely-used treatments, are reviewed, along with the implications of these observations for future therapeutic approaches. In this regard, bacterial species capable of facilitating optimal immune responses against cancer have been surveyed. According to the findings summarized here, we suggest that strategies incorporating probiotic bacteria and/or modulation of the intestinal microbiota can be used as immune adjuvants, aiming to optimize the efficacy of cancer immunotherapies and conventional anti-tumour treatments.

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<p>Comparison of Gut Microbiome in Human Colorectal Cancer in Paired Tumor and Adjacent Normal Tissues</p>

Cited by 29 publications

References 37 publications

Characteristics of fecal gut microbiota in patients with colorectal cancer at different stages and different sites

Characteristics of fecal gut microbiota in patients with colorectal cancer at different stages and different sites

A Novel Network Modelling for Metabolite Set Analysis: A Case Study on CRC Metabolomics

Modulation of Anti-Tumour Immune Responses by Probiotic Bacteria

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