Colorectal cancer (CRC) has a multifactorial etiology including genetic, environmental, life-style factors (diet) and diseases (obesity) and gut microbiota dysbiosis. The bacterial driver-passenger model proposes that driver bacteria may be involved in CRC initiation, while passengers in CRC progression. We reasoned that also gut microbiota-associated metabolites may be differentially enriched according to tumor stage. In addition, we aimed to identify and validate a new technique to collect mucosa-associated microbiota (MAM) and metabolome maintaining tissue integrity. Firstly, we analyzed mucosa-associated metabolites of 30 patients with low- or high-grade dysplastic adenomas, showing the feasibility and reproducibility of our new biopsy preserving approach. Furthermore, we performed an integrated analysis on MAM and mucosa-associated metabolome of 78 patients divided according to tumor histology. We identified candidate driver (B.fragilis, Bacteroides spp) and passenger (Anaerococcus, S. anginosus) bacteria enriched in low- or high- grade group respectively, supporting the driver-passenger hypothesis. Additionally, Pelomonas and Phascolarctobacterium, enriched in low-grade group, were negatively correlated with organonitrogen compounds, and with benzene and derivatives. Our data support the hypothesis of an involvement of the gut microbiota and its metabolites in CRC initiation and progression, and stress the importance of analyzing both MAM and metabolome. Finally, we analyzed MAM, lumen-associated microbiota (LAM) and diet of 116 patients, divided in normal-weight or obese, to dissect the interaction among diet, obesity, CRC and gut microbiota. Obese subjects showed a higher intake of processed and red/processed meat (p<0.05). Accordingly, these subjects' LAM showed an enrichment of Erysipelotrichaceae and B. massiliensis, positively correlated with meat consumption. Further studies are necessary to characterize the molecular basis of the observed correlations.

Role of microbiota and its metabolome in colorectal cancer (MIMEC)

CLAVENNA, MICHELA GIULIA
2023

Abstract

Colorectal cancer (CRC) has a multifactorial etiology including genetic, environmental, life-style factors (diet) and diseases (obesity) and gut microbiota dysbiosis. The bacterial driver-passenger model proposes that driver bacteria may be involved in CRC initiation, while passengers in CRC progression. We reasoned that also gut microbiota-associated metabolites may be differentially enriched according to tumor stage. In addition, we aimed to identify and validate a new technique to collect mucosa-associated microbiota (MAM) and metabolome maintaining tissue integrity. Firstly, we analyzed mucosa-associated metabolites of 30 patients with low- or high-grade dysplastic adenomas, showing the feasibility and reproducibility of our new biopsy preserving approach. Furthermore, we performed an integrated analysis on MAM and mucosa-associated metabolome of 78 patients divided according to tumor histology. We identified candidate driver (B.fragilis, Bacteroides spp) and passenger (Anaerococcus, S. anginosus) bacteria enriched in low- or high- grade group respectively, supporting the driver-passenger hypothesis. Additionally, Pelomonas and Phascolarctobacterium, enriched in low-grade group, were negatively correlated with organonitrogen compounds, and with benzene and derivatives. Our data support the hypothesis of an involvement of the gut microbiota and its metabolites in CRC initiation and progression, and stress the importance of analyzing both MAM and metabolome. Finally, we analyzed MAM, lumen-associated microbiota (LAM) and diet of 116 patients, divided in normal-weight or obese, to dissect the interaction among diet, obesity, CRC and gut microbiota. Obese subjects showed a higher intake of processed and red/processed meat (p<0.05). Accordingly, these subjects' LAM showed an enrichment of Erysipelotrichaceae and B. massiliensis, positively correlated with meat consumption. Further studies are necessary to characterize the molecular basis of the observed correlations.
2023
Inglese
Inglese
DIANZANI, Irma
Università degli Studi del Piemonte Orientale Amedeo Avogadro
Vercelli
136
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/82647
Il codice NBN di questa tesi è URN:NBN:IT:UNIUPO-82647