Tumorigenesis in Inflammatory Bowel Disease: Microbiota-Environment Interconnections

Mignini, Irene; Ainora, Maria Elena; Francesco, Sonia De; Galasso, Linda; Gasbarrini, Antonio; Zocco, Maria Assunta

doi:10.3390/cancers15123200

Cited by 3 publications

(1 citation statement)

References 219 publications

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“…As for CRC, the first hypothesis that the gut microbiota can contribute to its initiation and development was formulated in 1997 by Dove et al, who observed that gut bacteria were necessary for the growth of intestinal adenomas in mice, while germ-free mice developed 2-fold fewer adenomas in the small intestine than controls [11]. In the following years, the effect of the gut microbiota on colorectal tumorigenesis has been a major field of research, regarding not only sporadic CRC but also CRC occurring in high-risk conditions such as inflammatory bowel diseases, thus underlying microbiota's role both in the adenoma-carcinoma sequence typical of sporadic tumors and in the inflammationdysplasia-carcinoma process which is characteristic of inflammatory conditions [12]. Different studies focused their attention on specific bacteria which may contribute to CRC development, including colibactin-producing Escherichia coli, Bacteroides fragilis, Fusobacterium nucleatum, and Providencia, together with a significant decrease in butyrate-producing bacteria such as Roseburia and Fecalibacterium [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

From the Colon to the Liver: How Gut Microbiota May Influence Colorectal Cancer Metastatic Potential

Mignini,

Piccirilli,

Galasso

et al. 2024

JCM

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The gut microbiota’s influence on human tumorigenesis is a burning topic in medical research. With the new ontological perspective, which considers the human body and its pathophysiological processes as the result of the interaction between its own eukaryotic cells and prokaryotic microorganisms living in different body niches, great interest has arisen in the role of the gut microbiota on carcinogenesis. Indeed, dysbiosis is currently recognized as a cancer-promoting condition, and multiple molecular mechanisms have been described by which the gut microbiota may drive tumor development, especially colorectal cancer (CRC). Metastatic power is undoubtedly one of the most fearsome features of neoplastic tissues. Therefore, understanding the underlying mechanisms is of utmost importance to improve patients’ prognosis. The liver is the most frequent target of CRC metastasis, and new evidence reveals that the gut microbiota may yield an effect on CRC diffusion to the liver, thus defining an intriguing new facet of the so-called “gut-liver axis”. In this review, we aim to summarize the most recent data about the microbiota’s role in promoting or preventing hepatic metastasis from CRC, highlighting some potential future therapeutic targets.

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Section: Introductionmentioning

confidence: 99%

From the Colon to the Liver: How Gut Microbiota May Influence Colorectal Cancer Metastatic Potential

Mignini,

Piccirilli,

Galasso

et al. 2024

JCM

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Therapeutic effects of coptisine derivative EHLJ7 on colorectal cancer by inhibiting PI3K/AKT pathway

Chai,

Tang,

Chi

et al. 2024

Cellular Signalling

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Development and Validation of Machine Learning Algorithms for Prediction of Colorectal Polyps Based on Electronic Health Records

Ba,

Yuan,

Wang

et al. 2024

Biomedicines

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Background: Colorectal Polyps are the main source of precancerous lesions in colorectal cancer. To increase the early diagnosis of tumors and improve their screening, we aimed to develop a simple and non-invasive diagnostic prediction model for colorectal polyps based on machine learning (ML) and using accessible health examination records. Methods: We conducted a single-center observational retrospective study in China. The derivation cohort, consisting of 5426 individuals who underwent colonoscopy screening from January 2021 to January 2024, was separated for training (cohort 1) and validation (cohort 2). The variables considered in this study included demographic data, vital signs, and laboratory results recorded by health examination records. With features selected by univariate analysis and Lasso regression analysis, nine machine learning methods were utilized to develop a colorectal polyp diagnostic model. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The SHapley additive explanation method (SHAP) was used to rank the feature importance and explain the final model. Results: 14 independent predictors were identified as the most valuable features to establish the models. The adaptive boosting machine (AdaBoost) model exhibited the best performance among the 9 ML models in cohort 1, with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) of 0.632 (0.618–0.646), 0.635 (0.550–0.721), 0.674 (0.591–0.758), 0.593 (0.576–0.611), 0.673 (0.654–0.691), 0.608 (0.560–0.655) and 0.687 (0.626–0.749), respectively. The final model gave an AUC of 0.675 in cohort 2. Additionally, the precision recall (PR) curve for the AdaBoost model reached the highest AUPR of 0.648, positioning it nearest to the upper right corner. SHAP analysis provided visualized explanations, reaffirming the critical factors associated with the risk of colorectal polyps in the asymptomatic population. Conclusions: This study integrated the clinical and laboratory indicators with machine learning techniques to establish the predictive model for colorectal polyps, providing non-invasive, cost-effective screening strategies for asymptomatic individuals and guiding decisions for further examination and treatment.

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Tumorigenesis in Inflammatory Bowel Disease: Microbiota-Environment Interconnections

Cited by 3 publications

References 219 publications

From the Colon to the Liver: How Gut Microbiota May Influence Colorectal Cancer Metastatic Potential

From the Colon to the Liver: How Gut Microbiota May Influence Colorectal Cancer Metastatic Potential

Therapeutic effects of coptisine derivative EHLJ7 on colorectal cancer by inhibiting PI3K/AKT pathway

Development and Validation of Machine Learning Algorithms for Prediction of Colorectal Polyps Based on Electronic Health Records

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