Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction

Gut commensal Alistipes as a potential pathogenic factor in colorectal cancer

Although previous research has shown that inflammation is associated with development of colorectal cancer (CRC), questions remain about whether inflammatory factor-secreting bacteria play a crucial role in CRC development. The potential role of gut microbiota in secreting inflammatory factors involved in the carcinogenesis of CRC among Chinese patients was explored in this study. 16S rRNA sequencing was utilized to evaluate the distinct microbial characteristics between patients with CRC and colorectal adenoma. The serum levels of TNF-α, IL-6 and IL-10 were measured using Enzyme-linked immunosorbent assay (ELISA), while the expression of LRG1 and TGF-β1 in tissues was evaluated by immunohistochemistry. The correlation between gut microbiota and inflammatory factor signaling was analyzed. Compared with the adenoma group, CRC patients exhibit distinct pathologies. Moreover, elevated levels of CEA, erythrocytes and haemoglobin in the blood of CRC patients were found. In addition, CRC patients have significantly higher levels of TNF-α, IL-6, IL-10, LRG1 and TGF-β1. Spearman correlation analysis revealed that LRG1 was positively related to IL-6 and TNF-α, respectively. The correlation analysis results of TGF-β1 were consistent with the above. The abundance of Blautia and Streptococcus was lower in CRC patients, while the relative abundance of Alistipes, Peptostreptococcus and Porphyromonas was significantly elevated. Moreover, positive correlations between Alistipes and inflammatory factor signaling were also found. Our results suggest that gut commensal Alistipes is a key bacterium with pro-inflammatory properties in the CRC carcinogenesis. TNF-α and IL-6 associated with Alistipes might activate LRG1/TGF-β1 signaling which contributed to the carcinogenesis of CRC.

The Crucial Role of Inflammation and the Immune System in Colorectal Cancer Carcinogenesis: A Comprehensive Perspective

Chronic inflammation drives the growth of colorectal cancer through the dysregulation of molecular pathways within the immune system. Infiltration of immune cells, such as macrophages, into tumoral regions results in the release of proinflammatory cytokines (IL-6; IL-17; TNF-α), fostering tumor proliferation, survival, and invasion. Tumors employ various mechanisms to evade immune surveillance, effectively 'cloaking' themselves from detection and subsequent attack. A comprehensive understanding of these intricate molecular interactions is paramount for advancing novel strategies aimed at modulating the immune response against cancer.

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.