Characterization of gut dominant microbiota in obese patients with nonalcoholic fatty liver disease.

In obese patients, non-alcoholic fatty liver (NAFLD) is common. However, whether there is a connection between the gut microbiota and the onset of NAFLD in obese people is yet unknown. Using quantitative real-time PCR, the microbiota of feces of the eligible 181 obese individuals was identified to compare the differences in gut microbiota between obesity with NAFLD and simple obesity. According to the findings, the gut dominant microbiota was similar between obesity with NAFLD and simple obesity. Nonetheless, compared to the simple obesity group, the quantity of Faecalibacterium prausnitzii colonies was much lower in the obesity with the NAFLD group. Bacteroides were present in greater than 65% of both groups. Bacteroides, Clostridium leptum, and Clostridium butyricum accounted for more than 80% of the cases in the obesity with NAFLD group, whereas Bacteroides, Clostridium butyricum, and F. prausnitzii accounted for more than 80% of the cases in the simple obesity group. We look for potential contributing variables to obesity-related NAFLD and potential prevention measures for obese people. Based on a multi-factor logistic regression analysis, lymphocytes may be a risk factor for obesity with NAFLD while F. prausnitzii may be a protective factor. Additionally, F. prausnitzii is positively impacted by Bacteroides, Clostridium leptum, Clostridium butyricum, and Eubacterium rectale, yet adversely impacted by Enterobacteriaceae. Notably, lymphocytes and F. prausnitzii may help determine whether obese patients would develop NAFLD.

Added value of systemic inflammation markers for monitoring response to neoadjuvant chemotherapy in breast cancer patients.

BACKGROUND: Complete response after neoadjuvant chemotherapy (rNACT) elevates the surgical outcomes of patients with breast cancer, however, non-rNACT have a higher risk of death and recurrence. AIM: To establish novel machine learning (ML)-based predictive models for predicting probability of rNACT in breast cancer patients who intends to receive NACT. METHODS: A retrospective analysis of 487 breast cancer patients who underwent mastectomy or breast-conserving surgery and axillary lymph node dissection following neoadjuvant chemotherapy at the Hubei Cancer Hospital between January 1, 2013, and October 1, 2021. The study cohort was divided into internal training and testing datasets in a 70:30 ratio for further analysis. A total of twenty-four variables were included to develop predictive models for rNACT by multiple ML-based algorithms. A feature selection approach was used to identify optimal predictive factors. These models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance. RESULTS: Analysis identified several significant differences between the rNACT and non-rNACT groups, including total cholesterol, low-density lipoprotein, neutrophil-to-lymphocyte ratio, body mass index, platelet count, albumin-to-globulin ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. The areas under the curve of the six models ranged from 0.81 to 0.96. Some ML-based models performed better than models using conventional statistical methods in both ROC curves. The support vector machine (SVM) model with twelve variables introduced was identified as the best predictive model. CONCLUSION: By incorporating pretreatment serum lipids and serum inflammation markers, it is feasible to develop ML-based models for the preoperative prediction of rNACT and therefore facilitate the choice of treatment, particularly the SVM, which can improve the prediction of rNACT in patients with breast cancer.

Identification of potential core genes in triple negative breast cancer using bioinformatics analysis.

BACKGROUND: Triple-negative breast cancer (TNBC) is a subtype of breast cancer with poor clinical outcome and limited treatment options. Lacking molecular targets, chemotherapy is the main adjuvant treatment for TNBC patients. MATERIALS AND METHODS: To explore potential therapeutic targets for TNBC, we analyzed three microarray datasets (GSE38959, GSE45827, and GSE65194) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between TNBC and normal tissue. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery to identify the pathways and functional annotation of DEGs. Protein-protein interaction of these DEGs was analyzed based on the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. In addition, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic value of hub genes expression in breast cancer patients. RESULTS: A total of 278 upregulated DEGs and 173 downregulated DEGs were identified. Among them, ten hub genes with a high degree of connectivity were picked out. Overexpression of these hub genes was associated with unfavorable prognosis of breast cancer, especially, CCNB1 overexpression was observed and indicated poor outcome of TNBC. CONCLUSION: Our study suggests that CCNB1 was overexpressed in TNBC compared with normal breast tissue, and overexpression of CCNB1 was an unfavorable prognostic factor of TNBC patients. Further study is needed to explore the value of CCNB1 in the treatment of TNBC.

Fourier transform infrared spectroscopy for the distinction of MCF-7 cells treated with different concentrations of 5-fluorouracil.

BACKGROUND: In order to provide personalized treatment to patients with breast cancer, an accurate, reliable and cost-efficient analytical technique is needed for drug screening and evaluation of tumor response to chemotherapy. METHODS: Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) was used as a tool to assess cancer cell response to chemotherapy. MCF-7 cells (human breast adenocarcinoma cell line) were treated with different concentrations of 5-fluorouracil (5-FU). The inhibition of cell proliferation was monitored by MTT, and apoptosis rates were determined by flow cytometry. Finally, spectra of the cell populations were acquired by ATR-FTIR. RESULTS: The cell response to 5-FU was detectable at different concentrations by ATR-FTIR. First, a band observed at 1741 cm(-1), representing membrane phospholipids, was enhanced with increasing 5-FU concentrations. In addition, the MCF-7 cell spectrum shifted progressively from 1153 to 1170 cm(-1) with increasing drug doses. Finally, the normalized band intensity of 1741 cm(-1)/Amide I was highly correlated with the percentage of apoptotic cells as assessed by partial correlation analysis. CONCLUSIONS: These findings suggest that the effects of different concentrations of drugs can be monitored by ATR-FTIR, which may help evaluate the response to chemotherapy and improve treatment strategies.