Machine learning-driven mast cell gene signatures for prognostic and therapeutic prediction in prostate cancer.

Background: The role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. Methods: This study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. Results: MCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and ß-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. Conclusion: The combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

Long non-coding RNA MEG3 promotes tumor necrosis factor-alpha induced oxidative stress and apoptosis in interstitial cells of cajal via targeting the microRNA-21 /I-kappa-B-kinase beta axis.

Interstitial Cells of Cajal (ICC) plays a critical role in the peristaltic contractions of the gastrointestinal and urinary tract. The dysfunction and loss of ICC contributes to hypokinetic disease, such as gallstoneand ureteropelvic junction obstruction . In the present study, we identified the underlying driving molecular signals of oxidative stress and apoptosis in ICC. ICC was isolated from small intestine of Balb/c mice, and stimulated with tumor necrosis factor-alpha (TNF-α). MTT and flow cytometry were performed to assess cell viability, apoptosis, and the level of reactive oxygen species in ICC, respectively. The level of malondialdehyde, superoxide dismutase, and glutathione peroxidase in cells were measured to assess oxidative stress. The expression of inflammatory factors (interleukin, IL-1 and IL-6) and apoptosis-related proteins were detected by western blot. We observed that TNF-αinduced inflammation, oxidative stress and cell apoptosis in ICC. By using quantitative real-time PCR , we verified that the expression of long non-coding RNAMEG3 was elevated by TNF-α in ICC. Silencing MEG3 reversed inflammation, oxidative stress, and cell apoptosisin TNF-α-treated ICC. Subsequently, we confirmed that MEG3 sponged cytoprotective miR-21 to upregulate the expression of I-kappa-B-kinase beta (IKKB) and activate the nuclear factor kappa-B (NF-κB) pathway. Both miR-21 overexpression and IKKB knockdown reduced TNF-α-induced above symptoms in ICC. Taken together, we can conclude that MEG3 mediates inflammation, oxidative stress and apoptosis in TNF-α-treated ICC via the miR-21/IKKB-NF-κB axis. The study improves our understanding of the molecular mechanism of ICC reduction related diseases.

A Mutation Network Method for Transmission Analysis of Human Influenza H3N2.

Characterizing the spatial transmission pattern is critical for better surveillance and control of human influenza. Here, we propose a mutation network framework that utilizes network theory to study the transmission of human influenza H3N2. On the basis of the mutation network, the transmission analysis captured the circulation pattern from a global simulation of human influenza H3N2. Furthermore, this method was applied to explore, in detail, the transmission patterns within Europe, the United States, and China, revealing the regional spread of human influenza H3N2. The mutation network framework proposed here could facilitate the understanding, surveillance, and control of other infectious diseases.

Impact of population movement on the spread of 2019-nCoV in China.

Since Dec 2019, China has experienced an outbreak caused by a novel coronavirus, 2019-nCoV. A travel ban was implemented for Wuhan, Hubei on Jan 23 to slow down the outbreak. We found a significant positive correlation between population influx from Wuhan and confirmed cases in other cities across China (R2 = 0.85, P < 0.001), especially cities in Hubei (R2 = 0.88, P < 0.001). Removing the travel restriction would have increased 118% (91%-172%) of the overall cases for the coming week, and a travel ban taken three days or a week earlier would have reduced 47% (26%-58%) and 83% (78%-89%) of the early cases. We would expect a 61% (48%-92%) increase of overall cumulative cases without any restrictions on returning residents, and 11% (8%-16%) increase if the travel ban stays in place for Hubei. Cities from Yangtze River Delta, Pearl River Delta, and Capital Economic Circle regions are at higher risk.