Stage IV colon cancer patients without DENND2D expression benefit more from neoadjuvant chemotherapy

Analysis of Prospective Genetic Indicators for Prenatal Exposure to Arsenic in Newborn Cord Blood of Using Machine Learning

Using a machine learning methods, we aim to find biological effect biomarkers of prenatal arsenic exposure in newborn cord blood. From the Gene Expression Omnibus (GEO) database, two datasets (GSE48354 and GSE7967) pertaining to cord blood sequencing while exposed to arsenic were retrieved and merged for additional study. Using the "limma" package in the R, differentially expressed genes (DEGs) were eliminated. Machine learning techniques of the LASSO regression algorithm and SVM-RFE algorithm were used to find potential biological effect biomarkers for cord blood sequencing in pregnant women exposed to arsenic. To evaluate the efficacy of biomarkers, a receiver operating characteristic (ROC) curve was used. Furthermore, we investigated the proportion of invading immune cells in each sample using CIBERSORT, and we investigated the relationship between biomarkers and immune cells using the Spearman approach. Using LASSO regression and the SVM-RFE technique, 28 DEGs were discovered, and the main biomarkers of cord blood exposed to arsenic were discovered to be DENND2D, OLIG1, RGS18, CXCL16, DDIT4, FOS, G0S2, GPR183, JMJD6, and SOCS3. According to an immune infiltration analysis and correlation analysis, key biomarkers were substantially associated with the invading immune cells. Ten genes are important biomarkers of cord blood exposed to arsenic connected with infiltrating immune cells, and infiltrating immune cells may play important roles in cord blood exposed to arsenic, according to the study's findings.

Development of macrophage-associated genes prognostic signature predicts clinical outcome and immune infiltration for sepsis

Sepsis is a major global health problem, causing a significant burden of disease and death worldwide. Risk stratification of sepsis patients, identification of severe patients and timely initiation of treatment can effectively improve the prognosis of sepsis patients. We procured gene expression datasets for sepsis (GSE54514, GSE65682, GSE95233) from the Gene Expression Omnibus and performed normalization to mitigate batch effects. Subsequently, we applied weighted gene co-expression network analysis to categorize genes into modules that exhibit correlation with macrophage activity. To pinpoint macrophage-associated genes (MAAGs), we executed differential expression analysis and single sample gene set enrichment analysis. We then established a prognostic model derived from four MAAGs that were significantly differentially expressed. Functional enrichment analysis and immune infiltration assessments were instrumental in deciphering the biological mechanisms involved. Furthermore, we employed principal component analysis and conducted survival outcome analyses to delineate molecular subgroups within sepsis. Four novel MAAGs-CD160, CX3CR1, DENND2D, and FAM43A-were validated and used to create a prognostic model. Subgroup classification revealed distinct molecular profiles and a correlation with 28-day survival outcomes. The MAAGs risk score was developed through univariate Cox, LASSO, and multivariate Cox analyses to predict patient prognosis. Validation of the risk score upheld its prognostic significance. Functional enrichment implicated ribonucleoprotein complex biogenesis, mitochondrial matrix, and transcription coregulator activity in sepsis, with an immune infiltration analysis indicating an association between MAAGs risk score and immune cell populations. The four MAAGs exhibited strong diagnostic capabilities for sepsis. The research successfully developed a MAAG-based prognostic model for sepsis, demonstrating that such genes can significantly stratify risk and reflect immune status. Although in-depth mechanistic studies are needed, these findings propose novel targets for therapy and provide a foundation for future precise clinical sepsis management.

CircDENND2D Inhibits PD-L1-Mediated Non-Small Cell Lung Cancer Metastasis and Immune Escape by Regulating miR-130b-3p/STK11 Axis

Local recurrence and distant metastasis of non-small cell lung cancer (NSCLC) caused by immune escape is one of the root causes of treatment difficulties. We aim to investigate the mechanism of immune escape in NSCLC. NSCLC tissues were collected. Cell proliferation was detected by CCK-8 assay. Cell migration and invasion ability was measured by Transwell assay. The expressions of E-cadherin, N-cadherin and PD-L1 were detected by Western blot. NSCLC cells were co-cultured with CD8+ T cells to simulate tumor microenvironment in vitro. The proportion of CD8+ T cells and apoptosis were detected by flow cytometry. Dual-luciferase reporter gene assay confirmed the targeting relationship of circDENND2D and STK11. The expressions of circDENND2D and STK1 were down-regulated, while miR-130b-3p expression was up-regulated in NSCLC tissues. Overexpression of circDENND2D or STK11 inhibited NSCLC cells proliferation, migration and invasion, and attenuated the immune escape of NSCLC cells. CircDENND2D targeted miR-130b-3p to competitively promote STK11 expression. STK11 knockdown or miR-130b-3p overexpression attenuated the function of circDENND2D overexpression on NSCLC cells. CircDENND2D inhibited metastasis and immune escape of NSCLC by regulating miR-130b-3p/STK11 axis.