Wenkai Wang’s research while affiliated with Shanghai University of Traditional Chinese Medicine and other places

Objective Xiao‐Chai‐Hu‐Tang (XCHT) has been demonstrated to exert an antidepressant effect during long‐term clinical practices. However, the potential mechanisms of XCHT remain unknown. This study aims to investigate the effect of XCHT on chronic unpredictable mild stress‐induced mice with depressive‐like behaviors and to explore the underlying mechanisms. Methods The active compositions and potential related targets of XCHT in the brain were obtained through UPLC‐Q‐TOF‐MS, network pharmacology, and bioinformatics analyses, verified by experimental validation. Then, the protein–protein interaction (PPI), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and molecular docking were used to predict the core targets and mechanisms of XCHT on depression. After being treated with XCHT standard decoction, based on enzyme‐linked immunosorbent assay (ELISA), non‐targeted metabolism, targeted LC–MS analyses, RNA‐seq, quantitative RT‐PCR, immunofluorescence, and western blotting were determined to clarify the mechanism of XCHT in the treatment of anxiety and depression disorder. Results In total, 166 active ingredients and 525 related targets of XCHT were detected and selected from the network databases. The inflammatory response and metabolism of neurotransmitters were the main related signaling pathways predicted by KEGG enrichment analyses. Behavioral testing shows that XCHT has antidepressant effects, and untargeted metabolomic studies showed it significantly reduced levels of the neurotoxic substance quinoline acid. Combining the results of molecular docking, RNA‐seq, and western blot revealed that XCHT regulated nerve regeneration via BDNF/TrkB/CREB and PI3K/AKT signaling pathways. Immunofluorescence analysis revealed that XCHT downregulated the chronic stress‐induced activation of microglia and astrocytes in the hippocampus. Conclusion XCHT exerts antidepressant functions by modulating neuroinflammation and neuroregeneration.

Hexabromocyclododecane (HBCD), a brominated flame retardant, is linked to various health implications, including prostate cancer. This study explored the molecular mechanisms and potential biomarkers associated with HBCD exposure using data from the Comparative Toxicogenomics Database (CTD) and The Cancer Genome Atlas (TCGA). A total of 7,147 differentially expressed genes (DEGs) and 46 differentially expressed miRNAs were identified, with significant enrichment in cancer-related pathways and xenobiotic metabolism. Protein–protein interaction (PPI) network construction and enrichment analyses revealed four hub genes: DNAJC12, PKMYT1, RRM2, and SLC12A5. These genes displayed notable expression changes in response to HBCD exposure and were strongly correlated with survival outcomes in prostate cancer patients, as demonstrated by Cox regression and ROC curve analyses. Additionally, miRNA correlation analyses indicated robust positive associations, highlighting a coordinated regulatory network. Experimental expression analyses on HBCD-treated cell lines further validated these findings. This study sheds light on the significant impact of HBCD on gene and miRNA expression in prostate cancer, emphasizing the potential of the identified hub genes and miRNAs as prognostic biomarkers and therapeutic targets. By elucidating the pathways and regulatory networks influenced by HBCD, the findings provide a foundation for developing strategies to mitigate its carcinogenic effects and improve outcomes for prostate cancer patients.

Background: Kidney renal clear cell carcinoma (KIRC) is a common and clinically significant subtype of kidney cancer. A potential therapeutic target in KIRC is disulfidptosis, a novel mode of cell death induced by disulfide stress. The aim of this study was to develop a prognostic model to explore the clinical significance of different disulfidptosis gene typings from KIRC. Methods: A comprehensive analysis of the chromosomal localization, expression patterns, mutational landscape, copy number variations, and prognostic significance of 10 disulfide death genes was conducted. Patients were categorized into distinct subtypes using the Non-negative Matrix Factorization (NMF) typing method based on disulfidptosis gene expression patterns. Weighted Gene Co-expression Network Analysis (WGCNA) was used on the KIRC dataset to identify differentially expressed genes between subtype clusters. A risk signature was created using LASSO-Cox regression and validated by survival analysis. An interaction between risk score and immune cell infiltration, tumor microenvironment characteristics and pathway enrichment analysis were investigated. Results: Initial findings highlight the differential expression of specific DRGs in KIRC, with genomic instability and somatic mutation analysis revealing key insights into their role in cancer progression. NMF clustering differentiates KIRC patients into subgroups with distinct survival outcomes and immune profiles, and hierarchical clustering identifies gene modules associated with key biological and clinical parameters, leading to the development of a risk stratification model (LRP8, RNASE2, CLIP4, HAS2, SLC22A11, and KCTD12) validated by survival analysis and predictive of immune infiltration and drug sensitivity. Pathway enrichment analysis further delineates the differential molecular pathways between high-risk and low-risk patients, offering potential targets for personalized treatment. Lastly, differential expression analysis of model genes between normal and KIRC cells provides insights into the molecular mechanisms underlying KIRC, highlighting potential biomarkers and therapeutic targets. Conclusion: This study contributes to the understanding of KIRC and provides a potential prognostic model using disulfidptosis gene for personalized management in KIRC patients. The risk signature shows clinical applicability and sheds light on the biological mechanisms associated with disulfide-induced cell death.

Objective This study aimed to investigate the causal associations between several liver traits (liver iron content, percent liver fat, alanine transaminase levels, and liver volume) and colorectal cancer (CRC) risk using a Mendelian randomization (MR) approach to improve our understanding of the disease and its management. Methods Genetic variants were used as instrumental variables, extracted from genome-wide association studies (GWAS) datasets of liver traits and CRC. The Two-Sample MR package in R was used to conduct inverse variance weighted (IVW), MR Egger, Maximum likelihood, Weighted median, and Inverse variance weighted (multiplicative random effects) MR approaches to generate overall estimates of the effect. MR analysis was conducted with Benjamini-Hochberg method-corrected P values to account for multiple testing (P < 0.013). MR-PRESSO was used to identify and remove outlier genetic variants in Mendelian randomization (MR) analysis. The MR Steiger test was used to assess the validity of the assumption that exposure causes outcomes. Leave-one-out validation, pleiotropy, and heterogeneity testing were also conducted to ensure the reliability of the results. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. Results The MR analysis suggested a causal effect between liver volume and a reduced risk of CRC (OR 0.60; 95% CI, 0.44–0.82; P = 0.0010) but did not provide evidence for causal effects of liver iron content, percent liver fat, or liver alanine transaminase levels. The MR-PRESSO method did not identify any outliers, and the MR Steiger test confirmed that the causal direction of the analysis results was correct in the Mendelian randomization analysis. MR results were consistent with heterogeneity and pleiotropy analyses, and leave-one-out analysis demonstrated the overall values obtained were consistent with estimates obtained when all available SNPs were included in the analysis. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. Conclusion The study provides tentative evidence for a causal role of liver volume in CRC, while genetically predicted levels of liver iron content, percent liver fat, and liver alanine transaminase levels were not associated with CRC risk. The findings may inform the development of targeted therapeutic interventions for colorectal liver metastasis (CRLM) patients, and the study highlights the importance of MR as a powerful epidemiological tool for investigating causal associations between exposures and outcomes.

The high prevalence of depressive disorders in individuals with cancer and their contribution to tumour progression is a topic that is gradually gaining attention. Recent evidence has shown that there are prominent connections between immune gene variants and mood disorders. The homeostasis of the tumour immune microenvironment (TIME) and the infiltration and activation of immune cells play a very important role in the antitumour effect. In this study, we established a compound mouse model with chronic unpredictable mild stress (CUMS) and orthotopic colorectal cancer to simulate colorectal cancer (CRC) patients with depression. Using 10✕Genomics single-cell transcriptome sequencing technology, we profiled nearly 30,000 cells from tumour samples of 8 mice from the control and CUMS groups, revealed that immune cells in tumours under a chronic stress state trend toward a more immunosuppressive and exhaustive status, and described the crosstalk between the overall inflammatory environment and immunosuppressive landscape to provide mechanistic information or efficacious strategies for immune-oncology treatments in CRC with depressive disorders.

Objective This study aimed to investigate the causal associations between several liver traits (liver iron content, percent liver fat, alanine transaminase levels, and liver volume) and colorectal cancer (CRC) risk using a Mendelian randomization (MR) approach to improve our understanding of the disease and its management. Methods Genetic variants were used as instrumental variables, extracted from genome-wide association studies (GWAS) datasets of liver traits and CRC. The Two-Sample MR package in R was used to conduct inverse variance weighted (IVW), MR Egger, Maximum likelihood, Weighted median, and Inverse variance weighted (multiplicative random effects) MR approaches to generate overall estimates of the effect. MR analysis was conducted with Benjamini-Hochberg method-corrected P values to account for multiple testing (P < 0.013). MR-PRESSO was used to identify and remove outlier genetic variants in Mendelian randomization (MR) analysis. The MR Steiger test was used to assess the validity of the assumption that exposure causes outcomes. Leave-one-out validation, pleiotropy, and heterogeneity testing were also conducted to ensure the reliability of the results. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. Results The MR analysis suggested a causal effect between liver volume and a reduced risk of CRC (OR 0.60; 95% CI, 0.44-0.82; P = 0.0010) but did not provide evidence for causal effects of liver iron content, percent liver fat, or liver alanine transaminase levels. The MR-PRESSO method did not identify any outliers, and the MR Steiger test confirmed that the causal direction of the analysis results was correct in the Mendelian randomization analysis. MR results were consistent with heterogeneity and pleiotropy analyses, and leave-one-out analysis demonstrated the overall values obtained were consistent with estimates obtained when all available SNPs were included in the analysis. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. Conclusion The study provides tentative evidence for a causal role of liver volume in CRC, while genetically predicted levels of liver iron content, percent liver fat, and liver alanine transaminase levels were not associated with CRC risk. The findings may inform the development of targeted therapeutic interventions for colorectal liver Causal associations between liver traits and Colorectal cancer: a Mendelian randomization study