Biomedical subjects

Yang Wu

Publications and source records attributed to Yang Wu.

3 recordsLinked to original sources

PubMed2025

Mendel randomization confirmed gastroesophageal reflux disease may increase the risk of mental disorders.

BACKGROUND: The potential causal relationship between gastroesophageal reflux disease (GERD) and mental disorder was analyzed using the mendelian randomization (MR) method. METHODS: Data are derived from genome-wide association study (GWAS) summary data, using gastroesophageal reflux disease (GERD) as the exposure factor. Single nucleotide polymorphisms (SNPs) significantly associated with GERD were selected as instrumental variables (IVs), and mental disorders (bipolar disorder, major depression, Alzheimer's disease, anorexia nervosa, anxiety, and obsessive-compulsive disorder) were used as outcome variables. The inverse variance weighted (IVW) method is used as the main analysis method, and MR-Egger regression, weighted median (WM) method, simple mode and weighted mode are used as supplementary methods for Mendelian randomization (MR) analysis. Cochran's Q test and P value are used to quantify heterogeneity, MR-Egger regression was used to evaluate the multilevel effect test of SNPs, and leave-one-out method to determine whether there are potential SNPs, and to evaluate the stability of the results. Odds ratio (OR) and 95% confidence interval (CI) were used as effect indicators to evaluate whether there is a causal relationship between GERD and mental disorders. RESULTS: IVW demonstrated a causal relationship between GERD and bipolar disorder (OR = 1.70, 95%CI = 1.39-2.09, P < 0.05) and anorexia nervosa (OR = 0.71, 95%CI = 0.52-0.99, P < 0.05). Furthermore, there is a weak causal relationship between GERD and major depression (OR = 1.01, 95%CI = 1.01-1.02, P < 0.05) and anxiety (OR = 1.01, 95%CI = 1.01-1.01, P < 0.05). Similarly, there is no evidence of a causal relationship between GERD and Alzheimer's disease (OR = 0.95, 95%CI = 0.87-1.03, P > 0.05) or obsessive-compulsive disorder (OR = 0.95, 95%CI = 0.67-1.36, P > 0.05). Cochran's Q test for heterogeneity shows that there is no significant heterogeneity (P > 0.05) for bipolar disorder, anxiety, and obsessive-compulsive disorder. However, major depression, Alzheimer's disease, and anorexia nervosa have some degree of heterogeneity (P < 0.05). Horizontal pleiotropic analysis showed that the P values for six mental disorders (0.750, 0.296, 0.154, 0.798, 0.893, 0.451) were all greater than 0.05. Leave-one-out analysis and funnel plot showed that MR analysis results can be considered relatively stable. All F are > 10, indicating no weak IVs bias. CONCLUSION: GERD can obviously increase the risk of bipolar disorder; the increased risk of anxiety disorder is very slight. There is no clear evidence to support the causal relationship between GERD and four other mental disorders, including major depression, Alzheimer's disease, anorexia nervosa, and obsessive-compulsive disorder.

Humans↗

PubMed2025

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Deep Learning↗

PubMed2024

Transcriptomic analysis reveals key molecular signatures across recovery phases of hemorrhagic fever with renal syndrome.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS), a life-threatening zoonosis caused by hantavirus, poses significant mortality risks and lacks specific treatments. This study aimed to delineate the transcriptomic alterations during the recovery phases of HFRS. METHODS: RNA sequencing was employed to analyze the transcriptomic alterations in peripheral blood mononuclear cells from HFRS patients across the oliguric phase (OP), diuretic phase (DP), and convalescent phase (CP). Twelve differentially expressed genes (DEGs) were validated using quantitative real-time PCR in larger sample sets. RESULTS: Our analysis revealed pronounced transcriptomic differences between DP and OP, with 38 DEGs showing consistent expression changes across all three phases. Notably, immune checkpoint genes like CD83 and NR4A1 demonstrated a monotonic increase, in contrast to a monotonic decrease observed in antiviral and immunomodulatory genes, including IFI27 and RNASE2. Furthermore, this research elucidates a sustained attenuation of immune responses across three phases, alongside an upregulation of pathways related to tissue repair and regeneration. CONCLUSION: Our research reveals the transcriptomic shifts during the recovery phases of HFRS, illuminating key genes and pathways that may serve as biomarkers for disease progression and recovery.

Hemorrhagic Fever with Renal Syndrome↗