Biomedical subjects

Man Li

Publications and source records attributed to Man Li.

3 recordsLinked to original sources

PubMed2025

STT3A is essential for Wnt signaling and represents a target for cancers driven by RNF43 deficiency.

Abnormalities in the Wnt pathway are major drivers of cancer. RNF43 loss-of-function mutations are frequently detected in aggressive cancers lacking targeted therapies, underscoring the need to uncover key regulators and targets of this pathway. Using a double death trap (DDT) Wnt reporter and genome-wide CRISPR screen, we identified STT3A as an essential regulator of Wnt signaling. Genetic and pharmacological inhibition of STT3A suppressed aberrant Wnt activity caused by RNF43/ZNRF3 loss. Importantly, suppression of STT3A blocked the growth of RNF43-deficient cancer cell lines, patient-derived organoids, and spontaneous tumors. Mechanistically, STT3A regulates Wnt/β-catenin signaling via LRP6, but not LRP5. Glycosylation of LRP6 by STT3A is required for Wnt ligand binding. Notably, STT3A depletion displayed milder effects on bone homeostasis, as supported by phenotypes in STT3A-deficient patients. Together, this study established STT3A as a critical Wnt regulator through LRP6 glycosylation and a therapeutic target for RNF43-deficient cancers.

Humans↗

PubMed2025

Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) has become a serious disease that endangers human health, with high morbidity and mortality. Numerous studies have reported histone acetylation can result in the occurrence of cardiovascular diseases. This article aims to explore the potential biomarkers of histone acetylation regulatory genes (ARGs) in AMI patients. METHODS: Five AMI datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman's correlation analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to histone acetylation regulation. In the GSE60993 and GSE48060 datasets, the common differentially expressed genes (DEGs) between AMI and control samples were screened. Importantly, the intersecting genes were obtained by overlapping ARGs-related genes, common DEGs, and module genes. Then, the biomarkers in AMI were determined by machine learning, receiver operating characteristic (ROC) curves, and quantitative PCR (qPCR). In addition, immune analysis, drug prediction, molecular docking, and the lncRNA-miRNA-mRNA regulatory network targeting the biomarkers were analyzed, respectively. RESULTS: Here, a total of 18 intersecting genes were identified by overlapping 7,349 ARGs-related genes, 5,565 module genes, and 25 common DEGs. Further, five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were obtained, and a nomogram was constructed and verified based on these biomarkers. Notably, the biomarkers were significantly associated with CD8 T cells and neutrophils. In addition, the drugs related to biomarkers were predicted, and ATOGEPANT with the molecular target (S100A12) had a high binding affinity (docking score = -10 kcal/mol). CONCLUSION: AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI.

Humans↗

PubMed2025

Clinical and Multiorgan Proteomics Characteristics of the Diverse Fatal Phase in Super Elderly Patients With SARS-CoV-2 Infection: A Descriptive Study.

This study aims to identify the risk factors associated with clinical outcomes and the proteomic changes in organs related to fatal SARS-CoV-2 infection within the super-elderly population. This retrospective analysis included all elderly individuals with COVID-19 admitted to the Second Medical Center of PLA General Hospital from December 2022 to January 2023. The follow-up period ended on March 30, 2023. During this time, epidemiological, demographic, laboratory, and outcome data were analyzed descriptively. Proteomic sequencing was performed on super-elderly patients who died from COVID-19 at different stages of the disease. A total of 352 elderly COVID-19 patients, with a mean age of 89.84 ± 8.54 years, were included in this study. During a median follow-up period of 98 days, 79 patients died. Deceased patients were older and more likely to have cardiovascular and cerebrovascular diseases, with a lower prevalence of lipid-lowering therapy. The number of deaths in the acute and post-acute phases were 34 and 45, respectively. Proteomics data suggest that the immune systems of patients who died in the acute phase underwent a more rapid and severe onslaught. Patients in the post-acute phase showed higher levels of viral genome replication and a more robust immune response. However, the over-activation of the immune system led to systemic organ dysfunction. Effective management of comorbidities may improve the prognosis of COVID-19 in super-elderly patients. The continuous replication of the SARS-CoV-2 virus and its subsequent impact on the immune system are critical determinants of survival time in this demographic.

Humans↗