Background: As the increasing number of Corona Virus Disease 2019 (COVID-19) patients caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2), which caused an outbreak initiated from Wuhan, China in December, 2019, the clinical features and treatment of COVID-19 patients have been understood. However, it is urgent to need the rapid and accurate detection for SARS-CoV-2 infection diagnosis. We aimed to evaluate the antibodies-based and nucleic acid-based tests (NAT) for SARS-CoV-2-infected patients. Method: We retrospectively and observationally studied 133 patients diagnosed with SARS-CoV-2 and admitted in Renmin Hospital of Wuhan University, China, from Feb 17 to Mar 1, 2020. Demographic data, symptoms, clinical examination, laboratory tests, and clinical outcomes were collected. Data were compared between IgM-IgG antibody test and real-time RT-PCR detection for COVID-19 patients. Results: Of 133 patients with SARS-CoV-2 infection, there were 44 moderate cases, 52 severe cases, and 37 critical cases with no significant difference of gender and age among three subgroups. Overall, the positive ratio in IgM antibody test was higher than in RT-PCR detection. In RT-PCR detection, the positive ratio was 65.91%, 71.15%, and 67.57% in moderate, severe, and critical cases, respectively. Whereas, the positive ratio of IgM/IgG antibody detection in patients was 79.55%/93.18%, 82.69%/100%, and 72.97%/97.30% in moderate, severe, and critical cases, respectively. Moreover, the concentrations of antibodies were also measured in three subgroups. Conclusion: The IgM-IgG antibodies-based test exhibited a comparative superiority to the NAT for COVID-19 diagnosis, which provides an effective complement to the false negative results from NAT for SARS-CoV-2 infection diagnosis.

Background: Prostate cancer remains one of the leading causes of cancer-related mortality in men worldwide. The treatment of it is currently based on surgical removal, radiotherapy, and hormone therapy. It is crucial to improve therapeutic prospects for the diagnosis and treatment of prostate cancer via drug target screening. Methods: We integrated eQTL data from the eQTLGen Consortium and pQTL data from UK Biobank Proteome Plasma Proteins (UKB-PPP) and deCODE health datasets. MR analyses (SMR, heterogeneity in dependent instruments (HEIDI), IVW, Wald ratio, weighted median, and MR-Egger) were used to screen candidate genes associated with prostate adenocarcinoma (PRAD) risk. Candidate genes were further verified through TCGA-based gene expression profile, survival analysis, and immune microenvironment evaluations. TIDE analysis was utilized to investigate gene immunotherapy response. Single-cell RNA sequencing data from the GSE176031 dataset were used to investigate the gene expression patterns. The Drug Bank, Therapeutic Target Database and Drug Signatures Database were utilized to predict targeted drugs for candidate genes. Results: MTHFD1 and LGALS4 were identified as promising therapeutic targets for PRAD, with evidence provided at multi-omics levels. LGALS4 was predominantly expressed in malignant cells and was correlated with enhanced immune checkpoint pathways, increased TIDE scores, and immunotherapy resistance. In contrast, MTHFD1was expressed in both tumor and microenvironmental cells and was associated with poor survival. Drug target prediction suggested that there are no currently approved drugs specifically targeting MTHFD1 and LGALS4. Conclusions: Our study identified MTHFD1 and LGALS4 as potential preventive targets for PRAD. However, future experiments are warranted to assess the utility and effectiveness of these candidate proteins.

Background: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Identifying drug targets associated with CRC is crucial for developing targeted therapies. Methods: MR (IVW, Wald ratio, weighted median, and MR-Egger) and SMR analyses were used to screen candidate genes associated with CRC risk. Further validation was performed using The Cancer Genome Atlas (TCGA) to assess gene expression patterns and prognostic significance. Additionally, immune infiltration analysis was conducted to characterize the tumor immune microenvironment. Drug prediction was performed to explore potential therapeutic interventions. Results: Eight genes were identified associated with CRC. IGFBP3, CD72, SERPINH1, CHRDL2, LRP11, and SPARCL1 were linked to an increased risk of CRC, whereas DBI and HYAL1 were associated with a decreased risk of CRC. Notably, DBI exhibited a potentially favorable immune profile, negatively correlated with Tregs and MDSCs while positively associated with activated CD8⁺ and CD4⁺ T cells. Conclusions: Eight genes were identified as associated with CRC, among which DBI exhibited a potential protective role, correlating with improved patient survival, enhanced immune activation, and increased responsiveness to immunotherapy. The remaining proteins demonstrated diverse and complex functions within the tumor immune microenvironment, providing novel insights for the development of precision diagnostics and immunotherapeutic strategies.