Appraising the effectiveness of immune cells on thyroid cancer: a Mendelian randomization study.

BACKGROUND: The intricate interplay between the immune system and tumor plays a pivotal role in thyroid cancer (TC) pathogenesis, potentially influencing both the causation and therapeutic outcomes. Despite extensive research, existing literature offers ambiguous insights regarding the association between immune cell traits and thyroid cancer progression. METHODS: To elucidate the potential causal relationships, we conducted an integrated two-sample Mendelian randomization (MR) analysis. This study utilized publicly genetic datasets to explore the causalities between 731 immune cell traits (categorized into four trait types across seven panels) and thyroid cancer. We ensured the robustness of our findings through comprehensive sensitivity analyses, meticulously assessing potential sources of bias such as pleiotropy. RESULTS: After False Discovery Rate (FDR) correction, six immune cell traits were identified to be significantly associated with thyroid cancer risk (Inverse Variance Weighted, IVW): Absolute count of gamma delta T cells/ T-cell receptor gamma delta absolute count (TCRgd AC) 0.8464 (OR95% CI = 0.7477-0.9580, P = 0.0083, PFDR = 0.0103); CD8 on bright CD8 cells (CD8 on CD8br) 0.8867 (OR95% CI = 0.8159-0.9637, P = 0.0047, PFDR = 0.0093); CD127 on CD45RA negative CD4 T cells not regulatory T cells (CD127 on CD45RA- CD4 not Treg) 0.8969 (OR95% CI = 0.8192-0.9820, P = 0.0186, PFDR = 0.0186); CD80 on CD62L positive plasmacytoid dendritic cells (CD80 on CD62L+ plasmacytoid DC) 1.1091 (OR95% CI = 1.0267-1.1982, P = 0.0086, PFDR = 0.0103); CD80 on plasmacytoid DC 1.1283 (OR95% CI = 1.0462-1.2168, P = 0.0017, PFDR = 0.0093); Side scatter-area on bright CD8 cells (SSC - A on CD8br) 1.1622 (OR95% CI = 1.0507-1.2854, P = 0.0035, PFDR = 0.0093). CONCLUSIONS: Our study demonstrated the causalities between immune cell traits and thyroid cancers by Mendelian randomization study, thus guiding future mechanism studies.

Identification of Potential Drug Therapy for Dermatofibrosarcoma Protuberans with Bioinformatics and Deep Learning Technology.

BACKGROUND: Dermatofibrosarcoma protuberans (DFSP) is a rare mesenchymal tumor that is primarily treated with surgery. Targeted therapy is a promising approach to help reduce the high rate of recurrence. This study aims to identify the potential target genes and explore the candidate drugs acting on them effectively with computational methods. METHODS: Identification of genes associated with DFSP was conducted using the text mining tool pubmed2ensembl. Further gene screening was carried out by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-Protein Interaction (PPI) network was constructed by using the Search Tools for the Retrieval of Interacting (STRING) database and visualized in Cytoscape. The gene candidates were identified after a literature review. Drugs targeting these genes were selected from Pharmaprojects. The binding affinity scores of Drug-Target Interaction (DTI) were predicted by a deep learning algorithm Deep Purpose. RESULTS: A total of 121 genes were found to be associated with DFSP by text mining. The top 3 statistically functionally enriched pathways of GO and KEGG analysis included 36 genes, and 18 hub genes were further screened out by constructing a PPI networking and literature retrieval. A total of 42 candidate drugs targeted at hub genes were found by Pharmaprojects under our restrictions. Finally, 10 drugs with top affinity scores were predicted by DeepPurpose, including 3 platelet-derived growth factor receptor beta kinase (PDGFRB) inhibitors, 2 platelet-derived growth factor receptor alpha kinase (PDGFRA) inhibitors, 2 Erb-B2 receptor tyrosine kinase 2 (ErbB-2) inhibitors, 1 tumor protein p53 (TP53) stimulant, 1 vascular endothelial growth factor receptor (VEGFR) antagonist, and 1 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitor. CONCLUSION: Text mining and bioinformatics are useful methods for gene identification in drug discovery. DeepPurpose is an efficient and operative deep learning tool for predicting the DTI and selecting the drug candidates.

Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology.

Background: Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, immunotherapy, radiation therapy, etc. Targeted therapy is a promising approach to treating advanced melanomas, but resistance always occurs. This study is aimed at identifying the potential target genes and candidate drugs for drug-resistant melanoma effectively with computational methods. Methods: Identification of genes associated with drug-resistant melanomas was conducted using the text mining tool pubmed2ensembl. Further gene screening was carried out by GO and KEGG pathway enrichment analyses. The PPI network was constructed using STRING database and Cytoscape. GEPIA was used to perform the survival analysis and conduct the Kaplan-Meier curve. Drugs targeted at these genes were selected in Pharmaprojects. The binding affinity scores of drug-target interactions were predicted by DeepPurpose. Results: A total of 433 genes were found associated with drug-resistant melanomas by text mining. The most statistically differential functional enriched pathways of GO and KEGG analyses contained 348 genes, and 27 hub genes were further screened out by MCODE in Cytoscape. Six genes were identified with statistical differences after survival analysis and literature review. 16 candidate drugs targeted at hub genes were found by Pharmaprojects under our restrictions. Finally, 11 ERBB2-targeted drugs with top affinity scores were predicted by DeepPurpose, including 10 ERBB2 kinase inhibitors and 1 antibody-drug conjugate. Conclusion: Text mining and bioinformatics are valuable methods for gene identification in drug discovery. DeepPurpose is an efficient and operative deep learning tool for predicting the DTI and selecting the candidate drugs.

Global Trends of Stem Cell Precision Medicine Research (2018-2022): A Bibliometric Analysis.

Background: Stem cells are a group of cells that can self-renew and have multiple differentiation capabilities. Shinya Yamanaka first discovered a method to convert somatic cells into pluripotent stem cells in 2006. Stem cell therapy can be summarized into three aspects (regenerative treatment, therapy targeted at stem cells, and establishment of disease models). Disease models are mainly established by induced pluripotent stem cells, and the research of stem cell precision medicine has been promising in recent years. Based on the construction of 3D, patient-specific disease models from pluripotent induced stem cells, proper research on disease development and treatment prognosis can be realized. Bibliometric analysis is an efficient way to quickly understand global trends and hotspots in this field. Methods: A literature search of stem cell precision medicine research from 2018 to 2022 was carried out using the Web of Science Core Collection.VOSviewer, R-bibliometrix, and CiteSpace software programs were employed to perform the bibliometric analysis. Results: A total of 552 publications were retrieved from 2018 to 2022. Annual publication outputs trended upward and reached a peak of 172 in 2021. The United States contributed the most publications (160, 29.0%) to the field, followed by China (63, 11.4%) and Italy (60, 10.9%). International academic collaborations were active. CANCERS was considered the most productive journal with 18 documents. NATURE was the most co-cited journal with 1860 times citations. The most cited document was entitled "Induced Pluripotent Stem Cells for Cardiovascular Disease Modeling and Precision Medicine: A Scientific Statement From the American Heart Association" with 9 times local citations. " precision medicine" (n = 89, 12.64%), "personalized medicine" (n = 72, 10.23%), "stem cells" (n = 43, 4.40%), and "induced pluripotent stem cells" (n = 41, 5.82%), "cancer stem cells" (n = 31, 4%), "organoids" (n = 26, 3.69%) were the top 6 frequent keywords. Conclusion: The present study performs a comprehensive investigation concerning stem cell precision medicine (2018-2022) for the first time. This research field is developing, and a deeper exploration of 3D patient-specific organoid disease models is worth more research in the future.