Abstract: Objective: We aim at investigating the molecular mechanisms through which the Qingfei Jiedu decoction (QFJDD) regulates PD-L1 expression in lung adenocarcinoma (LUAD). Methods: Bioactive compounds and targets of QFJDD were screened from TCMSP, BATMAN-TCM, and literature. Then, GeneCard, OMIM, PharmGKB, Therapeutic Target, and DrugBank databases were used to identify LUAD-related genes. The protein-protein interaction (PPI) network was constructed using overlapping targets of bioactive compounds in LUAD with the Cytoscape software and STRING database. The potential functions and pathways in which the hub genes were enriched by GO, KEGG, and DAVID pathway analyses. Molecular docking of bioactive compounds and key genes was executed via AutoDock Vina. Qualitative and quantitative analyses of QFJDD were performed using UPLC-Q-TOF-MS and UPLC. Expressions of key genes were determined by qRT-PCR, immunoreactivity score (IRS) of PD-L1 was assessed by immunohistochemistry (IHC), while the CD8(+)PD-1(+)T% derived from spleen tissues of Lewis lung cancer (LLC) bearing-mice was calculated using flow cytometry (FCM). Results: A total of 53 bioactive compounds and 288 targets of QFJDD as well as 8151 LUAD associated genes were obtained. Further, six bioactive compounds, including quercetin, luteolin, kaempferol, wogonin, baicalein, and acacetin, and 22 hub genes were identified. The GO analysis showed that the hub genes were mainly enriched in DNA or RNA transcription. KEGG and DAVID pathway analyses revealed that 20 hub genes were primarily enriched in virus, cancer, immune, endocrine, and cardiovascular pathways. The EGFR, JUN, RELA, HIF1A, NFKBIA, AKT1, MAPK1, and MAPK14 hub genes were identified as key genes in PD-L1 expression and PD-1 checkpoint pathway. Moreover, ideal affinity and regions were identified between core compounds and key genes. Notably, QFJDD downregulated EGFR, JUN, RELA, HIF1A, NFKBIA, and CD274 expressions (p < 0.05), while it upregulated AKT1 and MAPK1 (p < 0.05) levels in A549 cells. The PD-L1 IRS of LLC tissue in the QFJDD high dose (Hd) group was lower than model group (p < 0.01). CD8(+)PD-1(+)T% was higher in the QFJDD Hd group than in normal and model groups (p < 0.05). Conclusion: QFJDD downregulates PD-L1 expression and increases CD8(+)PD-1(+)T% via regulating HIF-1, EGFR, JUN and NFkappaB signaling pathways. Therefore, QFJDD is a potential treatment option for LUAD.

Abstract: A major challenge in translating genomic variants of Tuberculosis (TB) into clinical implementation is to integrate the disease-associated variants and facilitate drug discovery through the concept of genomic-driven drug repurposing. Here, we utilized two established genomic databases, namely a Genome-Wide Association Study (GWAS) and a Phenome-Wide Association Study (PheWAS) to identify the genomic variants associated with TB disease and further utilize them for drug-targeted genes. We evaluated 3.425 genomic variants associated with TB disease which overlapped with 200 TB-associated genes. To prioritize the biological TB risk genes, we devised an in-silico pipeline and leveraged an established bioinformatics method based on six functional annotations (missense mutation, cis-eQTL, biological process, cellular component, molecular function, and KEGG molecular pathway analysis). Interestingly, based on the six functional annotations that we applied, we discovered that 14 biological TB risk genes are strongly linked to the deregulation of the biological TB risk genes. Hence, we demonstrated that 12 drug target genes overlapped with 40 drugs for other indications and further suggested that the drugs may be repurposed for the treatment of TB. We highlighted that CD44, CCR5, CXCR4, and C3 are highly promising proposed TB targets since they are connected to SELP and HLA-B, which are biological TB risk genes with high systemic scores on functional annotations. In sum, the current study shed light on the genomic variants involved in TB pathogenesis as the biological TB risk genes and provided empirical evidence that the genomics of TB may contribute to drug discovery.

Abstract: Clinical evidence supports the combination of cabozantinib with an immune checkpoint inhibitor for the treatment of metastatic clear cell renal cell carcinoma (mccRCC) and suggests a synergistic antitumour activity of this combination. Nevertheless, the biological basis of this synergy is not fully characterized. We studied the mechanisms underpinning the potential synergism of cabozantinib combined with a PD1 inhibitor in mccRCC and delved into cabozantinib monotherapy properties supporting its role to partner these combinations. To model physiological drug action, we used a machine learning-based technology known as Therapeutic Performance Mapping Systems, applying two approaches: Artificial Neural Networks and Sampling Methods. We found that the combined therapy was predicted to exert a wide therapeutic action in the tumour and the microenvironment. Cabozantinib may enhance the effects of PD1 inhibitors on immunosurveillance by modulating the innate and adaptive immune system, through the inhibition of VEGF-VEGFR and Gas6-AXL/TYRO3/MER (TAM) axes, while the PD1 inhibitors may boost the antiangiogenic and pro-apoptotic effects of cabozantinib by modulating angiogenesis and T-cell cytotoxicity. Cabozantinib alone was predicted to restore cellular adhesion and hamper tumour proliferation and invasion. These data provide a biological rationale and further support for cabozantinib plus PD1 inhibitor combination and may guide future nonclinical and clinical research.

Abstract: Background: Thyroid cancer is the most common endocrine malignancy. However, the molecular mechanism involved in its pathogenesis is not well characterized. Purpose: The objective of this study is to identify key cellular pathways and differentially expressed genes along the thyroid cancer pathogenesis sequence as well as to identify potential prognostic and therapeutic targets. Methods: Publicly available transcriptomics data comprising a total of 95 samples consisting of 41 normal, 28 non-aggressive and 26 metastatic papillary thyroid carcinoma (PTC) cases were used. Transcriptomics data were normalized and filtered identifying 9394 differentially expressed genes. The genes identified were subjected to pathway analysis using absGSEA identifying PTC related pathways. Three of the genes identified were validated on 508 thyroid cancer biopsies using RNAseq and TNMplot. Results: Pathway analysis revealed a total of 2193 differential pathways among non-aggressive samples and 1969 among metastatic samples compared to normal tissue. Pathways for non-aggressive PTC include calcium and potassium ion transport, hormone signaling, protein tyrosine phosphatase activity and protein tyrosine kinase activity. Metastatic pathways include growth, apoptosis, activation of MAPK and regulation of serine threonine kinase activity. Genes for non-aggressive are KCNQ1, CACNA1D, KCNN4, BCL2, and PTK2B and metastatic PTC are EGFR, PTK2B, KCNN4 and BCL2. Three of the genes identified were validated using clinical biopsies showing significant overexpression in aggressive compared to non-aggressive PTC; EGFR (p < 0.05), KCNN4 (p < 0.001) and PTK2B (p < 0.001). DrugBank database search identified several FDA approved drug targets including anti-EGFR Vandetanib used to treat thyroid cancer in addition to others that may prove useful in treating PTC. Conclusion: Transcriptomics analysis identified putative prognostic targets including EGFR, PTK2B, BCL2, KCNQ1, KCNN4 and CACNA1D. EGFR, PTK2B and KCN44 were validated using thyroid cancer clinical biopsies. The drug search identified FDA approved drugs including Vandetanib in addition to others that may prove useful in treating the disease.

Abstract: Pathway-level analysis is a powerful approach enabling the interpretation of post-genomic data at a higher level than that of individual molecules. Molecular-targeted therapy focusing on cascade signaling pathways has become a new paradigm in anticancer therapy, instead of a single protein. However, the approaches to narrowing down the long list of biological pathways are limited. Here, we proposed a strategy for in silico Drug Prescription on biological pathways across pan-Cancers (CDP), by connecting drugs to candidate pathways. Applying on a list of 120 traditional Chinese medicines (TCM), we especially identified the "TCM-pathways-cancers" triplet and constructed it into a heterogeneous network across pan-cancers. Applying them into TCMs, the computational prescribing methods deepened the understanding of the efficacy of TCM at the molecular level. Further applying them into Western medicines, CDP could promote drug reposition avoiding time-consuming developments of new drugs.

Abstract: The patient population suffering from pancreatic ductal adenocarcinoma (PDAC) presents, as a whole, with a high degree of molecular tumor heterogeneity. The heterogeneity of PDAC tumor composition has complicated treatment and stalled success in clinical trials. Current in vitro techniques insufficiently replicate the intricate stromal components of PDAC tumor microenvironments (TMEs) and fail to model a given tumor's unique genetic phenotype. The development of patient-derived organoids (PDOs) has opened the door for improved personalized medicine since PDOs are derived directly from patient tumors, thus preserving the tumors' unique behaviors and genetic phenotypes. This study developed a tumor-chip device engineered to mimic the PDAC TME by incorporating PDOs and stromal cells, specifically pancreatic stellate cells and macrophages. Establishing PDOs in a multicellular microfluidic chip device prolongs cellular function and longevity and successfully establishes a complex organotypic tumor environment that incorporates desmoplastic stroma and immune cells. When primary cancer cells in monoculture were subjected to stroma-depleting agents, there was no effect on cancer cell viability. However, targeting stroma in our tumor-chip model resulted in a significant increase in the chemotherapy effect on cancer cells, thus validating the use of this tumor-chip device for drug testing.

Abstract: Objective: We aimed to analyze the possible molecular mechanism of Xihuang pill (XHP) in the treatment of pancreatic cancer based on methods of network pharmacology, molecular docking, and bioinformatics. Methods: The main active components and targets were obtained through the TCMSP database, the BATMAN-TCM database, and the Chemistry database. The active ingredients were screened according to the "Absorption, Distribution, Metabolism, Excretion" (ADME) principle and supplemented with literature. We searched GeneCards, OMIM, TTD, and DrugBank databases for pancreatic cancer targets. The targets of disease and ingredients were intersected to obtain candidate key targets. Then, we constructed a protein-protein interaction (PPI) network for protein interaction analysis and a composition-key target map to obtain essential effective ingredients. Metascape was used to perform functional enrichment analysis to screen critical targets and pathways. The expression and prognosis of key targets were examined and analyzed, and molecular docking was carried out. Results: A total of 52 active ingredients of XHP, 121 candidate targets, and 52 intersecting targets were obtained. The core active ingredients of XHP for the treatment of pancreatic cancer were quercetin, 17-beta-estradiol, ursolic acid, and daidzein. The core targets were EGFR, ESR1, MAPK1, MAPK8, MAPK14, TP53, and JUN, which were highly expressed genes of pancreatic cancer. Among them, EGFR and MAPK1 were significantly correlated with the survival of pancreatic cancer patients. The key pathway was the EGFR/MAPK pathway. The molecular docking results indicated that four active compositions had good binding ability to key targets. Conclusion: The molecular mechanism of XHP for the treatment of pancreatic cancer involved multiple components, multiple targets, and multiple pathways. This research theoretically elucidated the ameliorative effect of XHP against pancreatic cancer and might provide new ideas for further research on the treatment of pancreatic cancer.

Abstract: Background: It is imperative to identify drugs that allow treating symptoms of severe COVID-19. Respiratory failure is the main cause of death in severe COVID-19 patients, and the host inflammatory response at the lungs remains poorly understood. Methods: Therefore, we retrieved data from post-mortem lungs from COVID-19 patients and performed in-depth in silico analyses of single-nucleus RNA sequencing data, inflammatory protein interactome network, and shortest pathways to physiological phenotypes to reveal potential therapeutic targets and drugs in advanced-stage COVID-19 clinical trials. Results: Herein, we analyzed transcriptomics data of 719 inflammatory response genes across 19 cell types (116,313 nuclei) from lung autopsies. The functional enrichment analysis of the 233 significantly expressed genes showed that the most relevant biological annotations were inflammatory response, innate immune response, cytokine production, interferon production, macrophage activation, blood coagulation, NLRP3 inflammasome complex, and the TLR, JAK-STAT, NF-kappaB, TNF, oncostatin M signaling pathways. Subsequently, we identified 34 essential inflammatory proteins with both high-confidence protein interactions and shortest pathways to inflammation, cell death, glycolysis, and angiogenesis. Conclusion: We propose three small molecules (baricitinib, eritoran, and montelukast) that can be considered for treating severe COVID-19 symptoms after being thoroughly evaluated in COVID-19 clinical trials.

Abstract: Background: Sjogren's syndrome (SS) is a chronic autoimmune disease characterized by progressive oral and ocular dryness that correlates poorly with autoimmune damage to the glands. CheReCunJin (CRCJ) formula is a prescription formulated according to the Chinese medicine theory for SS treatment. Objective: This study aimed to explore the underlying mechanisms of CRCJ against SS. Methods: The databases, including Traditional Chinese Medicine System Pharmacology, Encyclopedia of Traditional Chinese Medicine, Bioinformatics Analysis Tool for the molecular mechanism of Traditional Chinese Medicine, and Traditional Chinese Medicine Integrated Databases, obtained the active ingredients and predicted targets of CRCJ. Then, DrugBank, Therapeutic Target Database, Genecards, Comparative Toxicogenomics Database, and DisGeNET disease databases were used to screen the predicted targets of SS. Intersected targets of CRCJ and SS were visualized by using Venn diagrams. The overlapping targets were uploaded to the protein-protein interaction network analysis search tool. Cytoscape 3.8.2 software constructed a "compound-targets-disease" network. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses characterized potential targets' biological functions and pathways. AutoDock Vina 1.1.2 software was used to research and verify chemical effective drug components and critical targets. Results: From the database, we identified 878 active components and 2578 targets of CRCJ, and 827 SS-related targets. 246 SS-related genes in CRCJ were identified by intersection analysis, and then ten hub genes were identified as crucial potential targets from PPI, including ALB, IL-6, TNF, INS, AKT1, IL1B, VEGFA, TP53, JUN, and TLR4. The process of CRCJ action against SS was mainly involved in human cytomegalovirus infection and Th17 cell differentiation, as well as the toll-like receptor signaling and p53 signaling pathways. Molecular docking showed that the bioactive compounds of CRCJ had a good binding affinity with hub targets. Conclusions: The results showed that CRCJ could activate multiple pathways and treat SS through multiple compounds and targets. This study lays a foundation for better elucidation of the molecular mechanism of CRCJ in the treatment of SS, and also provides basic guidance for future research on Chinese herbal compounds.

Abstract: The novel coronavirus disease (COVID-19) caused by SARS-CoV-2 virus spreads rapidly to become a global pandemic. Researchers have been working to develop specific drugs to treat COVID-19. The main protease (M(pro)) of SARS-CoV-2 virus plays a pivotal role in mediating viral replication and transcription, which makes it a potential therapeutic drug target against COVID-19. In this study, a virtual drug screening method based on the M(pro) structure (Protein Data Bank ID: 6LU7) was proposed, and 8,820 compounds collected from the DrugBank database were used for molecular docking and virtual screening. A data set containing 1,545 drug molecules, derived from compounds with a low binding free energy score in the docking experiment, was established. N-1H-Indazol-5-yl-2-(6-methylpyridin-2-yl)quinazolin-4-amine, ergotamine, antrafenine, dihydroergotamine, and phthalocyanine outperformed the other compounds in binding conformation and binding free energy over the N3 inhibitor in the crystal structure. The bioactivity and ADMET properties of these five compounds were further investigated. These experimental results for five compounds suggested that they were potential therapeutics to be developed for clinical trials. To further verify the results of molecular docking, we also carried out molecular dynamics (MD) simulations on the complexes formed by the five compounds and M(pro). The five complexes showed stable affinity in terms of root mean square distance (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and hydrogen bond. It was further confirmed that the five compounds had potential inhibitory effects on SARS-CoV-2 M(pro).