Potential Small Molecule Drugs Research Articles

Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning Across Multiple Assays

The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying potential small-molecule drugs. Given traditional methods’ labor-intensive and time-consuming nature, deep learning has emerged as an essential tool for efficiently processing and extracting insights from complex biological data. To utilize deep learning techniques, particularly deep neural networks (DNN) enhanced with the synthetic minority oversampling technique (SMOTE), to enhance the classification of binding activities in anti-SARS-CoV-2 molecules across various bioassays. We used 11 bioassay datasets covering various SARS-CoV-2 interactions and inhibitory mechanisms. These assays ranged from spike-ACE2 protein-protein interaction to ACE2 enzymatic activity and 3CL enzymatic activity. To address the prevalent class imbalance in these datasets, the SMOTE technique was employed to generate new samples for the minority class. In our model-building approach, we divided the dataset into 80% training and 20% test sets, reserving 10% of the training set for validation. Our approach involved employing a DNN that integrates ReLU and sigmoid activation functions, incorporates batch normalization, and uses Adam optimization. The hyperparameters and architecture of the DNN were optimized through various tests on layers, minibatch sizes, epoch sizes, and learning rates. A 40% dropout rate was incorporated to mitigate overfitting. For model evaluation, we computed performance metrics, such as balanced accuracy (BACC), precision, recall, F1 score, Matthews’ correlation coefficient (MCC), and area under the curve (AUC). The performance of the DNN across 11 bioassay test sets revealed varying outcomes, significantly influenced by the ratios of active-to-inactive compounds. Assays, such as AlphaLISA and CoV-PPE, demonstrated robust performance across various metrics, including BACC, precision, recall, and AUC, when configured with more balanced ratios (1:3 and 1:1, respectively). This suggests the effective identification of active compounds in both cases. In contrast, assays with higher imbalance ratios, such as 3CL (1:38) and cytopathic effect (1:15), demonstrated higher recall but lower precision, highlighting challenges in accurately identifying active compounds among numerous inactive compounds. However, even in these challenging settings, the model achieved favorable BACC and recall scores. Overall, the DNN model generally performed well, as indicated by the BACC, MCC, and AUC values, especially when considering the degree of dataset imbalance in each assay. This study demonstrates the significant impact of deep learning, particularly DNN models enhanced with SMOTE, in improving the identification of active compounds in bioassay datasets for COVID-19 drug discovery, outperforming traditional machine learning models. Furthermore, this study highlights the efficacy of advanced computational techniques in addressing high-throughput screening data imbalances.

Small molecule drug discovery for glioblastoma treatment based on bioinformatics and cheminformatics approaches.

Background: Glioblastoma (GBM) is a common and highly aggressive brain tumor with a poor prognosis for patients. It is urgently needed to identify potential small molecule drugs that specifically target key genes associated with GBM development and prognosis. Methods: Differentially expressed genes (DEGs) between GBM and normal tissues were obtained by data mining the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene function annotation was performed to investigate the potential functions of the DEGs. A protein-protein interaction (PPI) network was constructed to explore hub genes associated with GBM. Bioinformatics analysis was used to screen the potential therapeutic and prognostic genes. Finally, potential small molecule drugs were predicted using the DGIdb database and verified using chemical informatics methods including absorption, distribution, metabolism, excretion, toxicity (ADMET), and molecular docking studies. Results: A total of 429 DEGs were identified, of which 19 hub genes were obtained through PPI analysis. The hub genes were confirmed as potential therapeutic targets by functional enrichment and mRNA expression. Survival analysis and protein expression confirmed centromere protein A (CENPA) as a prognostic target in GBM. Four small molecule drugs were predicted for the treatment of GBM. Conclusion: Our study suggests some promising potential therapeutic targets and small molecule drugs for the treatment of GBM, providing new ideas for further research and targeted drug development.

Analysis of network expression and immune infiltration of disulfidptosis-related genes in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a globally prevalent respiratory disease, and programmed cell death plays a pivotal role in the development of COPD. Disulfidptosis is a newly discovered type of cell death that may be associated with the progression of COPD. However, the expression and role of disulfidptosis-related genes (DRGs) in COPD remain unclear. The expression of DRGs was identified by analyzing RNA sequencing (RNA-seq) data in COPD. Further, COPD patients were classified into two subtypes by unsupervised cluster analysis to reveal their differences in gene expression and immune infiltration. Meanwhile, hub genes associated with disulfidptosis were screened by weighted gene co-expression network analysis. Subsequently, the hub genes were validated experimentally in cells and animals. In addition, we screened potential therapeutic drugs through the hub genes. We identified two distinct molecular clusters and observed significant differences in immune cell populations between them. In addition, we screened nine hub genes, and experimental validation showed that CDC71, DOHH, PDAP1, and SLC25A39 were significantly upregulated in cigarette smoke-induced COPD mouse lung tissues and bronchial epithelial cells (BEAS-2B) treated with cigarette smoke extract. Finally, we predicted 10 potential small molecule drugs such as Atovaquone, Taurocholic acid, Latamoxef, and Methotrexate. We highlighted the strong association between COPD and disulfidptosis, with DRGs demonstrating a discriminative capacity for COPD. Additionally, the expression of certain novel genes, including CDC71, DOHH, PDAP1, and SLC25A39, is linked to COPD and may aid in the diagnosis and assessment of this condition.

Predicting potential therapeutic targets and small molecule drugs for early-stage lung adenocarcinoma

Lung cancer is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting the majority, and its main subtype being lung adenocarcinoma (LUAD). Despite substantial advances in LUAD diagnosis and treatment, early diagnostic biomarkers inadequately fulfill clinical requirements. Thus, we conducted bioinformatics analysis to identify potential biomarkers and corresponding therapeutic drugs for early-stage LUAD patients. Here we identified a total of 10 differentially expressed genes (DEGs) with survival significance through the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Subsequently, we identified a promising small molecule drug, Aminopurvalanol A, based on the 10 key genes using the L1000FWD application, which was validated by molecular docking followed by in vivo and in vitro experiments. The results highlighted TOP2A, CDH3, ASPM, CENPF, SLC2A1, and PRC1 as potential detection biomarkers for early LUAD. We confirmed the efficacy and safety of Aminopurvalanol A, providing valuable insights for the clinical management of LUAD.

Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes

BackgroundOsteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes.MethodsWe obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website.ResultsWe identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified.Conclusion4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.

Antibody-Drug Conjugate Overview: a State-of-the-art Manufacturing Process and Control Strategy.

Antibody-drug conjugates (ADCs) comprise an antibody, linker, and drug, which direct their highly potent small molecule drugs to target tumor cells via specific binding between the antibody and surface antigens. The antibody, linker, and drug should be properly designed or selected to achieve the desired efficacy while minimizing off-target toxicity. With a unique and complex structure, there is inherent heterogeneity introduced by product-related variations and the manufacturing process. Here this review primarily covers recent key advances in ADC history, clinical development status, molecule design, manufacturing processes, and quality control.The manufacturing process, especially the conjugation process, should be carefully developed, characterized, validated, and controlled throughout its lifecycle. Quality control is another key element to ensure product quality and patient safety. A patient-centric strategy has been well recognized and adopted by the pharmaceutical industry for therapeutic proteins, and has been successfully implemented for ADCs as well, to ensure that ADC products maintain their quality until the end of their shelf life. Deep product understanding and process knowledge defines attribute testing strategies (ATS). Quality by design (QbD) is a powerful approach for process and product development, and for defining an overall control strategy. Finally, we summarize the current challenges on ADC development and provide some perspectives that may help to give related directions and trigger more cross-functional research to surmount those challenges.

Gene signatures to therapeutics: Assessing the potential of ivermectin against t(4;14) multiple myeloma.

Multiple myeloma (MM) is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow. The translocation, (t)(4;14), results in high-risk MM with limited treatment alternatives. Thus, there is an urgent need for identification and validation of potential treatments for this MM subtype. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets. To elucidate the molecular basis and search for potential effective drugs of t(4;14) MM subtype by employing a comprehensive approach. The transcriptional signature of t(4;14) MM was sourced from the Gene Expression Omnibus. Two datasets, GSE16558 and GSE116294, which included 17 and 15 t(4;14) MM bone marrow samples, and five and four normal bone marrow samples, respectively. After the differentially expressed genes were identified, the Cytohubba tool was used to screen for hub genes. Then, the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Using the STRING database and Cytoscape, protein-protein interaction networks and core targets were identified. Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis, respectively. In this study, a total of 258 differentially expressed genes with enriched functions in cancer pathways, namely cytokine receptor interactions, nuclear factor (NF)-κB signaling pathway, lipid metabolism, atherosclerosis, and Hippo signaling pathway, were identified. Ten hub genes (cd45, vcam1, ccl3, cd56, app, cd48, btk, ccr2, cybb, and cxcl12) were identified. Nine drugs, including ivermectin, deforolimus, and isoliquiritigenin, were predicted by the Connectivity Map database to have potential therapeutic effects on t (4;14) MM. In molecular docking, ivermectin showed strong binding affinity to all 10 identified targets, especially cd45 and cybb. Ivermectin inhibited t(4;14) MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro. Furthermore, ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14) MM cells. Collectively, the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14) MM diagnosis and treatment, with ivermectin emerging as a potential therapeutic alternative.

Integration of multi-omics and clinical treatment data reveals bladder cancer therapeutic vulnerability gene combinations and prognostic risks.

Bladder cancer (BCa) is a common malignancy of the urinary tract. Due to the high heterogeneity of BCa, patients have poor prognosis and treatment outcomes. Immunotherapy has changed the clinical treatment landscape for many advanced malignancies, opening new avenues for the precise treatment of malignancies. However, effective predictors and models to guide clinical treatment and predict immunotherapeutic outcomes are still lacking. We downloaded BCa sample data from The Cancer Genome Atlas to identify anti-PD-L1 immunotherapy-related genes through an immunotherapy dataset and used machine learning algorithms to build a new PD-L1 multidimensional regulatory index (PMRI) based on these genes. PMRI-related column-line graphs were constructed to provide quantitative tools for clinical practice. We analyzed the clinical characteristics, tumor immune microenvironment, chemotherapy response, and immunotherapy response of patients based on PMRI system. Further, we performed function validation of classical PMRI genes and their correlation with PD-L1 in BCa cells and screening of potential small-molecule drugs targeting PMRI core target proteins through molecular docking. PMRI, which consists of four anti-PD-L1 immunotherapy-associated genes (IGF2BP3, P4HB, RAC3, and CLK2), is a reliable predictor of survival in patients with BCa and has been validated using multiple external datasets. We found higher levels of immune cell infiltration and better responses to immunotherapy and cisplatin chemotherapy in the high PMRI group than in the low PMRI group, which can also be used to predict immune efficacy in a variety of solid tumors other than BCa. Knockdown of IGF2BP3 inhibited BCa cell proliferation and migration, and IGF2BP3 was positively correlated with PD-L1 expression. We performed molecular docking prediction for each of the core proteins comprising PMRI and identified 16 small-molecule drugs with the highest affinity to the target proteins. Our PD-L1 multidimensional expression regulation model based on anti-PD-L1 immunotherapy-related genes can accurately assess the prognosis of patients with BCa and identify patient populations that will benefit from immunotherapy, providing a new tool for the clinical management of intermediate and advanced BCa.

Interactive Nutrient Process (INP) in a Generative AI of a New Drug-6-Shogaol as a Potential Case.

The dynamically evolving science of pharmacology requires AI technology to advance a new path for drug development. The author proposes generative AI for future drugs, identifying suitable drug molecules, uncharacteristically to previous generations of medicines, incorporating the wisdom, experience, and intuit of traditional materia medica and the respective traditional medicine practitioners. This paper describes the guiding principles of the new drug development, springing from the tradition and practice of Tibetan medicine, defined as the Interactive Nutrient Process (INP). The INP provides traditional knowledge and practitioner's experience, contextualizing and teaching the new drug therapy. An illustrative example of the outcome of the INP is a potential small molecule drug, 6-Shogaol and related shogaol derivatives, from ginger roots (Zingiber officinalis fam. Zingiberaceae) evaluated clinically for 12 months for biological markers of iron homeostasis in patients with the myelodysplastic syndromes (MDS). The study's preliminary results indicate that 6-Shogaol and related shogaols may improve iron homeostasis in low-risk/intermediate-1 MDS patients without objective or subjective side effects.

Comprehensive Analysis of PANoptosis-Related Gene Signature of Ulcerative Colitis.

Accumulating evidence shows that the abnormal increase in the mortality of intestinal epithelial cells (IECs) caused by apoptosis, pyroptosis, and necroptosis is closely related to the function of mucous membrane immunity and barrier function in patients with ulcerative colitis (UC). As a procedural death path that integrates the above-mentioned many deaths, the role of PANoptosis in UC has not been clarified. This study aims to explore the characterization of PANoptosis patterns and determine the potential biomarkers and therapeutic targets. We constructed a PANoptosis gene set and revealed significant activation of PANoptosis in UC patients based on multiple transcriptome profiles of intestinal mucosal biopsies from the GEO database. Comprehensive bioinformatics analysis revealed five key genes (ZBP1, AIM2, CASP1/8, IRF1) of PANoptosome with good diagnostic value and were highly correlated with an increase in pro-inflammatory immune cells and factors. In addition, we established a reliable ceRNA regulatory network of PANoptosis and predicted three potential small-molecule drugs sharing calcium channel blockers that were identified, among which flunarizine exhibited the highest correlation with a high binding affinity to the targets. Finally, we used the DSS-induced colitis model to validate our findings. This study identifies key genes of PANoptosis associated with UC development and hypothesizes that IRF1 as a TF promotes PANoptosome multicomponent expression, activates PANoptosis, and then induces IECs excessive death.

WGCNA combined with machine learning to find potential biomarkers of liver cancer.

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

Analysis of damage-associated molecular patterns in amyotrophic lateral sclerosis based on ScRNA-seq and bulk RNA-seq data.

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disorder characterized by the progressive loss of motor neurons. Despite extensive research, the exact etiology of ALS remains elusive. Emerging evidence highlights the critical role of the immune system in ALS pathogenesis and progression. Damage-Associated Molecular Patterns (DAMPs) are endogenous molecules released by stressed or damaged cells, acting as danger signals and activating immune responses. However, their specific involvement in ALS remains unclear. We obtained single-cell RNA sequencing (scRNA-seq) data of ALS from the primary motor cortex in the Gene Expression Omnibus (GEO) database. To better understand genes associated with DAMPs, we performed analyses on cell-cell communication and trajectory. The abundance of immune-infiltrating cells was assessed using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. We performed univariate Cox analysis to construct the risk model and utilized the least absolute shrinkage and selection operator (LASSO) analysis. Finally, we identified potential small molecule drugs targeting ALS by screening the Connectivity Map database (CMap) and confirmed their potential through molecular docking analysis. Our study annotated 10 cell types, with the expression of genes related to DAMPs predominantly observed in microglia. Analysis of intercellular communication revealed 12 ligand-receptor pairs in the pathways associated with DAMPs, where microglial cells acted as ligands. Among these pairs, the SPP1-CD44 pair demonstrated the greatest contribution. Furthermore, trajectory analysis demonstrated distinct differentiation fates of different microglial states. Additionally, we constructed a risk model incorporating four genes (TRPM2, ROCK1, HSP90AA1, and HSPA4). The validity of the risk model was supported by multivariate analysis. Moreover, external validation from dataset GSE112681 confirmed the predictive power of the model, which yielded consistent results with datasets GSE112676 and GSE112680. Lastly, the molecular docking analysis suggested that five compounds, namely mead-acid, nifedipine, nifekalant, androstenol, and hydrastine, hold promise as potential candidates for the treatment of ALS. Taken together, our study demonstrated that DAMP entities were predominantly observed in microglial cells within the context of ALS. The utilization of a prognostic risk model can accurately predict ALS patient survival. Additionally, genes related to DAMPs may present viable drug targets for ALS therapy.

[Identification of SULF1 as a Shared Gene in Idiopathic Pulmonary Fibrosis
and Lung Adenocarcinoma].

Idiopathic pulmonary fibrosis (IPF) is an idiopathic chronic, progressive interstitial lung disease with a diagnosed median survival of 3-5 years. IPF is associated with an increased risk of lung cancer. Therefore, exploring the shared pathogenic genes and molecular pathways between IPF and lung adenocarcinoma (LUAD) holds significant importance for the development of novel therapeutic approaches and personalized precision treatment strategies for IPF combined with lung cancer. Bioinformatics analysis was conducted using publicly available gene expression datasets of IPF and LUAD from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis was employed to identify common genes involved in the progression of both diseases, followed by functional enrichment analysis. Subsequently, additional datasets were used to pinpoint the core shared genes between the two diseases. The relationship between core shared genes and prognosis, as well as their expression patterns, clinical relevance, genetic characteristics, and immune-related functions in LUAD, were analyzed using The Cancer Genome Atlas (TCGA) database and single-cell RNA sequencing datasets. Finally, potential therapeutic drugs related to the identified genes were screened through drug databases. A total of 529 shared genes between IPF and LUAD were identified. Among them, SULF1 emerged as a core shared gene associated with poor prognosis. It exhibited significantly elevated expression levels in LUAD tissues, concomitant with high mutation rates, genomic heterogeneity, and an immunosuppressive microenvironment. Subsequent single-cell RNA-seq analysis revealed that the high expression of SULF1 primarily originated from tumor-associated fibroblasts. This study further demonstrated an association between SULF1 expression and tumor drug sensitivity, and it identified potential small-molecule drugs targeting SULF1 highly expressed fibroblasts. This study identified a set of shared molecular pathways and core genes between IPF and LUAD. Notably, SULF1 may serve as a potential immune-related biomarker and therapeutic target for both diseases.

Age-related genes affecting the immune cell infiltration in ulcerative colitis revealed by weighted correlation network analysis and machine learning.

The crosstalk between age and immunity in the context of ulcerative colitis (UC) remains incompletely understood. Our objective is to elucidate the specific age-associated genetic factors that modulate immune cell infiltration in UC, with the aim of identifying innovative therapeutic targets for the treatment of this disease. Potential batch effects between samples were removed by R package "inSilicoMerging". Unsupervised clustering analysis via the "ConsensusClusterPlus" R package was utilized to perform consensus molecular subtyping of immune subtypes in UC. The construction of a heat map was accomplished through the utilization of the R package "pheatmap", while functional enrichment analysis was executed by means of the Metascape database. The identification of the age-related gene module was achieved by performing weighted gene co-expression network analysis (WGCNA) analysis using the R package "WGCNA". The support vector machine (SVM), least absolute shrinkage and selector operation (LASSO), and random forest algorithms were performed via the "e1071", "glmnet" and "randomForest" packages in R, respectively. The diagnostic performance of the parameter was assessed using the receiver operating characteristic (ROC) curve. Correlation analysis was performed by Spearman correlation. The "XSum" package in R was employed to identify potential small-molecule drugs for UC utilizing the Connectivity Map (CMap) database. Molecular docking was performed with Autodock Vina molecular docking software. A significantly greater frequency of UC patients aged below 40 years was observed in the group with extensive disease extent as compared to those with non-extensive disease extent (70% vs. 47%; Chi-square test, p = 0.02). The application of unsupervised clustering analysis allowed for the stratification of UC patients into two distinct immune subtypes, namely cluster C1 and cluster C2. The distribution of immune subtypes was significantly different between different age categories (Chi-square test, p = 0.00219). The UC samples that were grouped under cluster C1 were distinguished by a higher abundance of macrophages and an elevated number of neutrophils relative to those in cluster C2. Based on both WGCNA and Limma analysis, 146 age-related genes were identified, which exhibited a predominant enrichment in the biological process of cellular senescence. Two age-related genes (MIDN, and PLD6) affecting the immune cell infiltration in UC were identified based on machine learning algorithms (SVM, LASSO, and random forest). The diagnostic performance of MIDN (AUC = 0.93) and PLD6 (AUC = 0.90) in discerning UC patients belonging to cluster C1 was found to be satisfactory, as demonstrated by ROC curve analysis. MIDN demonstrated a positive correlation (r = 0.50, p < 0.0001) with Neutrophil, while PLD6 exhibited a negative correlation (r = -0.52, p < 0.0001) with Neutrophil levels. The "XSum" algorithm revealed that Entinostat has therapeutic potential for UC. The docking glide score between Entinostat and MIDN, and PLD6 protein was -8.9 kcal/mol and -6.8 kcal/mol, respectively. We have identified two age-related genes, MIDN and PLD6, that are involved in immune cell infiltration in patients with ulcerative colitis. Furthermore, a small molecule drug (Entinostat) with potential therapeutic effects for UC was screened out. This study presented new perspectives on personalized clinical management and therapy research for UC.

Extracellular Pgk1 interacts neural membrane protein enolase-2 to improve the neurite outgrowth of motor neurons

Understanding the molecular interaction between ligand and receptor is important for providing the basis for the development of regenerative drugs. Although it has been reported that extracellular phosphoglycerate kinase 1 (Pgk1) can promote the neurite outgrowth of motoneurons, the Pgk1-interacting neural receptor remains unknown. Here we show that neural membranous Enolase-2 exhibits strong affinity with recombinant Pgk1-Flag, which is also evidently demonstrated by immunoelectron microscopy. The 325th-417th domain of Pgk1 interacts with the 405th-431st domain of Enolase-2, but neither Enolase-1 nor Enolase-3, promoting neurite outgrowth. Combining Pgk1 incubation and Enolase-2 overexpression, we demonstrate a highly significant enhancement of neurite outgrowth of motoneurons through a reduced p-P38-T180/p-Limk1-S323/p-Cofilin signaling. Collectively, extracellular Pgk1 interacts neural membrane receptor Enolase-2 to reduce the P38/Limk1/Cofilin signaling which results in promoting neurite outgrowth. The extracellular Pgk1-specific neural receptor found in this study should provide a material for screening potential small molecule drugs that promote motor nerve regeneration.

Predicting response of immunotherapy and targeted therapy and prognosis characteristics for renal clear cell carcinoma based on m1A methylation regulators

In recent years, RNA methylation modification has been found to be related to a variety of tumor mechanisms, such as rectal cancer. Clear cell renal cell carcinoma (ccRCC) is most common in renal cell carcinoma. In this study, we get the RNA profiles of ccRCC patients from ArrayExpress and TCGA databases. The prognosis model of ccRCC was developed by the least absolute shrinkage and selection operator (LASSO) regression analysis, and the samples were stratified into low–high risk groups. In addition, our prognostic model was validated through the receiver operating characteristic curve (ROC). “pRRophetic” package screened five potential small molecule drugs. Protein interaction networks explore tumor driving factors and drug targeting factors. Finally, polymerase chain reaction (PCR) was used to verify the expression of the model in the ccRCC cell line. The mRNA matrix in ArrayExpress and TCGA databases was used to establish a prognostic model for ccRCC through LASSO regression analysis. Kaplan Meier analysis showed that the overall survival rate (OS) of the high-risk group was poor. ROC verifies the reliability of our model. Functional enrichment analysis showed that there was a obviously difference in immune status between the high-low risk groups. “pRRophetic” package screened five potential small molecule drugs (A.443654, A.770041, ABT.888, AG.014699, AMG.706). Protein interaction network shows that epidermal growth factor receptor [EGRF] and estrogen receptor 1 [ESR1] are tumor drivers and drug targeting factors. To further analyze the differential expression and pathway correlation of the prognosis risk model species. Finally, polymerase chain reaction (PCR) showed the expression of YTHN6-Methyladenosine RNA Binding Protein 1[YTHDF1], TRNA Methyltransferase 61B [TRMT61B], TRNA Methyltransferase 10C [TRMT10C] and AlkB Homolog 1[ALKBH1] in ccRCC cell lines. To sum up, the prognosis risk model we created not only has good predictive value, but also can provide guidance for accurately predicting the prognosis of ccRCC.

The brief overview, antivirus and anti-SARS-CoV-2 activity, quantitative methods, and pharmacokinetics of cepharanthine: a potential small-molecule drug against COVID-19.

To effectively respond to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an increasing number of researchers are focusing on the antiviral activity of cepharanthine (CEP), which is a clinically approved drug being used for over 70years. This review aims to provide a brief overview of CEP and summarize its recent findings in quantitative analysis, pharmacokinetics, therapeutic potential, and mechanism in antiviral and anti-SARS-CoV-2 activity. Given its remarkable capacity against SARS-CoV-2 infection in vitro and in vivo, with its primary target organ being the lungs, and its good pharmacokinetic profile; mature and stable manufacturing technique; and its advantages of safety, effectiveness, and accessibility, CEP has become a promising drug candidate for treating COVID-19 despite being an old drug.

Hub genes, diagnostic model, and predicted drugs in systemic sclerosis by integrated bioinformatics analysis.

Background: Systemic sclerosis (scleroderma; SSc), a rare and heterogeneous connective tissue disease, remains unclear in terms of its underlying causative genes and effective therapeutic approaches. The purpose of the present study was to identify hub genes, diagnostic markers and explore potential small-molecule drugs of SSc. Methods: The cohorts of data used in this study were downloaded from the Gene Expression Complex (GEO) database. Integrated bioinformatic tools were utilized for exploration, including Weighted Gene Co-Expression Network Analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, gene set enrichment analysis (GSEA), Connectivity Map (CMap) analysis, molecular docking, and pharmacokinetic/toxicity properties exploration. Results: Seven hub genes (THY1, SULF1, PRSS23, COL5A2, NNMT, SLCO2B1, and TIMP1) were obtained in the merged gene expression profiles of GSE45485 and GSE76885. GSEA results have shown that they are associated with autoimmune diseases, microorganism infections, inflammatory related pathways, immune responses, and fibrosis process. Among them, THY1 and SULF1 were identified as diagnostic markers and validated in skin samples from GSE32413, GSE95065, GSE58095 and GSE125362. Finally, ten small-molecule drugs with potential therapeutic effects were identified, mainly including phosphodiesterase (PDE) inhibitors (BRL-50481, dipyridamole), TGF-β receptor inhibitor (SB-525334), and so on. Conclusion: This study provides new sights into a deeper understanding the molecular mechanisms in the pathogenesis of SSc. More importantly, the results may offer promising clues for further experimental studies and novel treatment strategies.

Bioinformatics analysis to identify breast cancer-related potential targets and candidate small molecule drugs

ObjectiveThe purpose of this study is to identify potential targets associated with breast cancer and screen potential small molecule drugs using bioinformatics analysis. MethodsDEGs analysis of breast cancer tissues and normal breast tissues was performed using R language limma analysis on the GSE42568 and GSE205185 datasets. Functional enrichment analysis was conducted on the intersecting DEGs. The STRING analysis platform was used to construct a PPI network, and the top 10 core nodes were identified using Cytoscape software. QuartataWeb was utilized to build a target-drug interaction network and identify potential drugs. Cell survival and proliferation were assessed using CCK8 and colony formation assays. Cell cycle analysis was performed using flow cytometry. Western blot analysis was conducted to assess protein levels of PLK1, MELK, AURKA, and NEK2. ResultsA total of 54 genes were consistently upregulated in both datasets, which were functionally enriched in mitotic cell cycle and cell cycle-related pathways. The 226 downregulated genes were functionally enriched in pathways related to hormone level regulation and negative regulation of cell population proliferation. Ten key genes, namely CDK1, CCNB2, ASPM, AURKA, TPX2, TOP2A, BUB1B, MELK, RRM2, and NEK2 were identified. The potential drug Fostamatinib was predicted to target AURKA, MELK, CDK1, and NEK2. In vitro experiments demonstrated that Fostamatinib inhibited the proliferation of breast cancer cells, induced cell arrest in the G2/M phase, and down-regulated MELK, AURKA, and NEK2 proteins. ConclusionIn conclusion, Fostamatinib shows promise as a potential drug for the treatment of breast cancer by regulating the cell cycle and inhibiting the proliferation of breast cancer cells.

DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning

Targeting lncRNAs by small molecules (SM-lncR) to alter their expression levels has emerged as an important therapeutic modality for disease treatment. To date, no computational tools have been dedicated to predicting small molecule-mediated upregulation or downregulation of lncRNA expression. Here, we introduce DeepdlncUD, which integrates predictions of nine deep learning algorithms together, to infer the regulation types of small molecules on modulating lncRNA expression. Through systematic optimization on a training set of 771 upregulation and 739 downregulation SM-lncR pairs, each encoding 1369 sequence, representational, and physiochemical features, this method outperforms a recently released program, DeepsmirUD, by achieving 0.674 in AUC (area under the receiver operating characteristic curve), 0.722 in AUCPR (area under the precision-recall curve), 0.681 in F1-score, and 0.516 in Jaccard Index on a test set of 222 SM-lncR pairs. By extracting 125 upregulation and 46 downregulation SM-lncR pairs that involve disease-associated lncRNAs, DeepdlncUD is shown to gain an accuracy of 0.700 in the pathological context. Using connectivity scores, around half of the small molecules are correctly estimated as drugs to treat lncRNA-regulated diseases. This tool can be run at a fast speed to assist the discovery of potential small molecule drugs of lncRNA targets on a large scale. DeepdlncUD is publicly available at https://github.com/2003100127/deepdlncud.