Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap)

Ingenol ameliorates silicosis via targeting the PTGS2/PI3K/AKT signaling axis: Implications for therapeutic intervention

Silicosis, formerly known as silico, is an irreversible disease caused by prolonged inhalation of substantial amounts of free crystalline silica dust, characterized by pulmonary inflammation and extensive nodular fibrosis. The etiology of the disease remains unclear, which currently hinders the development of effective therapeutic drugs and interventions. Ingenol (Ing), a terpenoid active ingredient found in plants of the Euphorbiaceae family, including the entire herb of Euphorbia helioscopia, Euphorbia kansui, or Euphorbia lathyris, demonstrates significant anti-inflammatory and antiviral activities. In this study, we identified and confirmed that Ingenol can significantly ameliorate silicosis induced by silica dioxide by inhibiting the PTGS2/PI3K/AKT signaling pathway. In vivo, Ingenol improves pulmonary respiratory function and reduces inflammation and fibrosis in a murine model of CS-induced silicosis. In vitro, Ingenol inhibits the expression of cellular factors associated with inflammation and fibrosis, as well as macrophage apoptosis and fibroblast migration. Furthermore, it can modulate the expression of fibrosis-related proteins, thereby inhibiting CS-induced fibrotic responses. Mechanistically, a combination of bioinformatics, network pharmacology, and experimental validation indicates that Ingenol mitigates the progression of silicosis by modulating the PTGS2/PI3K/AKT signaling pathway. In summary, these findings suggest that Ingenol is a potential candidate for the treatment of silicosis.

BDNF is a prognostic biomarker involved in the immune infiltration of lung adenocarcinoma and associated with programmed cell death

It is well established that genes associated with cell death can serve as prognostic markers for patients with cancer. Programmed cell death (PCD) is known to play a role in cancer cell apoptosis and antitumor immunity. With the continuous discovery of new forms of PCD, the roles of PCD in lung adenocarcinoma (LUAD) require ongoing evaluation. In the present study, mRNA expression data and clinical information associated with 15 forms of PCD were extracted from publicly available databases and systematically analyzed. Utilizing these data, a robust risk prediction model was established that incorporates six PCD-related genes (PRGs). Datasets from the Gene Expression Omnibus database were employed to validate the six genes exhibiting risk-associated characteristics. The PRG-based model reliably predicted the prognosis of patients with LUAD, with the high-risk group showing a poor prognosis, reduced levels of immune infiltration molecules and diminished expression of human leukocyte antigens. Additionally, the relationships among PRGs, somatic mutations, tumor stemness index and immune infiltration were assessed. Based on these risk characteristics, a nomogram was constructed, patient stratification was performed, small-molecule drug candidates were predicted, and somatic mutations and chemotherapy responses were analyzed. Furthermore, reverse transcription-quantitative PCR was used to assess the expression of PDGs in vitro, and the critical role of brain-derived neurotrophic factor in LUAD development was identified through Mendelian randomization, gene knockdown, wound healing, western blot and colony formation assays. These findings offer new insights into the development of targeted therapies for LUAD, particularly in patients with high BDNF expression.

The role of endoplasmic reticulum stress in type 2 diabetes mellitus mechanisms and impact on islet function

Type 2 diabetes mellitus (T2DM) is a globally prevalent metabolic disorder characterized by insulin resistance and dysfunction of islet cells. Endoplasmic reticulum (ER) stress plays a crucial role in the pathogenesis and progression of T2DM, especially in the function and survival of β-cells. β-cells are particularly sensitive to ER stress because they require substantial insulin synthesis and secretion energy. In the early stages of T2DM, the increased demand for insulin exacerbates β-cell ER stress. Although the unfolded protein response (UPR) can temporarily alleviate this stress, prolonged or excessive stress leads to pancreatic cell dysfunction and apoptosis, resulting in insufficient insulin secretion. This review explores the mechanisms of ER stress in T2DM, particularly its impact on islet cells. We discuss how ER stress activates UPR signaling pathways to regulate protein folding and degradation, but when stress becomes excessive, these pathways may contribute to β-cell death. A deeper understanding of how ER stress impacts islet cells could lead to the development of novel T2DM treatment strategies aimed at improving islet function and slowing disease progression.

Phospholipase C Beta 2 as a Key Regulator of Tumor Progression and Epithelial-Mesenchymal Transition via PI3K/AKT Signaling in Renal Cell Carcinoma

Background: Renal cell carcinoma (RCC) represents the most common form of invasive kidney cancer in adults. Among the components critical to cellular regulation is Phospholipase C Beta 2 (PLCB2), a member of the phospholipase C enzyme family. This enzyme plays a vital role in managing key cellular functions such as growth, differentiation, migration, and survival. Despite its significant importance, the specific expression patterns and molecular mechanisms of PLCB2 in the progression of RCC are not well understood. Methods: This investigation employed a combination of bioinformatics analyses, scRNA-seq, functional assays, transcriptome sequencing, real-time quantitative PCR (RT-PCR), immunofluorescence, rescue experiments, and Western blotting to explore the regulatory function of PLCB2 in driving the epithelial-mesenchymal transition (EMT) in RCC through the PI3K/AKT signaling pathway. Results:PLCB2 expression is significantly elevated in RCC samples, and this increase is inversely correlated with patient prognosis. The knockdown of PLCB2 in RCC cell lines leads to a marked reduction in cell proliferation, invasion, migration, and EMT. Transcriptome sequencing further revealed that PLCB2 is significantly associated with the PI3K/AKT pathway. Notably, the PI3K activator 740Y-P was able to reverse the reductions in migration, invasion, and EMT caused by the PLCB2 knockdown. Conclusions: Our findings underscore the pivotal role of PLCB2 in regulating RCC invasion and metastasis by modulating the EMT via the PI3K/AKT signaling pathway. This highlights PLCB2 not only as a key prognostic biomarker, but also as a promising therapeutic target in the treatment of advanced-stage RCC, offering new avenues for more effective interventions.

The Diagnostic and Prognostic Value of Neurotransmitter Receptor-Related Genes in Colon Adenocarcinoma

Colorectal cancer (CRC) is a malignant tumor with high morbidity and mortality in the world. This study aimed to find receptor-related genes (NRGs) with diagnostic and prognostic value in colon adenocarcinoma (COAD). The Cancer Genome Atlas (TCGA) and the Human Protein Atlas database databases were applied to find differential expression NRGs between COAD and normal colonic tissues. Subsequently, Cox regression analysis and minimum absolute contraction and selection operator algorithm were used to construct a prognosis nomogram based on TCGA and Gene Expression Omnibus databases. Expression levels of 35 NRGs were significant differences in COAD and normal colonic tissues. ROC curves showed that 24 NRGs had high diagnostic accuracy (AUC > 0.850) in COAD. Risk score was constructed based on 10 NRGs for the first time. Cox regression analysis revealed risk score was an independent risk factor and a higher risk score predicts a later TNM stage. Finally, a prognostic nomogram containing risk score and clinical features was established. Calibration curves and C-index suggested the powerful predictable value of the model. This study identified the NRGs with diagnostic value and prognostic value, providing a direction for treatment of COAD patients.

GLP-1R mediates idebenone-reduced blood glucose in mice

GLP-1 receptor agonists (GLP-1RAs) are an innovative class of drugs with significant therapeutic value for type 2 diabetes mellitus (T2DM). The GLP-1RAs currently available on the market are biologic macromolecular peptide agents that are expensive to treat and not easy to take orally. Therefore, the development of small molecule GLP-1RAs is becoming one of the most sought-after research targets for hypoglycemic drugs. In this study, we sought to find a potential oral small molecule GLP-1RA and to evaluate its effect on insulin secretion in rat pancreatic β cells and on blood glucose in mice. We downloaded the mRNA expression profiles of GSE102194 and GSE37936 from the Gene Expression Omnibus database. Subsequently, the small molecule compound idebenone was screened through the connectivity map database. The results of molecular docking, biolayer interferometry, and cellular thermal shift assay indicated that idebenone could bind potently with GLP-1R. Furthermore, ibebenone elevated intracellular cAMP levels. The radioimmunoassay data showed that idebenone enhanced glucose-stimulated insulin secretion via agonism of GLP-1R. Moreover, the results of oral glucose tolerance tests in C57BL/6, Glp-1r-/-, and hGlp-1r mice demonstrated that the glucose-lowering effects of idebenone were mediated by GLP-1R and that there were no species differences in the agonistic effect of idebenone on GLP-1R. In summary, idebenone reduces blood glucose in mice by promoting insulin release through agonism of GLP-1R, suggesting that idebenone is probably a potential GLP-1RA, which is expected to provide a new therapeutic strategy for the prevention and treatment of metabolic diseases such as T2DM.

Identification of potential therapeutic targets for plaque vulnerability based on an integrated analysis

BACKGROUND AND AIMS

This study aimed to explore potential hub genes and pathways of plaque vulnerability and to investigate possible therapeutic targets for acute coronary syndrome (ACS).

METHODS AND RESULTS

Four microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene coexpression networks (WGCNA) and immune cell infiltration analysis (IIA) were used to identify the genes for plaque vulnerability. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology, Gene Ontology annotation and protein-protein interaction (PPI) network analyses were performed to explore the hub genes. Random forest and artificial neural networks were constructed for validation. Furthermore, the CMap and Herb databases were employed to explore possible therapeutic targets. A total of 168 DEGs with an adjusted P < 0.05 and approximately 1974 IIA genes were identified in GSE62646. Three modules were detected and associated with CAD-Class, including 891 genes that can be found in GSE90074. After removing duplicates, 114 hub genes were used for functional analysis. GO functions identified 157 items, and 6 pathways were enriched for the KEGG pathway at adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). Random forest and artificial neural network models were built based on the GSE48060 and GSE34822 datasets, respectively, to validate the previous hub genes. Five genes (GZMA, GZMB, KLRB1, KLRD1 and TRPM6) were selected, and only two of them (GZMA and GZMB) were screened as therapeutic targets in the CMap and Herb databases.

CONCLUSION

We performed a comprehensive analysis and validated GZMA and GZMB as a target for plaque vulnerability, which provides a therapeutic strategy for the prevention of ACS. However, whether it can be used as a predictor in blood samples requires further experimental verification.

Mining key circadian biomarkers for major depressive disorder by integrating bioinformatics and machine learning

OBJECTIVE

This study aimed to identify key clock genes closely associated with major depressive disorder (MDD) using bioinformatics and machine learning approaches.

METHODS

Gene expression data of 128 MDD patients and 64 healthy controls from blood samples were obtained. Differentially expressed were identified and weighted gene co-expression network analysis (WGCNA) was first performed to screen MDD-related key genes. These genes were then intersected with 1475 known circadian rhythm genes to identify circadian rhythm genes associated with MDD. Finally, multiple machine learning algorithms were applied for further selection, to determine the most critical 4 circadian rhythm biomarkers.

RESULTS

Four key circadian rhythm genes (ABCC2, APP, HK2 and RORA) were identified that could effectively distinguish MDD samples from controls. These genes were significantly enriched in circadian pathways and showed strong correlations with immune cell infiltration. Drug target prediction suggested that small molecules like melatonin and escitalopram may target these circadian rhythm proteins.

CONCLUSION

This study revealed discovered 4 key circadian rhythm genes closely associated with MDD, which may serve as diagnostic biomarkers and therapeutic targets. The findings highlight the important roles of circadian disruptions in the pathogenesis of MDD, providing new insights for precision diagnosis and targeted treatment of MDD.

Construction of a ceRNA network and screening of potential biomarkers and molecular targets in male smokers with chronic obstructive pulmonary disease

Background

Circular RNAs (circRNAs) play an important role in the occurrence and development of diseases. However, the role of circRNAs in male smokers with chronic obstructive pulmonary disease (COPD) remains unclear.

Methods

Stable COPD patients and healthy controls were recruited. Peripheral blood mononuclear cells (PBMCs) were extracted. After high-throughput RNA sequencing (RNA-Seq) of PBMCs, a bioinformatics method was used to analyse differentially expressed (DE) circRNAs (DEcircRNAs) and mRNAs (DEmRNAs).

Results

Total of 114 DEcircRNAs and 58 DEmRNAs were identified. Functional enrichment analysis showed that processes related to COPD include the regulation of interleukin (IL)-18, IL-5 and the NLRP3 inflammasome; differentiation of T helper type 1 (Th1), Th2, and Th17 cells, and the AMPK, Wnt, JAK-STAT, and PI3K-Akt signalling pathways. In the protein-protein interaction (PPI) network, the core genes were MYO16, MYL4, SCN4A, NRCAM, HMCN1, MYOM2, and IQSEC3. Small-molecule prediction results revealed potential drugs for the COPD treatment. Additionally, the circRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) regulatory network was constructed.

Conclusion

This study identified a set of dysregulated circRNAs and mRNAs and revealed potentially important genes, pathways, new small-molecule drugs and ceRNA regulatory networks in male smokers with COPD. These circRNAs might be prospective biomarkers or potential molecular targets of the ceRNA mechanism for COPD.

Unveiling biomarkers and therapeutic targets in IgA nephropathy through large-scale blood transcriptome analysis

INTRODUCTION

IgA nephropathy (IgAN) is the most prevalent form of glomerulonephritis. Unfortunately, molecular biomarkers for IgAN derived from omics studies are still lacking. This research aims to identify critical genes associated with IgAN through large-scale blood transcriptome analysis.

METHODS

We constructed novel blood transcriptome profiles from peripheral blood mononuclear cells (PBMCs) of 53 Chinese IgAN patients and 28 healthy individuals. Our analysis included GO, KEGG, and GSEA for biological pathways. We analyzed immune cell profiles with CIBERSORT and constructed PPI networks with STRING, visualized in Cytoscape. Key differentially expressed genes (DEGs) were identified using CytoHubba and MCODE. We assessed the correlation between gene expressions and clinical data to evaluate clinical significance and identified hub genes through machine learning, validated with an open-access dataset. Potential drugs were explored using the CMap database.

RESULTS

We identified 333 DEGs between IgAN patients and healthy controls, mainly related to immune response and inflammation. Key pathways included NK cell mediated cytotoxicity, complement and coagulation cascades, antigen processing, and B cell receptor signaling. Cytoscape revealed 16 clinically significant genes (including KIR2DL1, KIR2DL3, VISIG4, C1QB, and C1QC, associated with sub-phenotype and prognosis). Machine learning identified two hub genes (KLRC1 and C1QB) for a diagnostic model of IgAN with 0.92 accuracy, validated at 1.00 against the GSE125818 dataset. Sirolimus, calcifediol, and efaproxiral were suggested as potential therapeutic agents.

CONCLUSION

Key DEGs, particularly VISIG4, KLRC1, and C1QB, emerge as potential specific markers for IgAN, paving the way for future targeted personalized treatment options.

Comprehensive Analysis of Fatty Acid Metabolism in Diabetic Nephropathy from the Perspective of Immune Landscapes, Diagnosis and Precise Therapy

Objective

Diabetic nephropathy (DN) represents the principal cause of end-stage renal diseases worldwide, lacking effective therapies. Fatty acid (FA) serves as the primary energy source in the kidney and its dysregulation is frequently observed in DN. Nevertheless, the roles of FA metabolism in the occurrence and progression of DN have not been fully elucidated.

Methods

Three DN datasets (GSE96804/GSE30528/GSE104948) were obtained and combined. Differentially expressed FA metabolism-related genes were identified and subjected to DN classification using "ConsensusClusterPlus". DN subtypes-associated modules were discovered by "WGCNA", and module genes underwent functional enrichment analysis. The immune landscapes and potential drugs were analyzed using "CIBERSORT" and "CMAP", respectively. Candidate diagnostic biomarkers of DN were screened using machine learning algorithms. A prediction model was constructed, and the performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The online tool "Nephroseq v5" was conducted to reveal the clinical significance of the candidate diagnostic biomarkers in patients with DN. A DN mouse model was established to verify the biomarkers' expression.

Results

According to 39 dysregulated FA metabolism-related genes, DN samples were divided into two molecular subtypes. Patients in Cluster B exhibited worse outcomes with a different immune landscape compared with those in Cluster A. Ten potential small-molecular drugs were predicted to treat DN in Cluster B. The diagnostic model based on PRKAR2B/ANXA1 was created with ideal predictive values in early and advanced stages of DN. The correlation analysis revealed significant association between PRKAR2B/ANXA1 and clinical characteristics. The DN mouse model validated the expression patterns of PRKAR2B/ANXA1.

Conclusion

Our study provides new insights into the role of FA metabolism in the classification, immunological pathogenesis, early diagnosis, and precise therapy of DN.

ER stress and unfolded protein response (UPR) signaling modulate GLP-1 receptor signaling in the pancreatic islets

Insulin is essential for maintaining normoglycemia and is predominantly secreted in response to glucose stimulation by β-cells. Incretin hormones, such as glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide, also stimulate insulin secretion. However, as obesity and type 2 diabetes worsen, glucose-dependent insulinotropic polypeptide loses its insulinotropic efficacy, whereas GLP-1 receptor (GLP-1R) agonists continue to be effective owing to its signaling switch from Gs to Gq. Herein, we demonstrated that endoplasmic reticulum (ER) stress induced a transition from Gs to Gq in GLP-1R signaling in mouse islets. Intriguingly, chemical chaperones known to alleviate ER stress, such as 4-PBA and TUDCA, enforced GLP-1R's Gq utilization rather than reversing GLP-1R's signaling switch induced by ER stress or obese and diabetic conditions. In addition, the activation of X-box binding protein 1 (XBP1) or activating transcription factor 6 (ATF6), 2 key ER stress-associated signaling (unfolded protein response) factors, promoted Gs utilization in GLP-1R signaling, whereas Gq employment by ER stress was unaffected by XBP1 or ATF6 activation. Our study revealed that ER stress and its associated signaling events alter GLP-1R's signaling, which can be used in type 2 diabetes treatment.

Osteoarthritis related epigenetic variations in miRNA expression and DNA methylation

Osteoarthritis (OA) is chronic arthritis characterized by articular cartilage degradation. However, a comprehensive regulatory network for OA-related microRNAs and DNA methylation modifications has yet to be established. Thus, we aimed to identify epigenetic changes in microRNAs and DNA methylation and establish the regulatory network between miRNAs and DNA methylation. The mRNA, miRNA, and DNA methylation expression profiles of healthy or osteoarthritis articular cartilage samples were downloaded from Gene Expression Omnibus (GEO) database, including GSE169077, GSE175961, and GSE162484. The differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs), and differentially methylated genes (DMGs) were analyzed by the online tool GEO2R. DAVID and STRING databases were applied for functional enrichment analysis and protein-protein interaction (PPI) network. Potential therapeutic compounds for the treatment of OA were identified by Connectivity map (CMap) analysis. A total of 1424 up-regulated DEGs, 1558 down-regulated DEGs, 5 DEMs with high expression, 6 DEMs with low expression, 1436 hypermethylated genes, and 455 hypomethylated genes were selected. A total of 136 up-regulated and 65 downregulated genes were identified by overlapping DEGs and DEMs predicted target genes which were enriched in apoptosis and circadian rhythm. A total of 39 hypomethylated and 117 hypermethylated genes were obtained by overlapping DEGs and DMGs, which were associated with ECM receptor interactions and cellular metabolic processes, cell connectivity, and transcription. Moreover, The PPI network showed COL5A1, COL6A1, LAMA4, T3GAL6A, and TP53 were the most connective proteins. After overlapping of DEGs, DMGs and DEMs predicted targeted genes, 4 up-regulated genes and 11 down-regulated genes were enriched in the Axon guidance pathway. The top ten genes ranked by PPI network connectivity degree in the up-regulated and downregulated overlapping genes of DEGs and DMGs were further analyzed by the CMap database, and nine chemicals were predicted as potential drugs for the treatment of OA. In conclusion, TP53, COL5A1, COL6A1, LAMA4, and ST3GAL6 may play important roles in OA genesis and development.

DPADM: a novel algorithm for detecting drug-pathway associations based on high-throughput transcriptional response to compounds

Pathway genes functionally participate in the same biological process. They typically act cooperatively, and none is considered dispensable. The dominant paradigm in drug discovery is the one-to-one strategy, which aims to find the most sensitive drug to act on an individual target. However, many complex diseases, such as cancer, are caused by dysfunction among multiple-gene pathways, not just one. Therefore, identifying pathway genes that are responsive to synthetic compounds in a global physiological environment may be more effective in drug discovery. The high redundancy of crosstalk between biological pathways, though, hints that the covariance matrix, which only connects genes with strong marginal correlations, may miss higher-level interactions, such as group interactions. We herein report the development of DPADM-a Drug-Pathway association Detection Model that infers pathways responsive to specific drugs. This model elucidates higher-level gene-gene interactions by evaluating the conditional dependencies between genes under different drug treatments. The advantage of the proposed method is demonstrated using simulation studies by comparing with another two methods. We applied this model to the Connectivity Map data set (CMap), and demonstrated that DPADM is able to identify many drug-pathway associations, such as mitoxantrone (MTX)- PI3K/AKT association, which targets the topological conditions of DNA transcription. Surprisingly, apart from identifying pathways corresponding to specific drugs, our methodology also revealed new drug-related pathways with functions similarly to those of seed genes.

Identification of potential biomarkers and pathogenesis in neutrophil-predominant severe asthma: A comprehensive bioinformatics analysis

BACKGROUND

Airway neutrophilia has been associated with asthma severity and asthma exacerbations. This study attempted to identify biomarkers, pathogenesis, and therapeutic molecular targets for severe asthma in neutrophils using bioinformatics analysis.

METHODS

Fifteen healthy controls and 3 patients with neutrophilic severe asthma were screened from the Gene Expression Omnibus (GEO) database. Based on the analysis of differentially expressed genes (DEGs), functional and pathway enrichment analyses, gene set enrichment analysis, protein-protein interaction network construction, and analysis were performed. Moreover, small-molecule drug candidates have also been identified.

RESULTS

Three hundred and three upregulated and 59 downregulated genes were identified. Gene ontology function enrichment analyses were primarily related to inflammatory response, immune response, leukocyte migration, neutrophil chemotaxis, mitogen-activated protein kinase cascade, Jun N-terminal kinase cascade, I-kappaB kinase/nuclear factor-κB, and MyD88-dependent toll-like receptor signaling pathway. Pathway enrichment analyses and gene set enrichment analysis were mainly involved in cytokine-cytokine receptor interaction, the TNF signaling pathway, leukocyte transendothelial migration, and the NOD-like receptor signaling pathway. Furthermore, 1 important module and 10 hub genes (CXCL8, TLR2, CXCL1, ICAM1, CXCR4, FPR2, SELL, PTEN, TREM1, and LEP) were identified in the protein-protein interaction network. Moreover, indoprofen, mimosine, STOCK1N-35874, trapidil, iloprost, aminoglutethimide, ajmaline, levobunolol, ethionamide, cefaclor, dimenhydrinate, and bethanechol are potential drugs for the treatment of neutrophil-predominant severe asthma.

CONCLUSION

This study identified potential biomarkers, pathogenesis, and therapeutic molecular targets for neutrophil-predominant severe asthma.

Chromatin Separation Regulators Predict the Prognosis and Immune Microenvironment Estimation in Lung Adenocarcinoma

Background: Abnormal chromosome segregation is identified to be a common hallmark of cancer. However, the specific predictive value of it in lung adenocarcinoma (LUAD) is unclear.

Method: The RNA sequencing and the clinical data of LUAD were acquired from The Cancer Genome Atlas (TACG) database, and the prognosis-related genes were identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were carried out for functional enrichment analysis of the prognosis genes. The independent prognosis signature was determined to construct the nomogram Cox model. Unsupervised clustering analysis was performed to identify the distinguishing clusters in LUAD-samples based on the expression of chromosome segregation regulators (CSRs). The differentially expressed genes (DEGs) and the enriched biological processes and pathways between different clusters were identified. The immune environment estimation, including immune cell infiltration, HLA family genes, immune checkpoint genes, and tumor immune dysfunction and exclusion (TIDE), was assessed between the clusters. The potential small-molecular chemotherapeutics for the individual treatments were predicted via the connectivity map (CMap) database.

Results: A total of 2,416 genes were determined as the prognosis-related genes in LUAD. Chromosome segregation is found to be the main bioprocess enriched by the prognostic genes. A total of 48 CSRs were found to be differentially expressed in LUAD samples and were correlated with the poor outcome in LUAD. Nine CSRs were identified as the independent prognostic signatures to construct the nomogram Cox model. The LUAD-samples were divided into two distinct clusters according to the expression of the 48 CSRs. Cell cycle and chromosome segregation regulated genes were enriched in cluster 1, while metabolism regulated genes were enriched in cluster 2. Patients in cluster 2 had a higher score of immune, stroma, and HLA family components, while those in cluster 1 had higher scores of TIDES and immune checkpoint genes. According to the hub genes highly expressed in cluster 1, 74 small-molecular chemotherapeutics were predicted to be effective for the patients at high risk.

Conclusion: Our results indicate that the CSRs were correlated with the poor prognosis and the possible immunotherapy resistance in LUAD.

The Common Cellular Events in the Neurodegenerative Diseases and the Associated Role of Endoplasmic Reticulum Stress

Neurodegenerative diseases are inseparably linked with aging and increase as life expectancy extends. There are common dysfunctions in various cellular events shared among neurogenerative diseases, such as calcium dyshomeostasis, neuroinflammation, and age-associated decline in the autophagy-lysosome system. However, most of all, the prominent pathological feature of neurodegenerative diseases is the toxic buildup of misfolded protein aggregates and inclusion bodies accompanied by an impairment in proteostasis. Recent studies have suggested a close association between endoplasmic reticulum (ER) stress and neurodegenerative pathology in cellular and animal models as well as in human patients. The contribution of mutant or misfolded protein-triggered ER stress and its associated signaling events, such as unfolded protein response (UPR), to the pathophysiology of various neurodegenerative disorders, including Alzheimer's, Parkinson's, and Huntington's disease, amyotrophic lateral sclerosis, and prion disease, is described here. Impaired UPR action is commonly attributed to exacerbated ER stress, pathogenic protein aggregate accumulation, and deteriorating neurodegenerative pathologies. Thus, activating certain UPR components has been shown to alleviate ER stress and its associated neurodegeneration. However, uncontrolled activation of some UPR factors has also been demonstrated to worsen neurodegenerative phenotypes, suggesting that detailed molecular mechanisms around ER stress and its related neurodegenerations should be understood to develop effective therapeutics against aging-associated neurological syndromes. We also discuss current therapeutic endeavors, such as the development of small molecules that selectively target individual UPR components and address ER stress in general.

Endoplasmic Reticulum (ER) Stress and Its Role in Pancreatic β-Cell Dysfunction and Senescence in Type 2 Diabetes

An increased life span and accompanying nutritional affluency have led to a rapid increase in diseases associated with aging, such as obesity and type 2 diabetes, imposing a tremendous economic and health burden on society. Pancreatic β-cells are crucial for controlling glucose homeostasis by properly producing and secreting the glucose-lowering hormone insulin, and the dysfunction of β-cells determines the outcomes for both type 1 and type 2 diabetes. As the native structure of insulin is formed within the endoplasmic reticulum (ER), ER homeostasis should be appropriately maintained to allow for the proper metabolic homeostasis and functioning of β-cells. Recent studies have found that cellular senescence is critically linked with cellular stresses, including ER stress, oxidative stress, and mitochondrial stress. These studies implied that β-cell senescence is caused by ER stress and other cellular stresses and contributes to β-cells’ dysfunction and the impairment of glucose homeostasis. This review documents and discusses the current understanding of cellular senescence, β-cell function, ER stress, its associated signaling mechanism (unfolded protein response), and the effect of ER stress on β-cell senescence and dysfunction.

Targeting macrophage TFEB-14-3-3 epsilon Interface by naringenin inhibits abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a lethal cardiovascular disease, and there is no proven drug treatment for this condition. In this study, by using the Connectivity Map (CMap) approach, we explored naringenin, a naturally occurring citrus flavonoid, as a putative agent for inhibiting AAA. We then validated the prediction with two independent mouse models of AAA, calcium phosphate (CaPO₄)-induced C57BL/6J mice and angiotensin II-infused ApoE^−/− mice. Naringenin effectively blocked the formation of AAAs and the progression of established AAAs. Transcription factor EB (TFEB) is the master regulator of lysosome biogenesis. Intriguingly, the protective role of naringenin on AAA was abolished by macrophage-specific TFEB depletion in mice. Unbiased interactomics, combined with isothermal titration calorimetry (ITC) and cellular thermal shift assays (CETSAs), further revealed that naringenin is directly bound to 14-3-3 epsilon blocked the TFEB-14-3-3 epsilon interaction, and therefore promoted TFEB nuclear translocation and activation. On one hand, naringenin activated lysosome-dependent inhibition of the NLRP3 inflammasome and repressed aneurysmal inflammation. On the other hand, naringenin induced TFEB-dependent transcriptional activation of GATA3, IRF4, and STAT6 and therefore promoted reparative M2 macrophage polarization. In summary, naturally derived naringenin or macrophage TFEB activation shows promising efficacy for the treatment of AAA.

Epigenetic Biomarkers Screening of Non-Coding RNA and DNA Methylation Based on Peripheral Blood Monocytes in Smokers

This study aims to use bioinformatics methods to determine the epigenetic changes in microRNA expression and DNA methylation caused by cigarette smoking. The data of mRNA, miRNA expression, and methylation microarray were obtained from the GEO database to filter differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs), and methylated CpG probes (DMPs) through the limma package. The R clusterProfile package was used for functional annotation and enrichment analysis. The protein-protein interaction (PPI) network was constructed by the String database and visualized in Cytoscape software. Starbase database was employed to predict lncRNA and CirRNA based on the sequence of miRNA, and to establish a regulatory network of ceRNA. By overlapping DEG and DEM, 107 down-miRNA-targeted up-regulated genes and 65 up-miRNA-target down-regulated genes were obtained, which were mainly enriched in autophagy signaling pathways and protein ubiquitination pathways, respectively. In addition, 324 genes with low methylation and high expression and 204 genes with high methylation and low expression were respectively related to the degeneration of the nervous system and the function of the cardiovascular system. Interestingly, 43 genes were up-regulated under the dual regulation of reduced miRNA and hypomethylation, while 14 genes were down-regulated under the dual regulation of increased miRNA and hypermethylation. Ten chemicals have been identified as putative therapeutic agents for pathological conditions caused by smoking. In addition, among these genes, HSPA4, GRB2, PRKCA, and BCL2L1 could play a fundamental role in related diseases caused by smoking and may be used as the biomarkers for precise diagnosis and targets for future therapies of smoking-related diseases.

Prognostic Signatures and Therapeutic Value Based on the Notch Pathway in Renal Clear Cell Carcinoma

The Notch family of genes encodes a group of highly conserved cell surface membrane receptors, which are involved in one of the key pathways that determine cell growth, differentiation, and apoptosis in embryonic tissues. Furthermore, abnormal expression of Notch genes is closely related to the occurrence and development of several cancers. To date, no specific treatment of RCC has been reported to relate to the Notch pathway. Therefore, we detected Notch pathway genes in series of tumors, as well as potential compounds targeting the Notch pathway, with a focus on the mechanism of Notch pathway action in kidney renal clear cell carcinoma (KIRC). Samples from KIRC patients were divided into three clusters based on the mRNA expression of Notch pathway genes. In addition, we investigated the potential targets of the Notch pathway, predicted the IC₅₀ of several classical targeted therapies, and analyzed their correlation with the Notch pathway. Finally, LASSO regression analysis was performed to build a model to predict survival in KIRC patients. These results suggest that therapies targeting the Notch pathway could be more efficiently studied based on the Notch score and that we can predict the prognosis of patients with KIRC based on the expression of Notch pathway genes. Most importantly, these results may provide a solid theoretical basis for future research on therapeutic targets for patients with KIRC.

Bioinformatics analyses of the pathogenesis and new biomarkers of chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is one of the major cause of global death. The purpose of our analysis was to detect a more reliable biomarker and small-molecule drug candidates and to identify the precise mechanisms involved in COPD.

METHODS

Three data sets were downloaded from the Gene Expression Omnibus database and analysed by Gene Expression Omnibus 2R. Functional enrichment analyses were performed by Metascape. We use the STRING data to build a protein-protein interaction network. The targets of differentially expressed microRNA (DE miRNA) were predicted by the miRWalk database. Small-molecule drugs were predicted on connectivity map.

RESULTS

A total of 181 differentially expressed genes and 35 DE miRNAs were confirmed. The protein-protein interaction network including all integrated differentially expressed genes was constructed, and 4 modules were filtrated. The module genes were relative to immune, inflammatory and oxidative stress functions according to a pathway analysis. The top 20 key genes were screened. Among the DE miRNAs found to be regulating key genes, miR-194-3p, MiR-502-5p, MiR-5088-5p, MiR-3127-5p, and miR-23a-5p might be the most significant due to their high number of connecting nodes in COPD. In addition, cephaeline, emetine, gabapentin, and amrinone were found to be potential drugs to treat COPD patients.

CONCLUSION

Our study suggests that miR-194-3p, miR-502-5p, and miR-23a-5p might participate in the nosogenesis of COPD. In addition, 4 potential small-molecule drugs were considered potentially useful for treating COPD patients.

Screening and Identification of Hub Genes in the Corticosteroid Resistance Network in Human Airway Epithelial Cells via Microarray Analysis

Background and Objective: Corticosteroid resistance is a major barrier to chronic obstructive pulmonary disease (COPD), but the exact mechanism of corticosteroid resistance in COPD has been less well studied. Methods: The microarray dataset GSE11906, which includes genomic and clinical data on COPD, was downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified using R software. Gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes (KEGG) were utilized to enrich and analyze the gene cohort related to the response to steroid hormones, respectively. The Connectivity Map (CMap) database was used to screen corticosteroid resistance-related drugs that might exert a potential therapeutic effect. STRING was used to construct a protein-protein interaction (PPI) network of the gene cohort, and the CytoHubba plug-in of Cytoscape was used to screen the hub genes in the PPI network. The expression levels of hub genes in cigarette smoke extract (CSE)-stimulated bronchial epithelial cells were assayed by quantitative real-time PCR and western blotting. Results: Twenty-one genes were found to be correlated with the response to steroid hormones. In the CMap database, 32 small-molecule compounds that might exert a therapeutic effect on corticosteroid resistance in COPD were identified. Nine hub genes were extracted from the PPI network. The expression levels of the BMP4, FOS, FN1, EGFR, and SPP1 proteins were consistent with the microarray data obtained from molecular biology experiments. Scopoletin significantly restrained the increases in the levels of AKR1C3, ALDH3A1, FN1 and reversed the decreases of phosphorylated GR and HDAC2 caused by CSE exposure. Conclusion: The BMP4, FOS, FN1, EGFR, and SPP1 genes are closely correlated with CSE-induced glucocorticoid resistance in airway epithelial cells. Scopoletin may be a potential drug for the treatment of glucocorticoid resistance caused by CSE.

Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug-target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

Genomic and Transcriptome Analysis to Identify the Role of the mTOR Pathway in Kidney Renal Clear Cell Carcinoma and Its Potential Therapeutic Significance

The mTOR pathway, a major signaling pathway, regulates cell growth and protein synthesis by activating itself in response to upstream signals. Overactivation of the mTOR pathway may affect the occurrence and development of cancer, but no specific treatment has been proposed for targeting the mTOR pathway. In this study, we explored the expression of mTOR pathway genes in a variety of cancers and the potential compounds that target the mTOR pathway and focused on an abnormal type of cancer, kidney renal clear cell carcinoma (KIRC). Based on the mRNA expression of the mTOR pathway gene, we divided KIRC patient samples into three clusters. We explored possible therapeutic targets of the mTOR pathway in KIRC. We predicted the IC50 of some classical targeted drugs to analyze their correlation with the mTOR pathway. Subsequently, we investigated the correlation of the mTOR pathway with histone modification and immune infiltration, as well as the response to anti-PD-1 and anti-CTLA-4 therapy. Finally, we used a LASSO regression analysis to construct a model to predict the survival of patients with KIRC. This study shows that mTOR scores can be used as tools to study various treatments targeting the mTOR pathway and that we can predict the recovery of KIRC patients through the expression of mTOR pathway genes. These research results can provide a reference for future research on KIRC patient treatment strategies.

The Advantages of Connectivity Map Applied in Traditional Chinese Medicine

Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.

Crosstalk between endoplasmic reticulum stress and oxidative stress: Focus on protein disulfide isomerase and endoplasmic reticulum oxidase 1

Cellular stress and inflammation, establishing as disease pathology, have reached great heights in the last few decades. Stress conditions such as hyperglycemia, hyperlipidemia and lipoproteins are known to disturb proteostasis resulting in the accumulation of unfolded or misfolded proteins, alteration in calcium homeostasis culminating in unfolded protein response. Protein disulfide isomerase and endoplasmic reticulum oxidase-1 are the key players in protein folding. The protein folding process assisted by endoplasmic reticulum oxidase-1 results in the production of reactive oxygen species in the lumen of the endoplasmic reticulum. Production of reactive oxygen species beyond the quenching capacity of the antioxidant systems perturbs ER homeostasis. Endoplasmic reticulum stress also induces the production of cytokines leading to inflammatory responses. This has been proven to be the major causative factor for various pathophysiological states compared to other cellular triggers in diseases, which further manifests to increased oxidative stress, mitochondrial dysfunction, and altered inflammatory responses, deleterious to cellular physiology and homeostasis. Numerous studies have drawn correlations between the progression of several diseases in association with endoplasmic reticulum stress, redox protein folding, oxidative stress and inflammatory responses. This review aims to provide an insight into the role of protein disulfide isomerase and endoplasmic reticulum oxidase-1 in endoplasmic reticulum stress, unfolded protein response, mitochondrial dysfunction, and inflammatory responses, which exacerbate the progression of various diseases.

Identification of Osteosarcoma Metastasis-Associated Gene Biomarkers and Potentially Targeted Drugs Based on Bioinformatic and Experimental Analysis

BACKGROUND

Metastasis is the leading cause of death for patients with osteosarcoma (OS). In the present study, we explore the biomarkers for metastatic OS and provide potential therapeutic approaches.

MATERIALS AND METHODS

RNA-Seq data and clinical follow-up information were downloaded from TARGET and GEO databases. A Cox regression model was used to analyze metastatic events. L1000FWD, DGIdb, and CMap databases were used to identify potential drugs related to metastasis. Invasion and migration transwell assays and an adhesion assay were used to identify biological functions of genes.

RESULTS

A total of 15 metastasis-related signatures (MRSs) were associated with the prognosis based on the TARGET or GSE21257 cohorts, among which IL10RA and TLR7 genes were especially significant. In the DGIdb drug-gene interaction database, TLR7 and IFNGR1 were found to have potential interactions with drugs. After inhibiting the expression of TLR7, the migration, invasion, and adhesion ability of OS cells were significantly enhanced, which further promoted metastasis.

CONCLUSION

We identified a set of MRS that may be related to OS metastases. Among them, TLR7 plays a vital role and may be a potential target for OS metastasis treatment.

Exploring the Regulation Mechanism of Xihuang Pill, Olibanum and β-Boswellic Acid on the Biomolecular Network of Triple-Negative Breast Cancer Based on Transcriptomics and Chemical Informatics Methodology

Background

Xihuang Pill (XHP) is mainly used to treat “Ru Yan (breast cancer)”. Evidence-based medical evidence and showed that XHP improves the efficacy of chemotherapy and reduced chemotherapy-induced toxicity in breast cancer patients. However, the mechanism of XHP against breast cancer is not clear.

Methods

The effect of XHP extract on cell half-inhibitory concentration (IC50) and cell viability of MD-MB-231 cells was detected by CCK-8 method. The cell inhibition rate of MDA-MB-453 cells were detected by MTT method. Apoptosis was detected by flow cytometry, cell transfer ability was detected by Transwell method, and cell proliferation ability was detected by colony formation assay. The expression of Notch1, β-catenin and c-myc mRNA in MDA-MB-453 cells were detected by real-time fluorescence quantitative PCR. Then, chemical informatics and transcriptomics methodology was utilized to predict the potential compounds and targets of XHP, and collect triple negative breast cancer (TNBC) genes and the data of Olibanum and β-boswellic acid intervention MD-MB-231 cells (from GSE102891). The cytoscape software was utilized to undergo network construction and network analysis. Finally, the data from the network analysis was imported into the DAVID database for enrichment analysis of signaling pathways and biological processes.

Results

The IC50 was 15.08 g/L (for MD-MB-231 cells). After interfering with MD-MB-231 cells with 15.08 g/L XHP extract for 72 h, compared with the control group, the cell viability, migration and proliferation was significantly decreased, while early apoptosis and late apoptosis were significantly increased (P < 0.01). After interfering with MDA-MB-453 cells with 6 g/L XHP extract for 72 h, compared with the control group, the cell inhibition and apoptosis rate increased, while the expression of Notch1, β-catenin and c-myc mRNA decreased. (P < 0.05). The chemical informatics and transcriptomics analysis showed that four networks were constructed and analyzed: (1) potential compounds-potential targets network of XHP; (2) XHP-TNBC PPI network; (3) DEGs PPI network of Olibanum-treated MD-MB 231 cells; (4) DEGs PPI network of β-boswellic acid -treated MD-MB 231 cells. Several anti-TNBC biological processes, signaling pathways, targets and so on were obtained.

Conclusion

XHP may exert anti-TNBC effects through regulating biological processes, signaling pathways, targets found in this study.