CMap Analysis Research Articles

High expression of serine and arginine-rich splicing factor 9 (SRSF9) is associated with hepatocellular carcinoma progression and a poor prognosis

BackgroundSerine and arginine-rich splicing factor 9 (SRSF9) has been linked to the occurrence and progression of various cancers; however, its effects and mechanism of action hepatocellular carcinoma (HCC) have not been reported. In this study, we used a bioinformatics approach and in vitro assays to evaluate the expression of SRSF9 in HCC, its prognostic value, and its underlying regulatory mechanisms, including analyses of related pathways and the role of methylation.MethodsTranscriptomic and DNA methylation data for 357 HCC cases and 50 paratumor tissues in The Cancer Genome Atlas database were obtained. Additionally, protein expression data for cell lines and tissue samples were obtained from the Human Protein Atlas. The CMap databased was used to predict candidate drugs targeting SRSF9. Various cell lines were used for in vitro validation.ResultsSRSF9 expression was significantly elevated in HCC and was negatively regulated by its methylation site cg06116271. The low expression of SRSF9 and hypermethylation of cg06116271 were both associated with a longer overall survival time. A correlation analysis revealed ten genes that were co-expressed with SRSF9; levels of CDK4, RAN, DENR, RNF34, and ANAPC5 were positively correlated and levels of RBP4, APOC1, MASP2, HP, and HPX were negatively correlated with SRSF9 expression. The knockdown of SRSF9 in vitro inhibited the proliferation and migration of HCC cells and significantly reduced the expression of proteins in the Wnt signaling pathway (DVL2 and β-catenin) and cell cycle pathway (Cyclin D and Cyclin E). A CMap analysis identified two drugs, camptothecin and apigenin, able to target and inhibit the expression of SRSF9.ConclusionsThis study expands our understanding of the molecular biological functions of SRSF9 and cg06116271 and provides candidate diagnostic and therapeutic targets for HCC.

Identification of biomarkers, pathways and potential therapeutic agents for salt-sensitive hypertension using RNA-seq.

BackgroundSalt-sensitive hypertension (SSH) is a common type of essential hypertension in China. In recent years, although an increasing number of researches have focused on SSH, few studies have been researched on patients with SSH. The objective of this study was to explore the genes and pathways linked with SSH using RNA-sequencing (RNA-seq).Materials and methodsWe used RNA-seq to analyze the transcriptome of peripheral blood mononuclear cells (PBMCs) of five SSH patients and five SRH patients. Next, we analyzed the differentially expressed genes (DEGs) using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Set Enrichment (GSEA) enrichment analysis. Then, Cytoscape was used to construct the protein-protein interaction (PPI) network and the hub genes. Finally, CMAP analysis found that several small molecular compounds could reverse the altered DEGs.ResultsA total of 431 DEGs were found in the PBMC samples, including 294 up-regulated and 137 down-regulated genes. Functional enrichment analysis found significant enrichment in immune-related associations such as inflammation, chemokine, and cytokine-cytokine receptor interaction. The hub genes of the two modules were IL-6, IL-1A, CCL2, CCL3L3, and BUB1. In addition, we identified two small molecular compounds (iopromide and iloprost) that potentially interacted with DEGs.ConclusionThis study suggests some potential biomarkers for the diagnosis of SSH. It provides new insights into SSH diagnosis and possible future clinical treatment.

GNG12 as A Novel Molecular Marker for the Diagnosis and Treatment of Glioma.

PurposeGNG12 influences a variety of tumors; however, its relationship with glioma remains unclear. The aim of this study was to comprehensively investigate the relationship between GNG12 and the clinical characteristics and prognosis of glioma patients and reveal the mechanisms causing the malignant process of GNG12.Materials and MethodsWe obtained information on clinical samples from multiple databases. The expression level of GNG12 was validated using a RT-qPCR and IHC. KM curves were used to assess the correlation between the GNG12 expression and OS of glioma patients. An ROC curve was drawn to assess the predictive performance of GNG12. Univariate and multivariate Cox analyses were performed to analyze the factors affecting the prognosis of patients with glioma. GSEA and TIMER databases were used to estimate the relationship between GNG12 expression, possible molecular mechanisms, and immune cell infiltration. CMap analysis was used to screen candidate drugs for glioma. Subsequent in vitro experiments were used to validate the proliferation and migration of glioma cells and to explore the potential mechanisms by which GNG12 causes poor prognosis in gliomas.ResultsGNG12 was overexpressed in glioma patients and GNG12 expression level correlated closely with clinical features, including age and histological type, etc. Subsequently, the K-M survival analysis indicated that the expression level of GNG12 was relevant to the prognosis of glioma, and the ROC curve implied that GNG12 can predict glioma stability. Univariate and multivariate analyses showed that GNG12 represents a risk factor for glioma occurrence. GNG12 expression is closely associated with some immune cells. Additionally, several in vitro experiments demonstrated that down-regulation of GNG12 expression can inhibits the proliferation and migration capacity of glioma cells. Ultimately, the results for the GSEA and WB experiments revealed that GNG12 may promote the malignant progression of gliomas by regulating the cell adhesion molecule cell signaling pathway.ConclusionIn this study, we identified GNG12 as a novel oncogene elevated in gliomas. Reducing GNG12 expression inhibits the proliferation and migration of glioma cells. In summary, GNG12 can be used as a novel biomarker for the early diagnosis of human gliomas and as a potential therapeutic target.

A multiomics and network pharmacological study reveals the neuroprotective efficacy of Fu-Fang-Dan-Zhi tablets against glutamate-induced oxidative cell death

Neuroprotective therapy after ischemic stroke remains a significant need, but current measures are still insufficient. The Fu-Fang-Dan-Zhi tablet (FFDZT) is a proprietary Chinese medicine clinically employed to treat ischemic stroke in the recovery period. This work aims to systematically investigate the neuroprotective mechanism of FFDZT. A systems strategy that integrated metabolomics, transcriptomics, network pharmacology, and in vivo and in vitro experiments was used. First, middle cerebral artery occlusion (MCAO) model rats were treated with FFDZT. FFDZT treatment significantly reduced the infarct volume in the brains of middle cerebral artery occlusion (MCAO) model rats. Then, samples of serum and brain tissue were taken for metabolomics and transcriptomics studies, respectively; gene expression profiles of MCF7 cells treated with FFDZT and its 4 active compounds (senkyunolide I, formononetin, drilodefensin, and tanshinone IIA) were produced for CMAP analysis. Computational analysis of metabolomics and transcriptomics results suggested that FFDZT regulated glutamate and oxidative stress-related metabolites (2-hydroxybutanoic acid and 2-hydroxyglutaric acid), glutamate receptors (NMDAR, KA, and AMPA), glutamate involved pathways (glutamatergic synapse pathway; d-glutamine and d-glutamate metabolism; alanine, aspartate and glutamate metabolism), as well as the reactive oxygen species metabolic process. CMAP analysis indicated that two active ingredients of FFDZT (tanshinone ⅡA and senkyunolide I) could act as glutamate receptor antagonists. Next, putative therapeutic targets of FFDZT's active ingredients identified in the brain were collected from multiple resources and filtered by statistical criteria and tissue expression information. Network pharmacological analysis revealed extensive interactions between FFDZT's putative targets, anti-IS drug targets, and glutamate-related enzymes, while the resulting PPI network exhibited modular topology. The targets in two of the modules were significantly enriched in the glutamatergic synapse pathway. The interactions between FFDZT's ingredients and important targets were verified by molecular docking. Finally, in vitro experiments validated the effects of FFDZT and its ingredients in suppressing glutamate-induced PC12 cell injury and reducing the generation of reactive oxygen species. All of our findings indicated that FFDZT's efficacy for treating ischemic stroke could be due to its neuroprotection against glutamate-induced oxidative cell death.

Novel Drug Candidate Prediction for Intrahepatic Cholangiocarcinoma via Hub Gene Network Analysis and Connectivity Mapping

Simple SummaryOnly about 15% of intrahepatic cholangiocarcinoma (ICC) patients meet the criteria for resection at the time of diagnosis. For patients with advanced and/or metastatic disease, the development of novel therapeutic strategies is urgently needed. The aim of our study was to identify possible novel therapeutic targets and drugs for ICC by using transcriptomic profiles from the Gene Expression Omnibus databases and The Cancer Genome Atlas. The weighted co-expression gene network was constructed to screen hub genes. Potential drug candidates with promise in the treatment of ICC were identified by analyzing key protein–protein interaction (PPI) networks of the hub genes to identify potential interacting drugs based on the Connectivity Map database.Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy, and there is a need for effective systemic therapies. Gene expression profile-based analyses may allow for efficient screening of potential drug candidates to serve as novel therapeutics for patients with ICC. The RNA expression profile of ICC and normal biliary epithelial cells were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Function annotation and enrichment pathway analyses of the differentially expressed genes (DEGs) were finished using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. A weighted gene co-expression network (WGCN) was constructed by WGCN analysis (WGCNA). Key genes from the DEGs and co-expression gene modules were analyzed to generate a protein–protein interaction (PPI) network. The association between the top 10 screened hub genes and the overall and disease-free survival of ICC patients was examined. The Connectivity Map (cMap) analysis was performed to identify possible drugs for ICC using hub genes. A total of 151 key genes were selected from the overlapping genes of 1287 GSE-DEGs, 8183 TCGA-DEGs and 1226 genes in the mixed modules. A total of 10 hub genes of interest (CTNNB1, SPP1, COL1A2, COL3A1, SMAD3, SRC, VCAN, PKLR, GART, MRPS5) were found analyzing protein–protein interaction. Using the cMap, candidate drugs screened with potential efficacy for ICC included three tyrosine kinase inhibitors (dasatinib, NVP-BHG712, tivantinib), two cannabinoid receptor agonists (palmitoylethanolamide, arachidonamide), two antibiotics (moxifloxacin, amoxicillin), one estrogen receptor agonist (levonorgestrel), one serine/threonine protein kinase inhibitor (MK-2206) and other small molecules. Key genes from network and PPI analysis allowed us to identify potential drugs for ICC. The identification of novel gene expression profiles and related drug screening may accelerate the identification of potential novel drug therapies for ICC.

An Immune Signature for Risk Stratification and Therapeutic Prediction in Helicobacter pylori-Infected Gastric Cancer.

Simple SummaryHelicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC), and over half of the world’s population is colonized with HP. Up to now, many gene signatures are designed for predicting the prognosis of GC patients, while there are no signatures designed specifically for HP+ GC patients. Considering the tight association between HP infection and tumor immune microenvironment, we constructed an immune-related signature named IRSHG in this study, aiming to provide new insight into the treatment for HP+ GC patients. IRSHG was the first prognostic signature for HP+ GC patients, demonstrating high reliability and feasibility in predicting the prognosis. In addition, IRSHG could help investigate potential therapies and guide anti-PD-1 immunotherapy for HP+ GC patients, providing new insight for the personalized treatment of GC.Helicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC). Increasing evidence has clarified that tumor immune microenvironment (TIME) is closely related to the prognosis and therapeutic efficacy of HP-positive (HP+) GC patients. In this study, we aimed to construct a novel immune-related signature for predicting the prognosis and immunotherapy efficacy of HP+ GC patients. A total of 153 HP+ GC from three different cohorts were included in this study. An Immune-Related prognostic Signature for HP+ GC patients (IRSHG) was established using Univariate Cox regression, the LASSO algorithm, and Multivariate Cox regression. Univariate and Multivariate analyses proved IRSHG was an independent prognostic predictor for HP+ GC patients, and an IRSHG-integrated nomogram was established to quantitatively assessthe prognostic risk. The low-IRSHG group exhibited higher copy number load and distinct mutation profiles compared with the high-IRSHG group. In addition, the difference of hallmark pathways and immune cells infiltration between the two groups was investigated. Notably, tumor immune dysfunction and exclusion (TIDE) analysis indicated that the low-IRSHG group had a higher sensitivity to anti-PD-1 immunotherapy, which was validated by an external pabolizumab treatment cohort. Moreover, 98 chemotherapeutic drugs and corresponding potential biomarkers were identified for two groups, and several drugs with potential ability to reverse IRSHG score were identified using CMap analysis. Collectively, IRSHG may serve as a promising biomarker for survival outcome as well as immunotherapy efficacy. Furthermore, it can also help to prioritize potential therapeutics for HP+ GC patients, providing new insight for the personalized treatment of HP-infected GC.

Identification of Hub Genes and Therapeutic Agents for IgA Nephropathy Through Bioinformatics Analysis and Experimental Validation.

BackgroundIgA nephropathy (IgAN) is the most common primary glomerular disease and the leading cause of the end-stage renal disease in the world. The pathogenesis of IgAN has not been well elucidated, and yet treatment is limited. High-throughput microarray has been applied for elucidating molecular biomarkers and potential mechanisms involved in IgAN. This study aimed to identify the potential key genes and therapeutics associated with IgAN using integrative bioinformatics and transcriptome-based computational drug repurposing approach.MethodsThree datasets of mRNA expression profile were obtained from the gene expression omnibus database and differentially expressed genes (DEGs) between IgAN glomeruli and normal tissue were identified by integrated analysis. Gene ontology and pathway enrichment analyses of the DEGs were performed by R software, and protein-protein interaction networks were constructed using the STRING online search tool. External dataset and immunohistochemical assessment of kidney biopsy specimens were used for hub gene validation. Potential compounds for IgAN therapy were obtained by Connectivity Map (CMap) analysis and preliminarily verified in vitro. Stimulated human mesangial cells were collected for cell proliferation and cell cycle analysis using cell counting kit 8 and flow cytometry, respectively.Results134 DEGs genes were differentially expressed across kidney transcriptomic data from IgAN patients and healthy living donors. Enrichment analysis showed that the glomerular compartments underwent a wide range of interesting pathological changes during kidney injury, focused on anion transmembrane transporter activity and protein digestion and absorption mostly. Hub genes (ITGB2, FCER1G, CSF1R) were identified and verified to be significantly upregulated in IgAN patients, and associated with severity of renal lesions. Computational drug repurposing with the CMap identified tetrandrine as a candidate treatment to reverse IgAN hub gene expression. Tetrandrine administration significantly reversed mesangial cell proliferation and cell cycle transition.ConclusionThe identification of DEGs and related therapeutic strategies of IgAN through this integrated bioinformatics analysis provides a valuable resource of therapeutic targets and agents of IgAN. Especially, our findings suggest that tetrandrine might be beneficial for IgAN, which deserves future research.

Repurposing of ingenol mebutate for treating human colorectal cancer by targeting S100 calcium-binding protein A4 (S100A4)

Colorectal cancer (CRC) is the world's second most common cause of cancer-related death. Novel treatments are still urgently needed. S100 calcium-binding protein A4 (S100A4) was demonstrated to be an anticancer therapeutic target. Herein, we found that higher S100A4 expression was associated with a poorer prognosis in publicly available cohorts and a Taiwanese CRC patient cohort. To identify repurposed S100A4 inhibitors, we mined the Connectivity Map (CMap) database for clinical drugs mimicking the S100A4-knockdown gene signature. Ingenol mebutate, derived from the sap of the plant Euphorbia peplus, is approved as a topical treatment for actinic keratosis. The CMap analysis predicted ingenol mebutate as a potent S100A4 inhibitor. Indeed, both messenger RNA and protein levels of S100A4 were attenuated by ingenol mebutate in human CRC cells. In addition, CRC cells with higher S100A4 expressions and/or the wild-type p53 gene were more sensitive to ingenol mebutate, and their migration and invasion were inhibited by ingenol mebutate. Therefore, our results suggest the repurposing of ingenol mebutate for treating CRC by targeting S100A4.

Increased ATP2A1 Predicts Poor Prognosis in Patients With Colorectal Carcinoma.

Colorectal cancer is one of the most common malignant tumors in the digestive system. Traditional diagnosis and treatment methods have not significantly improved the overall survival of patients. In this study, we explored the value of ATP2A1 as a biomarker in predicting the prognosis of colorectal cancer patients. We used the TCGA database to reveal the relationship between ATP2A1 mRNA level and prognosis, methylation, and immune invasion in colorectal cancer. The results showed that the expression of ATP2A1 was increased in colorectal cancer. The overall survival of patients with high expression of ATP2A1 was significantly lower than patients with low expression of ATP2A1. Cox regression analysis showed that high expression of ATP2A1 was an independent risk factor for poor prognosis in colorectal cancer patients. In addition, we used three datasets to perform a meta-analysis, which further confirmed the reliability of the results. Furthermore, we revealed that ATP2A1 could significantly inhibit the proliferation of colorectal cancer cells by inhibiting the autophagy process and was associated with several immune cells, especially CD8 + T cells. Finally, four small molecule drugs with potential inhibition of ATP2A1 expression were found by CMap analysis. This study demonstrates for the first time that ATP2A1 is a potential pathogenic factor, which may play a significant role in colorectal cancer.

TGIF1 overexpression promotes glioma progression and worsens patient prognosis.

Transforming growth factor β-induced factor homeobox 1 (TGIF1) reportedly promotes the pathological processes of various malignant tumors. However, few studies have investigated the role of TGIF1 in gliomas. We aimed to explore the relationship between TGIF1 expression and the clinical characteristics of patients with glioma, including their overall survival. A total of thousands transcriptome datapoints were downloaded from public databases to determine the correlations between TGIF1 and various clinicopathological features using the Wilcoxon or Kruskal-Wallis tests. The Kaplan-Meier and Cox statistical methods were used to explore the prognostic significance of TGIF1. Gene set enrichment analysis (GSEA) was used to indirectly identify the pathological mechanisms modulated by TGIF1, and compounds that inhibit its expression were determined using a connectivity map (CMap). TGIF1 was significantly overexpressed in gliomas and was correlated with unfavorable prognostic factors and shorter overall survival. Cox analysis confirmed that TGIF1 expression was a significant predictor of poor prognosis in patients with glioma. GSEA revealed that the signaling pathways associated with TGIF1 expression in glioma included extracellular matrix receptor- and cell cycle-modulating proteins. CMap analysis showed that the small molecules scriptaid, torasemide, dexpropranolol, ipratropium bromide, and harmine were potential negative regulators of TGIF1. Finally, in vitro experiments demonstrated that knockdown of TGIF1 significantly inhibited the proliferation and invasion of glioma cell. Taken together, our study, which is the first to comprehensively analyze TGIF1 in gliomas, revealed it to be a novel oncogene in terms of its association with this disease. As such, TGIF1 may be a potential therapeutic target for individualized treatment of patients with glioma.

Exploring the Association between Glutathione Metabolism and Ferroptosis in Osteoblasts with Disuse Osteoporosis and the Key Genes Connecting them.

Disused osteoporosis is a kind of osteoporosis, a common age-related disease. Neurological disorders are major risk factors for osteoporosis. Though there are many studies on disuse osteoporosis, the genetic mechanisms for the association between glutathione metabolism and ferroptosis in osteoblasts with disuse osteoporosis are still unclear. The purpose of this study is to explore the key genes and other related mechanism of ferroptosis and glutathione metabolism in osteoblast differentiation and disuse osteoporosis. By weighted gene coexpression network analysis (WGCNA), the process of osteoblast differentiation-related genes was studied in GSE30393. And the related functional pathways were found through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. By combining GSE1367 and GSE100933 together, key genes which were separately bound up with glutathione metabolism and ferroptosis were located. The correlation of these key genes was analyzed by the Pearson correlation coefficient. GSTM1 targeted agonist glutathione (GSH) selected by connectivity map (CMap) analysis was used to interfere with the molding disused osteoporosis process in MC3T3-E1 cells. RT-PCR and intracellular reactive oxygen species (ROS) were performed. Two important pathways, glutathione metabolism and ferroptosis pathways, were found. GSTM1 and TFRC were thought as key genes in disuse osteoporosis osteoblasts with the two mechanisms. The two genes have a strong negative correlation. Our experiment results showed that the expression of TFRC was consistent with the negative correlation with the activation process of GSTM1. The strong relationship between the two genes was proved. Glutathione metabolism and ferroptosis are important in the normal differentiation of osteoblasts and the process of disuse osteoporosis. GSTM1 and TFRC were the key genes. The two genes interact with each other, which can be seen as a bridge between the two pathways. The two genes participate in the process of reducing ROS in disuse osteoporosis osteoblasts.

Integrated Analysis of Multiple Microarray Studies to Identify Core Gene-Expression Signatures Involved in Tubulointerstitial Injury in Diabetic Nephropathy.

Diabetic nephropathy is a leading cause of end-stage renal disease in both developed and developing countries. It is lack of specific diagnosis, and the pathogenesis remains unclarified in diabetic nephropathy, following the unsatisfactory effects of existing treatments. Therefore, it is very meaningful to find biomarkers with high specificity and potential targets. Two datasets, GSE30529 and GSE47184 from GEO based on diabetic nephropathy tubular samples, were downloaded and merged after batch effect removal. A total of 545 different expression genes screened with log2FC > 0.5 were weighted gene coexpression correlation network analysis, and green module and blue module were identified. The results of KEGG analyses both in green module and GSEA analysis showed the same two enriched pathway, focal adhesion and viral myocarditis. Based on the intersection among WGCNA focal adhesion/Viral myocarditis, GSEA focal adhesion/viral myocarditis, and PPI network, 17 core genes, ACTN1, CAV1, PRKCB, PDGFRA, COL1A2, COL6A3, RHOA, VWF, FN1, HLA-F, HLA-DPB1, ITGB2, HLA-DRA, HLA-DMA, HLA-DPA1, HLA-B, and HLA-DMB, were identified as potential biomarkers in diabetic tubulointerstitial injury and were further validated externally for expression at GSE99325 and GSE104954 and clinical feature at nephroseq V5 online platform. CMap analysis suggested that two compounds, LY-294002 and bufexamac, may be new insights for therapeutics of diabetic tubulointerstitial injury. Conclusively, it was raised that a series of core genes may be as potential biomarkers for diagnosis and two prospective compounds.

Immune Microenvironment Terms Signature Robustly Predicts the Prognosis and Immunotherapy Response in Bladder Cancer Based on Large Population Cohorts

Immune microenvironment is implicated in cancer progression. However, the role of immune microenvironment in bladder cancer has not been fully explored. Open-accessed datasets GSE120736, GSE128959, GSE13507, GSE31684, GSE32548, GSE48075, GSE83586, and The Cancer Genome Atlas (TCGA) database were enrolled in our study. Single-sample gene set enrichment analysis (ssGSEA) was used to quantify 53 immune terms in combined BLCA cohorts. The top 10 important immune terms were identified through random forest algorithm for model establishment. Our model showed satisfactory efficacy in prognosis prediction. Furthermore, we explored clinical and genomic feature differences between high- and low-risk groups. The results indicated that the patients in the high-risk group might be associated with worse clinical features. Gene set enrichment analysis showed that epithelial–mesenchymal translational, mTORC1 signaling, mitotic spindle, glycolysis, E2F target, and G2M checkpoint pathways were aberrantly activated in high-risk patients, partially explaining its worse prognosis. Patients in the low-risk group showed better immunotherapy response according to TIDE and TCIA analysis, indicating that our model could effectively predict the immunotherapy response rate. KCNH4, UGT1A1, TPO, SHANK1, PITX3, MYH1, MYH13, KRT3, DEC1, and OBP2A genes were identified as feature genes in the high- and low-risk patients. CMAP analysis was performed to identify potential compounds targeting the riskscore.

Abstract 363: Irf8 And Securinine: Seeking Treatments For Abdominal Aortic Aneurysm

Objective: Abdominal aortic aneurysm (AAA) is defined as local weakening and dilatation of the abdominal aorta ≥50% and has high incidence in elderly. Current therapy is limited to surgical repair, only, no pharmacological therapy has shown effectiveness against AAA development and growth. Untreated AAA rupture is highly associated with death, and aged patients are at augmented risk for surgical complications. We therefore sought to identify novel agents that could be employed for medical AAA therapy. Methods: Using whole genome DNA methylation analysis (RRBS-Seq) in a transgenerational model of murine AAA, combined with transcriptional analysis of murine AAA tissue, we identified interferon regulatory factors (IRFs: a group of immune-system-related transcription factors (TF)), as major drivers of AAA risk and pathophysiology. We performed TF enrichment analysis that specifically suggested IRF8 as being crucial for AAA development. Drug Signatures Database (DSigDB) and Connectivity Map (CMap) analysis identified securinine, an alkaloid and traditional Chinese herbal medicine derived from Securinega suffruticosa , as a probable IRF8 inhibitor. Its vascular effects are relatively unknown. Results: We studied the effects of securinine using the porcine pancreatic elastase (PPE) model of AAA. Sonographic imaging data of 28-day securinine-treated mice (15 mg/kg) showed smaller aneurysm diameters and significantly downregulated gene expression of IRF8 and IFN-γ compared to control PPE groups. Conclusion: These data suggest that securinine may have therapeutic benefits on AAA development, potentially through regulation of IRF8.

Immune-Associated Gene Signatures and Subtypes to Predict the Progression of Atherosclerotic Plaques Based on Machine Learning.

Objective: Experimental and clinical evidence suggests that atherosclerosis is a chronic inflammatory disease. Our study was conducted for uncovering the roles of immune-associated genes during atherosclerotic plaque progression. Methods: Gene expression profiling of GSE28829, GSE43292, GSE41571, and GSE120521 datasets was retrieved from the GEO database. Three machine learning algorithms, least absolute shrinkage, and selection operator (LASSO), random forest, and support vector machine–recursive feature elimination (SVM-RFE) were utilized for screening characteristic genes among atherosclerotic plaque progression- and immune-associated genes. ROC curves were generated for estimating the diagnostic efficacy. Immune cell infiltrations were estimated via ssGSEA, and immune checkpoints were quantified. CMap analysis was implemented to screen potential small-molecule compounds. Atherosclerotic plaque specimens were classified using a consensus clustering approach. Results: Seven characteristic genes (TNFSF13B, CCL5, CCL19, ITGAL, CD14, GZMB, and BTK) were identified, which enabled the prediction of progression of atherosclerotic plaques. Higher immune cell infiltrations and immune checkpoint expressions were found in advanced-stage than in early-stage atherosclerotic plaques and were positively linked to characteristic genes. Patients could clinically benefit from the characteristic gene-based nomogram. Several small molecular compounds were predicted based on the characteristic genes. Two subtypes, namely, C1 immune subtype and C2 non-immune subtype, were classified across atherosclerotic plaques. The characteristic genes presented higher expression in C1 than in C2 subtypes. Conclusion: Our findings provide several promising atherosclerotic plaque progression- and immune-associated genes as well as immune subtypes, which might enable to assist the design of more accurately tailored cardiovascular immunotherapy.

A Novel Immune-Related Prognostic Signature Predicting Survival in Patients with Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) carries the lowest survival rate of all major organ cancers, which is of dismal prognosis and high mortality rate. Thus, the present study attempted to identify a few novel prognostic biomarkers and establish an immune-related prognostic signature which could predict the prognosis of PAAD. Four prognostic immune-related genes (IRGs) including S100A6, S100A10, S100A16, and SDC1 were screened by differentially expressed gene (DEG) identification and weighted gene coexpression network analysis (WGCNA). Subsequent analysis proved the high expression of these IRGs in PAAD tissues, suggested by TCGA-PAAD data, merged microarray-acquired dataset (MMD), GEPIA, and Oncomine webtool. By using MMD and TCGA-PAAD data, S100A6 (MMD: AUC = 0.897; TCGA: AUC = 0.843), S100A10 (MMD: AUC = 0.880; TCGA: AUC = 0.780), S100A16 (MMD: AUC = 0.878; TCGA: AUC = 0.838), and SDC1 (MMD: AUC = 0.885; TCGA: AUC = 0.812) exhibited excellent diagnostic efficiency for PAAD. By conducting connectivity map (CMap) analysis, we concluded that three molecule drugs (sulpiride, famotidine, and nalidixic acid) might have worked in the treatment of PAAD. Then, an immune-related prognostic index was constructed, which was validated as an independent prognostic factor for PAAD patients (P=0.004). We further constructed a nomogram by using this immune-related signature and age, the prognostic value of which was validated by using concordance index (C-index = 0.780) and area under curve (AUC = 0.909). Moreover, the immune-related prognostic signature was associated with response to anti-PD-1/L1 immunotherapy. To sum up, four IRGs were screened out and verified to be novel immune-related prognostic biomarkers in PAAD. Besides, sulpiride, famotidine, and nalidixic acid might be potential choices in the treatment of PAAD. An immune-related signature was established to show great potential for prognosis prediction for PAAD, independently, which might guide more effective immunotherapy strategies. A nomogram is further established by using this immune-related prognostic index, which might contribute to more effective prognosis prediction in PAAD patients.

AGTR1, PLTP, and SCG2 associated with immune genes and immune cell infiltration in calcific aortic valve stenosis: analysis from integrated bioinformatics and machine learning.

Background: Calcific aortic valve stenosis (CAVS) is a crucial cardiovascular disease facing aging societies. Our research attempts to identify immune-related genes through bioinformatics and machine learning analysis. Two machine learning strategies include Least Absolute Shrinkage Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE). In addition, we deeply explore the role of immune cell infiltration in CAVS, aiming to study the potential therapeutic targets of CAVS and explore possible drugs. Methods: Download three data sets related to CAVS from the Gene Expression Omnibus. Gene set variation analysis (GSVA) looks for potential mechanisms, determines differentially expressed immune-related genes (DEIRGs) by combining the ImmPort database with CAVS differential genes, and explores the functions and pathways of enrichment. Two machine learning methods, LASSO and SVM-RFE, screen key immune signals and validate them in external data sets. Single-sample GSEA (ssGSEA) and CIBERSORT analyze the subtypes of immune infiltrating cells and integrate the analysis with DEIRGs and key immune signals. Finally, the possible targeted drugs are analyzed through the Connectivity Map (CMap). Results: GSVA analysis of the gene set suggests that it is highly correlated with multiple immune pathways. 266 differential genes (DEGs) integrate with immune genes to obtain 71 DEIRGs. Enrichment analysis found that DEIRGs are related to oxidative stress, synaptic membrane components, receptor activity, and a variety of cardiovascular diseases and immune pathways. Angiotensin II Receptor Type 1(AGTR1), Phospholipid Transfer Protein (PLTP), Secretogranin II (SCG2) are identified as key immune signals of CAVS by machine learning. Immune infiltration found that B cells naï ve and Macrophages M2 are less in CAVS, while Macrophages M0 is more in CAVS. Simultaneously, AGTR1, PLTP, SCG2 are highly correlated with a variety of immune cell subtypes. CMap analysis found that isoliquiritigenin, parthenolide, and pyrrolidine-dithiocarbamate are the top three targeted drugs related to CAVS immunity. Conclusion: The key immune signals, immune infiltration and potential drugs obtained from the research play a vital role in the pathophysiological progress of CAVS.

Identification of Diagnostic Biomarkers, Immune Infiltration Characteristics, and Potential Compounds in Rheumatoid Arthritis.

Aims This study is aimed at investigating the pathogenesis of rheumatoid arthritis (RA) by identifying key biomarkers, associated immune infiltration, and small-molecule compounds using bioinformatic analysis. Methods Six datasets were obtained from the Gene Expression Omnibus database, and the batch effect was adjusted. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyse differentially expressed genes (DEGs). Furthermore, candidate small-molecule drugs associated with RA were selected from the Connectivity Map (CMap) database. The least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and multivariate logistic regression analyses were performed on DEGs to screen for RA diagnostic markers. The receiver operating characteristic curve, concordance index, and GiViTi calibration band were the metrics used to assess the diagnostic markers of RA identified in this analysis. The single-sample gene set enrichment analysis was performed to calculate the scores of infiltrating immune cells and evaluate the activities of immune-related pathways. Finally, the correlation between screening markers and RA diagnosis was determined. Results A total of 227 DEGs were identified. Functional enrichment analysis and KEGG revealed that DEGs were enriched by the immune response. CMap analysis identified 11 small-molecule compounds with therapeutic potential for RA. In gene expression, the activities of 13 immune cells and 12 immune-related pathways significantly differed between patients with RA and healthy controls. DPYSL3 and SPP1 had the potential to diagnose RA. SPP1 expression was positively correlated with DPYSL3 in 11 immune cells and 10 immune-related pathways. Conclusion This study comprehensively analysed DEGs and immune infiltration and screened for potential diagnostic markers and small-molecule compounds of RA.

Identification and verification of the molecular mechanisms and prognostic values of the cadherin gene family in gastric cancer

While cadherin (CDH) genes are aberrantly expressed in cancers, the functions of CDH genes in gastric cancer (GC) remain poorly understood. The clinical significance and molecular mechanisms of CDH genes in GC were assessed in this study. Data from a total of 1226 GC patients included in The Cancer Genome Atlas (TCGA) and Kaplan–Meier plotter database were used to independently explore the value of CDH genes in clinical application. The TCGA RNA sequencing dataset was used to explore the molecular mechanisms of CDH genes in GC. Using enrichment analysis tools, CDH genes were found to be related to cell adhesion and calcium ion binding in function. In TCGA cohort, 12 genes were found to be differentially expressed between GC para-carcinoma and tumor tissue. By analyzing GC patients in two independent cohorts, we identified and verified that CDH2, CDH6, CDH7 and CDH10 were significantly associated with a poor GC prognosis. In addition, CDH2 and CDH6 were used to construct a GC risk score signature that can significantly improve the accuracy of predicting the 5-year survival of GC patients. The GSEA approach was used to explore the functional mechanisms of the four prognostic CDH genes and their associated risk scores. It was found that these genes may be involved in multiple classic cancer-related signaling pathways, such as the Wnt and phosphoinositide 3-kinase signaling pathways in GC. In the subsequent CMap analysis, three small molecule compounds (anisomycin, nystatin and bumetanide) that may be the target molecules that determine the risk score in GC, were initially screened. In conclusion, our current study suggests that four CDH genes can be used as potential biomarkers for GC prognosis. In addition, a prognostic signature based on the CDH2 and CDH6 genes was constructed, and their potential functional mechanisms and drug interactions explored.

Transcription factor network analysis based on single cell RNA-seq identifies that Trichostatin-a reverses docetaxel resistance in prostate Cancer

BackgroundOvercoming drug resistance is critical for increasing the survival rate of prostate cancer (PCa). Docetaxel is the first cytotoxic chemotherapeutical approved for treatment of PCa. However, 99% of PCa patients will develop resistance to docetaxel within 3 years. Understanding how resistance arises is important to increasing PCa survival.MethodsIn this study, we modeled docetaxel resistance using two PCa cell lines: DU145 and PC3. Using the Passing Attributes between Networks for Data Assimilation (PANDA) method to model transcription factor (TF) activity networks in both sensitive and resistant variants of the two cell lines. We identified edges and nodes shared by both PCa cell lines that composed a shared TF network that modeled changes which occur during acquisition of docetaxel resistance in PCa. We subjected the shared TF network to connectivity map analysis (CMAP) to identify potential drugs that could disrupt the resistant networks. We validated the candidate drug in combination with docetaxel to treat docetaxel-resistant PCa in both in vitro and in vivo models.ResultsIn the final shared TF network, 10 TF nodes were identified as the main nodes for the development of docetaxel resistance. CMAP analysis of the shared TF network identified trichostatin A (TSA) as a candidate adjuvant to reverse docetaxel resistance. In cell lines, the addition of TSA to docetaxel enhanced cytotoxicity of docetaxel resistant PCa cells with an associated reduction of the IC50 of docetaxel on the resistant cells. In the PCa mouse model, combination of TSA and docetaxel reduced tumor growth and final weight greater than either drug alone or vehicle.ConclusionsWe identified a shared TF activity network that drives docetaxel resistance in PCa. We also demonstrated a novel combination therapy to overcome this resistance. This study highlights the usage of novel application of single cell RNA-sequencing and subsequent network analyses that can reveal novel insights which have the potential to improve clinical outcomes.