CMap Analysis Research Articles

Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis.

The advancement of high-throughput sequencing and genomic analysis revealed that acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease. The abundance of such genetic data in ALL can also be utilized to identify potential targets for drug discovery and even drug repurposing. We aimed to determine potential genes for drug development and further guide the identification of candidate drugs repurposed for treating ALL through integrated genomic network analysis. Genetic variants associated with ALL were retrieved from the GWAS Catalog. We further applied a genomic-driven drug repurposing approach based on the six functional annotations to prioritize crucial biological ALL-related genes based on the scoring system. Lastly, we identified the potential drugs in which the mechanisms overlapped with the therapeutic targets and prioritized the candidate drugs using Connectivity Map (CMap) analysis. Forty-two genes were considered biological ALL-risk genes with ARID5B topping the list. Based on potentially druggable genes that we identified, palbociclib, sirolimus, and tacrolimus were under clinical trial for ALL. Additionally, chlorprothixene, sirolimus, dihydroergocristine, papaverine, and tamoxifen are the top five drug repositioning candidates for ALL according to the CMap score with dasatinib as a comparator. In conclusion, this study determines the practicability and the potential of integrated genomic network analysis in driving drug discovery in ALL.

Pirenperone relieves the symptoms of fragile X syndrome in Fmr1 knockout mice

Fragile X syndrome (FXS) is a neurodevelopmental disorder that is caused by the loss of Fragile X-linked mental retardation protein (FMRP), an RNA binding protein that can bind and recognize different RNA structures and regulate the target mRNAs’ translation involved in neuronal synaptic plasticity. Perturbations of this gene expression network have been related to abnormal behavioral symptoms such as hyperactivity, and impulsivity. Considering the roles of FMRP in the modulation of mRNA translation, we investigated the differentially expressed genes which might be targeted to revert to normal and ameliorate behavioral symptoms. Gene expression data was analyzed and used the connectivity map (CMap) to understand the changes in gene expression in FXS and predict the effective drug candidates. We analyzed the GSE7329 dataset that had 15 control and 8 FXS patients’ lymphoblastoid samples. Among 924 genes, 42 genes were selected as signatures for CMap analysis, and 24 associated drugs were found. Pirenperone was selected as a potential drug candidate for FXS for its possible antipsychotic effect. Treatment of pirenperone increased the expression level of Fmr1 gene. Moreover, pirenperone rescued the behavioral deficits in Fmr1 KO mice including hyperactivity, spatial memory, and impulsivity. These results suggest that pirenperone is a new drug candidate for FXS, which should be verified in future studies.

A risk model developed based on necroptosis to assess progression for ischemic cardiomyopathy and identify possible therapeutic drugs.

Object: Ischemic cardiomyopathy (ICM), with high morbidity and mortality, is the most common cause of heart failure. Cardiovascular remodeling secondary to chronic myocardial ischemia is the main cause of its progression. A recently identified type of programmed cell death called necroptosis is crucial in the development of various cardiovascular diseases. However, the function role of necroptosis in cardiac remodeling of ICM has not been elucidated. Our study aimed to screen for genes associated with necroptosis and construct a risk score to assess the progression and evaluate the prognosis of ICM patients, and further to search for potentially therapeutic drugs. Methods: The gene expression profiling was obtained from the GEO database. LASSO regression analysis was used to construct necroptosis-related gene signatures associated with ICM progression and prognosis. TF-gene and miRNA-gene networks were constructed to identify the regulatory targets of potential necroptosis-related signature genes. Pathway alterations in patients with high necroptosis-related score (NRS) were analyzed by GO, KEGG, GSEA analysis, and immune cell infiltration was estimated by ImmuCellAI analysis. CMap analysis was performed to screen potential small molecule compounds targeting patients with high NRS. Independent risk analyses were performed using nomograms. Results: Six necroptosis-related signature genes (STAT4, TNFSF10, CHMP5, CHMP18, JAK1, and CFLAR) were used to define the NRS, with areas under the ROC curves of 0.833, 0.765, and 0.75 for training test, test set, and validation set, respectively. Transcription factors FOXC1 and hsa-miR-124-3p miRNA may be regulators of signature genes. Patients with higher NRS have pathway enriched in fibrosis and metabolism and elevated nTreg cells. AZD-7762 may be an effective drug to improve the prognosis of patients with high NRS. A feature-based nomogram was constructed from which patients could derive clinical benefit. Conclusion: Our results reveal 6 necroptosis gene signatures that can evaluate the progression and prognosis of ICM with high clinical value, and identify potential targets that could help improve cardiovascular remodeling.

N6-methyladenosine regulators-related immune genes enable predict graft loss and discriminate T-cell mediate rejection in kidney transplantation biopsies for cause.

The role of m6A modification in kidney transplant-associated immunity, especially in alloimmunity, still remains unknown. This study aims to explore the potential value of m6A-related immune genes in predicting graft loss and diagnosing T cell mediated rejection (TCMR), as well as the possible role they play in renal graft dysfunction. Renal transplant-related cohorts and transcript expression data were obtained from the GEO database. First, we conducted correlation analysis in the discovery cohort to identify the m6A-related immune genes. Then, lasso regression and random forest were used respectively to build prediction models in the prognosis and diagnosis cohort, to predict graft loss and discriminate TCMR in dysfunctional renal grafts. Connectivity map (CMap) analysis was applied to identify potential therapeutic compounds for TCMR. The prognostic prediction model effectively predicts the prognosis and survival of renal grafts with clinical indications (P< 0.001) and applies to both rejection and non-rejection situations. The diagnostic prediction model discriminates TCMR in dysfunctional renal grafts with high accuracy (area under curve = 0.891). Meanwhile, the classifier score of the diagnostic model, as a continuity index, is positively correlated with the severity of main pathological injuries of TCMR. Furthermore, it is found that METTL3, FTO, WATP, and RBM15 are likely to play a pivotal part in the regulation of immune response in TCMR. By CMap analysis, several small molecular compounds are found to be able to reverse TCMR including fenoldopam, dextromethorphan, and so on. Together, our findings explore the value of m6A-related immune genes in predicting the prognosis of renal grafts and diagnosis of TCMR.

Identifying Prognostic Biomarkers Related to m6A Modification and Immune Infiltration in Renal Cell Carcinoma.

Renal cell carcinoma (RCC) is the largest category of kidney tumors and usually does not have a good prognosis. N6-methyladenosine(m6A) and immune infiltration have received increased attention because of their great influence on the clinical outcome and prognosis of cancer patients. We identified hub genes through multi-dimensional screening, including DEGs, PPI analysis, LASSO regression, and random forest. Meanwhile, GO/KEGG enrichment, cMAP analysis, prognostic analysis, m6A prediction, and immune infiltration analysis were performed to understand the potential mechanism and screen therapeutic drugs. We screened 275 downregulated and 185 upregulated genes using three GEO datasets and the TCGA dataset. In total, 82 candidate hub genes were selected using STRING and Cytoscape. Enrichment analysis illustrated that the top 3 biological process terms and top 1 KEGG term were related to immunity. cMAP analysis showed some antagonistic molecules can be candidate drugs for the treatment of RCC. Then, six hub genes (ERBB2, CASR, P2RY8, CAT, PLAUR, and TIMP1) with strong predictive values for prognosis and clinicopathological features were selected. Meanwhile, P2RY8, ERBB2, CAT, and TIMP1 may obtain m6A modification by binding METTL3 or METTL14. On the other hand, differential expression of CAT, ERBB2, P2RY8, PLAUR, and TIMP1 affects the infiltration of the majority of immune cells. We identified six hub genes through multi-dimensional screening. They all possess strong predictive value for prognosis and clinicopathological features. Meanwhile, hub genes may regulate the progression of RCC via an m6A- and immunity-dependent mechanism.

Integrated analysis reveals effects of bioactive ingredients from Limonium Sinense (Girard) Kuntze on hypoxia-inducible factor (HIF) activation.

Limonium Sinense (Girard) Kuntze is a traditional Chinese medicinal herb, showing blood replenishment, anti-tumour, anti-hepatitis, and immunomodulation activities amongst others. However, the mechanism of its pharmacological activities remains largely unknown. Here, we investigated the effects of bioactive ingredients from Limonium Sinense using an integrated approach. Water extracts from Limonium Sinense (LSW) showed a strong growth inhibitory effect on multiple cells in both 2D and 3D cultures. Global transcriptomic profiling and further connectivity map (CMap) analysis identified several similarly acting therapeutic candidates, including Tubulin inhibitors and hypoxia-inducible factor (HIF) modulators. The effect of LSW on the cell cycle was verified with flow cytometry showing a G2/M phase arrest. Integrated analysis suggested a role for gallic acid in mediating HIF activation. Taken together, this study provides novel insights into the bioactive ingredients in Limonium Sinense, highlighting the rich natural resource and therapeutic values of herbal plants.

Identification of circRNA-miRNA-mRNA networks to explore the molecular mechanism and immune regulation of postoperative neurocognitive disorder.

Postoperative neurocognitive disorder (PND) is a common complication in older patients. However, its pathogenesis has still remained elusive. Recent studies have shown that circular RNA (circRNA) plays an important role in the development of neurodegenerative diseases, such as PND after surgery. CircRNA, as a competitive endogenous RNA (ceRNA), mainly acts as a molecular sponge for miRNA to "adsorb" microRNA (miRNA) and to reduce the inhibitory effects of miRNAs on target mRNA. The sequencing data of circRNA were obtained from the Gene Expression Omnibus (GEO) database. By bioinformatic methods, circAtlas, miRDB, miRTarBase and miRwalk databases were applied to construct circRNA-miRNA-mRNA networks and screen differentially expressed mRNAs. To improve the accuracy of the data, we randomly divided aging mice into control (non-PND group) and PND groups, and used high-throughput sequencing to analyze their brain hippocampal tissue for analysis. Three key genes were cross-detected in the data of both groups, which were Unc13c, Tbx20 and St8sia2 (as hub genes), providing new targets for PND treatment. According to the results of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, immune cell infiltration analysis, gene set enrichment analysis (GSEA), Connectivity Map (CMap) analysis, quantitative real-time polymerase chain reaction (qRT-PCR), the genes that were not related to the central nervous system were removed, and finally, mmu_circ_0000331/miR-1224-3p/Unc13c and mmu_circ_0000406/miR-24-3p/St8sia2 ceRNA networks were identified. In addition, the CMap method was used to select the top 4 active compounds with the largest negative correlation absolute values, including cimaterol, Rucaparib, FG-7142, and Hydrocortisone.

Systematic Analysis of Immune Infiltration and Predicting Prognosis in Clear Cell Renal Cell Carcinoma Based on the Inflammation Signature.

Clear cell renal cell carcinoma (ccRCC) is the most frequent kind of kidney malignancy. Inflammation is a physiological response of the immune system to harmful stimuli. Notably, the role inflammation plays in ccRCC is still unknown. In this study, consensus clustering analysis sorted the ccRCC specimens from the TCGA dataset into C1 and C2 clusters. The C2 cluster comprised ccRCC specimens with a high TNM stage and tumor grade. These specimens were characterized by the activation of the inflammatory response and an immunosuppressive microenvironment. A seven-gene inflammation-related risk signature was designed employing the LASSO and Cox regression analyses for the inflammation-related genes. The ccRCC specimens were classified into two groups with high and low risk by calculating the risk scores. The specimens in the group with high risk showed a poor prognosis and were positively correlated with immune inhibitory factors. Moreover, a nomogram was created by incorporating inflammation-related risk signatures and clinical characteristics. The ROC and DCA curves indicated a satisfactory efficiency of the nomogram for predicting the survival outcomes. Furthermore, we identified the potential therapeutic drug molecules through CMap analysis. The findings of our study may act as a guide for further research on new prognostic biomarkers and therapies.

Comprehensive analysis of epigenomics and transcriptome data to identify potential target genes associated with obesity.

DNA methylation is closely related to the occurrence and development of many diseases, but its role in obesity is still unclear. This study aimed to find the potential differentially methylated genes associated with obesity occurrence and development. By combining methylation and transcriptome analysis, we identified the key genes in adipose tissue affecting the occurrence and development of obesity and revealed the possible molecular mechanisms involved in obesity pathogenesis. We first screened 14 methylation-related differential genes and verified their expression in adipose tissue by quantitative polymerase chain reaction (qPCR). Seven genes with the same expression pattern were identified as key genes, namely, CCRL2, GPT, LGALS12, PC, SLC27A2, SLC4A4, and TTC36. Then, the immune microenvironment of adipose tissue was quantified by CIBERSORT, and we found that the content of M0 macrophages and T follicular helper cells in adipose tissue was significantly increased and decreased, respectively, in the obese group. Furthermore, the relationship between key genes and the immune microenvironment was analyzed. Additionally, the metabolic pathway activity of each sample was calculated based on the ssGSEA algorithm, and the key gene–metabolic network was constructed. Moreover, we performed a CMAP analysis based on the differential genes in adipose tissue to screen out drugs potentially effective in obesity treatment. In conclusion, we identified seven methylation-related key genes closely related to obesity pathogenesis and explored the potential mechanism of their role in obesity. This study provided novel insights into the molecular mechanisms and management of obesity.

Screening and identification of CNIH4 gene associated with cell proliferation in gastric cancer based on a large-scale CRISPR-Cas9 screening database DepMap

BackgroundGastric cancer is a common malignant tumor of digestive tract. The study aimed to identify candidate genes associated with the proliferation and survival of gastric cancer cell through CRISPR-cas9 screening data, which may provide new therapeutic targets for gastric cancer patients. MethodsCandidate genes related to gastric cancer cell viability by CRISPR-cas9 screening from Depmap and genes differentially expressed between gastric cancer tissues and normal gastric tissues from TCGA were overlapped. WGCNA and KEGG analysis was conducted to performed to identify key pathways and genes. Using CMap, we identified small molecules that might reverse candidate gene expression of gastric cancer. LASSO regression was used to construct a signature to predict overall survival of gastric cancer patients. CCK8 assay was performed to assess the effects of candidate gene on gastric cancer cell proliferation. ResultsA total of 710 candidate genes related to gastric cancer cell viability in the DepMap were identified and overlapped with differentially expressed genes in TCGA database, which were enriched in the cell cycle pathway. CMap analysis suggested that molecule drug LY294002 might be a novel choice for gastric cancer treatment. Using Cox univariate analysis and Lasso analysis, we developed a prognostic model including 12 candidate genes, and conducted subgroup analysis and external validation. Moreover, knockdown of the key candidate gene CNIH4 inhibited the proliferation of gastric cancer cells. ConclusionCell cycle pathway and CNIH4, identified by CRISPR-cas9 screening, were a key pathway and gene that regulate cell viability in gastric cancer. CNIH4 has significant prognostic values and can serve as a new target for gastric cancer patient treatment.

Identification of ATP1B1, a key copy number driver gene in diffuse large B-cell lymphoma and potential target for drugs.

Copy number variations (CNVs) participate in the development and progression of cancer by altering the expression levels of genes. However, it is unclear whether this correlation exists in diffuse large B-cell lymphoma (DLBCL). Differentially expressed genes (DEGs) were identified from the GSE25638 and GSE56315 datasets. Modules that were highly related to DLBCL prognosis were obtained by Weighted Gene Co-expression Network Analysis (WGCNA). We performed an integrated analysis between CNV and differential gene expression in The Cancer Genome Atlas (TCGA) DLBCL. The DEGs were then overlapped with the module genes and expression-copy number variations-related (Exp-CNV-related) genes to obtain the common key genes. Time-dependent receiver operating characteristic (ROC) analysis was utilized to evaluate the accuracy of the key gene in predicting the prognosis of DLBCL. Next, we conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to explore the key gene. The potential molecule drugs of the key gene were identified by Connectivity Map (Cmap) analysis. A turquoise module with 160 genes was identified as the signature module. ATP1B1 is overexpressed in DLBCL cell lines, compared to Cluster of Differentiation 19+B (CD19+B) cells. The ROC curve indicated that ATP1B1 could be a biomarker for diagnosing DLBCL, and the forest map suggested that ATP1B1 gene expression levels had a greater impact on the prognosis of patients with DLBCL. The area under curve (AUC) value of the time-dependent ROC curve with values based on the 1-, 3-, and 5-year survivability were 0.576, 0.663, and 0.706, respectively. Pathway analysis demonstrated the relationship between ATP1B1 and focal adhesion, etc. The inhibitory effects of ATP1B1 downregulation on DLBCL cell proliferation, cell migration, invasion, and cell adhesion were also examined. We found out that the higher proliferation ability in ATP1B1-overexpression cells was rescued with roxithromycin. ATP1B1 is a copy number driver gene that could potentially be adopted as a diagnostic biomarker and therapeutic target of DLBCL.

Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer.

Background: Colorectal cancer is the fourth most deadly cancer worldwide. Although current treatment regimens have prolonged the survival of patients, the prognosis is still unsatisfactory. Inflammation and lncRNAs are closely related to tumor occurrence and development in CRC. Therefore, it is necessary to establish a new prognostic signature based on inflammation-related lncRNAs to improve the prognosis of patients with CRC. Methods: LASSO-penalized Cox analysis was performed to construct a prognostic signature. Kaplan-Meier curves were used for survival analysis and ROC curves were used to measure the performance of the signature. Functional enrichment analysis was conducted to reveal the biological significance of the signature. The R package “maftool” and GISTIC2.0 algorithm were performed for analysis and visualization of genomic variations. The R package “pRRophetic”, CMap analysis and submap analysis were performed to predict response to chemotherapy and immunotherapy. Results: An effective and independent prognostic signature, IRLncSig, was constructed based on sixteen inflammation-related lncRNAs. The IRLncSig was proved to be an independent prognostic indicator in CRC and was superior to clinical variables and the other four published signatures. The nomograms were constructed based on inflammation-related lncRNAs and detected by calibration curves. All samples were classified into two groups according to the median value, and we found frequent mutations of the TP53 gene in the high-risk group. We also found some significantly amplificated regions in the high-risk group, 8q24.3, 20q12, 8q22.3, and 20q13.2, which may regulate the inflammatory activity of cancer cells in CRC. Finally, we identified chemotherapeutic agents for high-risk patients and found that these patients were more likely to respond to immunotherapy, especially anti-CTLA4 therapy. Conclusion: In short, we constructed a new signature based on sixteen inflammation-related lncRNAs to improve the outcomes of patients in CRC. Our findings have proved that the IRLncSig can be used as an effective and independent marker for predicting the survival of patients with CRC.

A novel cuproptosis-related subtypes and gene signature associates with immunophenotype and predicts prognosis accurately in neuroblastoma.

BackgroundNeuroblastoma (NB) is the most frequent solid tumor in pediatrics, which accounts for roughly 15% of cancer-related mortality in children. NB exhibited genetic, morphologic, and clinical heterogeneity, which limited the efficacy of available therapeutic approaches. Recently, a new term ‘cuproptosis’ has been used to denote a unique biological process triggered by the action of copper. In this instance, selectively inducing copper death is likely to successfully overcome the limitations of conventional anticancer drugs. However, there is still a gap regarding the role of cuproptosis in cancer, especially in pediatric neuroblastoma.MethodsWe characterized the specific expression of cuproptosis-related genes (CRGs) in NB samples based on publicly available mRNA expression profile data. Consensus clustering and Lasso-Cox regression analysis were applied for CRGs in three independent cohorts. ESTIMATE and Xcell algorithm was utilized to visualize TME score and immune cell subpopulations’ relative abundances. Tumor Immune Dysfunction and Exclusion (TIDE) score was used to predict tumor response to immune checkpoint inhibitors. To decipher the underlying mechanism, GSVA was applied to explore enriched pathways associated with cuproptosis signature and Connectivity map (CMap) analysis for drug exploration. Finally, qPCR verified the expression levels of risk-genes in NB cell lines. In addition, PDHA1 was screened and further validated by immunofluorescence in human clinical samples and loss-of-function assays.ResultsWe initially classified NB patients according to CRGs and identified two cuproptosis-related subtypes that were associated with prognosis and immunophenotype. After this, a cuproptosis-related prognostic model was constructed and validated by LASSO regression in three independent cohorts. This model can accurately predict prognosis, immune infiltration, and immunotherapy responses. These genes also showed differential expression in various characteristic groups of all three datasets and NB cell lines. Loss-of-function experiments indicated that PDHA1 silencing significantly suppressed the proliferation, migration, and invasion, in turn, promoted cell cycle arrest at the S phase and apoptosis of NB cells.ConclusionsTaken together, this study may shed light on new research areas for NB patients from the cuproptosis perspective.

Identification of the circRNA-miRNA-mRNA regulatory network in osteoarthritis using bioinformatics analysis.

Background: Osteoarthritis (OA) is a degenerative joint disease that seriously affects the quality of people. Unfortunately, the pathogenesis of OA has not been fully known. Therefore, this study aimed to construct a ceRNA regulatory network related to OA to explore the pathogenesis of OA. Methods: Differentially expressed circRNAs (DEcircRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were obtained from the Gene Expression Omnibus microarray data (GSE175959, GSE105027, and GSE169077). The miRNA response elements and target mRNAs were identified using bioinformatics approaches. Additionally, a circRNA–miRNA–mRNA network was established using Cytoscape version 3.8.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs in the network were conducted to explore the possible mechanisms underlying OA development. Protein–protein interaction (PPI) analysis was performed to determine the hub genes. Based on the hub genes, a sub network was constructed using Cytoscape 3.8.0 version. Finally, connectivity map (CMap) and drug–gene interaction database (DGIdb) analyses were performed to identify the potential therapeutic targets for OA. Results: Altogether, five DEcircRNAs, 89 DEmiRNAs, and 345 DEmRNAs were identified. Moreover, a circRNA–miRNA–mRNA network was established using three circRNAs, seven miRNAs, and 37 mRNAs. GO and KEGG analyses demonstrated that the mRNAs in the network could be related to the occurrence and development of OA. PPI analysis was performed and six key genes, namely serpin family H member 1 [SERPINH1], collagen type VIII alpha 2 chain [COL8A2], collagen type XV alpha 1 chain [COL15A1], collagen type VI alpha 3 chain [COL6A3], collagen type V alpha 1 chain [COL5A1], and collagen type XI alpha 1 chain [COL11A1], were identified. Furthermore, a circRNA–miRNA–hub gene subnetwork was established in accordance with two circRNAs (hsa_circ_0075320 and hsa_circ_0051428), two miRNAs (hsa-miR-6124 and hsa-miR-1207-5p), and six hub genes (COL11A1, SERPINH1, COL6A3, COL5A1, COL8A2, and COL15A1). Finally, three chemicals (noscapine, diazepam, and TG100-115) based on CMap analysis and two drugs (collagenase Clostridium histolyticum and ocriplasmin) based on DGIdb were discovered as potential treatment options for OA. Conclusion: This study presents novel perspectives on the pathogenesis and treatment of OA based on circRNA-related competitive endogenous RNA regulatory networks.

The Natural Products Withaferin A and Withanone from the Medicinal Herb Withania somnifera Are Covalent Inhibitors of the SARS-CoV-2 Main Protease.

The current COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) created a global health crisis. The ability of vaccines to protect immunocompromised individuals and from emerging new strains are major concerns. Hence antiviral drugs against SARS-CoV-2 are essential. The SARS-CoV-2 main protease Mpro is vital for replication and an important target for antivirals. Using CMap analysis and docking studies, withaferin A (wifA) and withanone (win), two natural products from the medicinal herb Withania somnifera (ashwagandha), were identified as promising candidates that can covalently inhibit the viral protease Mpro. Cell culture, enzymatic, LC-MS/MS, computational, and equilibrium dialysis based assays were performed. DFT calculations indicated that wifA and win can form stable adducts with thiols. The cytotoxicity of Mpro was significantly reduced by wifA and win. Both wifA and win were found to irreversibly inhibit 0.5 μM Mpro with IC50 values of 0.54 and 1.8 μM, respectively. LC-MS/MS analysis revealed covalent adduct formation with wifA at cysteines 145 and 300 of Mpro. The natural products wifA and win can irreversibly inhibit the SARS-CoV-2 main protease Mpro. Based on the work presented here we propose that both wifA and win have the potential to be safely used as preventative and therapeutic interventions for COVID-19.

Identification of endoplasmic reticulum stress-associated genes and subtypes for prediction of Alzheimer's disease based on interpretable machine learning.

Introduction: Alzheimer’s disease (AD) is a severe dementia with clinical and pathological heterogeneity. Our study was aim to explore the roles of endoplasmic reticulum (ER) stress-related genes in AD patients based on interpretable machine learning. Methods: Microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. We performed nine machine learning algorithms including AdaBoost, Logistic Regression, Light Gradient Boosting (LightGBM), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest, K-nearest neighbors (KNN), Naïve Bayes, and support vector machines (SVM) to screen ER stress-related feature genes and estimate their efficiency of these genes for early diagnosis of AD. ROC curves were performed to evaluate model performance. Shapley additive explanation (SHAP) was applied for interpreting the results of these models. AD patients were classified using a consensus clustering algorithm. Immune infiltration and functional enrichment analysis were performed via CIBERSORT and GSVA, respectively. CMap analysis was utilized to identify subtype-specific small-molecule compounds. Results: Higher levels of immune infiltration were found in AD individuals and were markedly linked to deregulated ER stress-related genes. The SVM model exhibited the highest AUC (0.879), accuracy (0.808), recall (0.773), and precision (0.809). Six characteristic genes (RNF5, UBAC2, DNAJC10, RNF103, DDX3X, and NGLY1) were determined, which enable to precisely predict AD progression. The SHAP plots illustrated how a feature gene influence the output of the SVM prediction model. Patients with AD could obtain clinical benefits from the feature gene-based nomogram. Two ER stress-related subtypes were defined in AD, subtype2 exhibited elevated immune infiltration levels and immune score, as well as higher expression of immune checkpoint. We finally identified several subtype-specific small-molecule compounds. Conclusion: Our study provides new insights into the role of ER stress in AD heterogeneity and the development of novel targets for individualized treatment in patients with AD.

The molecular mechanism of Ang II induced-AAA models based on proteomics analysis in ApoE−/− and CD57BL/6J mice

Apolipoprotein knockout (ApoE−/−) and CD57BL/6J mouse models of angiotensin II (Ang II)-induced abdominal aortic aneurysm (AAA) are commonly used in AAA research. However, the similarities and differences in the molecular mechanisms of AAA in these two genotypes have not been reported. In our study, we analyzed proteomics data from ApoE−/− and CD57BL/6J mouse models of Ang II-induced AAA and control mice by LC–MS/MS. Gene set enrichment analysis (GSEA) of differentially abundance proteins (DAPs) in the ApoE−/− or CD57BL/6J mouse groups was performed in R software, and infiltration of immune cells in groups was assessed. DAP that showed the same trend in abundance in ApoE−/− and CD57BL/6J mice (S-DAP) were identified and subjected to GO enrichment, KEGG pathway, and connectivity map (CMap) analyses. The protein–protein interaction (PPI) network of the S-DAP was drawn, the key S-DAP were identified by MCODE, and the transcription factors (TFs) of crucial S-DAP were predicted by iRegulon in Cytoscape. Male ApoE−/− and CD57BL/6J mouse models of Ang II-induced AAA are commonly used in AAA research, and extracellular matrix organization is associated with AAA in both of these models. However, there are some differences between the mechanisms underlying AAA in these two genotypes, and these differences need to be considered when studying AAA and selecting models. SignificanceOur research provided the first insight into the similarity and differential mechanisms of Ang II infused AAA models using ApoE−/− and CD57BL/6J mice. This study might provide the some advises for the selection of Ang II infused AAA models for further AAA researches.

Deciphering the molecular mechanism and crosstalk between Parkinson's disease and breast cancer through multi-omics and drug repurposing approach

Epidemiological studies indicate a higher occurrence of breast cancer (BRCA) in patients with Parkinson's disease. However, the exact molecular mechanism is still not precise. Herein, we tested the hypothesis that this inverse comorbidity result from shared genetic and molecular processes. We conducted an integrated omics analysis to identify the common gene signatures associated with PD and BRCA. Secondly, several dysregulated biological processes in both indications were analyzed by functional enrichment methods, and significant overlapping processes were identified. To establish common regulatory mechanisms, information about transcription factors and miRNAs associated with both the disorders was extracted. Finally, disease-specific gene expression signatures were compared through LINCS L1000 analysis to identify potential repurposing drugs for PD. The potential repurposed drug candidates were then correlated with PD-specific gene signatures by Cmap analysis. In conclusion, this study highlights the shared genes, biological pathways and regulatory signatures associated with PD and BRCA with an improved understanding of crosstalk involved. Additionally, the role of therapeutics was investigated in context with their comorbid associations. These findings could help to explain the complex molecular patterns of associations between PD and BRCA.

Pan-cancer gene expression analysis: Identification of deregulated autophagy genes and drugs to target them

Identifying suitable deregulated targets in autophagy pathway is essential for developing autophagy modulating cancer therapies. With this aim, we systematically analyzed the expression levels of genes that contribute to the execution of autophagy in 21 cancers. Deregulated genes for 21 cancers were analyzed using the level 3 mRNA data from TCGAbiolinks. A total of 574 autophagy genes were mapped to the deregulated genes across 21 cancers. PPI network, cluster analysis, gene enrichment, gene ontology, KEGG pathway, patient survival, protein expression and cMap analysis were performed. Among the autophagy genes, 260 were upregulated, and 43 were downregulated across pan-cancer. The upregulated autophagy genes - CDKN2A and BIRC5 - were the most frequent signatures in cancers and could be universal cancer biomarkers. Significant involvement of autophagy process was found in 8 cancers (CHOL, HNSC, GBM, KICH, KIRC, KIRP, LIHC and SARC). Fifteen autophagy hub genes (ATP6V0C, BIRC5, HDAC1, IL4, ITGB1, ITGB4, MAPK3, mTOR, cMYC, PTK2, SRC, TCIRG1, TP63, TP73 and ULK1) were found to be linked with patients survival and also expressed in cancer patients tissue samples, making them as potential drug targets for these cancers. The deregulated autophagy genes were further used to identify drugs Losartan, BMS-345541, Embelin, Abexinostat, Panobinostat, Vorinostat, PD-184352, PP-1, XMD-1150, Triplotide, Doxorubicin and Ouabain, which could target one or more autophagy hub genes. Overall, our findings shed light on the most frequent cancer-associated autophagy genes, potential autophagy targets and molecules for cancer treatment. These findings can accelerate autophagy modulation in cancer therapy.

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.