Integrated Gene Expression Data-Driven Identification of Molecular Signatures, Prognostic Biomarkers, and Drug Targets for Glioblastoma.

Glioblastoma (GBM) is a highly prevalent and deadly brain tumor with high mortality rates, especially among adults. Despite extensive research, the underlying mechanisms driving its progression remain poorly understood. Computational analysis offers a powerful approach to explore potential prognostic biomarkers, drug targets, and therapeutic agents for GBM. In this study, we utilized three gene expression datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with GBM progression. Our goal was to uncover key molecular players implicated in GBM pathogenesis and potential avenues for targeted therapy. Analysis of the gene expression datasets revealed a total of 78 common DEGs that are potentially involved in GBM progression. Through further investigation, we identified nine hub DEGs that are highly interconnected in protein-protein interaction (PPI) networks, indicating their central role in GBM biology. Gene Ontology (GO) and pathway enrichment analyses provided insights into the biological processes and immunological pathways influenced by these DEGs. Among the nine identified DEGs, survival analysis demonstrated that increased expression of GMFG correlated with decreased patient survival rates in GBM, suggesting its potential as a prognostic biomarker and preventive target for GBM. Furthermore, molecular docking and ADMET analysis identified two compounds from the NIH clinical collection that showed promising interactions with the GMFG protein. Besides, a 100 nanosecond molecular dynamics (MD) simulation evaluated the conformational changes and the binding strength. Our study highlights the potential of GMFG as both a prognostic biomarker and a therapeutic target for GBM. The identification of GMFG and its associated pathways provides valuable insights into the molecular mechanisms driving GBM progression. Moreover, the identification of candidate compounds with potential interactions with GMFG offers exciting possibilities for targeted therapy development. However, further laboratory experiments are required to validate the role of GMFG in GBM pathogenesis and to assess the efficacy of potential therapeutic agents targeting this molecule.

CRP and IHF act as host regulators in Royal Jelly's antibacterial activity.

Royal Jelly (RJ) is a natural substance produced by honeybees, serving not only as nutrition for bee brood and queens but also as a functional food due to its health-promoting properties. Despite its well-known broad-spectrum antibacterial activity, the precise molecular mechanism underlying its antibacterial action has remained elusive. In this study, we investigated the impact of RJ on the bacteria model MG1655 at its half-maximal inhibitory concentration, employing LC-MS/MS to analyze proteomic changes. The differentially expressed proteins were found to primarily contribute to the suppression of gene expression processes, specifically transcription and translation, disrupting nutrition and energy metabolism, and inducing oxidative stress. Notably, RJ treatment led to a marked inhibition of superoxide dismutase and catalase activities, resulting in heightened oxidative damage and lipid peroxidation. Furthermore, through a protein-protein interaction network analysis using the STRING database, we identified CRP and IHF as crucial host regulators responsive to RJ. These regulators were found to play a pivotal role in suppressing essential hub genes associated with energy production and antioxidant capabilities. Our findings significantly contribute to the understanding of RJ's antibacterial mechanism, highlighting its potential as a natural alternative to conventional antibiotics. The identification of CRP and IHF as central players highlights the intricate regulatory networks involved in RJ's action, offering new targets for developing innovative antimicrobial strategies.

Machine Learning Identify Ferroptosis-Related Genes as Potential Diagnostic Biomarkers for Gastric Intestinal Metaplasia.

BACKGROUND: Gastric intestinal metaplasia(GIM) is an independent risk factor for GC, however, its pathogenesis is still unclear. Ferroptosis is a new type of programmed cell death, which may be involved in the process of GIM. The purpose of this study was to analyze the expression of ferroptosis-related genes (FRGs) in GIM tissues and to explore the relationship between ferroptosis and GIM. METHOD: The results of GIM tissue full transcriptome sequencing were downloaded from Gene Expression Omnibus(GEO) database. R software (V4.2.0) and R packages were used for screening and enrichment analysis of differentially expressed genes(DEGs). The key genes were screened by least absolute shrinkage and selection operator(LASSO) and support vector machine-recursive feature elimination(SVM-RFE) algorithm. Receiver operating characteristic(ROC) curve was used to evaluate the diagnostic efficacy of key genes in GIM. Clinical samples were used to further validate hub genes. RESULTS: A total of 12 differentially expressed ferroptosis-related genes (DEFRGs) were identified. Using two machine learning algorithms, GOT1, ALDH3A2, ACSF2 and SESN2 were identified as key genes. The area under ROC curve (AUC) of GOT1, ALDH3A2, ACSF2 and SESN2 in the training set were 0.906, 0.955, 0.899 and 0.962 respectively, and the AUC in the verification set were 0.776, 0.676, 0.773 and 0.880, respectively. Clinical samples verified the differential expression of GOT1, ACSF2, and SESN2 in GIM. CONCLUSION: We found that there was a significant correlation between ferroptosis and GIM. GOT1, ACSF2 and SESN2 can be used as diagnostic markers to effectively identify GIM.

Unraveling the molecular pathogenesis of Type 2 Diabetes and its impact on female infertility: A bioinformatics and systems biology approach.

Type 2 diabetes mellitus (T2D) has been linked with female infertility (FI). Nevertheless, our understanding of the molecular hallmarks and underlying mechanisms remains elusive. This research article aimed to find the hub genes, pathways, transcription factors, and miRNA involved. For this study, softwares like cytoscape, string, Enrichr, FFL loop, etc., were utilized. This research article employed differentially expressed genes (DEGs) to identify multiple biological targets to understand the association between T2D and female infertility (FI). Between T2D and FI, we found 3869 differentially expressed genes. We have also analyzed different pathways like thyroid hormone signaling pathways, AGE-RAGE signaling pathways in diabetic complications and ubiquitin-mediated proteolysis through pathway analysis. Moreover, hub genes MED17, PRKCG, THRA, FOXO1, NCOA2, PLCG2, COL1A1, CXCL8, PRPF19, ANAPC5, UBE2I, XIAP and KEAP1 have been identified. Additionally, these hub genes were subjected to identify the miRNA-mRNA regulation network specific to T2D-associated female infertility. In the FFL study (Feed Forward Loop), transcription factor (SP1, NFKB1, RELA and FOX01), miRNA (has-mir-7-5p, has-let-7a-5p, hsa-mir-16-5p, hsa-mir-155-5p, has-mir-122-5p, has-let-7b-5p, has-mir-124-3p, has-mir-34a-5p, has-mir-130a-3p, has-let-7i-5p, and hsa-mir-27a-3p) and six genes (XIAP, THRA, NCOA2, MED17, FOXO1, and COL1A1) among the thirteen key genes were recognized as regulator and inhibitor. Our analysis reveals that these genes can serve as a significant biomarker for female infertility linked with Type 2 Diabetes, through the prioritization of candidate genes. This study gives us insight into the molecular and cellular mechanism of T2D-associated FI. This finding helps in developing novel therapeutic approaches and will improve efficacy and reduce side effects of the treatment. This research requires further experimental investigation of the principal targets.

New insights into PSAT1 as a therapeutic target for myelodysplastic syndrome (MDS).

The metabolomic landscape in myelodysplastic syndrome (MDS) is highly deregulated and presents promising avenues for understanding disease pathogenesis and potential molecular dependencies. Here, we evaluated the transcriptomic landscape in MDS in multiple independent studies focusing more on metabolomics pathways. Identifying molecular dependencies will pave the way for a more precise disease stratification as well as the development of novel personalized treatment strategies. The study adopted a retrospective, cross-sectional approach, utilizing transcriptomic data from multiple MDS studies. The transcriptomic data were then subjected to comprehensive analyses, including differential gene expression, gene enrichment analysis, gene co-expression analysis, protein-protein interaction analyses, and survival analyses. PSAT1 showed a significant upregulation profile in MDS patients. This observed upregulation is correlated with the deregulation of immune-related pathways in MDS samples. This observation suggests a novel role for PSAT1 in immune modulation and potentially in augmenting immune evasion, which may lead to poor prognosis. This was evident in other tumors in the TCGA database, where cancer patients with high PSAT1 expression have a shorter overall survival. This study unveils a novel potential therapeutic avenue in MDS. Identifying the role of the PSAT1 gene sheds light on the disease's intricate biology, highlighting the ongoing cross-talk between metabolism and immune regulation, which may pave the way for innovative treatment modalities.

Identification of the novel exhausted T cell CD8 + markers in breast cancer.

Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein-protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the separation of patients with different immune relevance and survival. CRTAM, CLEC2D, and KLRB1 were identified as CD8Tex hub genes and were demonstrated to have potential clinical relevance and immune therapy impact. This study provides a unique view of the critical CD8Tex hub genes for cancer immune therapy.

Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction.

Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach.

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

Identification of diagnostic genes and drug prediction in metabolic syndrome-associated rheumatoid arthritis by integrated bioinformatics analysis, machine learning, and molecular docking.

Background: Interactions between the immune and metabolic systems may play a crucial role in the pathogenesis of metabolic syndrome-associated rheumatoid arthritis (MetS-RA). The purpose of this study was to discover candidate biomarkers for the diagnosis of RA patients who also had MetS. Methods: Three RA datasets and one MetS dataset were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) were employed to identify hub genes in MetS-RA. Enrichment analysis was used to explore underlying common pathways between MetS and RA. Receiver operating characteristic curves were applied to assess the diagnostic performance of nomogram constructed based on hub genes. Protein-protein interaction, Connectivity Map (CMap) analyses, and molecular docking were utilized to predict the potential small molecule compounds for MetS-RA treatment. qRT-PCR was used to verify the expression of hub genes in fibroblast-like synoviocytes (FLS) of MetS-RA. The effects of small molecule compounds on the function of RA-FLS were evaluated by wound-healing assays and angiogenesis experiments. The CIBERSORT algorithm was used to explore immune cell infiltration in MetS and RA. Results: MetS-RA key genes were mainly enriched in immune cell-related signaling pathways and immune-related processes. Two hub genes (TYK2 and TRAF2) were selected as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning and proved to have a high diagnostic value (area under the curve, TYK2, 0.92; TRAF2, 0.90). qRT-PCR results showed that the expression of TYK2 and TRAF2 in MetS-RA-FLS was significantly higher than that in non-MetS-RA-FLS (nMetS-RA-FLS). The combination of CMap analysis and molecular docking predicted camptothecin (CPT) as a potential drug for MetS-RA treatment. In vitro validation, CPT was observed to suppress the cell migration capacity and angiogenesis capacity of MetS-RA-FLS. Immune cell infiltration results revealed immune dysregulation in MetS and RA. Conclusion: Two hub genes were identified in MetS-RA, a nomogram for the diagnosis of RA and MetS was established based on them, and a potential therapeutic small molecule compound for MetS-RA was predicted, which offered a novel research perspective for future serum-based diagnosis and therapeutic intervention of MetS-RA.

Network Pharmacology Followed by Experimental Validation to Explore the Mechanism of Stigmasterol in Sangbaipi Decoction Regulating PI3K/Akt Signaling to Alleviate Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

Purpose: Sangbaipi decoction (SBPD), a traditional Chinese medicine (TCM) prescription, has been widely used to treat acute exacerbation of chronic obstructive pulmonary disease (AECOPD), while the underlying pharmacological mechanism remains unclear due to the complexity of composition. Methods: A TCM-active ingredient-drug target network of SBPD was constructed utilizing the TCM-Systems-Pharmacology database. AECOPD-relevant proteins were gathered from Gene Cards and the Online-Mendelian-Inheritance-in-Man database. Protein-protein interaction, GO and KEGG enrichment analyses of the targets from the intersection of SBPD and AECOPD targets were performed to identify the core signaling pathway, followed by molecular docking verification of its interaction with active ingredients. The network pharmacology results were checked using in-vivo experiments. To induce AECOPD, rats were exposure to combined tobacco smoke and lipopolysaccharide (LPS). Then rats underwent gavage with stigmasterol (SM) after successful modeling. The involvement of phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) signaling was investigated using its inhibitor, LY294002. Lung function and histopathology were examined. The levels of inflammatory cytokines in the lung and serum were assessed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), Western blot and/or Enzyme-linked immunosorbent assay (ELISA). Results: SM was recognized as an active ingredient of SBPD and stably bound to Akt1. SM improved lung function and histological abnormalities, concomitant with suppressed PI3K/Akt signaling, downregulated lung and serum Interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α) levels and serum transforming growth factor-ß (TGF-ß) levels and upregulated lung and serum Interleukin 10 (IL-10) levels in AECOPD rats. In AECOPD rats, LY294002 restored lung function, and it also improved lung histological abnormalities and inflammation, which was found to be potentiated by SM. Conclusion: SM targets PI3K/Akt signaling to reduce lung injury and inflammation in AECOPD rats.

Common ground on immune infiltration landscape and diagnostic biomarkers in diabetes-complicated atherosclerosis: an integrated bioinformatics analysis.

Introduction: Type 2 diabetes mellitus (T2DM) is a major cause of atherosclerosis (AS). However, definitive evidence regarding the common molecular mechanisms underlying these two diseases are lacking. This study aimed to investigate the mechanisms underlying the association between T2DM and AS. Methods: The gene expression profiles of T2DM (GSE159984) and AS (GSE100927) were obtained from the Gene Expression Omnibus, after which overlapping differentially expressed gene identification, bioinformatics enrichment analyses, protein-protein interaction network construction, and core genes identification were performed. We confirmed the discriminatory capacity of core genes using receiver operating curve analysis. We further identified transcription factors using TRRUST database to build a transcription factor-mRNA regulatory network. Finally, the immune infiltration and the correlation between core genes and differential infiltrating immune cells were analyzed. Results: A total of 27 overlapping differentially expressed genes were identified under the two-stress conditions. Functional analyses revealed that immune responses and transcriptional regulation may be involved in the potential pathogenesis. After protein-protein interaction network deconstruction, external datasets, and qRT-PCR experimental validation, four core genes (IL1B, C1QA, CCR5, and MSR1) were identified. ROC analysis further showed the reliable value of these core genes. Four common differential infiltrating immune cells (B cells, CD4+ T cells, regulatory T cells, and M2 macrophages) between T2DM and AS datasets were selected based on immune cell infiltration. A significant correlation between core genes and common differential immune cells. Additionally, five transcription factors (RELA, NFκB1, JUN, YY1, and SPI1) regulating the transcription of core genes were mined using upstream gene regulator analysis. Discussion: In this study, common target genes and co-immune infiltration landscapes were identified between T2DM and AS. The relationship among five transcription factors, four core genes, and four immune cells profiles may be crucial to understanding T2DM complicated with AS pathogenesis and therapeutic direction.

Network analysis and experimental verification of Salvia miltiorrhiza Bunge-Reynoutria japonica Houtt. drug pair in the treatment of non-alcoholic fatty liver disease.

CONTEXT: There are currently no approved specific clinical drugs for non-alcoholic fatty liver disease (NAFLD). Salvia miltiorrhiza Bunge-Reynoutria japonica Houtt. drug pair (SRDP) has been widely used in the treatment of chronic liver diseases. However, the mechanism of SRDP treating NAFLD remains unclear. OBJECTIVE: Based on network analysis and in vitro experimental verification, we investigated the effect of SRDP on lipid deposition and explored its possible mechanism for the treatment of NAFLD. METHODS: The TCMSP platform was used to screen the active metabolites of SRDP and corresponding targets. The GeneCards and OMIM databases were used to screen the NAFLD targets. The drug-disease intersecting targets were extracted to obtain the potential targets. Then the protein-protein interaction (PPI) and drug-active metabolites-target-disease network map was constructed. The DAVID database was performed to GO and KEGG pathway enrichment analysis for the intersecting targets. The core active metabolite and signaling pathway were verified by in vitro experiments. RESULTS: Network analysis predicted 59 active metabolites and 89 targets of SRDP for the treatment of NAFLD. 112 signaling pathways were enriched for KEGG pathways, including PI3K-AKT signaling pathway,etc. It was confirmed that luteolin, the core active metabolite of SRDP, effectively reduced fat accumulation and intracellular triglyceride content in HepG2 fatty liver cell model. Luteolin could inhibit mTOR pathway by inhibiting PI3K-AKT signaling pathway phosphorylation, thereby activating autophagy to alleviate NAFLD. DISCUSSION AND CONCLUSION: The results of this study validate and predict the possible role of various active metabolites of SRDP in the treatment of NAFLD through multiple targets and signaling pathways. The core active metabolite of SRDP, luteolin can alleviate NAFLD by acting on the PI3K-AKT-mTOR signaling pathway to induce autophagy.

Identification of regulatory networks and crosstalk factors in brown adipose tissue and liver of a cold-exposed cardiometabolic mouse model.

BACKGROUND: Activation of brown adipose tissue (BAT) has gained attention due to its ability to dissipate energy and counteract cardiometabolic diseases (CMDs). METHODS: This study investigated the consequences of cold exposure on the BAT and liver proteomes of an established CMD mouse model based on LDL receptor-deficient (LdlrKO) mice fed a high-fat, high-sucrose, high-cholesterol diet for 16 weeks. We analyzed energy metabolism in vivo and performed untargeted proteomics on BAT and liver of LdlrKO mice maintained at 22 °C or 5 °C for 7 days. RESULTS: We identified several dysregulated pathways, miRNAs, and transcription factors in BAT and liver of cold-exposed Ldlrko mice that have not been previously described in this context. Networks of regulatory interactions based on shared downstream targets and analysis of ligand-receptor pairs identified fibrinogen alpha chain (FGA) and fibronectin 1 (FN1) as potential crosstalk factors between BAT and liver in response to cold exposure. Importantly, genetic variations in the genes encoding FGA and FN1 have been associated with cardiometabolic-related phenotypes and traits in humans. DISCUSSION: This study describes the key factors, pathways, and regulatory networks involved in the crosstalk between BAT and the liver in a cold-exposed CMD mouse model. These findings may provide a basis for future studies aimed at testing whether molecular mediators, as well as regulatory and signaling mechanisms involved in tissue adaption upon cold exposure, could represent a target in cardiometabolic disorders.

Repurposing Proximity-Dependent Protein Labeling (BioID2) for Protein Interaction Mapping in E. coli.

Methods that identify protein-protein interactions are essential for understanding molecular mechanisms controlling biological systems. Proximity-dependent labeling has proven to be a valuable method for revealing protein-protein interaction networks in living cells. A mutant form of the biotin protein ligase enzyme from Aquifex aeolicus (BioID2) underpins this methodology by producing biotin that is attached to proteins that enter proximity to it. This labels proteins for capture, extraction, and identification. In this chapter, we present a toolkit for BioID2 specifically adapted for use in E. coli, exemplified by the chemotaxis protein CheA. We have created plasmids containing BioID2 as expression cassettes for proteins (e.g., CheA) fused to BioID2 at either the N or C terminus, optimized with an 8 × GGS linker. We provide a methodology for expression and verification of CheA-BioID2 fusion proteins in E. coli cells, the in vivo biotinylation of interactors by protein-BioID2 fusions, and extraction and analysis of interacting proteins that have been biotinylated.

Exploring the Mechanism of Myrrh in the Treatment of Breast Cancer Based on Network Pharmacology and Cell Experiments.

This study aimed to investigate the mechanism of action of myrrh in breast cancer (BC) treatment and identify its effective constituents. Data on the compounds and targets of myrrh were collected from the TCMSP, PubChem, and Swiss Target Prediction databases. BC-related targets were obtained from the Genecard database. A protein-protein interaction (PPI) analysis, gene ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted on the intersecting targets of the disease and drug. The key targets of myrrh in BC treatment were identified based on the PPI network. The active constituents of myrrh were determined through reverse-screening using the top 20 KEGG pathways. Macromolecular docking studies, molecular dynamic (MD) simulations, and cell assays were utilized to validate the active constituents and critical targets. Network pharmacology indicated that VEGFA, TP53, ESR1, EGFR, and AKT1 are key targets of myrrh. Pelargonidin chloride, Quercetin, and Naringenin were identified as the active constituents of myrrh. Macromolecular docking showed that Quercetin and Naringenin have strong docking capabilities with ESR1. The results of MD simulation experiments align with those of molecular docking experiments. Cell and western blot assays demonstrated that Quercetin and Naringenin could inhibit MCF-7 cells and significantly reduce the expression of ESR1 protein. The findings reveal the active constituents, key targets, and molecular mechanisms of myrrh in BC treatment, providing scientific evidence that supports the role of myrrh in BC therapy. Furthermore, the results suggest that network pharmacology predictions require experimental validation for reliability.

Systems biology-based analysis exploring shared biomarkers and pathogenesis of myocardial infarction combined with osteoarthritis.

Background: More and more evidence supports the association between myocardial infarction (MI) and osteoarthritis (OA). The purpose of this study is to explore the shared biomarkers and pathogenesis of MI complicated with OA by systems biology. Methods: Gene expression profiles of MI and OA were downloaded from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-Expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify the common DEGs. The shared genes related to diseases were screened by three public databases, and the protein-protein interaction (PPI) network was built. GO and KEGG enrichment analyses were performed on the two parts of the genes respectively. The hub genes were intersected and verified by Least absolute shrinkage and selection operator (LASSO) analysis, receiver operating characteristic (ROC) curves, and single-cell RNA sequencing analysis. Finally, the hub genes differentially expressed in primary cardiomyocytes and chondrocytes were verified by RT-qPCR. The immune cell infiltration analysis, subtypes analysis, and transcription factors (TFs) prediction were carried out. Results: In this study, 23 common DEGs were obtained by WGCNA and DEGs analysis. In addition, 199 common genes were acquired from three public databases by PPI. Inflammation and immunity may be the common pathogenic mechanisms, and the MAPK signaling pathway may play a key role in both disorders. DUSP1, FOS, and THBS1 were identified as shared biomarkers, which is entirely consistent with the results of single-cell RNA sequencing analysis, and furher confirmed by RT-qPCR. Immune infiltration analysis illustrated that many types of immune cells were closely associated with MI and OA. Two potential subtypes were identified in both datasets. Furthermore, FOXC1 may be the crucial TF, and the relationship of TFs-hub genes-immune cells was visualized by the Sankey diagram, which could help discover the pathogenesis between MI and OA. Conclusion: In summary, this study first revealed 3 (DUSP1, FOS, and THBS1) novel shared biomarkers and signaling pathways underlying both MI and OA. Additionally, immune cells and key TFs related to 3 hub genes were examined to further clarify the regulation mechanism. Our study provides new insights into shared molecular mechanisms between MI and OA.

Identification of differentially expressed genes, pathways, and immune infiltration in diabetes.

This study aimed to perform exhaustive bioinformatic analysis by using GSE29221 micro-array maps obtained from healthy controls and Type 2 Diabetes (T2DM) patients. Raw data are downloaded from the Gene Expression Omnibus database and processed by the limma package in R software to identify Differentially Expressed Genes (DEGs). Gene ontology functional analysis and Kyoto Gene Encyclopedia and Genome Pathway analysis are performed to determine the biological functions and pathways of DEGs. A protein interaction network is constructed using the STRING database and Cytoscape software to identify key genes. Finally, immune infiltration analysis is performed using the Cibersort method. This study has implications for understanding the underlying molecular mechanism of T2DM and provides potential targets for further research.

The mechanism of geniposide in patients with COVID-19 and atherosclerosis: A pharmacological and bioinformatics analysis.

In patients with severe acute respiratory syndrome coronavirus 2 (which causes coronavirus disease 2019 [COVID-19]), oxidative stress (OS) is associated with disease severity and death. OS is also involved in the pathogenesis of atherosclerosis (AS). Previous studies have shown that geniposide has anti-inflammatory and anti-viral properties, and can protect cells against OS. However, the potential target(s) of geniposide in patients with COVID-19 and AS, as well as the mechanism it uses, are unclear. We combined pharmacology and bioinformatics analysis to obtain geniposide against COVID-19/AS targets, and build protein-protein interaction network to filter hub genes. The hub genes were performed an enrichment analysis by ClueGO, including Gene Ontology and KEGG. The Enrichr database and the target microRNAs (miRNAs) of hub genes were predicted through the MiRTarBase via Enrichr. The common miRNAs were used to construct the miRNAs-mRNAs regulated network, and the miRNAs' function was evaluated by mirPath v3.0 software. Two hundred forty-seven targets of geniposide were identified in patients with COVID-19/AS comorbidity by observing the overlap between the genes modulated by geniposide, COVID-19, and AS. A protein-protein interaction network of geniposide in patients with COVID-19/AS was constructed, and 27 hub genes were identified. The results of enrichment analysis suggested that geniposide may be involved in regulating the OS via the FoxO signaling pathway. MiRNA-mRNA network revealed that hsa-miR-34a-5p may play an important role in the therapeutic mechanism of geniposide in COVID-19/AS patients. Our study found that geniposide represents a promising therapy for patients with COVID-19 and AS comorbidity. Furthermore, the target genes and miRNAs that we identified may aid the development of new treatment strategies against COVID-19/AS.

Hypoxia-related signature to risk stratify patients for the benefit of immune checkpoint inhibitors therapy in head and neck squamous cell carcinoma: An experimental study.

BACKGROUND: Increasing evidence has shown that hypoxia is a biomarker of tumor proliferation and metastasis. This research aimed to identify a hypoxia-associated gene prognostic index (HAGPI) in head and neck squamous cell carcinoma (HNSCC) and based on HAGPI-defined subgroups to predict prognosis and response to immune checkpoint inhibitors therapy. METHODS: RNA-sequencing transcriptomic data for patients with HNSCC were downloaded from The Cancer Genome Atlas (TCGA). Protein-protein interaction network analysis was performed to select hypoxia-related hub genes. Univariate and multivariate cox regression analyses were used to identify hub genes to develop the HAGPI. Afterward expression data were imported into CIBERSORT to evaluate the relative proportion of 22 immune cells and compared the relative proportions of immune cells between the 2 HAGPI subgroups. The relationship between immunopheno score (IPS) and HAGPI was validated for immune checkpoint inhibitors (ICIs) response in TCGA cohorts. RESULTS: The HAGPI was constructed based on HS3ST1, HK1, PGK1, STC2, SERPINE1, PKLR genes. In high-HAGPI patients, the primary and secondary endpoint events in TCGA and GEO cohorts were significantly lower than low-HAGPI groups (P < .05). HAGPI-high patients exhibited a poorer prognosis than HAGPI-low patients did. The abundance of M2 macrophages and NK cell were significantly enhanced in the high-HAGPI while T cells regulatory and T cells CD8, were markedly elevated in the low-HAGPI. Meanwhile, patients in the low-HAGPI patients had higher levels of immunosuppressant expression and less aggressive phenotypes. Furthermore, IPS analysis showed that the low-HAGPI group with higher IPS represented a more immunogenic phenotype. CONCLUSION: The current study developed and verified a HAPGI model that can be considered as an independent prognostic biomarker and elucidated the tumor immune microenvironment of HNSCC.

Investigation of the relationship between COVID-19 and pancreatic cancer using bioinformatics and systems biology approaches.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, poses a huge threat to human health. Pancreatic cancer (PC) is a malignant tumor with high mortality. Research suggests that infection with SARS-CoV-2 may increase disease severity and risk of death in patients with pancreatic cancer, while pancreatic cancer may also increase the likelihood of contracting SARS-CoV-2, but the link is unclear. METHODS: This study investigated the transcriptional profiles of COVID-19 and PC patients, along with their respective healthy controls, using bioinformatics and systems biology approaches to uncover the molecular mechanisms linking the 2 diseases. Specifically, gene expression data for COVID-19 and PC patients were obtained from the Gene Expression Omnibus datasets, and common differentially expressed genes (DEGs) were identified. Gene ontology and pathway enrichment analyses were performed on the common DEGs to elucidate the regulatory relationships between the diseases. Additionally, hub genes were identified by constructing a protein-protein interaction network from the shared DEGs. Using these hub genes, we conducted regulatory network analyses of microRNA/transcription factors-genes relationships, and predicted potential drugs for treating COVID-19 and PC. RESULTS: A total of 1722 and 2979 DEGs were identified from the transcriptome data of PC (GSE119794) and COVID-19 (GSE196822), respectively. Among these, 236 common DEGs were found between COVID-19 and PC based on protein-protein interaction analysis. Functional enrichment analysis indicated that these shared DEGs were involved in pathways related to viral genome replication and tumorigenesis. Additionally, 10 hub genes, including extra spindle pole bodies like 1, holliday junction recognition protein, marker of proliferation Ki-67, kinesin family member 4A, cyclin-dependent kinase 1, topoisomerase II alpha, cyclin B2, ubiquitin-conjugating enzyme E2 C, aurora kinase B, and targeting protein for Xklp2, were identified. Regulatory network analysis revealed 42 transcription factors and 23 microRNAs as transcriptional regulatory signals. Importantly, lucanthone, etoposide, troglitazone, resveratrol, calcitriol, ciclopirox, dasatinib, enterolactone, methotrexate, and irinotecan emerged as potential therapeutic agents against both COVID-19 and PC. CONCLUSION: This study unveils potential shared pathogenic mechanisms between PC and COVID-19, offering novel insights for future research and therapeutic strategies for the treatment of PC and SARS-CoV-2 infection.