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The term "Long-COVID" (LC) is characterized by the aftereffects of COVID-19 infection. Various studies have suggested that Epstein-Barr virus (EBV) reactivation is among the significant reported causes of LC. However, there is a lack of in-depth research that could largely explore the pathogenic mechanism and pinpoint the key genes in the EBV and LC context. This study mainly aimed to predict the potential disease-associated common genes between EBV reactivation and LC condition using next-generation sequencing (NGS) data and reported naturally occurring biomolecules as inhibitors. We applied the bulk RNA-Seq from LC and EBV-infected peripheral blood mononuclear cells (PBMCs), identified the differentially expressed genes (DEGs) and the Protein-Protein interaction (PPI) network using the STRING database, identified hub genes using the cytoscape plugins CytoHubba and MCODE, and performed enrichment analysis using ClueGO. The interaction analysis of a hub gene was performed against naturally occurring bioflavonoid molecules using molecular docking and the molecular dynamics (MD) simulation method. Out of 357 common genes, 22 genes (CCL2, CCL20, CDCA2, CEP55, CHI3L1, CKAP2L, DEPDC1, DIAPH3, DLGAP5, E2F8, FGF1, NEK2, PBK, TOP2A, CCL3, CXCL8, DEPDC1, IL6, RETN, MMP2, LCN2, and OLR1) were classified as hub genes, and the remaining ones were classified as neighboring genes. Enrichment analysis showed the role of hub genes in various pathways such as immune-signaling pathways, including JAK-STAT signaling, interleukin signaling, protein kinase signaling, and toll-like receptor pathways associated with the symptoms reported in the LC condition. ZNF and MYBL TF-family were predicted as abundant TFs controlling hub genes' transcriptional machinery. Furthermore, OLR1 (PDB: 7XMP) showed stable interactions with the five shortlisted refined naturally occurring bioflavonoids, i.e., apigenin, amentoflavone, ilexgenin A, myricetin, and orientin compounds. The total binding energy pattern was observed, with amentoflavone being the top docked molecule (with a binding affinity of -8.3 kcal/mol) with the lowest total binding energy of -18.48 kcal/mol. In conclusion, our research has predicted the hub genes, their molecular pathways, and the potential inhibitors between EBV and LC potential pathogenic association. The in vivo or in vitro experimental methods could be utilized to functionally validate our findings, which would be helpful to cure LC or to prevent EBV reactivation. [ABSTRACT FROM AUTHOR]

Abstract Recently, we have reported that extracellular vesicles (EVs) from the bone marrow mesenchymal stromal cells (BM-MSC) of aplastic anemia (AA) patients inhibit hematopoietic stem and progenitor cell (HSPC) proliferative and colony-forming ability and promote apoptosis. One mechanism by which AA BM-MSC EVs might contribute to these altered HSPC functions is through microRNAs (miRNAs) encapsulated in EVs. However, little is known about the role of BM-MSC EVs derived miRNAs in regulating HSPC functions in AA. Therefore, we performed miRNA profiling of EVs from BM-MSC of AA (n = 6) and normal controls (NC) (n = 6) to identify differentially expressed miRNAs. The Integrated DEseq2 analysis revealed 34 significantly altered mature miRNAs, targeting 235 differentially expressed HSPC genes in AA. Hub gene analysis revealed 10 HSPC genes such as IGF-1R, IGF2R, PAK1, PTPN1, etc., which are targeted by EV miRNAs and had an enrichment of chemokine, MAPK, NK cell-mediated cytotoxicity, Rap1, PI3k-Akt, mTOR signalling pathways which are associated with hematopoietic homeostasis. We further showed that miR-139-5p and its target, IGF-1R (hub-gene), might regulate HSPC proliferation and apoptosis, which may serve as potential therapeutic targets in AA. Overall, the study highlights that AA BM-MSC EV miRNAs could contribute to impaired HSPC functions in AA.

Abstract Psoriasis, a chronic inflammatory skin disorder, is associated with comorbidities such as acute myocardial infarction (AMI). However, the molecular mechanisms connecting these conditions are unclear. In this study, we conducted bioinformatics analyses using gene expression datasets to identify differentially expressed genes and hub genes associated with both psoriasis and AMI. Our findings emphasize the involvement of immune-related pathways in the pathogenesis of both conditions. Furthermore, we investigated the expression levels of hub genes in AMI patients and myocardial infarction (MI) mice. ELISA measurements revealed significantly higher levels of CXCL8, IL1B, S100A9, and S100A12 in the serum of AMI patients compared to normal individuals. Immunohistochemical staining of heart tissue from MI mice showed a progressive increase in the expression of CXCL8 and IL-1B as MI advanced, while S100A9 exhibited high expression at day 3 post-MI. mRNA expression analysis validated these findings. Additionally, we explored the skin lesions of psoriasis patients and found significantly higher expression of CXCL8, IL-1B, S100A9, and S100A12 in the affected skin areas compared to unaffected regions. These results highlight the consistent upregulation of hub genes in both AMI and psoriasis patients, as well as in myocardial infarction mice, underscoring their potential as reliable markers for disease diagnosis. Moreover, molecular docking simulations revealed potential interactions between simvastatin and key target proteins, suggesting a potential therapeutic avenue. Overall, our study uncovers shared molecular signatures and potential therapeutic targets, providing a foundation for future investigations targeting common pathways in psoriasis and AMI.

Recently, we have reported that extracellular vesicles (EVs) from the bone marrow mesenchymal stromal cells (BM-MSC) of aplastic anemia (AA) patients inhibit hematopoietic stem and progenitor cell (HSPC) proliferative and colony-forming ability and promote apoptosis. One mechanism by which AA BM-MSC EVs might contribute to these altered HSPC functions is through microRNAs (miRNAs) encapsulated in EVs. However, little is known about the role of BM-MSC EVs derived miRNAs in regulating HSPC functions in AA. Therefore, we performed miRNA profiling of EVs from BM-MSC of AA (n = 6) and normal controls (NC) (n = 6) to identify differentially expressed miRNAs. The Integrated DEseq2 analysis revealed 34 significantly altered mature miRNAs, targeting 235 differentially expressed HSPC genes in AA. Hub gene analysis revealed 10 HSPC genes such as IGF-1R, IGF2R, PAK1, PTPN1, etc., which are targeted by EV miRNAs and had an enrichment of chemokine, MAPK, NK cell-mediated cytotoxicity, Rap1, PI3k-Akt, mTOR signalling pathways which are associated with hematopoietic homeostasis. We further showed that miR-139-5p and its target, IGF-1R (hub-gene), might regulate HSPC proliferation and apoptosis, which may serve as potential therapeutic targets in AA. Overall, the study highlights that AA BM-MSC EV miRNAs could contribute to impaired HSPC functions in AA. [ABSTRACT FROM AUTHOR]

Psoriasis, a chronic inflammatory skin disorder, is associated with comorbidities such as acute myocardial infarction (AMI). However, the molecular mechanisms connecting these conditions are unclear. In this study, we conducted bioinformatics analyses using gene expression datasets to identify differentially expressed genes and hub genes associated with both psoriasis and AMI. Our findings emphasize the involvement of immune-related pathways in the pathogenesis of both conditions. Furthermore, we investigated the expression levels of hub genes in AMI patients and myocardial infarction (MI) mice. ELISA measurements revealed significantly higher levels of CXCL8, IL1B, S100A9, and S100A12 in the serum of AMI patients compared to normal individuals. Immunohistochemical staining of heart tissue from MI mice showed a progressive increase in the expression of CXCL8 and IL-1B as MI advanced, while S100A9 exhibited high expression at day 3 post-MI. mRNA expression analysis validated these findings. Additionally, we explored the skin lesions of psoriasis patients and found significantly higher expression of CXCL8, IL-1B, S100A9, and S100A12 in the affected skin areas compared to unaffected regions. These results highlight the consistent upregulation of hub genes in both AMI and psoriasis patients, as well as in myocardial infarction mice, underscoring their potential as reliable markers for disease diagnosis. Moreover, molecular docking simulations revealed potential interactions between simvastatin and key target proteins, suggesting a potential therapeutic avenue. Overall, our study uncovers shared molecular signatures and potential therapeutic targets, providing a foundation for future investigations targeting common pathways in psoriasis and AMI. [ABSTRACT FROM AUTHOR]

The term "Long-COVID" (LC) is characterized by the aftereffects of COVID-19 infection. Various studies have suggested that Epstein–Barr virus (EBV) reactivation is among the significant reported causes of LC. However, there is a lack of in-depth research that could largely explore the pathogenic mechanism and pinpoint the key genes in the EBV and LC context. This study mainly aimed to predict the potential disease-associated common genes between EBV reactivation and LC condition using next-generation sequencing (NGS) data and reported naturally occurring biomolecules as inhibitors. We applied the bulk RNA-Seq from LC and EBV-infected peripheral blood mononuclear cells (PBMCs), identified the differentially expressed genes (DEGs) and the Protein–Protein interaction (PPI) network using the STRING database, identified hub genes using the cytoscape plugins CytoHubba and MCODE, and performed enrichment analysis using ClueGO. The interaction analysis of a hub gene was performed against naturally occurring bioflavonoid molecules using molecular docking and the molecular dynamics (MD) simulation method. Out of 357 common genes, 22 genes (CCL2, CCL20, CDCA2, CEP55, CHI3L1, CKAP2L, DEPDC1, DIAPH3, DLGAP5, E2F8, FGF1, NEK2, PBK, TOP2A, CCL3, CXCL8, DEPDC1, IL6, RETN, MMP2, LCN2, and OLR1) were classified as hub genes, and the remaining ones were classified as neighboring genes. Enrichment analysis showed the role of hub genes in various pathways such as immune-signaling pathways, including JAK-STAT signaling, interleukin signaling, protein kinase signaling, and toll-like receptor pathways associated with the symptoms reported in the LC condition. ZNF and MYBL TF-family were predicted as abundant TFs controlling hub genes' transcriptional machinery. Furthermore, OLR1 (PDB: 7XMP) showed stable interactions with the five shortlisted refined naturally occurring bioflavonoids, i.e., apigenin, amentoflavone, ilexgenin A, myricetin, and orientin compounds. The total binding energy pattern was observed, with amentoflavone being the top docked molecule (with a binding affinity of –8.3 kcal/mol) with the lowest total binding energy of −18.48 kcal/mol. In conclusion, our research has predicted the hub genes, their molecular pathways, and the potential inhibitors between EBV and LC potential pathogenic association. The in vivo or in vitro experimental methods could be utilized to functionally validate our findings, which would be helpful to cure LC or to prevent EBV reactivation.

Background and aim: The complex dynamic interplay between different biological pathways involved in atherosclerosis development has rendered the identification of specific therapeutic targets a challenging quest. We aimed to identify specific genes and mechanistic pathways associated with the early development of fibro-atheromas in a swine model of atherosclerosis. Methods: The Wisconsin Miniature Swine™ model of Familial Hypercholesterolemia (WMS-FH, n = 11) and genetically related WMS controls (WMS-N, n = 11) were used. The infrarenal aorta was harvested from both groups for histopathologic and transcriptomic profiling at 12 months. Bioinformatic analysis was performed to identify hub genes and pathways central to disease pathophysiology. The expression of ITGB2, the top ranked hub gene, was manipulated in cell culture and the expression of interconnected genes was tested. Results: Fibro-atheromatous lesions were documented in all WMS-FH aortic tissues and displayed internal elastic lamina (IEL) disruption, significant reduction of myofibroblast presence and disorganized collagen deposition. No fibro-atheromas were observed in the control group. A total of 266 differentially expressed genes (DEGs) were upregulated in WMS-FH aortic tissues, while 29 genes were downregulated. Top identified hub genes included ITGB2, C1QA, LCP2, SPI1, CSF1R, C5AR1, CTSS, MPEG1, C1QC, and CSF2RB. Overexpression of ITGB2 resulted in elevated expression of other interconnected genes expressed in porcine endothelial cells. Conclusion: In a swine translational model of atherosclerosis, transcriptomic analysis identified ITGB2 as a central hub gene associated inflammation and early fibroatheroma development making it a potential therapeutic target at this stage of disease.

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC. [ABSTRACT FROM AUTHOR]

Simple Summary: Aflatoxin B1 a highly distributed and hepatotoxicant leads to liver damage and the subsequent development of liver cancer. The aim is to study the combined genes and their function in liver damage progression to prevent reversible liver fibrosis which is a dynamic and bidirectional process to become irreversible liver cancer as a result of aflatoxin B1 exposure. The present study revealed that the combined differential expressed genes of AFB1-fibrosis-related and liver cancer-related were connected to cell process disruption, the top ten core genes were identified using four different algorithm methods and the combined core genes showed that the BUB1B and RRM2 genes were core genes of AFB1-liver fibrosis-liver cancer. The inflammatory-related signaling ssGSEA score for BUB1B high expression results in a significant increase in the expression of JAK-STAT regulation and TLR signaling with no effect on the RRM2 gene also the immune checkpoint chemotherapy-related high expression of the BUB1B gene was showed to have a significant change in CTLA4 Blockage in TCHA LIHC patients. Our study will contribute to, the aflatoxin b1 hepatotoxicant damaging progression could be controlled by BUB1B and RRM2 gene study. The molecular mechanism of the hepatotoxicant aflatoxin B1 to induce liver fibrosis and hepatocellular carcinoma (HCC) remains unclear, to offer fresh perspectives on the molecular mechanisms underlying the onset and progression of AFB1-Fibrosis-HCC, which may offer novel targets for the detection and therapy of HCC caused by AFB1. In this study, expression profiles of AFB1, liver fibrosis and liver cancer-related datasets were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by the GEO2R tool. The STRING database, CytoHubba, and Cytoscape software were used to create the protein-protein interaction and hub genes of the combined genes, and the ssGSEA score for inflammatory cells related gene sets, the signaling pathway, and immunotherapy were identified using R software and the GSEA database. The findings revealed that AFB1-associated liver fibrosis and HCC combined genes were linked to cell process disruptions, the BUB1B and RRM2 genes were identified as hub genes, and the BUB1B gene was significantly increased in JAK-STAT signaling gene sets pathways as well as having an immunotherapy-related impact. In conclusion, BUB1B and RRM2 were identified as potential biomarkers for AFB1-induced fibrosis and HCC progression. [ABSTRACT FROM AUTHOR]

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study’s findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Background: Despite decades of research, an effective schistosomiasis vaccine remains elusive. The radiation-attenuated (RA) cercarial vaccine remains the best model for eliciting high levels of protection. We have recently explored this model in mice to identify potentially protective pathways by examining gene expression patterns in peripheral blood mononuclear cells (PBMC)., Methods: Herein, we reanalyzed the transcriptomic data from PBMC obtained from vaccinated and infected C57BL/6 mice in three timepoints (Days 7 and 17 after infection or vaccination and Day 7 post-challenge). In addition, we generated new data on PBMC collected 35 days after infection. Deconvolution analysis was performed to estimate immune cell composition by CIBERSORTx. Gene co-expression networks and over-representation analysis (ORA) were performed using the CEMiTool package. Protein-protein interaction networks were constructed using STRING, and the hub proteins for each module were identified using Cytoscape., Results: Co-expression network analysis identified a module (M2) associated with the infection process, grouping genes related to a Th2 immune response, and a second module (M6) associated with the vaccination process, displaying pathways related to a Th1 response, CD8 + T cells and NK cells. Within each module, five hub proteins were identified based on protein-protein interaction networks. The M2 infection module revealed Chil3, Il4, Cx3cr1, Emr1 and Ccl2 as hubs, while module M6, associated with vaccination, disclosed Prf1, Klrc1, IFN-γ, Ncr1 and Tbx21 as hub proteins., Conclusions: Our data point to the potentiald role of NK cells that may contribute to the RA vaccine response through the production of IFN-γ orchestrated by the T-bet transcription factor (Tbx21)., Competing Interests: Declarations Ethics approval and consent to participate The study was approved by Committee for the Ethical Use of Animals in Experimentation of the Butantan Institute (São Paulo, Brazil) under license 1030/13. Consent for publication All authors have reviewed and approved the final version of this manuscript and consent to its publication. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Melanoma is the third most common malignant skin tumor and has increased in morbidity and mortality over the previous decade due to its rapid spread into the bloodstream or lymphatic system. This study used integrated bioinformatics and network-based methodologies to reliably identify molecular targets and small molecular medicines that may be more successful for Melanoma diagnosis, prognosis and treatment. The statistical LIMMA approach utilized for bioinformatics analysis in this study found 246 common differentially expressed genes (cDEGs) between case and control samples from two microarray gene-expression datasets (GSE130244 and GSE15605). Protein-protein interaction network study revealed 15 cDEGs (PTK2, STAT1, PNO1, CXCR4, WASL, FN1, RUNX2, SOCS3, ITGA4, GNG2, CDK6, BRAF, AGO2, GTF2H1 and AR) to be critical in the development of melanoma (KGs). According to regulatory network analysis, the most important transcriptional and post-transcriptional regulators of DEGs and hub-DEGs are ten transcription factors and three miRNAs. We discovered the pathogenetic mechanisms of MC by studying DEGs' biological processes, molecular function, cellular components and KEGG pathways. We used molecular docking and dynamics modeling to select the four most expressed genes responsible for melanoma malignancy to identify therapeutic candidates. Then, utilizing the Connectivity Map (CMap) database, we analyzed the top 4-hub-DEGs-guided repurposable drugs. We validated four melanoma cancer drugs (Fisetin, Epicatechin Gallate, 1237586-97-8 and PF 431396) using molecular dynamics simulation with their target proteins. As a result, the results of this study may provide resources to researchers and medical professionals for the wet-lab validation of MC diagnosis, prognosis and treatments.Communicated by Ramaswamy H. Sarma.

Gastric cancer is the fifth most common malignancy and the third leading cause of cancer-related mortality worldwide. Immunotherapy offers promising new treatment options for gastric cancer patients; however, it is only effective in a limited fraction of patients. In this study, we evaluated the composition of 22 tumor-infiltrating lymphocytes (TILs) in TCGA Stomach Adenocarcinoma (STAD) using deconvolution-based method by analyzing the publicly available bulk tumor RNA-seq data. The patients were classified into high-TIL and low-TIL subtypes based on their immune cell profiles and prognosis outputs. The differentially expressed genes (DEGs) between the two subtypes were identified, and GO/KEGG analysis showed that broad immune genes, such as PD-L1 and PD-1, were highly expressed in the high-TIL subtype. A comprehensive protein–protein interaction (PPI) network centered on DEGs was built, and 16 hub genes of the network were further identified. Based on the hub genes, an elastic model with 11 gene signatures (NKG7, GZMB, IL2RB, CCL5, CD8A, IDO1, MYH1, GNLY, CXCL11, GBP5 and PRF1) was developed to predict the high-TIL subtype. In summary, our findings showed that the compositions of TILs within the tumor immune microenvironment of stomach cancer patients are highly heterogeneous, and the profiles of TILs have the potential to be predictive markers of patients' responses and overall survival outcomes. [ABSTRACT FROM AUTHOR]

The molecular mechanism of the hepatotoxicant aflatoxin B1 to induce liver fibrosis and hepatocellular carcinoma (HCC) remains unclear, to offer fresh perspectives on the molecular mechanisms underlying the onset and progression of AFB1-Fibrosis-HCC, which may offer novel targets for the detection and therapy of HCC caused by AFB1. In this study, expression profiles of AFB1, liver fibrosis and liver cancer-related datasets were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by the GEO2R tool. The STRING database, CytoHubba, and Cytoscape software were used to create the protein-protein interaction and hub genes of the combined genes, and the ssGSEA score for inflammatory cells related gene sets, the signaling pathway, and immunotherapy were identified using R software and the GSEA database. The findings revealed that AFB1-associated liver fibrosis and HCC combined genes were linked to cell process disruptions, the BUB1B and RRM2 genes were identified as hub genes, and the BUB1B gene was significantly increased in JAK-STAT signaling gene sets pathways as well as having an immunotherapy-related impact. In conclusion, BUB1B and RRM2 were identified as potential biomarkers for AFB1-induced fibrosis and HCC progression.

Background: Breast cancer is a complex and heterogeneous disease, and understanding its regulatory mechanisms and network characteristics is essential for identifying therapeutic targets and developing effective treatment strategies. This study aimed to unravel the intricate network of interactions involving differentially expressed genes, microribonucleic acid (miRNAs), and proteins in breast cancer through an integrative analysis of multi-omic data from Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset., Methods: The TCGA-BRCA dataset was used for data acquisition, which included RNA sequencing data for gene expression, miRNA sequencing data for miRNA expression, and protein expression quantification data. Various R packages, such as TCGAbiolinks, limma, and RPPA, were employed for data preprocessing and integration. Differential expression analysis, network construction, miRNA regulation exploration, pathway enrichment analysis, and independent dataset validation were performed., Results: Eight consistently upregulated hub genes-including ACTB, HSP90AA1, FN1, HSPA8, CDC42, CDH1, UBC, and EP300-were identified in breast cancer, indicating their potential significance in driving the disease. Pathway enrichment analysis revealed highly enriched pathways in breast cancer, including proteoglycans in cancer, PI3K-Akt, and mitogen-activated protein kinase signaling., Conclusion: This integrated multi-omic data analysis provides valuable insights into the regulatory mechanisms, network characteristics, and functional roles of genes, miRNAs, and proteins in breast cancer. The findings contribute to our understanding of the molecular landscape of breast cancer, facilitate the identification of potential therapeutic targets, and inform strategies for effective treatment., Competing Interests: The authors declare that they have no competing interests., (© 2024 Iran University of Medical Sciences.)

Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths globally. To reduce HCC-related mortality, early diagnosis and therapeutic improvement are essential. Hub differentially expressed genes (HubGs) may serve as potential diagnostic and prognostic biomarkers, also offering therapeutic targets for precise therapies. Therefore, we aimed to identify top-ranked hub genes for the diagnosis, prognosis, and therapy of HCC., Methods: Through a systematic literature review, 202 HCC-related HubGs were derived from 59 studies, yet consistent detection across these was lacking. Then, we identified top-ranked HubGs (tHubGs) by integrated bioinformatics analysis, highlighting their functions, pathways, and regulators that might be more representative of the diagnosis, prognosis, and therapies of HCC., Results: In this study, eight HubGs (CDK1, AURKA, CDC20, CCNB2, TOP2A, PLK1, BUB1B, and BIRC5) were identified as the tHubGs through the protein-protein interaction (PPI) network and survival analysis. Their differential expression in different stages of HCC, validated using The Cancer Genome Atlas (TCGA) Program database, suggests their potential as early HCC markers. The enrichment analyses revealed some important roles in HCC-related biological processes (BPs), molecular functions (MFs), cellular components (CCs), and signaling pathways. Moreover, the gene regulatory network analysis highlighted key transcription factors (TFs) and microRNAs (miRNAs) that regulate these tHubGs at transcriptional and post-transcriptional. Finally, we selected three drugs (CD437, avrainvillamide, and LRRK2-IN-1) as candidate drugs for HCC treatment as they showed strong binding with all of our proposed and published protein receptors., Conclusions: The findings of this study may provide valuable resources for early diagnosis, prognosis, and therapies for HCC.

Background and Aim: The complex dynamic interplay between different biological pathways involved in atherosclerosis development has rendered the identification of specific therapeutic targets a challenging quest. We aimed to identify specific genes and mechanistic pathways associated with the early development of fibro-atheromas in a swine model of atherosclerosis., Methods: The Wisconsin Miniature Swine™ model of Familial Hypercholesterolemia (WMS-FH, n = 11) and genetically related WMS controls (WMS-N, n = 11) were used. The infrarenal aorta was harvested from both groups for histopathologic and transcriptomic profiling at 12 months. Bioinformatic analysis was performed to identify hub genes and pathways central to disease pathophysiology. The expression of ITGB2, the top ranked hub gene, was manipulated in cell culture and the expression of interconnected genes was tested., Results: Fibro-atheromatous lesions were documented in all WMS-FH aortic tissues and displayed internal elastic lamina (IEL) disruption, significant reduction of myofibroblast presence and disorganized collagen deposition. No fibro-atheromas were observed in the control group. A total of 266 differentially expressed genes (DEGs) were upregulated in WMS-FH aortic tissues, while 29 genes were downregulated. Top identified hub genes included ITGB2, C1QA, LCP2, SPI1, CSF1R, C5AR1, CTSS, MPEG1, C1QC, and CSF2RB. Overexpression of ITGB2 resulted in elevated expression of other interconnected genes expressed in porcine endothelial cells., Conclusion: In a swine translational model of atherosclerosis, transcriptomic analysis identified ITGB2 as a central hub gene associated inflammation and early fibroatheroma development making it a potential therapeutic target at this stage of disease., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Aims: Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings., Background: Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance., Objective: This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection., Results: In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Clear cell renal cell carcinoma (ccRCC) is one of the common subtypes of kidney cancer. Circular RNAs (circRNAs) act as competing endogenous RNAs (ceRNAs) to affect the expression of microRNAs (miRNAs), and hence the expression of genes involved in the development and progression of ccRCC. However, these interactions have not been sufficiently explored., Methods: The differential expression of circRNAs (DEC) was extracted from the GEO database, and the expression of circRNAs was analyzed by the Limma R package. The interaction of miRNAs with circRNAs was predicted using (cancer-specific circRNA database) CSCD and circinteractome database. The genes affected by the miRNAs were predicted by miRwalk version 3, and the differential expression was retrieved using TCGA. Functional enrichment was assessed and a PPI network was created using DAVID and Cytoscape, respectively. The genes with significant interactions (hub-genes) were screened, and the total survival rate of ccRCC patients was extracted from the Gene Expression Profiling Interactive Analysis (GEPIA) database. To confirm the expression of OS genes we used the Immunohistochemistry (IHC) data and TCGA database. The correlation between gene expression and immune cell infiltration was investigated using TIMER2.0. Finally, potential drug candidates were predicted by the cMAP database., Results: Four DECs (hsa_circ_0003340, hsa_circ_0007836, hsa_circ_0020303, and hsa_circ_0001873) were identified, along with 11 interacting miRNAs (miR-1224-3p, miR-1294, miR-1205, miR-1231, miR-615-5p, miR-940, miR-1283, and miR-1305). These miRNAs were predicted to affect 1282 target genes, and function enrichment was used to identify the genes involved in cancer biology. 18 hub-genes (CCR1, VCAM1, NCF2, LAPTM5, NCKAP1L, CTSS, BTK, LILRB2, CD53, MPEG1, C3AR1, GPR183, C1QA, C1QC, P2RY8, LY86, CYBB, and IKZF1) were identified from a PPI network. VCAM1, NCF2, CTSS, LILRB2, MPEG1, C3AR1, P2RY8, and CYBB could affect the survival of ccRCC patients. The hub-gene expression was correlated with tumor immune cell infiltration and patient prognosis. Two potantial drug candidates, naphazoline and lithocholic acid could play a role in ccRCC therapy, as well other cancers., Conclusion: This bioinformatics analysis brings a new insight into the role of circRNA/miRNA/mRNA interactions in ccRCC pathogenesis, prognosis, and possible drug treatment or immunotherapy., (Copyright © 2021. Published by Elsevier Ltd.)

Background: Gastric cancer (GC) is a complex disorder with an inadequate response to treatment. Although many efforts have been made to clarify the development of GC, the exact etiology and molecular mechanisms of this malignancy remain unclear. This study was designed to identify and characterize essential associated genes with GC to construct a prognostic model., Methods: In this Insilco study, the gene expression microarray dataset GSE122401 was downloaded from the Gene Expression Omnibus (GEO). The raw data were processed and quantile-normalized with the edgeR package of R ver.3.5.3. The module-trait relationship and hub-genes associated with GC were analyzed with Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Cluepedia and Enrichr Database. Finally, hub-genes were screened and validated by GEPIA online database., Results: According to the WGCNA results, the blue module was found to be strongly correlated with the GC (r=0.91, p-value=1e-57). DEGs analysis was performed by edgeR package of R and indicated a total of 47 genes as hub-genes. Verifying the hub-genes expression using GEPIA online database showed a significantly increased level of ACAN gene expression in primary cancer cell line compared to metastatic cell line. On the other hand, the expression of MDFI and CHST1 genes in primary cell lines were lower compared to metastatic cancer cell lines., Conclusions: This study provides a framework of the co-expression gene modules ACAN, MDFI, and CHST1 as hub-genes. These hub-genes might offer candidate biomarkers to targeted therapy against GC. Further experiment validation and animal models are needed to reveal the exact mechanism of the above-mentioned genes in the pathogenesis and prognoses of GC.