Apigenin as a Multi-Targeted Agent in Gastrointestinal Cancers: A Systems Pharmacology Approach

BackgroundCervical cancer belongs to one of the most common female cancers; yet, the exact underlying mechanisms are still elusive. Recently, microarray and sequencing technologies have been widely used for screening biomarkers and molecular mechanism discovery in cancer studies. In this study, we aimed to analyse the microarray datasets using comprehensive bioinformatics tools and identified novel biomarkers associated with the prognosis of patients with cervical cancer.MethodsThe differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) datasets including GSE138080, GSE113942 and GSE63514 were analysed using GEO2R tool. The functional enrichment analysis was performed using g:Profiler tool. The protein–protein interaction (PPI) network construction and hub genes identification were performed using the STRING database and Cytoscape software, respectively. The hub genes were subjected to expression and survival analysis in the cervical cancer. The EdU incorporation and Cell Counting Kit-8 assays were performed to evaluate the effects of hub gene knockdown on the proliferation of cervical cancer cells.ResultsA total of 89 overlapping DEGs (63 up-regulated and 26 down-regulated genes) were identified in the microarray datasets. The functional enrichment analysis indicated that the overlapping DEGs were mainly associated with “DNA replication” and “cell cycle”. Furthermore, the PPI network analysis revealed that the network contains 87 nodes and 309 edges. Sub-module analysis using the Molecular Complex Detection tool identified 21 hub genes from the PPI network. The expression levels of the 21 hub genes were all up-regulated in the cervical cancer tissues when compared to normal cervical tissues as analysed by GEPIA tool. The survival analysis showed that the low expression of cell division cycle 45 (CDC45), GINS complex subunit 2 (GINS2), minichromosome maintenance complex component 2 (MCM2) and proliferating cell nuclear antigen (PCNA) was significantly correlated with the shorter overall survival of patients with cervical cancer. Moreover, the protein expression levels of GINS2, MCM2 and PCNA, but not CDC45, were significantly up-regulated in the cervical cancer tissues when compared to normal cervical tissues. Finally, knockdown of MCM2 significantly suppressed the proliferation of HeLa and SiHa cells.ConclusionIn conclusion, we screened a total of 89 overlapping DEGs from the GEO datasets, and further analysis identified four hub genes (CDC45, GINS2, MCM2 and PCNA) that were likely associated with the prognosis of patients with cervical cancer. MCM2 knockdown repressed the cervical cancer cell proliferation. The current findings may provide novel insights into understanding the pathophysiology of cervical cancer and develop therapeutic targets for patients with cervical cancer.

This study aimed to explore the hub genes and related key pathways in Spinal Cord Injury (SCI) based on the bioinformatics analysis. Two microarray datasets (GSE45006, GSE45550) were obtained from the GEO database and were merged and batch-corrected. The differentially expressed genes (DEGs) in SCI were explored with the Limma, and the weighted gene co-expression network analysis (WGCNA) was conducted to explore the module genes. Functional enrichment analysis and Gene set variation analysis (GSVA) were used to investigate the biological functions and key pathways of the key genes related to SCI. Then the protein-protein interaction (PPI) network was generated using the STING online tool, and the hub genes in SCI were identified. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic value of the selected hub genes. We identified 554 DEGs in SCI, and 1236 key genes in SCI were selected via WGCNA. Totally 111 key genes related to SCI were discovered. Furthermore, the functional enrichment analysis showed that these key mRNAs were primarily enriched in the extracellular matrix (ECM)-related pathways and processes associated with wound healing and cell growth. The PPI network further filtered six hub genes (Cd44, Timp1, Loxl1, Col6a1, Col3a1, Col5a1) ranked by the degree, and the diagnostic value of the six hub genes was confirmed by the ROC curves. Six hub genes including Cd44, Timp1, Loxl1, Col6a1, Col3a1, and Col5a1 were identified in SCI, with differential expression and excellent diagnostic value, which might provide insight into the targeted therapy of SCI.

Colorectal cancer (CRC) is the fifth leading cause of death in India. Until now, the exact pathogenesis concerning CRC signaling pathways is largely unknown; however, the diseased condition is believed to deteriorate with lifestyle, aging, and inherited genetic disorders. Hence, the identification of hub genes and therapeutic targets is of great importance for disease monitoring. Identification of hub genes and targets for identification of candidate hub genes for CRC diagnosis and monitoring. The present study applied gene expression analysis by integrating two profile datasets (GSE20916 and GSE33113) from NCBI-GEO database to elucidate the potential key candidate genes and pathways in CRC. Differentially expressed genes (DEGs) between CRC (195 CRC tissues) and healthy control (46 normal mucosal tissue) were sorted using GEO2R tool. Further, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed using Cluster Profiler in Rv. 3.6.1. Moreover, protein-protein interactions (PPI), module detection, and hub gene identification were accomplished and visualized through the Search Tool for the Retrieval of Interacting Genes, Molecular Complex Detection (MCODE) plug-in of Cytoscape v3.8.0. Further hub genes were imported into ToppGene webserver for pathway analysis and prognostic expression analysis was conducted using Gene Expression Profiling Interactive Analysis webserver. A total of 2221 DEGs, including 1286 up-regulated and 935down-regulated genes mainly enriched in signaling pathways of NOD-like receptor, FoxO, AMPK signalling and leishmaniasis. Three key modules were detected from PPI network using MCODE. Besides, top 20 high prioritized hub genes were selected. Further, prognostic expression analysis revealed ten of the hub genes, namely IL1B, CD44, Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, MMP9, CREB1, STAT1, vascular endothelial growth factor (VEGFA), CDC5 L, Ataxia-telangiectasia mutated (ATM + and CDH1 to be differently expressed in normal and cancer patients. The present study proposed five novel therapeutic targets, i.e., ATM, GAPDH, CREB1, VEGFA, and CDH1 genes that might provide new insights into molecular oncogenesis of CRC.

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.

Arsenic is a toxic metalloid that can cause acute and chronic adverse health problems. Unfortunately, rice, the primary staple food for more than half of the world's population, is generally regarded as a typical arsenic-accumulating crop plant. Evidence indicates that arsenic stress can influence the growth and development of the rice plant, and lead to high concentrations of arsenic in rice grain. But the underlying mechanisms remain unclear. In the present research, the possible molecules and pathways involved in rice roots in response to arsenic stress were explored using bioinformatics methods. Datasets that involving arsenic-treated rice root and the "study type" that was restricted to "Expression profiling by array" were selected and downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the arsenic-treated group and the control group were obtained using the online web tool GEO2R. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to investigate the functions of DEGs. The protein-protein interactions (PPI) network and the molecular complex detection algorithm (MCODE) of DEGs were analyzed using STRING and Cystoscope, respectively. Important nodes and hub genes in the PPI network were predicted and explored using the Cytoscape-cytoHubba plug-in. Two datasets, GSE25206 and GSE71492, were downloaded from Gene Expression Omnibus (GEO) database. Eighty common DEGs from the two datasets, including sixty-three up-regulated and seventeen down-regulated genes, were then selected. After functional enrichment analysis, these common DEGs were enriched mainly in 10 GO items, including glutathione transferase activity, glutathione metabolic process, toxin catabolic process, and 7 KEGG pathways related to metabolism. After PPI network and MCODE analysis, 49 nodes from the DEGs PPI network were identified, filtering two significant modules. Next, the Cytoscape-cytoHubba plug-in was used to predict important nodes and hub genes. Finally, five genes [Os01g0644000, PRDX6 (Os07g0638400), PRX112 (Os07g0677300), ENO1(Os06g0136600), LOGL9 (Os09g0547500)] were verified and could serve as the best candidates associated with rice root in response to arsenic stress. In summary, we elucidated the potential pathways and genes in rice root in response to arsenic stress through a comprehensive bioinformatics analysis.

BackgroundGastric cancer (GC) is one of the most common cancer worldwide. With the high rates of metastasis and recurrence, its overall survival remains poor at the present time. Hence, seeking new potential therapeutic targets of GC is important and urgent.MethodsWe retrieved the gene expression profiles and clinical data from The Cancer Genome Atlas (TCGA) datasets. After screening differentially expressed genes (DEGs), we carried out the survival analysis for overall survival to pick out robust DEGs. To explore the role of these robust DEGs, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Subsequently, protein interactions network was constructed utilizing the Search Tool for the Retrieval of Interacting Genes (STRING) database. We then presented the module analysis and filtered out hub genes by the Cytoscape software. Finally, Kaplan-Meier analysis was utilized to demonstrate the prognostic role of these hub genes.ResultsAccording to the gene expression profiles of TCGA and the survival analysis, 238 robust DEGs were filtered out, consisting of 140 up-regulated and 98 down-regulated genes. The up-regulated DEGs were mainly enriched in systemic lupus erythematosus, cytokine activity, and alcoholism, while down-regulated DEGs were mainly enriched in steroid hormone receptor activity, immune response, and metabolism. Through the construction of the protein-protein interaction (PPI) network, eight hub genes were finally screened out, including CCR8, HIST1H3B, HIST1H2AH, HIST1H2AJ, NPY, HIST2H2BF, GNG7, and CCL25.ConclusionsOur study picked out eight hub genes, which might be potential prognostic biomarkers for GC and even be treatment targets for clinical implication in the future.

Screening and identification of potential hub genes and immune cell infiltration in the synovial tissue of rheumatoid arthritis by bioinformatic approach

Background: Hepatocellular carcinoma (HCC) is a major threat to public health. However, few effective therapeutic strategies exist. We aimed to identify potentially therapeutic target genes of HCC by analyzing three gene expression profiles. Methods: The gene expression profiles were analyzed with GEO2R, an interactive web tool for gene differential expression analysis, to identify common differentially expressed genes (DEGs). Functional enrichment analyses were then conducted followed by a protein-protein interaction (PPI) network construction with the common DEGs. The PPI network was employed to identify hub genes, and the expression level of the hub genes was validated via data mining the Oncomine database. Survival analysis was carried out to assess the prognosis of hub genes in HCC patients. Results: A total of 51 common up-regulated DEGs and 201 down-regulated DEGs were obtained after gene differential expression analysis of the profiles. Functional enrichment analyses indicated that these common DEGs are linked to a series of cancer events. We finally identified 10 hub genes, six of which (OIP5, ASPM, NUSAP1, UBE2C, CCNA2, and KIF20A) are reported as novel HCC hub genes. Data mining the Oncomine database validated that the hub genes have a significant high level of expression in HCC samples compared normal samples (t-test, p < 0.05). Survival analysis indicated that overexpression of the hub genes is associated with a significant reduction (p < 0.05) in survival time in HCC patients. Conclusions: We identified six novel HCC hub genes that might be therapeutic targets for the development of drugs for some HCC patients.

Synovial inflammation influences the progression of osteoarthritis (OA). Herein, we aimed to identify potential biomarkers and analyze transcriptional regulatory-immune mechanism of synovitis in OA using weighted gene coexpression network analysis (WGCNA). A data set of OA synovium samples (GSE55235) was analyzed based on WGCNA. The most significant module with OA was identified and function annotation of the module was performed, following which the hub genes of the module were identified using Pearson correlation and a protein-protein interaction network was constructed. A transcriptional regulatory network of hub genes was constructed using the TRRUST database. The immune cell infiltration of OA samples was evaluated using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The hub genes coexpressed in multiple tissues were then screened out using data sets of synovium, cartilage, chondrocyte, subchondral bone, and synovial fluid samples. Finally, transcriptional factors and coexpressed hub genes were validated via experiments. The turquoise module of GSE55235 was identified via WGCNA. Functional annotation analysis showed that "mineral absorption" and "FoxO signaling pathway" were mostly enriched in the module. JUN, EGR1, FOSB, and KLF4 acted as central nodes in protein-protein interaction network and transcription factors to connect several target genes. "Activated B cell," "activated CD4T cell," "eosinophil," "neutrophil," and "type 17 T helper cell" showed high immune infiltration, while FOSB, KLF6, and MYBL2 showed significant negative correlation with type 17 T helper cell. Our results suggest that the expression level of apolipoprotein D (APOD) was correlated with OA. Furthermore, transcriptional regulatory-immune network was constructed, which may contribute to OA therapy.

Prostate cancer (PCa) is the most common and the second leading cause of cancer-related death among men in America. As the molecular mechanism of PCa has not yet been completely discovered, identification of hub genes and potential drug of this disease is an important area of research that could provide new insights into exploring the mechanisms underlying PCa. The aim of this study was to identify potential biomarkers and novel drug for prostate cancer treatment. The differentially expressed genes (DEGs) between prostate cancer and normal cells were screened using microarray data obtained from the Gene Expression Omnibus database. Gene ontology (GO) and pathway enrichment analyses were performed in order to investigate the functions of DEGs, and the protein-protein interaction (PPI) network of the DEGs was constructed using the Cytoscape software. DEGs were then mapped to the connectivity map database to identify molecular agents associated with the underlying mechanisms of PCa. Totally, 359 genes (155 upregulated and 204 downregulated genes) were found to be differentially expressed between prostate cancer and normal cells. The GO terms significantly enriched by DEGs included cell adhesion, protein binding involved in cell-cell adhesion, response to BMP, extracellular region and extracellular region part. KEGG pathway analysis showed that the most significant pathways included cell adhesion molecules (CAMs) and TGF-beta signaling pathway. The PPI network of up-regulated DEGs and down-regulated DEGs were established, respectively. While CDH1, BMP2, NKX3-1, PPARG and PRKAR2B were identified as the hub genes in the PPI network. The BMP2, PPARG and PRKAR2B genes may therefore be potential biomarkers in the treatment of PCa. Additionally, the small molecular agent phenoxybenzamine may be a potential drug for PCa.

BackgroundSpinal cord injury (SCI) is a common trauma in clinical practices. Subacute SCI is mainly characterized by neuronal apoptosis, axonal demyelination, Wallerian degeneration, axonal remodeling, and glial scar formation. It has been discovered in recent years that inflammatory responses are particularly important in subacute SCI. However, the mechanisms mediating inflammation are not completely clear.MethodsThe gene expression profiles of GSE20907, GSE45006, and GSE45550 were downloaded from the GEO database. The models of the three gene expression profiles were all for SCI to the thoracic segment of the rat. The differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA) were performed using R software, and functional enrichment analysis and protein–protein interaction (PPI) network were performed using Metascape. Module analysis was performed using Cytoscape. Finally, the relative mRNA expression level of central genes was verified by RT-PCR.ResultsA total of 206 candidate genes were identified, including 164 up-regulated genes and 42 down-regulated genes. The PPI network was evaluated, and the candidate genes enrichment results were mainly related to the production of tumor necrosis factors and innate immune regulatory response. Twelve core genes were identified, including 10 up-regulated genes and 2 down-regulated genes. Finally, seven hub genes with statistical significance in both the RT-PCR results and expression matrix were identified, namely Itgb1, Ptprc, Cd63, Lgals3, Vav1, Shc1, and Casp4. They are all related to the activation process of microglia.ConclusionIn this study, we identified the hub genes and signaling pathways involved in subacute SCI using bioinformatics methods, which may provide a molecular basis for the future treatment of SCI.

Breast cancer (BC) remains one of the leading causes of cancer-related mortality among women worldwide, with distant metastasis being the primary contributor to poor prognosis. However, the molecular mechanisms driving BC metastasis are not yet fully understood. We integrated three public microarray datasets (GSE14776, GSE103357, and GSE32489) to identify the differentially expressed genes (DEGs) associated with breast cancer metastasis. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and hub gene identification were performed using bioinformatics tools including DAVID, STRING, Cytoscape, and R. The prognostic significance of hub genes was assessed using Kaplan-Meier plotter and GEPIA. Expression validation was conducted through UALCAN, immunohistochemistry (IHC), and single-cell RNA sequencing (scRNA-seq) analysis from the GSE180286 dataset. A total of 295 co-DEGs were identified across the three datasets, enriched in pathways such as MAPK signaling, Rap1 signaling, and cell adhesion molecules. Twenty hub genes were identified from the PPI network, with eight showing strong prognostic value. Among them, PRC1 and POLR3H emerged as potential novel biomarkers. IHC confirmed the differential protein expression of PRC1, CDCA8, KIF14, and POLR3H. scRNA-seq analysis revealed that these hub genes were predominantly expressed in malignant epithelial and EMT (epithelial-mesenchymal transition) cells, particularly those from metastatic lymph node sites. This integrative analysis combining bulk and single-cell transcriptomic data identified key metastasis-associated genes in breast cancer. PRC1 and POLR3H, in particular, may serve as novel prognostic biomarkers and potential therapeutic targets.

The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein–protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine–cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the world, and more molecular mechanisms should be illuminated to meet the urgent need of developing novel detection and therapeutic strategies. We analyzed the related microarray data to find the possible hub genes and analyzed their prognostic values using bioinformatics methods. The mRNA microarray datasets GSE62452, GSE15471, GSE102238, GSE16515, and GSE62165 were finally chosen and analyzed using GEO2R. The overlapping genes were found by Venn Diagrams, functional and pathway enrichment analyses were performed using the DAVID database, and the protein-protein interaction (PPI) network was constructed by STRING and Cytoscape. OncoLnc, which was linked to TCGA survival data, was used to investigate the prognostic values. In total, 179 differentially expressed genes (DEGs) were found in PDAC, among which, 130 were up-regulated genes and 49 were down-regulated. DAVID showed that the up-regulated genes were significantly enriched in extracellular matrix and structure organization, collagen catabolic and metabolic process, while the down-regulated genes were mainly involved in proteolysis, reactive oxygen species metabolic process, homeostatic process and cellular response to starvation. From the PPI network, the 21 nodes with the highest degree were screened as hub genes. Based on Molecular Complex Detection (MCODE) plug-in, the top module was formed by ALB, TGM, PLAT, PLAU, EGF, MMP7, MMP1, LAMC2, LAMA3, LAMB3, COLA1, FAP, CDH11, COL3A1, ITGA2, and VCAN. OncoLnc survival analysis showed that, high expression of ITGA2, MMP7, ITGB4, ITGA3, VCAN and PLAU may predict poor survival results in PDAC. The present study identified hub genes and pathways in PDAC, which may be potential targets for its diagnosis, treatment, and prognostic prediction.

Background Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal cancers in the world, and more molecular mechanisms should be illuminated to meet the urgent need of developing novel detection and therapeutic strategies. We analysed the related microarray data to find the possible hub genes and analysed their prognostic values using bioinformatics methods. Methods The mRNA microarray datasets GSE62452, GSE15471, GSE102238, GSE16515, and GSE62165, were finally chosen and analysed using GEO2R. The overlapping genes were found by Venn Diagrams, functional and pathway enrichment analyses were performed using the DAVID database, and the protein-protein interaction (PPI) network was constructed by STRING and Cytoscape. OncoLnc, which linked TCGA survival data was used to investigate the prognostic values. Results In total, 179 differentially expressed genes (DEGs) were found in PDAC, among which, 130 were up-regulated genes, and 49 were down-regulated. DAVID showed that the up-regulated genes were significantly enriched in extracellular matrix and structure organisation, collagen catabolic and metabolic process, while the down-regulated genes were mainly involved in proteolysis, reactive oxygen species metabolic process, homeostatic process and cellular response to starvation. From the PPI network, the 22 nodes with the highest degree were screened as hub genes. Based on Molecular Complex Detection (MCODE) plug-in, the top module was formed by EGF, PLAT, MMP7, ALB, TIMP1, F8, PLAU, LAM3, MMP1, ITGA2, LAMB3 and LAMC2. OncoLnc survival analysis showed that high expression of ITGA2, MMP7, ITGB4, ITGA3, VCAN and PLAU might predict poor survival results in PDAC. Conclusions The present study identified hub genes and pathways in PDAC, which may be a potential target for its diagnosis, treatment, and prognostic prediction.