Anti-NSCLC role of SCN4B by negative regulation of the cGMP-PKG pathway: Integrated utilization of bioinformatics analysis and in vitro assay validation

Non-small cell lung cancer (NSCLC) is a malignant tumor with low overall cure and survival rates. Uncovering abnormally expressed genes is significantly important for developing novel targeted therapies in NSCLC. This study aimed to discover new differentially expressed genes (DEGs) of NSCLC. The DEGs of NSCLC were identified in eight data sets from Gene Expression Omnibus (GEO) database. The expression profiles and the prognostic significance of SCN4B in LUAD and LUSC were analyzed using GEPIA database. LinkedOmics was used to identify co-expressed genes with SCN4B, which were further subjected to KEGG pathway enrichment analysis. SCN4B-overexpressing plasmid (pcDNA/SCN4B) was transfected into A549 and NCI-H2170 cells to elevate the expression of SCN4B. MTT and TUNEL assays were performed to evaluate cell viability and apoptosis. Relying on the screened DEGs from GEO database, we identified that SCN4B was significantly downregulated in LUAD and LUSC. We confirmed the downregulation of SCN4B in NSCLC tissues using GEPIA database. SCN4B has a prognostic value in LUAD, but not LUSC. KEGG pathway enrichment analysis of SCN4B-related genes showed that cGMP-PKG signaling pathway might be involved in the role of SCN4B in NSCLC. Overexpression of SCN4B in A549 and NCI-H2170 cells inhibited the cell viability. Besides, SCN4B overexpression induced apoptosis of A549 and NCI-H2170 cells. SCN4B inhibited the expression of PKG1 and p-CREB in NSCLC cells. Moreover, the inhibitory effects of SCN4B on tumor malignancy were attenuated by the activator of PKG. In conclusion, integrated bioinformatical analysis proved that SCN4B was downregulated and had a prognostic significance in NSCLC. In vitro experimental studies demonstrated that SCN4B regulated NSCLC cells viability and apoptosis via inhibiting cGMP-PKG signaling pathway.

Navigating the Chemical Space of ENR Inhibitors: A Comprehensive Analysis

Antimicrobial resistance is a global health threat that requires innovative strategies against drug-resistant bacteria. Our study focuses on enoyl-acyl carrier protein reductases (ENRs), in particular FabI, FabK, FabV, and InhA, as potential antimicrobial agents. Despite their promising potential, the lack of clinical approvals for inhibitors such as triclosan and isoniazid underscores the challenges in achieving preclinical success. In our study, we curated and analyzed a dataset of 1412 small molecules recognized as ENR inhibitors, investigating different structural variants. Using advanced cheminformatic tools, we mapped the physicochemical landscape and identified specific structural features as key determinants of bioactivity. Furthermore, we investigated whether the compounds conform to Lipinski rules, PAINS, and Brenk filters, which are crucial for the advancement of compounds in development pipelines. Furthermore, we investigated structural diversity using four different representations: Chemotype diversity, molecular similarity, t-SNE visualization, molecular complexity, and cluster analysis. By using advanced bioinformatics tools such as matched molecular pairs (MMP) analysis, machine learning, and SHAP analysis, we were able to improve our understanding of the activity cliques and the precise effects of the functional groups. In summary, this chemoinformatic investigation has unraveled the FAB inhibitors and provided insights into rational antimicrobial design, seamlessly integrating computation into the discovery of new antimicrobial agents.

Do testosterone and sex hormone-binding globulin affect cancer risk? A Mendelian randomization and bioinformatics study

Using Mendelian Randomization (MR) and large-scale Genome-Wide Association Study (GWAS) data, this study aimed to investigate the potential causative relationship between testosterone and sex hormone-binding globulin (SHBG) levels and the onset of several cancers, including pathway enrichment analyses of single nucleotide polymorphisms (SNPs) associated with cancer allowed for a comprehensive bioinformatics approach, which offered a deeper biological understanding of these relationships. The results indicated that increased testosterone levels in women were associated with a higher risk of breast and cervical cancers but a lower risk of ovarian cancer. Conversely, increased testosterone was linked to lower stomach cancer risk for men, whereas high SHBG levels were related to decreased risks of breast and prostate cancers. The corresponding genes of the identified SNPs, as revealed by pathway enrichment analysis, were involved in significant metabolic and proliferative pathways. These findings emphasize the need for further research into the biological mechanisms behind these associations, paving the way for potential targeted interventions in preventing and treating these cancers.

Exploring the Molecular Targets for the Antidepressant and Antisuicidal Effects of Ketamine Enantiomers by Using Network Pharmacology and Molecular Docking

Ketamine, a racemic mixture of esketamine (S-ketamine) and arketamine (R-ketamine), has received particular attention for its rapid antidepressant and antisuicidal effects. NMDA receptor inhibition has been indicated as one of the main mechanisms of action of the racemic mixture, but other pharmacological targets have also been proposed. This study aimed to explore the possible multiple targets of ketamine enantiomers related to their antidepressant and antisuicidal effects. To this end, targets were predicted using Swiss Target Prediction software for each ketamine enantiomer. Targets related to depression and suicide were collected by the Gene Cards database. The intersections of targets were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Network pharmacology analysis was performed using Gene Mania and Cytoscape software. Molecular docking was used to predict the main targets of the network. The results indicated that esketamine and arketamine share some biological targets, particularly NMDA receptor and phosphodiesterases 3A, 7A, and 5A but have specific molecular targets. While esketamine is predicted to interact with the GABAergic system, arketamine may interact with macrophage migration inhibitory factor (MIF). Both ketamine enantiomers activate neuroplasticity-related signaling pathways and show addiction potential. Our results identified novel, poorly explored molecular targets that may be related to the beneficial effects of esketamine and arketamine against depression and suicide.

Comprehensive Analysis of Purine-Metabolism-Related Gene Signature for Predicting Ovarian Cancer Prognosis, Immune Landscape, and Potential Treatment Options

Purine metabolism is an important branch of metabolic reprogramming and has received increasing attention in cancer research. Ovarian cancer is an extremely dangerous gynecologic malignancy for which there are no adequate tools to predict prognostic risk. Here, we identified a prognostic signature consisting of nine genes related to purine metabolism, including ACSM1, CACNA1C, EPHA4, TPM3, PDIA4, JUNB, EXOSC4, TRPM2, and CXCL9. The risk groups defined by the signature are able to distinguish the prognostic risk and the immune landscape of patients. In particular, the risk scores offer promising personalized drug options. By combining risk scores with clinical characteristics, we have created a more detailed composite nomogram that allows for a more complete and individualized prediction of prognosis. In addition, we demonstrated metabolic differences between platinum-resistant and platinum-sensitive ovarian cancer cells. In summary, we have performed the first comprehensive analysis of genes related to purine metabolism in ovarian cancer patients and created a feasible prognostic signature that will aid in risk prediction and support personalized medicine.

Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous clinical phenotypes. Patients often experience abnormal sensory perception, which may further affect the ASD core phenotype, significantly and adversely affecting their quality of life. However, biomarkers for the diagnosis of ASD sensory perception abnormality are currently elusive. We sought to identify potential biomarkers related to ASD sensory perception abnormality to construct a prediction model that could facilitate the early identification of and screening for ASD. Differentially expressed genes in ASD were obtained from the Gene Expression Omnibus database and were screened for genes related to sensory perception abnormality. After enrichment analysis, the random forest method was used to identify disease-characteristic genes. A prediction model was constructed with an artificial neural network. Finally, the results were validated using data from the dorsal root ganglion, cerebral cortex, and striatum of the BTBR T+ Itpr3tf/J (BTBR) ASD mouse model. A total of 1869 differentially expressed genes in ASD were screened, among which 16 genes related to sensory perception abnormality were identified. According to enrichment analysis, these 16 genes were mainly related to actin, cholesterol metabolism, and tight junctions. Using random forest, 15 disease-characteristic genes were screened for model construction. The area under the curve of the training set validation result was 0.999, and for the model function validation, the result was 0.711, indicating high accuracy. The validation of BTBR mice confirmed the reliability of using these disease-characteristic genes for prediction of ASD. In conclusion, we developed a highly accurate model for predicting ASD sensory perception abnormality from 15 disease-characteristic genes. This model provides a new method for the early identification and diagnosis of ASD sensory perception abnormality.

TRIM21‑a potential biomarker for the prognosis of thyroid cancer

Thyroid cancer (THCA) is one of the commonest malignancies associated with increased recurrence. Therefore, identifying the putative molecular markers and therapeutic targets to improve the treatment of THCA is essential. The present study analyzed the potential role of tripartite motif-containing 21 (TRIM21), a member of the TRIM family belonging to the subfamily of E3 ubiquitin ligases, in the progression of THCA. Using bioinformatics analysis and immunohistochemistry of THCA tissues, it was observed that TRIM21 is overexpressed in THCA tissues. The present study also found that TRIM21 is associated with lymph node metastasis and high-risk recurrence of THCA. Furthermore, it identified a promotional role of TRIM21 in THCA cell migration and invasion. In addition, the present study analyzed TRIM21-enriched pathways and co-expressed genes in THCA. The present study suggested that TRIM21 may serve as a potential biomarker for THCA prognosis.

Identification of ferroptosis-related genes in syncytiotrophoblast-derived extracellular vesicles of preeclampsia

Preeclampsia (PE), defined as new-onset hypertension and multi-organ systemic complication during pregnancy, is the leading cause of maternal and neonatal mortality and morbidity. With extracellular vesicles research progresses, current data refers to the possibility that ferroptosis may play a role in exosomal effects. Evidence has suggested that ferroptosis may contribute to the pathogenesis of preeclampsia by bioinformatics analyses. The purpose of the current study is to identify the potential ferroptosis-related genes in syncytiotrophoblast-derived extracellular vesicles (STB-EVs) of preeclampsia using bioinformatics analyses. Clinical characteristics and gene expression data of all samples were obtained from the NCBI GEO database. The differentially expressed mRNAs (DE-mRNAs) in STB-EVs of preeclampsia were screened and then were intersected with ferroptosis genes. Functional and pathway enrichment analyses of ferroptosis-related DE-mRNAs in STB-EVs were performed. Ferroptosis-related hub genes in STB-EVs were identified by Cytoscape plugin CytoHubba with a Degree algorithm using a protein-protein interaction network built constructed from the STRING database. The predictive performance of ferroptosis-related hub genes was determined by a univariate analysis of receiver operating characteristic (ROC). The miRNA-hub gene regulatory network was constructed using the miRwalk database. A total of 1976 DE-mRNAs in STB-EVs were identified and the most enriched item identified by gene set enrichment analysis was signaling by G Protein-Coupled Receptors (normalized enrichment score = 1.238). These DE-mRNAs obtained 26 ferroptosis-related DE-mRNAs. Ferroptosis-related DE-mRNAs of gene ontology terms and Encyclopedia of Genes and Genomes pathway enrichment analysis were enriched significantly in response to oxidative stress and ferroptosis. Five hub genes (ALB, NOX4, CDKN2A, TXNRD1, and CAV1) were found in the constructed protein-protein interaction network with ferroptosis-related DE-mRNAs and the areas under the ROC curves for ALB, NOX4, CDKN2A, TXNRD1, and CAV1 were 0.938 (CI: 0.815-1.000), 0.833 (CI: 0.612-1.000), 0.875 (CI: 0.704-1.000), 0.958 (CI: 0.862-1.000), and 0.854 (CI: 0.652-1.000) in univariate analysis of ROC. We constructed a regulatory network of miRNA-hub gene and the findings demonstrate that hsa-miR-26b-5p, hsa-miR-192-5p, hsa-miR-124-3p, hsa-miR-492, hsa-miR-34a-5p and hsa-miR-155-5p could regulate most hub genes. In this study, we identified several central genes closely related to ferroptosis in STB-EVs (ALB, NOX4, CDKN2A, TXNRD1, and CAV1) that are potential biomarkers related to ferroptosis in preeclampsia. Our findings will provide evidence for the involvement of ferroptosis in preeclampsia and improve the understanding of ferroptosis-related molecular pathways in the pathogenesis of preeclampsia.

Construction of three-gene-based prognostic signature and analysis of immune cells infiltration in children and young adults with B-acute lymphoblastic leukemia

Background

Although B‐acute lymphoblastic leukemia (B‐ALL) patients' survival has been improved dramatically, some cases still relapse. This study aimed to explore the prognosis‐related novel differentially expressed genes (DEGs) for predicting the overall survival (OS) of children and young adults (CAYAs) with B‐ALL and analyze the immune‐related factors contributing to poor prognosis.

Methods

GSE48558 and GSE79533 from Gene Expression Omnibus (GEO) and clinical sample information and mRNA‐seq from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were retrieved. Prognosis‐related key genes were enrolled to build a Cox proportional model using multivariate Cox regression. Five‐year OS of patients, clinical characteristic relevance and clinical independence were assessed based on the model. The mRNA levels of prognosis‐related genes were validated in our samples and the difference of immune cells composition between high‐risk and low‐risk patients were compared.

Results

One hundred and twelve DEGs between normal B cells and B‐ALL cells were identified based on GSE datasets. They were mainly participated in protein binding and HIF‐1 signaling pathway. One hundred and eighty‐nine clinical samples were enrolled in the study, both Kaplan–Meier (KM) analysis and univariate Cox regression analysis showed that CYBB, BCL2A1, IFI30, and EFNB1 were associated with prognosis, CYBB, BCL2A1, and EFNB1 were used to construct prognostic risk model. Moreover, compared to clinical indicators, the three‐gene signature was an independent prognostic factor for CAYAs with B‐ALL. Finally, the mRNA levels of CYBB, BCL2A1, and EFNB1 were significantly lower in B‐ALL group as compared to controls. The high‐risk group had a significantly higher percentage of infiltrated immune cells.

Conclusion

We constructed a novel three‐gene signature with independent prognostic factor for predicting 5‐year OS of CAYAs with B‐ALL. Additionally, we discovered the difference of immune cells composition between high‐risk and low‐risk groups. This study may help to customize individual treatment and improve prognosis of CAYAs with B‐ALL.

Identification of key genes and pathways at the downstream of S100PBP in pancreatic cancer cells by integrated bioinformatical analysis

Background

The aim of the present study was to identify key genes and pathways downstream of S100PPBP in pancreatic cancer cells.

Methods

The microarray datasets GSE35196 (S100PBP knockdown) and GSE35198 (S100PBP overexpression) were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were obtained separately from GEO2R, and heatmaps showing clustering analysis of DEGs were generated using R software. Gene Ontology and pathway enrichment analyses were performed for identified DEGs using the Database for Annotation, Visualization, and Integrated Discovery and Kyoto Encyclopedia of Genes and Genomes, respectively. A protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes and Cytoscape software. Relevant expression datasets of key identified genes were downloaded from The Cancer Genome Atlas, and overall survival (OS) analysis was performed with R software. Finally, Gene Expression Profiling Interactive Analysis was used to evaluate the expression of key DEGs in pancreatic cancer tissues.

Results

A total of 34 DEGs (11 upregulated and 23 downregulated) were screened out from the two datasets. Gene Ontology enrichment analysis revealed that the identified DEGs were mainly functionally enriched in ATPase activity, production of siRNA involved in RNA interference, and production of miRNAs involved in gene silencing by miRNA. The pathway enrichment analysis of the identified DEGs showed enrichment mainly in apoptosis, non-homologous end-joining, and miRNA pathways in cancer. The protein–protein interaction network was composed of 21 nodes and 30 edges. After survival analysis and gene expression analysis, 4 genes associated with poor prognosis were selected, including LMNB1, PRKRA, SEPT2, and XRCC5.

Conclusions

LMNB1, PRKRA, SEPT2, and XRCC5 could be key downstream genes of the S100PBP gene in the inhibition of pancreatic cancer cell adhesion.

In silico identification and verification of ferroptosis-related genes in type 2 diabetic islets

Type 2 diabetes (T2D) is a major global public health burden, with β-cell dysfunction a key component in its pathogenesis. However, the exact pathogenesis of β-cell dysfunction in T2D is yet to be fully elucidated. Ferroptosis, a recently discovered regulated form of non-apoptotic cell death, plays a vital role in the development of diabetes and its complications. The current study aimed to identify the key molecules involved in β-cell ferroptosis3 in patients with T2D using the mRNA expression profile data of GSE25724 by bioinformatic approaches. The differentially expressed mRNAs (DE-mRNAs) in human islets of patients with T2D were screened using the islet mRNA expression profiling data from the Gene Expression Omnibus and their intersection with ferroptosis genes was then obtained. Ferroptosis-related DE-mRNA functional and pathway enrichment analysis in T2D islet were performed. Using a protein-protein interaction (PPI) network constructed from the STRING database, Cytoscape software identified ferroptosis-related hub genes in the T2D islet with a Degree algorithm. We constructed a miRNA-hub gene network using the miRWalk database. We generated a rat model of T2D to assess the expression of hub genes. A total of 1,316 DE-mRNAs were identified in the islet of patients between T2D and non-T2D (NT2D), including 221 and 1,095 up- and down-regulated genes. Gene set enrichment analysis revealed that the ferroptosis-related gene set was significantly different in islets between T2D and NT2D at an overall level. A total of 33 ferroptosis-related DE-mRNAs were identified, most of which were significantly enriched in pathways including ferroptosis. The established PPI network with ferroptosis-related DE-mRNAs identified five hub genes (JUN, NFE2L2, ATG5, KRAS, and HSPA5), and the area under the ROC curve of these five hub genes was 0.929 in the Logistic regression model. We constructed a regulatory network of hub genes and miRNAs, and the results showed that suggesting that hsa-miR-6855-5p, hsa-miR-9985, and hsa-miR-584-5p could regulate most hub genes. In rat model of T2D, the protein expression levels of JUN and NFE2L2 in pancreatic tissues were upregulated and downregulated, respectively. These results contribute to further elucidation of ferroptosis-related molecular mechanisms in the pathogenesis of β-cell dysfunction of T2D.

Identifying the hub genes and immune infiltration related to pyroptosis in rheumatoid arthritis

Rheumatoid arthritis (RA) is one of the most common autoimmune joint disorders globally, but its pathophysiological mechanisms have not been thoroughly investigated. Pyroptosis significantly correlates with programmed cell death. However, targeting pyroptosis has not been considered as a therapeutic strategy in RA due to a lack of systematic studies on validated biomarkers. The present study aimed to identify hub pyroptosis biomarkers and immune infiltration in RA. The gene expression profiles of synovial tissues were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed pyroptosis genes (DEPGs). Meanwhile, the CIBERSORT algorithm was used to explore the association between immune infiltration and RA. Consequently, two hub DEPGs (EGFR and JUN) were identified as critical genes in RA. Through gene ontology and pathway enrichment analysis. EGFR and JUN were found to be primarily involved in the ErbB signaling pathway, PD-1 checkpoint pathway, GnRH signaling pathway, etc. Furthermore, for immune infiltration analysis, the pyroptosis genes EGFR and JUN were closely connected with four and one immune cell types, respectively. Overall, this study presents a novel method to identify hub DEPGs and their correlation with immune infiltration, which may provide novel perspectives into the diagnosis and treatment of patients with RA.

Prediction of Key Candidate Genes for Platinum Resistance in Ovarian Cancer

Purpose

Ovarian cancer is one of the common malignant tumors of female reproductive organs, which seriously threatens the life and health of women. Resistance to chemotherapeutic drugs for ovarian cancer is the root cause of recurrence in most patients. The purpose of this study is to determine the differentially expressed genes of platinum resistance in ovarian cancer, and to screen out molecular targets and diagnostic markers that could be used to treat ovarian cancer platinum resistance.

Methods

We downloaded 5 gene microarray datasets GSE58470, GSE45553, GSE41499, GSE33482, and GSE15372 from the Gene Expression Omnibus database, all of which are associated with ovarian cancer platinum resistance. Subsequently, the intersection of the statistically significant differentially expressed genes in 5 gene chips was taken, and relevant bioinformatics and clinical parameters were performed on the screened differential genes. qRT-PCR was utilized to examine the mRNA expression levels in ovarian cancer sensitive and cisplatin-resistant cells.

Results

Three differential genes, IFI27, JAG1, DNM3, may be closely related to platinum resistance of ovarian cancer, were screened by microarray datasets. According to the combined verification of bioinformatics, clinical case analyses and experiments, it was inferred that the increased expression of DNM3 was beneficial to patients with platinum resistance, but the high expression of IFI27 and JAG1 may lead to the risk of platinum resistance.

Conclusion

IFI27, JAG1 and DNM3 screened by relevant gene chips may serve as new biomarkers of platinum resistance in ovarian cancer.

Analysis of Communal Molecular Mechanism and Potential Therapeutic Targets in Heart Failure and Type 2 Diabetes Mellitus

Background

Although increasing evidence has suggested an interaction between heart failure (HF) and Type 2 diabetes mellitus (T2DM), the common mechanisms of the two diseases remain unclear. Therefore, this study aimed to obtain the differentially expressed genes (DEGs) and potential biomarkers or therapeutic targets in HF and T2DM.

Methods

The communal DEGs of HF and T2DM were identified by analyzing the two microarray datasets (GSE84796 and GSE95849), and functional annotation was performed for the communal DEGs to uncover the potential molecular mechanisms of HF and T2DM. Subsequently, STRING database and Cytoscape software were used to construct the protein-protein interaction (PPI) network and screen the hub genes. Finally, co-expression and drug-gene interaction prediction analysis and mRNA-miRNA regulatory network analysis were performed for hub genes.

Results

A total of 233 up-regulated genes and 3 down-regulated genes were found between HF and T2DM. The functional enrichment of DEGs and genes in each four modules were mainly involved in immunity. In addition, five hub genes were identified from PPI network, including SYK, SELL, RAC2, TLR8 and ITGAX.

Conclusion

The communal DEGs and hub genes identified in this research contribute to discover the underlying biological mechanisms and presents potential biomarkers or therapeutic targets in HF and T2DM.

Computational analysis of human host binding partners of chikungunya and dengue viruses during coinfection

Mosquito-borne viral diseases like chikungunya and dengue infections can cause severe illness and have become major public health concerns. Chikungunya virus (CHIKV) and dengue virus (DENV) infections share similar primary clinical manifestations and are transmitted by the same vector. Thus, the probability of their coinfection gets increased with more severe clinical complications in the patients. The present study was undertaken to elucidate the common human interacting partners of CHIKV and DENV proteins during coinfection. The viral-host protein-protein interactome was constructed using Cytoscape. Subsequently, significant host interactors were identified during coinfection. The network analysis elucidated 57 human proteins interacting with both CHIKV and DENV, represented as hub-bottlenecks. The functional and biological analyses of the 40 hub-bottlenecks revealed that they are associated with phosphoinositide 3-kinases (PI3K)/AKT, p53 signaling pathways, regulation of cell cycle and apoptosis during coinfection. Moreover, the molecular docking analysis uncovered the tight and robust binding of selected hub-bottlenecks with CHIKV/DENV proteins. Additionally, 23 hub-bottlenecks were predicted as druggable candidates that could be targeted to eradicate the host-viral interactions. The elucidated common host binding partners during DENV and CHIKV coinfection as well as indicated approved drugs can support the therapeutics development.

Comprehensive Analysis of the Expression and Prognosis for Laminin Genes in Ovarian Cancer

Survival is low in ovarian cancer (OC). Most OC patients demonstrate advanced metastases, and recurrence is common. Dysregulation of laminin interactions is associated with cancer development. However, it is unknown whether laminin subunits can be considered as biomarkers for OC diagnosis, prognosis, and treatment. We used cBioPortal, GEO, ONCOMINE, GEPIA, Human Protein Atlas, Kaplan-Meier Plotter, TIMER, and Metascape to determine the associations among laminin expression, prognosis, and immune cell infiltration in OC. LAMA5, LAMB3, and LAMC2 mRNAs and LAMA3, LAMB1/B2/B3, and LAMC1/C2 proteins were overexpressed in OC tissues compared with normal ovaries. LAMA4, LAMB1, and LAMC1 mRNA upregulation was positively correlated with worse overall survival (OS) and progression-free survival (PFS) in OC. Elevated LAMA2 and LAMC2 mRNA expression levels were related to better PFS or OS, respectively. The results speculated that LAMA5 could potentially be a good prognostic factor in OC. Its expression proves valuable for predicting OS in patients diagnosed with stage Ⅳ and grade 3 OC and PFS in patients diagnosed with all OC stages or grades. LAMB3 and LAMC2 expression was correlated with platinum resistance development. ROC analysis of laminins in OC sets revealed that LAMA2/A4/A5, LAMB1/B2/B3, and LAMC2 could be used to differentiate between malignant tumors and non-neoplastic tissues. LAMA1/A5 and LAMC1 were significantly and negatively correlated with various tumor immune infiltrates (TILs), especially with dendritic cells, CD8⁺ T cells or neutrophil. LAMA4 and LAMB1 might be associated with tumor purity in OC. Overall, LAMA5 and LAMC1 could help predict OC survival and diagnosis and might be deemed important OC oncogenes.

Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC)

Background

Metabolic disorders have attracted increasing attention from scientists who conduct research on various tumours, especially hepatocellular carcinoma (HCC). The purpose of this study was to assess the prognostic significance of metabolism in HCC.

Methods

The expression profiles of metabolism-related genes (MRGs) of 349 surviving HCC patients were extracted from The Cancer Genome Atlas (TCGA) database. Subsequently, a series of biomedical computational algorithms were used to identify a seven-MRG signature as a prognostic model. GSEA indicated the function and pathway enrichment of these MRGs. Then, drug sensitivity analysis was used to identify the hub gene, which was tested using IHC staining.

Results

A total of 420 differential MRGs and 116 differentially expressed transcription factors (TFs) were identified in HCC patients based on data from the TCGA database. The GO and KEGG enrichment analyses indicated that metabolic disturbance might be involved in the development of HCC. LASSO regression analysis was used to construct a seven-MRG signature (DHDH, ENO1, G6PD, LPCAT1, PDE6D, PIGU and PPAT) that could predict the prognosis of HCC patients. GSEA revealed the functional and pathway enrichment of these seven MRGs. Then, drug sensitivity analysis indicated that G6PD might play a key role in the prognosis of HCC by promoting chemoresistance. Finally, we used IHC staining to demonstrate the relationship between G6PD expression levels and clinical parameters in HCC patients.

Conclusion

The results of this study provide a potential method for predicting the prognosis of HCC patients and avenues for further studies of HCC metabolism. Moreover, the function of G6PD may play a key role in the development and progression of HCC.

Identification of key miRNAs and their targets in peripheral blood mononuclear cells of IgA nephropathy using bioinformatics analysis

BACKGROUND

Currently, renal biopsy is the gold standard for clinical diagnosis and evaluation the degrees of IgA nephropathy. However, renal biopsy is an invasive examination and not suitable for long-term follow-up IgA nephropathy. The activation of peripheral blood mononuclear cells (PBMCs) are related to IgA nephropathy, but the key molecular marker and target of PBMCs for evaluating the progression and prognosis of IgA nephropathy is still unclear.

METHODS

We downloaded gene expression omnibus series 25590 (GSE25590) datasets, of which PBMCs from IgA nephrology (IgAN) and healthy patients, from the gene expression omnibus (GEO) database. Differentially expressed miRNAs (DEMs) between IgAN and healthy patients were identified. The Funrich software was used to predict the differentially expressed genes (DEGs). Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyzes of overlapping genes were analyzed at the function level on DAVID 6.8. We used search Tool for the retrieval of interacting genes (STRING) online database constructed the protein-protein interaction (PPI) network. Then we further analyzed the hub genes by Cytoscape software and the hub miRNA by TargetScan.

RESULTS

We identified 418 DEMs from the GSE25590 datasets. The upstream transcription factors SP1 regulates most DEMs. According to the GO and KEGG results, the DEGs were enriched in the MAPK signaling pathway and small GTPase mediated signal transduction. SYN1, SYT4, RBFOX1, KCNC1, VAMP2, FBXO11, ASB9, SYT9, KLHL5, and KRAS were identified as hub genes. Hsa-miR-532-5p, hsa-miR-92a, hsa-miR-328, hsa-miR-137, hsa-miR-153, hsa-miR-9-5p, hsa-miR-140-5p, hsa-miR-217, hsa-miR-155, and hsa-miR-212 were predicted as hub miRNAs.

CONCLUSIONS

The DEMs and DEGs re-analysis provided potential key genes and hub miRNA of PMBCs, which may help to monitor the happening and prognosis of IgAN.

Identification of Independent and Communal Differentially Expressed Genes as Well as Potential Therapeutic Targets in Ischemic Heart Failure and Non-Ischemic Heart Failure

Background

Heart failure (HF) is a rapidly growing public health problem, and its two main etiological types are non-ischemic heart failure (NIHF) and ischemic heart failure (IHF). However, the independent and common mechanisms of NIHF and IHF have not been fully elucidated. Here, bioinformatic analysis was used to characterize the difference and independent pathways for IHF and NIHF, and more importantly, to unearth the common potential markers and therapeutic targets in IHF and NIHF.

Methods

Two data sets with accession numbers GSE26887 and GSE84796 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the independent and communal DEGs of NIHF and IHF, a functional annotation, protein-protein interaction (PPI) network analysis, co-expression and drug-gene interaction prediction analysis, and mRNA-miRNA regulatory network analysis were performed for DEGs.

Results

We found 1146 independent DEGs (DEGs2) of NIHF mainly enriched in transcription-related and 2595 independent DEGs (DEGs3) of IHF mainly enriched in immune-related. Moreover, 185 communal DEGs (DEGs1) were found between NIHF and IHF, including 93 upregulated genes and 92 downregulated genes. Pathway enrichment analysis results showed that GPCR pathways and biological processes are closely related to the occurrence of HF. In addition, three hub genes were identified from PPI network, including CCL5, C5 and TLR3.

Conclusion

The identification of DEGs and hub genes in this study contributes to a novel perception for potential functional mechanisms and biomarkers or therapeutic targets in NIHF and IHF.

In silico Identification of 10 Hub Genes and an miRNA-mRNA Regulatory Network in Acute Kawasaki Disease

Kawasaki disease (KD) causes acute systemic vasculitis and has unknown etiology. Since the acute stage of KD is the most relevant, the aim of the present study was to identify hub genes in acute KD by bioinformatics analysis. We also aimed at constructing microRNA (miRNA)–messenger RNA (mRNA) regulatory networks associated with acute KD based on previously identified differentially expressed miRNAs (DE-miRNAs). DE-mRNAs in acute KD patients were screened using the mRNA expression profile data of GSE18606 from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DE-mRNAs were performed with the DAVID database. Target genes of DE-miRNAs were predicted using the miRWalk database and their intersection with DE-mRNAs was obtained. From a protein–protein interaction (PPI) network established by the STRING database, Cytoscape software identified hub genes with the two topological analysis methods maximal clique centrality and Degree algorithm to construct a miRNA-hub gene network. A total of 1,063 DE-mRNAs were identified between acute KD and healthy individuals, 472 upregulated and 591 downregulated. The constructed PPI network with these DE-mRNAs identified 38 hub genes mostly enriched in pathways related to systemic lupus erythematosus, alcoholism, viral carcinogenesis, osteoclast differentiation, adipocytokine signaling pathway and tumor necrosis factor signaling pathway. Target genes were predicted for the up-regulated and down-regulated DE-miRNAs, 10,203, and 5,310, respectively. Subsequently, 355, and 130 overlapping target DE-mRNAs were obtained for upregulated and downregulated DE-miRNAs, respectively. PPI networks with these target DE-mRNAs produced 15 hub genes, six down-regulated and nine upregulated hub genes. Among these, ten genes (ATM, MDC1, CD59, CD177, TRPM2, FCAR, TSPAN14, LILRB2, SIRPA, and STAT3) were identified as hub genes in the PPI network of DE-mRNAs. Finally, we constructed the regulatory network of DE-miRNAs and hub genes, which suggested potential modulation of most hub genes by hsa-miR-4443 and hsa-miR-6510-5p. SP1 was predicted to potentially regulate most of DE-miRNAs. In conclusion, several hub genes are associated with acute KD. An miRNA–mRNA regulatory network potentially relevant for acute KD pathogenesis provides new insights into the underlying molecular mechanisms of acute KD. The latter may contribute to the diagnosis and treatment of acute KD.

Screening and bioinformatical analysis of differentially expressed genes in nasopharyngeal carcinoma

Objective: To identify differentially expressed genes via bioinformatical analysis for nasopharyngeal carcinoma (NPC) and explore potential biomarkers for NPC.

Methods: We downloaded the NPC gene expression datasets (GSE40290, GSE53819) and obtained differentially expressed genes (DEGs) via GEO2R. Functional analysis of DEGs was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In order to explore the interaction of DEGs and screen the core genes, we established protein-protein interaction (PPI) network. Then the expression level, prognostic and diagnostic analysis of the core genes in NPC were performed to reveal their potential effects on NPC. Furthermore, we obtained the transcription factors (TF) and microRNAs of core genes to construct the coregulatory network.

Results: We obtained 124 up-regulated genes and 190 down-regulated genes in total. These genes were found to be related to signal transduction, extracellular matrix organization and cell adhesion based on GO analysis. KEGG analysis revealed that the NF-kappa B (NF-κB) signaling pathway, pathways in cancer were mainly enriched signaling pathways. 25 core genes were obtained by constructing PPI network. Then the high expression of 10 core genes in NPC were verified via GEPIA, Oncomine databases and laboratory experiments. The TF-microRNA coregulatory network of the 10 core genes was built. Survival and diagnostic analysis indicated that SPP1 had negative influence on the prognosis of NPC patients based on two datasets and nine up-regulated core genes (FN1, MMP1, MMP3, PLAU, PLAUR, SERPINE1, SPP1, COL8A1, COL10A1) might be diagnostic markers for NPC.

Conclusions: Core genes of NPC were screened out by bioinformatical analysis in the present study and these genes may serve as prognostic and diagnostic biomarkers for NPC.

Construction and Analysis of a Diagnostic Model Based on Differential Expression Genes in Patients With Major Depressive Disorder

Background: Major depressive disorder (MDD) is a common and severe psychiatric disorder with a heavy burden on the individual and society. However, the prevalence varies significantly owing to the lack of auxiliary diagnostic biomarkers. To identify the shared differential expression genes (DEGs) with potential diagnostic value in both the hippocampus and whole blood, a systematic and integrated bioinformatics analysis was carried out. Methods: Two datasets from the Gene Expression Omnibus database (GSE53987 and GSE98793) were downloaded and analyzed separately. A weighted gene co-expression network analysis was performed to construct the co-expression gene network of DEGs from GSE53987, and the most disease-related module was extracted. The shared DEGs from the module and GSE98793 were identified using a Venn diagram. Functional pathway prediction was used to identify the most disease-related DEGs. Finally, several DEGs were chosen, and their potential diagnostic value was determined by receiver operating characteristic curve analysis. Results: After weighted gene co-expression network analysis, the most MDD-related module (MEgrey) was identified, and 623 DEGs were extracted from this module. The intersection between MEgrey and GSE98793 was calculated, and 163 common DEGs were identified. The co-expression network of 163 DEGs from these was then reconstructed. All hub genes were identified based on the connective degree of the reconstructed co-expression network. Based on the results of functional pathway enrichment, 17 candidate hub genes were identified. Finally, logistic regression and receiver operating characteristic curves showed that three candidate hub genes (CEP350, SMAD5, and HSPG2) had relatively high auxiliary value in the diagnosis of MDD. Conclusion: Our results showed that the combination of CEP350, SMAD5, and HSPG2 has a relatively high diagnostic value for MDD. Pathway enrichment analysis also showed that these genes may play an important role in the pathogenesis of MDD. These results suggest a potentially important role for this gene combination in clinical practice.

The effect of gestational diabetes on identification of key genes and pathways in human umbilical vein endothelial cell by integrated bioinformatics analysis

Maternal diabetes may lead to long-term risks for the offspring. The study aims at identifying the potential crucial genes and pathways associated with foetal metabolism and malformation of gestational diabetes mellitus (GDM). Gene Expression Series 49524 and 87295 were downloaded from Gene Expression Omnibus database, including eight from GDM and eight from non-GDM. A total of 35 differentially expressed genes were identified. Gene ontology functional annotation and signalling pathway analyses were performed. Four hub genes were identified by protein-protein interaction network: SHH, E2F1, STAT1, and HOXA9. The four hub genes were assessed by western blot and real-time quantitative PCR in clinical samples. The results of this data mining and integration help to reveal the pathophysiologic and molecular mechanism imprinted in primary umbilical cord-derived cells from GDM offspring. These genes and pathways identified are potential stratification biomarkers and provide further insight for developing therapeutic intervention for the offspring of diabetic mothers.Impact statementWhat is already known on this subject? Maternal diabetes may lead to long-term risks for the offspring. A high glucose environment might change the umbilical cord expression of genes implicated in foetal metabolism and development. However, underlying molecular mechanisms have not been investigated thoroughly.What do the results of this study add? GO functional annotation showed that the biological functions of differentially expressed genes mainly involved in metanephros development, salivary gland morphogenesis, fat cell differentiation, vasculogenesis, muscle cell proliferation, heart morphogenesis and Wnt signalling pathway. Signalling pathway analyses found that these differentially expressed genes mainly implicated in the apoptosis, cell cycle, Hedgehog, P53, and NOTCH signalling pathway. Four hub genes were identified by protein-protein interaction network: SHH, E2F1, STAT1 and HOXA9.What are the implications of these findings for clinical practice and/or further research? The genes and pathways identified in the present study are potential stratification biomarkers and provide further insight for developing therapeutic intervention for the offspring of diabetic mothers.

Identification of Common Differentially Expressed Genes and Potential Therapeutic Targets in Ulcerative Colitis and Rheumatoid Arthritis

Ulcerative colitis (UC) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases (IMIDs) with similar symptoms and common genomics. However, the relationship between UC and RA has not been investigated thoroughly. Therefore, this study aimed to establish the differentially expressed genes (DEGs) and potential therapeutic targets in UC and RA. Three microarray datasets (GSE38713, GSE1919, and GSE12251) were selected from the Gene Expression Omnibus (GEO) database for analysis. We used R software to identify the DEGs and performed enrichment analyses. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape software were used to construct the protein-protein interaction (PPI) network and identify the hub genes. A regulatory network based on the constructed PPI was generated using StarBase and PROMO databases. We identified a total of 1542 and 261 DEGs in UC and RA. There were 169 common DEGs identified in both UC and RA, including 63 upregulated genes (DEGs1) and nine downregulated genes (DEGs2). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs1 and DEGs2 in the PPI network revealed that the genes enriched were involved in immunity. A total of 45 hub genes were selected based on high scores of correlation; three hub genes (SRGN, PLEK, and FCGR3B) were found to be upregulated in UC and RA, and downregulated in UC patients with response to infliximab treatment. The identification of novel DEGs and hub genes in the current study contributes to a novel perception for latent functional mechanisms and presents potential prognostic indicators and therapeutic targets in UC and RA.

[Spindle assembly checkpoint complex-related genes TTK and MAD2L1 are over-expressed in lung adenocarcinoma: a big data and bioinformatics analysis]

OBJECTIVE

To screen the key genes related to the prognosis of lung adenocarcinoma through big data analysis and explore their clinical value and potential mechanism.

METHODS

We analyzed GSE18842, GSE27262, and GSE33532 gene expression profile data obtained from the Gene Expression Omnibus (GEO). Bioinformatics methods were used to screen the differentially expressed genes in lung adenocarcinoma tissues and KEGG and GO enrichment analysis was performed, followed by PPI interaction network analysis, module analysis, differential expression analysis, and prognosis analysis. The expressions of MAD2L1 and TTK by immunohistochemistry were verified in 35 non-small cell lung cancer specimens and paired adjacent tissues.

RESULTS

We identified a total of 256 genes that showed significant differential expressions in lung adenocarcinoma, including 66 up-regulated and 190 down-regulated genes. Thirty-two up-regulated core genes were screened by functional analysis, and among them 29 were shown to significantly correlate with a poor prognosis of patients with lung adenocarcinoma. All the 29 genes were highly expressed in lung adenocarcinoma tissues compared with normal lung tissues and were mainly enriched in cell cycle pathways. Seven of these key genes were closely related to the spindle assembly checkpoint (SAC) complex and responsible for regulating cell behavior in G2/M phase. We selected SAC-related proteins TTK and MAD2L1 to test their expressions in clinical tumor samples, and detected their overexpression in lung adenocarcinoma tissues as compared with the adjacent tissues.

CONCLUSIONS

Seven SAC complex-related genes, including TTK and MAD2L1, are overexpressed in lung adenocarcinoma tissues with close correlation with the prognosis of the patients.

Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis

Purpose

Stomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancer in the world with both high mortality and high metastatic capacity. Therefore, the present study aimed to investigate novel therapeutic targets and prognostic biomarkers that can be used for STAD treatment.

Materials and Methods

We acquired four original gene chip profiles, namely GSE13911, GSE19826, GSE54129, and GSE65801 from the Gene Expression Omnibus (GEO). The datasets included a total of 114 STAD tissues and 110 adjacent normal tissues. The GEO2R online tool and Venn diagram software were used to discriminate differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enriched pathways were also performed for annotation and visualization with DEGs. The STRING online database was used to identify the functional interactions of DEGs. Subsequently, we selected the most significant DEGs to construct the protein-protein interaction (PPI) network and to reveal the core genes involved. Finally, the Kaplan-Meier Plotter online database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to analyze the prognostic information of the core DEGs.

Results

A total of 114 DEGs (35 upregulated and 79 downregulated) were identified, which were abnormally expressed in the GEO datasets. GO analysis demonstrated that the majority of the upregulated DEGs were significantly enriched in collagen trimer, cell adhesion, and identical protein binding. The downregulated DEGs were involved in extracellular space, digestion, and inward rectifier potassium channel activity. Signaling pathway analysis indicated that upregulated DEGs were mainly enriched in receptor interaction, whereas downregulated DEGs were involved in gastric acid secretion. A total of 80 DEGs were screened into the PPI network complex, and one of the most important modules with a high degree was detected. Furthermore, 10 core genes were identified, namely COL1A1, COL1A2, FN1, COL5A2, BGN, COL6A3, COL12A1, THBS2, CDH11, and SERPINH1. Finally, the results of the prognostic information further demonstrated that all 10 core genes exhibited significantly higher expression in STAD tissues compared with that noted in normal tissues.

Conclusion

The multiple molecular mechanisms of these novel core genes in STAD are worthy of further investigation and may reveal novel therapeutic targets and biomarkers for STAD treatment.

Identification of Significant Genes in HIV/TB via Bioinformatics Analysis

OBJECTIVE

Tuberculosis (TB) is the most common cause of acquired immune deficiency syndrome (AIDS)-related deaths worldwide. The purpose of this study was to identify genes that are significant for the mechanisms involved in Mycobacterium tuberculosis (MTB) and human immunodeficiency virus (HIV) co-infection.

MATERIALS AND METHODS

We selected 113 HIV/TB and 109 HIV/LTBI (latent TB infection) genes from GSE37250 and GSE69581 datasets. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and gene ontology (GO) analyses. The protein-protein interaction (PPI) network of common differentially expressed genes (DEGs) were visualized using Cytoscape software with Search Tool for the Retrieval of Interacting Genes (STRING).

RESULTS

A total of 83 DEGs were found to be common to both datasets. These included 64 up-regulated genes and 19 down-regulated genes. The PPI network was analyzed, and 12 up-regulated genes were identified. Re-analysis using DAVID found no significant signaling pathways enriched by these twelve genes (CAMP, CTSG, DEFA1, DEFA1B, DEFA3, DEFA4, ELANE, HP, HPSE, OLFM4, PGLYRP1, TCN1).

CONCLUSIONS

Twelve significantly up-regulated DEGs that may be potential therapeutic targets for HIV/TB were identified using a series of bioinformatics analytical methods.

The Dysregulation and Prognostic Analysis of STRIPAK Complex Across Cancers

The striatin-interacting phosphatase and kinase (STRIPAK) is the highly conserved complex, which gains increased attention in physiology and pathology process recently. However, limited studies reported the details of STRIPAK complex in cancers while some results strongly suggested it plays a vital role in tumorigenesis. Hence, we systematically analyzed the molecular and survival profiles of 18 STRIPAK genes to assess the value of STRIPAK complex across cancers. Our findings revealed the low frequencies of DNA aberrances and incomparable expression difference of STRIPAK genes between normal and tumor tissues, but they showed strong prognostic value in cancers, especially the liver hepatocellular carcinoma (LIHC) and kidney renal clear cell carcinoma (KIRC). Interestingly, STRIPAK genes were observed the opposite pattern of survival and expression in the above two cancer types. PPP2R1A and TRAF3IP3 were proposed as the oncogenic genes in LIHC and KIRC, respectively. The STRIPAK genes serve as oncogenes may due to the methylation heterogeneity. Taken together, our comprehensive molecular analysis of STRIPAK complex provides resource to facilitate the understanding of mechanism and utilize the potential therapies to tumors.

Lipid Metabolism is the common pathologic mechanism between Type 2 Diabetes Mellitus and Parkinson's disease

Although increasing evidence has suggested crosstalk between Parkinson's disease (PD) and type 2 diabetes mellitus (T2DM), the common mechanisms between the two diseases remain unclear. The aim of our study was to characterize the interconnection between T2DM and PD by exploring their shared biological pathways and convergent molecules. The intersections among the differentially expressed genes (DEGs) in the T2DM dataset GSE95849 and PD dataset GSE6613 from the Gene Expression Omnibus (GEO) database were identified as the communal DEGs between the two diseases. Then, an enrichment analysis, protein-protein interaction (PPI) network analysis, correlation analysis, and transcription factor-target regulatory network analysis were performed for the communal DEGs. As a result, 113 communal DEGs were found between PD and T2DM. They were enriched in lipid metabolism, including protein modifications that regulate metabolism, lipid synthesis and decomposition, and the biological effects of lipid products. All these pathways and their biological processes play important roles in both diseases. Fifteen hub genes identified from the PPI network could be core molecules. Their function annotations also focused on lipid metabolism. According to the correlation analysis and the regulatory network analysis based on the 15 hub genes, Sp1 transcription factor (SP1) could be a key molecule since it affected other hub genes that participate in the common mechanisms between PD and T2DM. In conclusion, our analyses reveal that changes in lipid metabolism could be a key intersection between PD and T2DM, and that SP1 could be a key molecule regulating these processes. Our findings provide novel points for the association between PD and T2DM.

Comprehensive bioinformatics analysis of mRNA expression profiles and identification of a miRNA-mRNA network associated with lupus nephritis

OBJECTIVE

Lupus nephritis (LN) is one of the serious complications of systemic lupus erythematosus. The aim of this study was to identify core genes and pathways involved in the pathogenesis of LN.

METHODS

We screened differentially expressed genes (DEGs) in LN patients using mRNA expression profile data from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DEGs was performed utilizing the Database for annotation, Visualization and Integrated Discovery. Target genes with differentially expressed miRNAs (DEMIs) were predicted using the miRTarBase database, and the intersection between these target genes and DEGs was selected to be studied further.

RESULTS

In total, 107 common DEGs (CDEGs) were identified from the Tub_LN group and Glom_LN group, and 66 DEMIs were identified. Fifty-three hub genes and two significant modules were identified from the protein-protein interaction (PPI) network, and a miRNA-mRNA network was constructed. The CDEGs, module genes in the PPI network and genes intersecting with the CDEGs and target genes of DEMIs were all associated with the PI3K-Akt signalling pathway.

CONCLUSION

In summary, this study reveals some crucial genes and pathways potentially involving in the pathogenesis of LN. These findings provide a new insight for the research and treatment of LN.

Identification key genes, key miRNAs and key transcription factors of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is one of the most common cancers worldwide. The etiology and pathophysiology of LUAD remain unclear. The aim of the present study was to identify the key genes, miRNAs and transcription factors (TFs) associated with the pathogenesis and prognosis of LUAD.

Methods

Three gene expression profiles (GSE43458, GSE32863, GSE74706) of LUAD were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by GEO2R.The Gene Ontology (GO) terms, pathways, and protein-protein interactions (PPIs) of these DEGs were analyzed. Bases on DEGs, the miRNAs and TFs were predicted. Furthermore, TF-gene-miRNA co-expression network was constructed to identify key genes, miRNAs and TFs by bioinformatic methods. The expressions and prognostic values of key genes, miRNAs and TFs were carried out through The Cancer Genome Atlas (TCGA) database and Kaplan Meier-plotter (KM) online dataset.

Results

A total of 337 overlapped DEGs (75 upregulated and 262 downregulated) of LUAD were identified from the three GSE datasets. Moreover, 851 miRNAs and 29 TFs were identified to be associated with these DEGs. In total, 10 hub genes, 10 key miRNAs and 10 key TFs were located in the central hub of the TF-gene-miRNA co-expression network, and validated using The Cancer Genome Atlas (TCGA) database. Specifically, seven genes (PHACTR2, MSRB3, GHR, PLSCR4, EPB41L2, NPNT, FBXO32), two miRNAs (hsa-let-7e-5p, hsa-miR-17-5p) and four TFs (STAT6, E2F1, ETS1, JUN) were identified to be associated with prognosis of LUAD, which have significantly different expressions between LUAD and normal lung tissue. Additionally, the miRNA/gene co-expression analysis also revealed that hsa-miR-17-5p and PLSCR4 have a significant negative co-expression relationship (r=−0.33, P=1.67e-14) in LUAD.

Conclusions

Our study constructed a regulatory network of TF-gene-miRNA in LUAD, which may provide new insights about the interaction between genes, miRNAs and TFs in the pathogenesis of LUAD, and identify potential biomarkers or therapeutic targets for LUAD.

Down-regulated Solute Carrier Family 4 Member 4 Predicts Poor Progression in Colorectal Cancer

Aim: To identify potential key candidate genes, whose expression and clinical significance was further assessed in colorectal cancer (CRC).

Methods: Three original microarray datasets (GSE41328, GSE22598, and GSE23878) from NCBI-GEO were used to analyze differentially expressed genes (DEGs) in CRC. Online database analyses through Oncomine and GEIPA were performed to evaluate SLC4A4 expression and explore the prognostic merit of SLC4A4 expression, which was further confirmed by analyses from QPCR based cDNA array and IHC based tissue microarray (TMA). STRING website was used to explore the interaction between SLC4A4 with other DEGs based on the protein-protein interaction (PPI) networks.

Results: Analysis of three original microarray datasets from GEO identified 82 shared, differentially expressed genes (28 upregulated and 54 down-regulated) in CRC tissues. Online analyses from Oncomine and GEIPA revealed lower SLC4A4 mRNA expression in CRC tissues compared to adjacent normal tissues, which were further confirmed by QPCR based cDNA array and IHC based TMA analyses on both mRNA and protein levels. Survival analyses through GEIPA and from TMA demonstrated that low SLC4A4 expression is correlated with worse overall survival among patients with CRC. Survival analysis from Kaplan-meier plotter demonstrated that low SLC4A4 expression is significantly associated with poor progression (including relapse-free survival, overall survival, distant metastasis-free survival, post-progression survival) of patients with breast cancer, lung cancer, gastric cancer, and ovarian cancer. PPI analysis found that SLC4A4 is highly correlated with various genes, including SLC9A3, SLC26A6, ENSG00000214921, SLC26A4, SLC9A3R1, and SLC9A1.

Conclusion: The mRNA and protein levels of SLC4A4 were decreased in CRC tissues, and low expression of SLC4A4 significantly correlated with shorter survival of CRC patients and poorer progression of patients with breast cancer, lung cancer, gastric cancer and ovarian cancer, suggesting potential role of SLC4A4 on tumor suppression and prognostic prediction in multiple malignancies including CRC.

Bioinformatics analysis and identification of potential genes related to pathogenesis of cervical intraepithelial neoplasia

The aim of this study was to explore and identify the key genes and signal pathways contributing to cervical intraepithelial neoplasia (CIN). The gene expression profiles of GSE63514 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened performing with packages in software R. After Gene ontology terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyzing, and Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA) was used to analyze these genes. Then sub-modules were subsequently analyzed base CIN grade, and protein-protein interaction (PPI) network of DEGs were constructed. 537 DEGs were screened in total, consisting 331 up-regulated genes and 206 down-regulated genes in CIN samples compared to normal samples. The most DEGs were enriched in chromosomal region in cellular component (CC), organelle fission inbiological process (BP) and ATPase activity in molecular function (MF). KEGG pathway enrichment analyzing found the DEGs were mainly concentrated in 10 pathways. The results of GSEA mainly enriched in 4 functional sets: E2F-Targets, G2M-Checkpoint, Mitotic-Spindle and Spermatogenesis. A total of 6 modules were identified by WCGNA. Subsequently, grey module was the highest correlation (Cor=0.78, P=5e-22) and 31 genes were taken as candidate hub genes for CIN high grade risk (weighted correlation coefficients >0.80). Finally, diagnostic analysis showed that in addition to CCDC7, the expression levels of the remaining 13 DEGs have a high diagnostic value (AUC>0.8 and P<0.05). These findings provided a new sight into the understanding of molecular functions for CIN.

ASPM promotes glioblastoma growth by regulating G1 restriction point progression and Wnt-β-catenin signaling

Increasing evidence has indicated that the disorganized expression of certain genes promotes tumour progression. In this study, we elucidate the potential key differentially expressed genes (DEGs) between glioblastoma (GBM) and normal brain tissue by analysing three different mRNA expression profiles downloaded from the Gene Expression Omnibus (GEO) database. DEGs were sorted, and key candidate genes and signalling pathway enrichments were analysed. In our analysis, the highest fold change DEG was found to be abnormal spindle-like microcephaly associated (ASPM). The ASPM expression pattern from the database showed that it is highly expressed in GBM tissue, and patients with high expression of ASPM have a poor prognosis. Moreover, ASPM showed aberrantly high expression in GBM cell lines. Loss-of-function assay indicated that ASPM enhances tumorigenesis in GBM cells in vitro. Xenograft growth verified the oncogenic activity of ASPM in vivo. Furthermore, downregulation of ASPM could arrest the cell cycle of GBM cells at the G0/G1 phase and attenuate the Wnt/β-catenin signalling activity in GBM. These data suggest that ASPM may serve as a new target for the therapeutic treatment of GBM.

Integrated analysis of lncRNA CTD-2357A8.3 expression and its potential roles in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC), one of the most common malignant tumors, endangers human health. Recently, the incidence of HNSCC has kept increasing: However, its prognosis has not significantly improved. Understanding the molecular mechanism underlying HNSCC development will therefore provide new strategies for therapy. The present study attempted to identify differentially expressed (DE) long non-coding (lnc)RNAs and investigated their functional role in HNSCC development. Expression profiles of HNSCC and normal samples were downloaded from The Cancer Genome Atlas (TCGA) database. DElncRNAs between the HNSCC and normal samples were highlighted and their potential functions were investigated through lncRNA-micro (mi)RNA-mRNA network by using Gene Expression Profiling Interactive Analysis, UALCAN, DIANA-LncBase v.2 and miRWalk 3.0 databases. A total of 343 dysregulated lncRNAs were identified. Among these DElncRNAs, CTD-2357A8.3 had the highest fold-change and was significantly associated with poor overall survival in patients with HNSCC. Furthermore, CTD-2357A8.3 was associated with ‘signaling pathways regulating stem cell pluripotency’, ‘proteoglycans in cancer’, ‘transcriptional misregulation in cancer’ and ‘chemokine signaling pathway’. Further analysis demonstrated that CTD-2357A8.3 acted as a ‘sponge’ in order to competitively adsorb miRNA to regulate the expression of target gene caveolin 1 (CAV1) in HNSCC. In conclusion, CTD-2357A8.3 may be considered a promising diagnosis biomarker or a therapeutic target for the treatment of HNSCC.

Identification of Potential Biomarkers in Association With Progression and Prognosis in Epithelial Ovarian Cancer by Integrated Bioinformatics Analysis

Epithelial ovarian cancer (EOC) is one of the malignancies in women, which has the highest mortality. However, the microlevel mechanism has not been discussed in detail. The expression profiles GSE27651, GSE38666, GSE40595, and GSE66957 including 188 tumor and 52 nontumor samples were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were filtered using R software, and we performed functional analysis using the clusterProfiler. Cytoscape software, the molecular complex detection plugin and database STRING analyzed DEGs to construct protein-protein interaction network. We identified 116 DEGs including 81 upregulated and 35 downregulated DEGs. Functional analysis revealed that they were significantly enriched in the extracellular region and biosynthesis of amino acids. We next identified four bioactive compounds (vorinostat, LY-294002,trichostatin A, and tanespimycin) based on ConnectivityMap. Then 114 nodes were obtained in protein-protein interaction. The three most relevant modules were detected. In addition, according to degree ≥ 10, 14 core genes including FOXM1, CXCR4, KPNA2, NANOG, UBE2C, KIF11, ZWINT, CDCA5, DLGAP5, KIF15, MCM2, MELK, SPP1, and TRIP13 were identified. Kaplan-Meier analysis, Oncomine, and Gene Expression Profiling Interactive Analysis showed that overexpression of FOXM1, SPP1, UBE2C, KIF11, ZWINT, CDCA5, UBE2C, and KIF15 was related to bad prognosis of EOC patients. CDCA5, FOXM1, KIF15, MCM2, and ZWINT were associated with stage. Receiver operating characteristic (ROC) curve showed that messenger RNA levels of these five genes exhibited better diagnostic efficiency for normal and tumor tissues. The Human Protein Atlas database was performed. The protein levels of these five genes were significantly higher in tumor tissues compared with normal tissues. Functional enrichment analysis suggested that all the hub genes played crucial roles in citrate cycle tricarboxylic acid cycle. Furthermore, the univariate and multivariate Cox proportional hazards regression showed that ZWINT was independent prognostic indictor among EOC patients. The genes and pathways discovered in the above studies may open a new direction for EOC treatment.

Key genes and pathways in tumor-educated dendritic cells by bioinformatical analysis

Specific tumor microenvironment signaling might prevent the maturation of dendritic cells (DCs) with tolerogenic and immunosuppressive potential accounting for antigen-specific unresponsiveness in the lymphoid organs and in the periphery. In the present study, dendritic cells treated with LLC lung cancer cell or 4T1 breast cancer cell culture supernatants significantly down-regulated the expression of co-stimulatory molecules MHC-II, CD40, CD80, but up-regulated the inhibitory molecule PD-L1/L2, VISTA, and increased the messengerRNA levels of interleukin (IL)-6, arginase I, and IL-10, but decreased tumor necrosis factor-α and IL-12a. RNA was isolated from the dendritic cells with or without tumor supernatant stimulation and RNA sequencing was done. Then the differential expression genes were sorted, the candidate genes were analyzed and pathway enrichment analysis was done, and the associated protein-protein interaction network (PPI) was established. After integrated bioinformatical analysis, 405 (279 up-regulated and 126 down-regulated) consistently differential expression genes were identified. Using gene ontology and pathway analysis, it was found that differential expression genes were mainly enriched in the immune response, cell-cell interaction, hemostasis, and cell surface interactions with the vascular wall. The PPI data demonstrated that 236 nodes were classified with 1072 edges, and the most remarkable three modules involved 53 central node genes associated with cell survival, cell-substrate adhesion, chemotaxis, migration, immune response, and complement receptor mediated signaling pathway. These findings revealed the immune status of dendritic cells in the tumor environment.

Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis

Glioblastoma (GBM), characterized by high morbidity and mortality, is one of the most common lethal diseases worldwide. To identify the molecular mechanisms that contribute to the development of GBM, three cohort profile datasets (GSE50161, GSE90598 and GSE104291) were integrated and thoroughly analyzed; these datasets included 57 GBM cases and 22 cases of normal brain tissue. The current study identified differentially expressed genes (DEGs), and analyzed potential candidate genes and pathways. Additionally, a DEGs-associated protein-protein interaction (PPI) network was established for further investigation. Then, the hub genes associated with prognosis were identified using a Kaplan-Meier analysis based on The Cancer Genome Atlas database. Firstly, the current study identified 378 consistent DEGs (240 upregulated and 138 downregulated). Secondly, a cluster analysis of the DEGs was performed based on functions of the DEGs and signaling pathways were analyzed using the enrichment analysis tool on DAVID. Thirdly, 245 DEGs were identified using PPI network analysis. Among them, two co-expression modules comprising of 30 and 27 genes, respectively, and 35 hub genes were identified using Cytoscape MCODE. Finally, Kaplan-Meier analysis of the hub genes revealed that the increased expression of calcium-binding protein 1 (CABP1) was negatively associated with relapse-free survival. To summarize, all enriched Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways may participate in mechanisms underlying GBM occurrence and progression, however further studies are required. CABP1 may be a key gene associated with the biological process of GBM development and may be involved in a crucial mechanism of GBM progression.

Integrin α-5 as a potential biomarker of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant tumors that endanger human health. In recent years, the incidence of HNSCC has been increasing, without any significant improvement in the prognosis. Therefore, increased knowledge on the molecular mechanism underlying HNSCC development will allow the development of new strategies for therapy. The present study attempted to identify key genes involved in HNSCC development. Expression profiles of HNSCC and normal samples were downloaded from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) between the HNSCC and normal samples were identified and subjected to Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis. A protein-protein interaction (PPI) network was constructed, and Cytoscape CentiScape and Gene Expression Profiling Interactive Analysis were used to identify key DEGs. Finally, expression profiles of HNSCCs, including 500 HNSCCs and 44 normal samples, were included in the analysis. A total of 1,181 DEGs were screened, among which 354 genes were upregulated and 827 genes were downregulated in HNSCC compared with normal tissues. The GO enrichment analysis showed that the DEGs were mainly involved in chloride transmembrane transporter, metalloendopeptidase and substrate-specific channel activities. The KEGG pathway analysis revealed that the DEGs were mainly associated with ‘protein digestion and absorption’, as well as ‘extracellular matrix-receptor interaction’. Integrin α-5 (ITGA5) was identified as a hub gene, based on the PPI network complex, and was confirmed to be significantly associated with the overall survival rate. Moreover, ITGA5 was overexpressed specifically in HNSCC. The genes found, notably ITGA5, are potential diagnostic biomarkers and therapeutic targets in HNSCC.

Bioinformatic validation identifies candidate key genes in diffuse large-B cell lymphoma

Aim: In this study, four datasets concerning 167 diffuse large B-cell lymphoma (DLBCL) patients versus 56 controls and seven datasets involving 280 germinal center B-cell like (GCB) versus 224 activated B-cell like (ABC) DLBCL were included. Materials & methods: We identified 80 different expression genes (DEGs) for DLBCL versus nontumor and 77 DEGs for GCB versus ABC DLBCL. Results: These DEGs were found to be enriched in cell activity, signal transduction and extracellular region. Then ten central node genes for DLBCL versus nontumor and two hub genes for GCB versus ABC DLBCL were identified. Last, PAICS, IRF4 and PTPN1 were explored to be correlated with poor prognosis in DLBCL patients. Conclusion: Our study has identified critical genes from transcriptional profiles for DLBCL.

Identification of genes and pathways in human antigen-presenting cell subsets in response to polio vaccine by bioinformatical analysis

BACKGROUND

Polio eradication has been achieved in the world except for three countries due to the widespread use of the inactivated poliovirus vaccine (IPV) and the live-attenuated oral poliovirus vaccine. Following polio eradication, the IPV would be the only polio vaccine available. However, the mechanisms of the interactions between IPV and human antigen-presenting cells (APCs) remain largely unclear.

METHODS

To investigate the involvement of the IPV in human monocytes, we downloaded the gene chip GSE44721 from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the GEO2R analysis tool. Functional and pathway enrichment analyses were performed for DEGs using the Metascape database. DEG-associated protein-protein-interactions (PPIs) were established by the Search Tool for the Retrieval of Interacting Genes website and visualized by Cytoscape.

RESULTS

There were 240 DEGs (51 upregulated and 189 downregulated genes) identified from the GSE44721 data set, and they were significantly enriched in several biological processes, including antigen processing and presentation of lipid antigen via MHC class Ib, adaptive immune response, and response to interferon-gamma. One hundred thirty-six nodes were screened from the DEG PPI network. There were six significant hub proteins (WDR36, MRTO4, RPF2, PPAN, CD40, and BMS1) that regulated the IPV in human monocytes.

CONCLUSIONS

In summary, using bioinformatical analysis, we have information for the immunization activated by the IPV in monocytes. Moreover, hormones and cytokines regulate the activation of APCs.

Identification of core genes and clinical roles in pregnancy-associated breast cancer based on integrated analysis of different microarray profile datasets

More women are delaying child-birth. Thus, the diagnosis of pregnancy-associated breast cancer (PABC) will continue to increase. The aim of this study was to identify core candidate genes of PABC, and the relevance of the genes on the prognosis of PABC. GSE31192 and GSE53031 microarray profile datasets were downloaded from the Gene Expression Omnibus database and differentially expressed genes were analyzed using the R package and GEO2R tool. Then, Gene Ontology and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Moreover, the Search Tool for the Retrieval of Interacting Genes and the Molecular Complex Detection Cytoscape software plug-in were utilized to visualize protein–protein interactions and to screen candidate genes. A total of 239 DEGs were identified in PABC, including 101 up-regulated genes mainly enriched in fatty acid activation and the fibroblast growth factor signaling pathway, while 138 down-regulated genes particularly involved in activation of DNA fragmentation factor and apoptosis-induced DNA fragmentation. Fourteen hub genes with a high degree of connectivity were selected, including CREB1, ARF3, UBA5, SIAH1, KLHL3, HECTD1, MMP9, TRIM69, MEX3C, ASB6, UBE2Q2, FBXO22, EIF4A3, and PXN. Overall survival (OS) analysis of core candidate genes was performed using the Gene Expression Profiling Interactive Analysis and UALCAN websites. High ASB6 expression was associated with worse OS of PABC patients. Molecular subtypes and menopause status were also associated with worse OS for PABC patients. In conclusion, ASB6 could be a potential predictor and therapeutic target in patient with PABC.

Identification of Key Biomarkers and Potential Molecular Mechanisms in Renal Cell Carcinoma by Bioinformatics Analysis

Renal cell carcinoma (RCC) is the most common form of kidney cancer, caused by renal epithelial cells. RCC remains to be a challenging public health problem worldwide. Metastases that are resistant to radiotherapy and chemotherapy are the major cause of death from cancer. However, the underlying molecular mechanism regulating the metastasis of RCC is poorly known. Publicly available databases of RCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using GEO2R analysis, whereas the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by Gene Set Enrichment Analysis (GSEA) and Metascape. Protein-protein interaction (PPI) network of DEGs was analyzed by STRING online database, and Cytoscape software was used for visualizing PPI network. Survival analysis of hub genes was conducted using GEPIA online database. The expression levels of hub genes were investigated from The Human Protein Atlas online database and GEPIA online database. Finally, the comparative toxicogenomics database (CTD; http://ctdbase.org) was used to identify hub genes associated with tumor or metastasis. We identified 229 DEGs comprising 135 downregulated genes and 94 upregulated genes. Functional analysis revealed that these DEGs were associates with cell recognition, regulation of immune, negative regulation of adaptive immune response, and other functions. And these DEGs mainly related to P53 signaling pathway, cytokine-cytokine receptor interaction, Natural killer cell mediated cytotoxicity, and other pathways are involved. Ten genes were identified as hub genes through module analyses in the PPI network. Finally, survival analysis of 10 hub genes was conducted, which showed that the MMP2 (matrix metallo peptidase 2), DCN, COL4A1, CASR (calcium sensing receptor), GPR4 (G protein-coupled receptor 4), UTS2 (urotensin 2), and LDLR (low density lipoprotein receptor) genes were significant for survival. In this study, the DEGs between RCC and metastatic RCC were analyzed, which assist us in systematically understanding the pathogeny underlying metastasis of RCC. The MMP2, DCN, COL4A1, CASR, GPR4, UTS2, and LDLR genes might be used as potential targets to improve diagnosis and immunotherapy biomarkers for RCC.

Integrative bioinformatics analysis of miRNA and mRNA expression profiles and identification of associated miRNA-mRNA network in aortic dissection

BACKGROUND

Aortic dissection (AD) is one of the most lethal cardiovascular diseases. The aim of this study was to identify core genes and pathways revealing pathogenesis in AD.

METHODS

We screened differentially expressed mRNAs and miRNAs using mRNA and miRNA expression profile data of AD from Gene Expression Omnibus. Then functional and pathway enrichment analyses of differential expression genes (DEGs) was performed utilizing the database for annotation, visualization, and integrated discovery (DAVID). Target genes with differential expression miRNAs (DEMIs) were predicted using the miRWalk database, and the intersection between these predictions and DEGs was selected as differentially expressed miRNA-target genes. In addition, a protein-protein interaction (PPI) network and miRNA-mRNA regulatory network were constructed.

RESULTS

In total, 130 DEGs and 47 DEMIs were identified from mRNA and miRNA microarray, respectively, and 45 DEGs were DEMI-target genes. The PPI and miRNA-mRNA network included 79 node genes and 74 node genes, respectively, while 23 hub genes and 2 hub miRNAs were identified. The DEGs, PPI and modules differential expression miRNA-target genes were all mainly enriched in cell cycle, cell proliferation and cell apoptosis signaling pathways.

CONCLUSION

Taken above, the study reveals some candidate genes and pathways potentially involving molecular mechanisms of AD. These findings provide a new insight for research and treatment of AD.

Identification of key candidate genes and pathways in oral squamous cell carcinoma by integrated Bioinformatics analysis

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignant head and neck tumor, which poses a serious threat to human health. In recent years, the incidence of OSCC has been increasing, while the prognosis has not significantly improved. Elucidation of the molecular mechanisms underlying the development of OSCC may provide novel therapeutic strategies. In the present study, the gene expression profiles from 4 datasets, including 244 OSCC and 95 normal oral mucosa samples, were subjected to statistical and Bioinformatics analysis. A total of 34 differentially expressed genes (DEGs) were identified, among which 14 were upregulated and 20 were downregulated in OSCC compared with normal oral mucosa tissues. Gene Ontology enrichment analysis indicated that the DEGs were mainly involved in regulation of the immune response, cell adhesion and cell proliferative processes. The Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the DEGs were mainly associated with the phosphoinositide-3 kinase Akt and Toll-like receptor signaling pathway. The key candidate DEGs were identified from the complex protein-protein interaction network, and secreted phosphoprotein 1 (SPP1), integrin subunit α 3 and plasminogen activator, urokinase (PLAU) were confirmed to be significantly associated with the survival rate. Cell Counting Kit-8 and Transwell assays demonstrated that SPP1 and PLAU regulate cell proliferation, migration and invasion. The candidate genes/pathways identified in the present study may include promising diagnostic biomarkers or therapeutic targets for OSCC.

Identification of Grade-associated MicroRNAs in Brainstem Gliomas Based on Microarray Data

Gliomas arising in the brainstem are rare tumours that are difficult to surgically resect, and the microRNAs (miRNAs) and signalling pathways associated with brainstem gliomas (BSGs) are largely unknown. To identify grade-associated miRNAs in BSGs, a microarray analysis of 10 low-grade and 15 high-grade BSGs was performed in this study. Differentially expressed miRNAs (DE-miRNAs) were identified, and the functional DE-miRNAs were selected. The potential target genes and enriched pathways were analysed, and a target gene-associated protein-protein interaction (PPI) network was generated. Grade-associated functional DE-miRNAs were confirmed by real-time quantitative PCR. First, 28 functional DE-miRNAs, including 13 upregulated miRNAs and 15 downregulated miRNAs, were identified. Second, 2546 target genes that were involved in BSG-related pathways, such as signalling pathways regulating the pluripotency of stem cells, the AMPK signalling pathway, the HIF-1 signalling pathway, the PI3K-Akt signalling pathway, the Wnt signalling pathway and the Hippo signalling pathway, were screened. Third, PHLPP2 and VEGFA were identified as hub genes in the PPI network. Last, we found that hsa-miR-34a-5p inhibits BSG cell invasion in vitro. In summary, using integrated bioinformatics analysis, we have identified the potential target genes and pathways of grade-associated functional DE-miRNAs in BSGs, which could improve the accuracy of prognostic evaluation. Furthermore, these hub genes and pathways could be therapeutic targets for the treatment of BSGs.

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.

Targeting glypican-4 overcomes 5-FU resistance and attenuates stem cell-like properties via suppression of Wnt/β-catenin pathway in pancreatic cancer cells

The existences of cancer stem cells in patients with pancreatic cancer are considered as pivotal factors contributing to chemoresistance and disease relapse. Glypican-4 (GPC4) is one of the members of the glypicans family, which underlies human congenital malformations and multiple diseases. However, its potential biological function in pancreatic cancer still remains elusive. In this study, we are the first to demonstrate that GPC4 was involved in 5-fluorouracil (5-FU) resistance and pancreatic cancer stemness through comprehensive bioinformatical analysis. Functional experiments showed that knockdown of GPC4 sensitized pancreatic cancer cells to 5-FU and attenuated stem cell-like properties. In terms of mechanism research, knockdown of GPC4 suppressed the activation of Wnt/β-catenin pathway and its downstream targets. Furthermore, the expression of GPC4 was significantly upregulated in pancreatic cancer tissues compared with normal tissues and remarkably correlated with patients' overall survival according to the data derived from the Cancer Genome Atlas database. Taken together, our results suggest that GPC4 is a key regulator in chemoresistance and pancreatic cancer stemness. Thus, targeting GPC4 may serve as a promising strategy for pancreatic cancer therapy.

Identification of differentially expressed genes in synovial tissue of rheumatoid arthritis and osteoarthritis in patients

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the common joints disorder in the world. Although they have showed the analogous clinical manifestation and overlapping cellular and molecular foundation, the pathogenesis of RA and OA were different. The pathophysiologic mechanisms of arthritis in RA and OA have not been investigated thoroughly. Thus, the aim of study is to identify the potential crucial genes and pathways associated with RA and OA and further analyze the molecular mechanisms implicated in genesis. First, we compared gene expression profiles in synovial tissue between RA and OA from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene Expression Series (GSE) 1919, GSE55235, and GSE36700 were downloaded from the GEO database, including 20 patients of OA and 21 patients of RA. Differentially expressed genes (DEGs) including "CXCL13," "CD247," "CCL5," "GZMB," "IGKC," "IL7R," "UBD///GABBR1," "ADAMDEC1," "BTC," "AIM2," "SHANK2," "CCL18," "LAMP3," "CR1," and "IL32." Second, Gene Ontology analyses revealed that DEGs were significantly enriched in integral component of extracellular space, extracellular region, and plasma membrane in the molecular function group. Signaling pathway analyses indicated that DEGs had common pathways in chemokine signaling pathway, cytokine-cytokine receptor interaction, and cytosolic DNA-sensing pathway. Third, DEGs showed the complex DEGs protein-protein interaction network with the Coexpression of 83.22%, Shared protein domains of 8.40%, Colocalization of 4.76%, Predicted of 2.87%, and Genetic interactions of 0.75%. In conclusion, the novel DEGs and pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms of RA.