Genome-wide identification and in silico characterization of major RNAi gene families in date palm (Phoenix dactylifera).

BACKGROUND: Dates contain various minerals that are essential for good health. The major RNA interference (RNAi) gene families play a vital role in plant growth and development by controlling the expression of protein-coding genes against different biotic and abiotic stresses. However, these gene families for date palm are not yet studied. Therefore, this study has explored major RNAi genes and their characteristics in date palm. RESULTS: We have identified 4 PdDCLs, 7 PdAGOs, and 3 PdRDRs as RNAi proteins from the date palm genome by using AtRNAi genes as query sequences in BLASTp search. Domain analysis of predicted RNAi genes has revealed the Helicase_C, Dicer_dimer, PAZ, RNase III, and Piwi domains that are associated with the gene silencing mechanisms. Most PdRNAi proteins have been found in the nucleus and cytosol associated with the gene silencing actions. The gene ontology (GO) enrichment analysis has revealed some important GO terms including RNA interference, dsRNA fragmentation, and ribonuclease_III activity that are related to the protein-coding gene silencing mechanisms. Gene regulatory network (GRN) analysis has identified PAZ and SNF2 as the transcriptional regulators of PdRNAi genes. Top-ranked 10 microRNAs including Pda-miR156b, Pda-miR396a, Pda-miR166a, Pda-miR167d, and Pda-miR529a have been identified as the key post-transcriptional regulators of PdRNAi genes that are associated with different biotic/abiotic stresses. The cis-acting regulatory element analysis of PdRNAi genes has detected some vital cis-acting elements including ABRE, MBS, MYB, MYC, Box-4, G-box, I-box, and STRE that are linked with different abiotic stresses. CONCLUSION: The results of this study might be valuable resources for the improvement of different characteristics in date palm by further studies in wet-lab.

Integrated bioinformatics analysis of SEMA3C in tongue squamous cell carcinoma using machine-learning strategies.

Tongue squamous cell carcinoma (TSCC) is an aggressive oral cancer with a high incidence of metastasis and poor prognosis. We aim to identify and verify potential biomarkers for TSCC using bioinformatics analysis. To begin with, we examined clinical and RNA expression information of individuals with TSCC from the Gene Expression Omnibus (GEO) database. Differential expression analysis and functional analysis were conducted. Multiple machine-learning strategies were next employed to screen and determine the hub gene, and receiver operating characteristic (ROC) analysis was used to assess diagnostic value. Semaphorin3C (SEMA3C) was identified as a critical biomarker, presenting high diagnostic accuracy for TSCC. In the validation cohorts, SEMA3C exhibited high expression levels in TSCC. The high expression of SEMA3C was a poor prognostic factor in TSCC by the Kaplan-Meier curve. Based on the Gene Ontology (GO) analysis, SEMA3C was mapped in terms related to cell adhesion, positive regulation of JAK-STAT, positive regulation of stem cell maintenance, and positive regulation of NF-κB activity. Single-cell RNA sequencing (ScRNA-seq) analysis showed cells expressing SEMA3C were predominantly tumor cells. Then, we further verified that SEMA3C had high expression in TSCC clinical samples. In addition, the knockdown of SEMA3C suppressed the proliferation, migration, and invasion of TSCC cells in vitro. This study is the first to report the involvement of SEMA3C in TSCC, suggesting that upregulated SEMA3C could be a novel and critical potential biomarker for future predictive diagnostics, prevention, prognostic assessment, and personalized medical services in TSCC.

Integrated Bioinformatics Analysis and Target Drug Prediction of Inflammatory Bowel Disease Co-existent Diabetes Mellitus.

INTRODUCTION: Inflammatory bowel disease (IBD) has become one of the public problems worldwide and its incidence rate is increasing year by year. Its concomitant disease i.e. diabetes mellitus (DM) has attracted more and more attention due to DM altering the progression of IBD and leading to long periods of intermittent recurrence and deterioration. The common mechanism and potential target drug of IBD with comorbid chronic conditions of DM were explored. METHODS: Gene expression profile data were downloaded from the Gene Expression Omnibus (GEO) public database. The differentially expressed genes (DEGs) were identified by R software. GO annotation and pathway enrichment were performed, a protein-protein interaction (PPI) network was constructed, associated lncRNAs were predicted and drug prediction targeting key genes was made. Additionally, the regulatory network among core genes, associated pathways, and predicted lncRNA in IBD with coexistent DM were visualized. RESULTS: We identified the critical gene MMP3 with lncRNA CDKN2BAS involved in the PPAR pathway, which uncovered the underlying regulatory mechanism of IBD with coexistent DM. We also predicted the potential therapeutic compound ZINC05905909 acting on MMP3. CONCLUSION: Our findings revealed the regulatory mechanism chain of critical gene MMP3, lncRNA CDKN2BAS, and PPAR pathway and provided potential therapeutic compound ZINC05905909 for drug therapy to treat comorbid IBD DM.

Integrated Bioinformatics Analysis Confirms the Diagnostic Value of Nourin-Dependent miR-137 and miR-106b in Unstable Angina Patients.

The challenge of rapidly diagnosing myocardial ischemia in unstable angina (UA) patients presenting to the Emergency Department (ED) is due to a lack of sensitive blood biomarkers. This has prompted an investigation into microRNAs (miRNAs) related to cardiac-derived Nourin for potential diagnostic application. The Nourin protein is rapidly expressed in patients with acute coronary syndrome (ACS) (UA and acute myocardial infarction (AMI)). MicroRNAs regulate gene expression through mRNA binding and, thus, may represent potential biomarkers. We initially identified miR-137 and miR-106b and conducted a clinical validation, which demonstrated that they were highly upregulated in ACS patients, but not in healthy subjects and non-ACS controls. Using integrated comprehensive bioinformatics analysis, the present study confirms that the Nourin protein targets miR-137 and miR-106b, which are linked to myocardial ischemia and inflammation associated with ACS. Molecular docking demonstrated robust interactions between the Nourin protein and miR137/hsa-miR-106b, involving hydrogen bonds and hydrophobic interactions, with -10 kcal/mol binding energy. I-TASSER generated Nourin analogs, with the top 10 chosen for structural insights. Antigenic regions and MHCII epitopes within the Nourin SPGADGNGGEAMPGG sequence showed strong binding to HLA-DR/DQ alleles. The Cytoscape network revealed interactions of -miR137/hsa-miR--106b and Phosphatase and tensin homolog (PTEN) in myocardial ischemia. RNA Composer predicted the secondary structure of miR-106b. Schrödinger software identified key Nourin-RNA interactions critical for complex stability. The study identifies miR-137 and miR-106b as potential ACS diagnostic and therapeutic targets. This research underscores the potential of miRNAs targeting Nourin for precision ACS intervention. The analysis leverages RNA Composer, Schrödinger, and I-TASSER tools to explore interactions and structural insights. Robust Nourin-miRNA interactions are established, bolstering the case for miRNA-based interventions in ischemic injury. In conclusion, the study contributes to UA and AMI diagnosis strategies through bioinformatics-guided exploration of Nourin-targeting miRNAs. Supported by comprehensive molecular analysis, the hypoxia-induced miR-137 for cell apoptosis (a marker of cell damage) and the inflammation-induced miR-106b (a marker of inflammation) confirmed their potential clinical use as diagnostic biomarkers. This research reinforces the growing role of miR-137/hsa-miR-106b in the early diagnosis of myocardial ischemia in unstable angina patients.

Integrating Bioinformatics and Drug Sensitivity Analyses to Identify Molecular Characteristics Associated with Targeting Necroptosis in Breast Cancer and their Clinical Prognostic Significance.

BACKGROUND: Breast cancer accounts for over 1.8 million new cases worldwide annually, and prompt diagnosis and treatment are imperative to prevent mortality. Necroptosis, a form of programmed cell death, is thought to be a critical pathway for cancer cell apoptosis, yet, its relationship with breast cancer progression and molecular mechanisms remains largely unexplored. OBJECTIVES: Our study aims to investigate the molecular characteristics and clinical prognostic value of necroptosis-related genes in breast cancer using a comprehensive approach that involves integrated bioinformatics analysis along with drug sensitivity assessment. METHODS: Transcriptional, clinical, and tumor mutation burden (TMB) data related to breast cancer from the TCGA and GEO databases were integrated, and the necroptosis gene set was downloaded from the GSEA website for analysis. The screening conditions were set as adjusted P<0.05 and |log2FC(fold change)|>0.585 to screen for differential expression genes related to breast cancer necroptosis. Survival prognosis analysis was conducted on breast cancer necroptosis genes. Further analysis was conducted on prognosis-related necroptosis genes, including immune infiltration analysis and GO/KEGG enrichment analysis, to explore the potential biological functions and signaling pathway mechanisms of breast cancer necroptosis genes. Drug sensitivity screening was conducted on the prognosis-related necroptosis to identify potential drugs that target the promotion of necroptosis gene expression, and ultimately, single-gene analysis was performed on the core target genes obtained. RESULTS: Through integrated bioinformatics analysis, 29 differentially expressed mRNAs related to BRCA-Necroptosis were identified, including 18 upregulated mRNAs and 11 downregulated mRNAs. In addition, single-factor analysis of differential genes showed that the expression of HSPA4, PLK1, TNFRSF1B, FLT3, and LEF1 was closely related to BRCA survival prognosis. Based on the expression of these genes, a breast cancer prognosis model was constructed, and it was found that the area under the curve (AUC) of the curve of the risk genes for necroptosis was the largest, indicating that these genes have a certain clinical predictive significance for the occurrence and prognosis of BRCA. Additionally, there were significant differences in clinical characteristics of BRCA patients with different necroptosis gene expressions. Furthermore, GSVA and immune infiltration analysis revealed that Necroptosis-DEGs mainly affect the occurrence and progression of BRCA by participating in immune functions such as APC co-inhibition, APC co-stimulation, CCR, checkpoint, as well as infiltrating immune cells such as B cells naive, plasma cells, and T cells CD8. Moreover, the necroptosis gene group column chart indicated a 1-year survival rate of 0.979, a 3-year survival rate of 0.883, and a 5-year survival rate of 0.774. The necroptosis gene group and column chart are important indicators for evaluating BRCA prognosis. Finally, drug sensitivity screening of BRCA-Necroptosis genes showed that compounds such as A-770041, AC220, AP-24534, Bexarotene, and BMS-509744 have certain effects as potential targeted drugs for the treatment of BRCA necroptosis genes. CONCLUSION: Necroptosis genes are implicated in the pathogenesis and progression of breast cancer and are thought to impact the prognosis and response to drug treatments in individuals with BRCA. Consequently, understanding the role of these genes in breast cancer may aid in identifying more precise and efficacious therapeutic targets.

Integrated bioinformatics analysis of differentially expressed genes in the temporomandibular joint internal derangement.

OBJECTIVES: This study aimed to identify significant mechanisms and potential treatments for temporomandibular joint internal derangement (TMJD) through integrated bioinformatics analysis. MATERIALS AND METHODS: Gene expression data sets (GSE66864) from the Gene Expression Omnibus (GEO) database were downloaded. Differentially expressed genes (DEGs) were identified both in the treatment groups and in controls by R packages. Network analysis of protein-protein interaction (PPI) and Human Protein Atlas was used to explore DEGs' potential function. DGIdb database was utilized to gain potential drug targets. RESULTS: In conclusion, 126 DEGs were selected for TMJD through bioinformatics analysis. Both GO and Kyoto Encyclopedia of Genes and Genomes analyses combined showed the pathways involved in TMJD. A PPI network was constructed to select the top 10 hub genes, of which five hub genes were chosen for further investigation. Moreover, the microenvironment of immune cells related to hub genes was evaluated by R packages. Macrophages play an important role in inflammation and oral-related tumors. The Human Protein Atlas analysis indicated that the five hub genes are highly related to head and neck cancer. Finally, eight potential drugs were selected for two genes using the DGIdb database. CONCLUSION: Our integrated bioinformatics analysis identified DEGs in TMJD and provided potential ideas for further research and treatment approaches. However, experimental validation of the hub genes and potential drug targets is still needed. The key mechanisms of the identified genes and their potential roles as biomarkers or drug targets in TMJD require further investigation.

Screening of the shared pathogenic genes of ulcerative colitis and colorectal cancer by integrated bioinformatics analysis.

Ulcerative colitis (UC) is one of the high-risk pathogenic factors for colorectal cancer (CRC). However, the shared gene and signalling mechanisms between UC and CRC remain unclear. The goal of this study was to delve more into the probable causal relationship between UC and CRC. CRC and UC datasets were downloaded from the Gene Expression Omnibus database. Using R software and Perl, differentially expressed genes (DEGs) in both UC and CRC tissues were re-annotated and screened. The biological activities and signalling pathways involved in DEGs were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The STRING database and Cytoscape software were used to construct the gene interaction network. A total of 384 DEGs were selected for further investigation, and functional analysis revealed that inflammatory and immunological responses were crucial in the development of the two diseases. Moreover, the top 15 key genes involved in the UC and CRC were screened using cytoHubba, including IL1B, CXCL10, CCL20, MMP9, ICAM1, CCL4, CXCR1, MMP3, TLR2, PTGS2, IL1RN, IL6, COL1A2, TIMP1 and CXCL1. The identification of these genes in the present study may provide a novel perspective for the prediction, prevention and personalized medicine of UC and CRC patients.

Identification of necroptosis-related genes in Parkinson's disease by integrated bioinformatics analysis and experimental validation.

Background: Parkinson's disease (PD) is the second most common neurodegeneration disease worldwide. Necroptosis, which is a new form of programmed cell death with high relationship with inflammation, plays a vital role in the progression of PD. However, the key necroptosis related genes in PD are not fully elucidated. Purpose: Identification of key necroptosis-related genes in PD. Method: The PD associated datasets and necroptosis related genes were downloaded from the GEO Database and GeneCards platform, respectively. The DEGs associated with necroptosis in PD were obtained by gap analysis, and followed by cluster analysis, enrichment analysis and WGCNA analysis. Moreover, the key necroptosis related genes were generated by PPI network analysis and their relationship by spearman correlation analysis. Immune infiltration analysis was used for explore the immune state of PD brain accompanied with the expression levels of these genes in various types of immune cells. Finally, the gene expression levels of these key necroptosis related genes were validated by an external dataset, blood samples from PD patients and toxin-induced PD cell model using real-time PCR analysis. Result: Twelve key necroptosis-related genes including ASGR2, CCNA1, FGF10, FGF19, HJURP, NTF3, OIP5, RRM2, SLC22A1, SLC28A3, WNT1 and WNT10B were identified by integrated bioinformatics analysis of PD related dataset GSE7621. According to the correlation analysis of these genes, RRM2 and WNT1 were positively and negatively correlated with SLC22A1 respectively, while WNT10B was positively correlated with both OIF5 and FGF19. As the results from immune infiltration analysis, M2 macrophage was the highest population of immune cell in analyzed PD brain samples. Moreover, we found that 3 genes (CCNA1, OIP5 and WNT10B) and 9 genes (ASGR2, FGF10, FGF19, HJURP, NTF3, RRM2, SLC22A1, SLC28A3 and WNT1) were down- and up- regulated in an external dataset GSE20141, respectively. All the mRNA expression levels of these 12 genes were obviously upregulated in 6-OHDA-induced SH-SY5Y cell PD model while CCNA1 and OIP5 were up- and down- regulated, respectively, in peripheral blood lymphocytes of PD patients. Conclusion: Necroptosis and its associated inflammation play fundamental roles in the progression of PD and these identified 12 key genes might be served as new diagnostic markers and therapeutic targets for PD.

Weighted gene co-expression network analysis reveals the hub genes associated with pulmonary hypertension.

Pulmonary hypertension (PH) is a cardiopulmonary vascular disease that acutely endangers human health and can be fatal. It progresses rapidly and has a high mortality rate. Its pathophysiology is complicated and still not completely elucidated; therefore, achieving treatment breakthroughs are difficult. In this study, data from 58 normal controls and 135 patients with PH were extracted from the GSE24988, GSE113439, and GSE117261 datasets in the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (DEGs). In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Weighted gene co-expression network analysis (WGCNA) was used to identify the key modules and hub genes associated with PH. Eight PH-associated hub genes were identified. Furthermore, correlation analysis between immune cell infiltration and hub genes was performed, and the receiver operating characteristic (ROC) curves showed that TARDBP had the best diagnostic efficacy. Moreover, a rat hypoxic pulmonary hypertension (HPH) model was generated, and the expression of hub genes in the lungs and pulmonary arteries of HPH rats was verified using western blotting assays. Our results showed that mTOR, PSMD2, RBM8A, SMARCA4, TARDBP, and UBXN7 were highly expressed in the lungs. In addition, EFTUD2, mTOR, RBM8A, SMARCA4, TARDBP, and UBXN7 were significantly upregulated, whereas DDB1 was significantly downregulated in the pulmonary arteries of HPH rats compared with those of controls. In conclusion, we identified PH hub genes with diagnostic and predictive value by performing WGCNA on data from the GEO database. Furthermore, we provided novel insights of PH that might be utilized to evaluate potential biomarker genes and therapeutic targets.

Cell cycle protein BORA is associated with colorectal cancer progression by AURORA-PLK1 cascades: a bioinformatics analysis.

Colorectal cancer (CRC) is the third most diagnosed cancer in the world. A better understanding of the molecular mechanism of CRC is essential for making novel strategies for the CRC management and its prevention. The present study aims to explore the molecular mechanism through integrated bioinformatics analysis by analyzing genes and their co-expression pattern in normal and CRC states. GSE110223, GSE110224 and GSE113513 gene expression profiles were analyzed in this study. The co-expression networks for normal and tumor samples were constructed separately and analyzed to identify the modules, sub-networks and key genes. Gene regulatory network analysis was done to understand the regulatory mechanism of selected genes. Survival analysis was performed for the identified sub-networks and key genes to understand their role in CRC progression. A total of seven modules were detected and the KEGG pathway analysis revealed these modules were mainly enriched with cell cycle, metabolism and signaling-related pathways. E2F6 and ETV4 transcription factors regulating the activity of multiple genes of identified modules were found to be up-regulated in CRC. Six Sub-networks and seven key genes, BORA, CCT7, DTL, RUVBL1, RUVBL2, THEM6 and TMEM97 associated with the CRC progression were identified. Disease-gene association analysis identified a novel association of the BORA gene with CRC that activates and regulates the AURORA-PLK1 cascades in the cell cycle. Survival analysis indicates that the overexpressed BORA is associated with unfavourable overall survival in CRC. The mechanistic role of BORA in the regulation of cell cycle progression suggests that BORA might act as a potential therapeutic target for CRC.

Potential of immune-related genes as promising biomarkers for premature coronary heart disease through high throughput sequencing and integrated bioinformatics analysis.

Background: Coronary heart disease (CHD) is the most common progressive disease that is difficult to diagnose and predict in the young asymptomatic period. Our study explored a mechanistic understanding of the genetic effects of premature CHD (PCHD) and provided potential biomarkers and treatment targets for further research through high throughput sequencing and integrated bioinformatics analysis. Methods: High throughput sequencing was performed among recruited patients with PCHD and young healthy individuals, and CHD-related microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by using R software. Enrichment analysis and CIBERSORT were performed to explore the enriched pathways of DEGs and the characteristics of infiltrating immune cells. Hub genes identified by protein-protein interaction (PPI) networks were used to construct the competitive endogenous RNA (ceRNA) networks. Potential drugs were predicted by using the Drug Gene Interaction Database (DGIdb). Results: A total of 35 DEGs were identified from the sequencing dataset and GEO database by the Venn Diagram. Enrichment analysis indicated that DEGs are mostly enriched in excessive immune activation pathways and signal transduction. CIBERSORT exhibited that resting memory CD4 T cells and neutrophils were more abundant, and M2 macrophages, CD8 T cells, and naïve CD4 T cells were relatively scarce in patients with PCHD. After the identification of 10 hub gens, three ceRNA networks of CD83, CXCL8, and NR4A2 were constructed by data retrieval and validation. In addition, CXCL8 might interact most with multiple chemical compounds mainly consisting of anti-inflammatory drugs. Conclusions: The immune dysfunction mainly contributes to the pathogenesis of PCHD, and three ceRNA networks of CD83, CXCL8, and NR4A2 may be potential candidate biomarkers for early diagnosis and treatment targets of PCHD.

Identification of novel biomarkers in septic cardiomyopathy via integrated bioinformatics analysis and experimental validation.

Purpose: Septic cardiomyopathy (SCM) is an important world public health problem with high morbidity and mortality. It is necessary to identify SCM biomarkers at the genetic level to identify new therapeutic targets and strategies. Method: DEGs in SCM were identified by comprehensive bioinformatics analysis of microarray datasets (GSE53007 and GSE79962) downloaded from the GEO database. Subsequently, bioinformatics analysis was used to conduct an in-depth exploration of DEGs, including GO and KEGG pathway enrichment analysis, PPI network construction, and key gene identification. The top ten Hub genes were identified, and then the SCM model was constructed by treating HL-1 cells and AC16 cells with LPS, and these top ten Hub genes were examined using qPCR. Result: STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP were significantly elevated in the established SCM cells model. Conclusion: After bioinformatics analysis and experimental verification, it was demonstrated that STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP might play important roles in SCM.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies.

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease.

Objective: Nonalcoholic fatty liver disease (NAFLD) is a serious threat to human health worldwide. In this study, the aim is to analyze diagnosis biomarkers in NAFLD and its relationship with the immune microenvironment based on bioinformatics analysis. Methods: We downloaded microarray datasets (GSE48452 and GSE63067) from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The hub genes were screened by a series of machine learning analyses, such as support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and weighted gene co-expression network analysis (WGCNA). It is worth mentioning that we used the gene enrichment analysis to explore the driver pathways of NAFLD occurrence. Subsequently, the aforementioned genes were validated by external datasets (GSE66676). Moreover, the CIBERSORT algorithm was used to estimate the proportion of different types of immune cells. Finally, the Spearman analysis was used to verify the relationship between hub genes and immune cells. Results: Hub genes (CAMK1D, CENPV, and TRHDE) were identified. In addition, we found that the pathogenesis of NAFLD is mainly related to nutrient metabolism and the immune system. In correlation analysis, CENPV expression had a strong negative correlation with resting memory CD4 T cells, and TRHDE expression had a strong positive correlation with naive B cells. Conclusion: CAMK1D, CENPV, and TRHDE play regulatory roles in NAFLD. In particular, CENPV and TRHDE may regulate the immune microenvironment by mediating resting memory CD4 T cells and naive B cells, respectively, and thus influence disease progression.

Integrated Bioinformatics Analysis of the Hub Genes Involved in Irinotecan Resistance in Colorectal Cancer.

Different drug combinations including irinotecan remain some of the most important therapeutic modalities in treating colorectal cancer (CRC). However, chemotherapy often leads to the acquisition of cancer drug resistance. To bridge the gap between in vitro and in vivo models, we compared the mRNA expression profiles of CRC cell lines (HT29, HTC116, and LoVo and their respective irinotecan-resistant variants) with patient samples to select new candidate genes for the validation of irinotecan resistance. Data were downloaded from the Gene Expression Omnibus (GEO) (GSE42387, GSE62080, and GSE18105) and the Human Protein Atlas databases and were subjected to an integrated bioinformatics analysis. The protein-protein interaction (PPI) network of differently expressed genes (DEGs) between FOLFIRI-resistant and -sensitive CRC patients delivered several potential irinotecan resistance markers: NDUFA2, SDHD, LSM5, DCAF4, COX10 RBM8A, TIMP1, QKI, TGOLN2, and PTGS2. The chosen DEGs were used to validate irinotecan-resistant cell line models, proving their substantial phylogenetic heterogeneity. These results indicated that in vitro models are highly limited and favor different mechanisms than in vivo, patient-derived ones. Thus, cell lines can be perfectly utilized to analyze specific mechanisms on their molecular levels but cannot mirror the complicated drug resistance network observed in patients.

Identification of Functional CircRNA-miRNA-mRNA Regulatory Network in Dorsolateral Prefrontal Cortex Neurons of Patients With Cocaine Use Disorder.

Increasing evidence has indicated that circular RNAs (circRNAs) act as competing endogenous RNAs (ceRNAs) regulatory network to regulate the expression of target genes by sponging microRNAs (miRNAs), and therefore play an essential role in many neuropsychiatric disorders, including cocaine use disorder. However, the functional roles and regulatory mechanisms of circRNAs as ceRNAs in dorsolateral prefrontal cortex (dlPFC) of patients with cocaine use disorder remain to be determined. In this study, an expression profiling for dlPFC in 19 patients with cocaine use disorder and 17 controls from Gene Expression Omnibus datasets was used for the differentially expressed circRNAs analysis and the differentially expressed mRNAs analysis. Several tools were used to predict the miRNAs targeted by the circRNAs and the miRNAs targeted mRNAs, which then overlapped with the cocaine-associated differentially expressed mRNAs to determine the functional roles of circRNAs. Functional analysis for the obtained mRNAs was performed via Gene Ontology (GO) in Metascape database. Integrated bioinformatics analysis was conducted to further characterize the circRNA-miRNA-mRNA regulatory network and identify the functions of distinct circRNAs. We found a total of 41 differentially expressed circRNAs, and 98 miRNAs were targeted by these circRNAs. The overlapped mRNAs targeted by the miRNAs and the differentially expressed mRNAs constructed a circRNA-miRNA-mRNA regulation network including 24 circRNAs, 43 miRNAs, and 82 mRNAs in the dlPFC of patients with cocaine use disorder. Functional analysis indicated the regulation network mainly participated in cell response-related, receptor signaling-related, protein modification-related and axonogenesis-related pathways, which might be involved with cocaine use disorder. Additionally, we determined four hub genes (HSP90AA1, HSPA1B, YWHAG, and RAB8A) from the protein-protein interaction network and constructed a circRNA-miRNA-hub gene subnetwork based on the four hub genes. In conclusion, our findings provide a deeper understanding of the circRNAs-related ceRNAs regulatory mechanisms in the pathogenesis of cocaine use disorder.

Identity of MMP1 and its effects on tumor progression in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignant tumor worldwide with high morbidity and mortality. However, the diagnosis and molecular mechanisms of HNSCC remains poor. METHODS: Robust rank aggregation method was performed to excavate the differentially expressed genes (DEGs) in five datasets (GSE6631, GSE13601, GSE23036, GSE30784, GSE107591) from the Gene Expression Omnibus. Search Tool for the Retrieval of Interacting Genes (STRING) database extracted hub genes from the protein-protein interaction network. The expression of the hub genes was validated using expression profile from The Cancer Genome Atlas and Oncomine database. The module analysis and disease-free survival analysis of the hub genes were analyzed by Cytoscape and the Kaplan-Meier curve, respectively. The expression of hub genes was verified in clinical specimens. The functions of MMP1 which is most important in hub genes were explored in vitro and in vivo. RESULTS: Totally, 235 DEGs were identified in the present study which consists of 103 up-regulated and 132 down-regulated genes which were significantly enriched in the molecular function of calcium ion binding followed in the biological process of skin development. The mainly enriched pathways were ECM (extracellular matrix)-receptor interaction (hsa04512) and protein digestion and absorption (hsa04974). Six hub genes were screened out which showed dramatically increased expression in HNSCC samples compared with normal samples, including COL4A1, MMP1, PLAU, RBP1, SEMA3C, and COL4A2. These hub genes all showed worse disease-free survival with higher expression and were up-regulated in HNSCC clinical samples. MMP1 was proved to promote cell growth, migration, and phosphorylation of AKT in vitro and to promote liver metastasis in vivo. CONCLUSION: Bioinformatics analysis identified six key genes in HNSCC. Of these, MMP1 is the most likely biomarker. It activates the AKT pathway and promotes tumor progression.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer.

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein-protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

Identification of IMPA2 as the hub gene associated with colorectal cancer and liver metastasis by integrated bioinformatics analysis.

BACKGROUND AND OBJECTIVES: Colorectal cancer (CRC) is one of the most common malignant tumors worldwide with high incidence and mortality rate, while colorectal liver metastasis (CRLM) is one of the major causes of cancer-related deaths. Therefore, the present study aims to identify the hub gene associated with CRC carcinogenesis and liver metastasis, and then explore its diagnostic and prognostic value as well as the potential regulation mechanism. METHODS: The overlapping differential co-expression genes among CRC, CRLM, and normal tissues were explored on the GSE49355 and GSE81582 datasets from the Gene Expression Omnibus (GEO) database by integrated bioinformatics analysis. Then, the hub prognostic genes were selected from the overlapping genes by univariate Cox proportional hazard analysis and online database Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Subsequently, the clinical value of the hub genes was evaluated in the TCGA and GSE39582 cohorts. Finally, the underlying mechanisms of the hub gene regulating CRC carcinogenesis and metastasis were explored by Gene function annotation and DNA methylation analysis. RESULTS: Inositol mono-phosphatase 2 (IMPA2) was identified as the hub gene associated with CRC carcinogenesis and liver metastasis. IMPA2 had an excellent diagnostic efficiency, and its expression was significantly decreased in CRC and liver metastasis samples, being positively correlated with poor prognosis. Moreover, its low expression was associated with AJCC stage III+IV, T4, N1+2, and M1. In addition, our results revealed that the potential mechanisms used by IMPA2 to mediate CRC carcinogenesis and metastasis could be associated with lipid metabolism and epithelial mesenchymal transition (EMT). Finally, IMPA2 expression could be regulated by DNA methylation. CONCLUSIONS: IMPA2 was identified and reported for the first time as a hub gene biomarker in the diagnosis and prognosis of CRC, which could regulate CRC carcinogenesis and liver metastasis through the regulation of lipid metabolism, EMT, and DNA methylation.

Integrated Bioinformatics Analysis and Validation of the Prognostic Value of RBM10 Expression in Hepatocellular Carcinoma.

Background: RBM10's function in hepatocellular carcinoma (HCC) has rarely been addressed. We intend to explore the prognostic significance and therapeutic meaning of RBM10 in HCC in this study. Methods: Multiple common databases were integrated to analyze the expression status and prognostic meaning of RBM10 in HCC. The relationship between RBM10 mRNA level and clinical features was also assessed. Multiple enrichment analyses of the differentially expressed genes between RBM10 high- and low- transcription groups were constructed by using R software (version 4.0.2). A Search Tool for Retrieval of Interacting Genes database was used to construct the protein-protein interaction network between RBM10 and other proteins. A tumor immune estimation resource database was employed to identify the relationship between RBM10 expression and immune cell infiltrates. The prognostic value of RBM10 expression was validated in our HCC cohort by immunohistochemistry test. Results: The transcription of RBM10 mRNA was positively correlated with tumor histologic grade (p < 0.001), T classification (p < 0.001), and tumor stage (p < 0.001). High transcription of RBM10 in HCC predicted a dismal overall survival (p = 0.0037) and recurrence-free survival (p < 0.001). Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis all revealed that RBM10 was involved in the regulation of cell cycle, DNA replication, and immune-related pathways. Tumor immune estimation analysis revealed that RBM10 transcription was positively related to multiple immune cell infiltrates and the expressions of PD-1 and PD-L1. Conclusion: RBM10 was demonstrated to be a dismal prognostic factor and a potential biomarker for immune therapy in HCC in that it may be involved in the immune-related signaling pathways.