Gene Expression Profiling Datasets Research Articles

KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species.

Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thalianaand Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sitesand chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysisand Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.

Interspecies co-expression analysis of lateral root development using inducible systems in rice, Medicago, and Arabidopsis.

Lateral roots are crucial for plant growth and development, making them an important target for research aiming to improve crop yields and food security. However, their endogenous ontogeny and, as it were, stochastic appearance challenge their study. Lateral Root Inducible Systems (LRIS) can be used to overcome these challenges by inducing lateral roots massively and synchronously. The combination of LRISs with transcriptomic approaches significantly advanced our insights in the molecular control of lateral root formation, in particular for Arabidopsis. Despite this success, LRISs have been underutilized for other plant species or for lateral root developmental stages later than the initiation. In this study, we developed and/or adapted LRISs in rice, Medicago, and Arabidopsis to perform RNA-sequencing during time courses that cover different developmental stages of lateral root formation and primordium development. As such, our study provides three extensive datasets of gene expression profiles during lateral root development in three different plant species. The three LRISs are highly effective but timing and spatial distribution of lateral root induction vary among the species. Detailed characterization of the stages in time and space in the respective species enabled an interspecies co-expression analysis to identify conserved players involved in lateral root development, as illustrated for the AUX/IAA and LBD gene families. Overall, our results provide a valuable resource to identify potentially conserved regulatory mechanisms in lateral root development, and as such will contribute to a better understanding of the complex regulatory network underlying lateral root development.

Consensus cluster analysis of apoptosis-related genes in patients with osteoarthritis and their correlation with immune cell infiltration.

Osteoarthritis (OA) progression involves multiple factors, including cartilage erosion as the basic pathological mechanism of degeneration, and is closely related to chondrocyte apoptosis. To analyze the correlation between apoptosis and OA development, we selected apoptosis genes from the differentially expressed genes (DEGs) between OA and normal samples from the Gene Expression Omnibus (GEO) database, used lasso regression analysis to identify characteristic genes, and performed consensus cluster analysis to further explore the pathogenesis of this disease. The Gene expression profile datasets of OA samples, GSE12021 and GSE55235, were downloaded from GEO. The datasets were combined and analyzed for DEGs. Apoptosis-related genes (ARGs) were collected from the GeneCards database and intersected with DEGs for apoptosis-related DEGs (ARDEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to obtain characteristic genes, and a nomogram was constructed based on these genes. A consensus cluster analysis was performed to divide the patients into clusters. The immune characteristics, functional enrichment, and immune infiltration statuses of the clusters were compared. In addition, a protein-protein interaction network of mRNA drugs, mRNA-transcription factors (TFs), and mRNA-miRNAs was constructed. A total of 95 DEGs were identified, of which 47 were upregulated and 48 were downregulated, and 31 hub genes were selected as ARDEGs. LASSO regression analysis revealed nine characteristic genes: growth differentiation factor 15 (GDF15), NAMPT, TLR7, CXCL2, KLF2, REV3L, KLF9, THBD, and MTHFD2. Clusters A and B were identified, and neutrophil activation and neutrophil activation involved in the immune response were highly enriched in Cluster B, whereas protein repair and purine salvage signal pathways were enriched in Cluster A. The number of activated natural killer cells in Cluster B was significantly higher than that in Cluster A. GDF15 and KLF9 interacted with 193 and 32 TFs, respectively, and CXCL2 and REV3L interacted with 48 and 82 miRNAs, respectively. ARGs could predict the occurrence of OA and may be related to different degrees of OA progression.

Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths globally, with limited diagnostic and clinically significant therapeutic targets. Identifying the genes and processes involved in developing and progressing LUAD is crucial for developing effective targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression profile datasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO) databases. The differential gene expression and the downstream analysis determined clinically significant biomarkers using a network-based approach. These therapeutic targets predominantly enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD patients. The hydrophobic residues of the AURKA-TPX2 interaction were considered as the target site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the AURKA destabilization. This in silico study lays the foundation for repurposing targeted therapeutic options to impede the Protein-Protein Interactions (PPIs) in LUAD progression and aid in future translational investigations.

Identification and experimental validation of autophagy-related genes in abdominal aortic aneurysm

AimAutophagy plays essential roles in abdominal aortic aneurysm (AAA) development and progression. The objective of this study was to verify the autophagy-related genes (ARGs) underlying AAA empirically and using bioinformatics analysis.MethodsTwo gene expression profile datasets GSE98278 and GSE57691 were downloaded from the Gene Expression Omnibus (GEO) database, and principal component analysis was performed. Following, the R software (version 4.0.0) was employed to analyze potentially differentially expressed genes related with AAA and autophagy. Subsequently, the candidate genes were screened using protein–protein interaction (PPI), gene ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the RNA expression levels of the top five selected abnormal ARGs in clinical samples obtained from the normal and AAA patients.ResultsAccording to the information contained (97 AAA patients and 10 healthy controls) in the two datasets, a total of 44 differentially expressed autophagy-related genes (6 up-regulated genes and 38 down-regulated genes) were screened. GO enrichment analysis of differentially expressed autophagy-related genes (DEARGs) demonstrated that some enrichment items were associated with inflammation, and PPI analysis indicated interaction between these genes. RT-qPCR results presented that the expression levels of IL6, PPARG, SOD1, and MAP1LC3B were in accordance with the bioinformatics prediction results acquired from the mRNA chip.ConclusionBioinformatics analysis identified 44 potential autophagy-related differentially expressed genes in AAA. Further verification by RT- qPCR presented that IL6, PPARG, SOD1, and MAP1LC3B may affect the development of AAA by regulating autophagy. These findings might help explain the pathogenesis of AAA and be helpful in its diagnosis and treatment.

Bioinformatical analysis of the key differentially expressed genes for screening potential biomarkers in Wilms tumor

Wilms tumor (WT) is the most common pediatric renal malignant tumor in the world. Overall, the prognosis of Wilms tumor is very good. However, the prognosis of patients with anaplastic tumor histology or disease relapse is still poor, and their recurrence rate, metastasis rate and mortality are significantly increased compared with others. Currently, the combination of histopathological examination and molecular biology is essential to predict prognosis and guide the treatment. However, the molecular mechanism has not been well studied. Genetic profiling may be helpful in some way. Hence, we sought to identify novel promising biomarkers of WT by integrating bioinformatics analysis and to identify genes associated with the pathogenesis of WT. In the presented study, the NCBI Gene Expression Omnibus was used to download two datasets of gene expression profiles related to WT patients for the purpose of detecting overlapped differentially expressed genes (DEGs). The DEGs were then uploaded to DAVID database for enrichment analysis. In addition, the functional interactions between proteins were evaluated by simulating the protein–protein interaction (PPI) network of DEGs. The impact of selected hub genes on survival in WT patients was analyzed by using the online tool R2: Genomics Analysis and Visualization Platform. The correlation between gene expression and the degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA. Top 12 genes were identified for further study after constructing a PPI network and screening hub gene modules. Kinesin family member 2C (KIF2C) was identified as the most significant gene predicting the overall survival of WT patients. The expression of KIF2C in WT was further verified by quantitative real-time polymerase chain reaction and immunohistochemistry. Furthermore, we found that KIF2C was significantly correlated with immune cell infiltration in WT. Our present study demonstrated that altered expression of KIF2C may be involved in WT and serve as a potential prognostic biomarker for WT patients.

ENO2 as a Biomarker Regulating Energy Metabolism to Promote Tumor Progression in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common and metastatic type of renal cell carcinoma. Despite significant advancements, the current diagnostic biomarkers for ccRCC lack the desired specificity and sensitivity, necessitating the identification of novel biomarkers and elucidation of their underlying mechanisms. Three gene expression profile datasets were obtained from the GEO database, and differentially expressed genes (DEGs) were screened. Gene Ontology and KEGG pathway analysis were conducted in ccRCC. To clarify the diagnosis and prognostic role of ENO2, Kaplan-Meier analysis and Cox proportional hazards regression analysis were performed. Functional experiments were also carried out to verify the significant role of ENO2 in ccRCC. Finally, tumor mutational burden analysis was utilized to investigate the potential role of ENO2 in gene mutations in ccRCC. The study showed that ENO2 is a potential biomarker for the diagnosis of ccRCC and can independently predict the clinical prognosis of ccRCC. Furthermore, we found that ENO2 can promote the occurrence and progression of ccRCC by affecting the glycolysis level of cells through the "Warburg effect". These findings provide new theories for the occurrence and development of ccRCC and can help formulate new strategies for its diagnosis and treatment.

Hypoxia-related genes and PD-L1 inhibitor resistance in hepatocellular carcinoma.

158 Background: PD-L1 inhibitor in combination with anti-angiogenic drug has become the first-line treatment strategy for unresectable hepatocellular carcinoma (HCC). However, PD-L1 inhibitor resistance remains an essential issue in treating HCC. It has been proved in the tumor microenvironment that hypoxia-induced increase in PD-L1 expression makes a significant impact on drug resistance. Methods: Two public datasets of gene expression profiles (GSE 14520 and GSE 41666) from the Gene Expression Omnibus (GEO) database were analyzed using bioinformatics: (1) HCC tumor versus adjacent normal tissue (N = 214) and (2) normoxia versus anoxia of HepG2 cells (N = 6). HCC-signature and hypoxia-related genes were identified as the differentially expressed genes (DEGs). Gene set enrichment analyses (GSEA) were performed on the DEGs. Furthermore, multiple regression analysis on the TCGA-LIHC dataset (N=371) and construction of the protein-protein interaction (PPI) network were performed to investigate potential PD-L1 regulatory genes and hub genes. Results: A total of 52 genes overlapped in HCC-signature and hypoxia-related DEGs. Five genes were identified by GSEA as being concurrently associated with the PD-L1 expression pathways. Multiple regression analysis results showed 14 potential PD-L1 regulatory genes. Ten hub genes were identified in the PPI network. Finally, three genes (DLGAP5, KIF20A, and TPX2) were found in common and may be regulatory genes affecting PD-L1 expression. Conclusions: In conclusion, our study provides new insights into the potential hypoxia-related mechanisms of PD-L1 inhibitor resistance and contributes to exploring new therapeutic strategies for treating unresectable HCC.

Identification of Key Biomarkers in Hepatocellular Carcinoma Induced by Non-alcoholic steatohepatitis or Metabolic Syndrome via Integrated Bioinformatics Analysis.

The burden of hepatocellular carcinoma (HCC) is steadily growing because obesity, type 2 diabetes, and nonalcoholic fatty liver disease (NAFLD) are replacing viral- and alcohol-related liver disease as major pathogenic promoters. The current study attempted to identify the key genes and pathways in the non-alcoholic steatohepatitis (NASH) induced development of HCC using integrated bioinformatics analyses. Two gene expression profiling datasets, GSE102079 and GSE164760 were downloaded. Differentially expressed genes (DEGs) from HCC and healthy control samples were screened. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Expression and survival analysis of hub genes, methylation and genetic mutation analysis were explored withGEPIA2, UALCAN, GSCA, and TIMER2.0 databases. We identified 158 overlapping genes from the 2 datasets. Up-regulated genes were mainly related to the proliferation, adhesion and metastasis of tumors, while down-regulated genes were mainly related to oxidative stress and energy metabolism. CDKN2A, SPP1, CYP2C9 and CYP4A11 were associated with prognostic performance and were considered the potential crucial genes, which SPP1, CYP2C9 and CYP4A11 were identified as the DNA methylation-driven genes. In different mutation statuses of HCC, gene expression of CDKN2A, SPP1, CYP2C9 and CYP4A11 showed significant differences. CDKN2A and SPP1 were identified as risk genes, while CYP2C9 and CYP4A11 were identified as protective genes, which may affect the transformation of NASH into HCC.

Ferroptosis-Related Immune Genes in Hematological Diagnosis of Parkinson's Diseases.

Emerging evidence suggested that ferroptosis and immune activation, as well as their interactions, played a crucial role in the occurrence and progression of Parkinson's disease (PD). However, whether this interaction could serve as the basis for a hematological diagnosis of PD remained poorly understood. This study aimed to construct a novel hematological model for PD diagnosis based on the ferroptosis-related immune genes. The brain imaging of PD patients was obtained from the Affiliated Hospital of Nantong University. We used least absolute shrinkage and selection operator (LASSO) to identify the optimal signature ferroptosis-related immune genes based on six gene expression profile datasets of substantia nigra (SN) and peripheral blood of PD patients. Then we used the support vector machine (SVM) classifier to construct the hematological diagnostic model named Ferr.Sig for PD. Gene set enrichment analysis was utilized to execute gene functional annotation. The brain imaging and functional annotation analysis revealed prominent iron deposition and immune activation in the SN region of PD patients. We identified a total of 17 signature ferroptosis-related immune genes using LASSO method and imported them to SVM classifier. The Ferr.Sig model exhibited a high diagnostic accuracy, and its area under the curve (AUC) for distinguishing PD patients from healthy controls in the training and internal validation cohort reached 0.856 and 0.704, respectively. We also used the Ferr.Sig into other external validation cohorts, and a comparable AUC with the internal cohort was obtained, with the AUC of 0.727 in Scherzer's cohort, 0.745 in Roncagli's cohort, and 0.778 in Meiklejohn's cohort. Furthermore, the diagnostic performance of Ferr.Sig was not interfered by the other neurodegenerative diseases. This study revealed the value of ferroptosis-related immune genes in PD diagnosis, which may provide a novel direction and strategy for the development of novel biomarkers with less invasiveness, low cost, and high accuracy for PD screening and diagnosis.

Exploring the Immune Landscape of Cirrhosis through Weighted Gene Co-expression Network Analysis.

Cirrhosis is a persistent hepatic ailment that emerges from a range of causes, including viral infections, alcoholic liver disease, and non-alcoholic fatty liver disease. It is distinguished by the replacement of normal liver parenchyma with fibrous scar tissue, culminating in the development of hepatic insufficiency, portal hypertension, and eventual liver collapse. Several molecular and cellular mechanisms contribute to cirrhosis' pathogenesis, including activation of immune cells and dysregulation of immune-related pathways. Weighted Gene Co-expression Network Analysis (WGCNA) is a powerful data mining application used to identify gene modules and hub genes that are closely associated with specific phenotypes or conditions of interest. In this study, we performed WGCNA on publicly available gene expression datasets and subsequently assessed the roles of immune-related genes in the etiology and progression of cirrhosis, intending to explore potential therapeutic targets for this disease. GSE36411 gene expression profiling was extracted from the Gene Expression Omnibus repository (GEO). The transcriptomic data were submitted to Weighted Gene Co-expression Network Analysis (WGCNA) to screen for the presence of key genes, and immune-related genes were filtered by comparison to the InateDB database. Cancer Genome Atlas (TCGA) was included in the study to validate the significant modules generated from WGCNA. The key gene interaction network was constructed using GeneMANIA and Metascape. Kaplan-Meier method and Spearman correlation were used to evaluate the correlation of immune-related genes with prognosis, tumor microenvironment, and immune cell infiltration. Finally, we explored a possible mechanism using gene set enrichment (GSEA) analyses. In total, 2,102 differentially expressed genes (DEGs) were identified from the gene expression profile dataset. A weighted gene co-expression network analysis was performed, resulting in the classification of genes into 3 modules. Among these modules, the turquoise module was found to be most closely associated with cirrhosis. By comparing the turquoise module genes with an InateDB immune-related gene set, we identified 157 immune-associated genes. In addition, our study found that many hub genes are strongly associated with the number of immune-related genes in liver cirrhosis, in addition to a few modules associated with immune infiltration. It turns out that these hub genes were engaged in migration, activation, and immune cell regulation, as well as in the signaling pathways that drive the immune response to infection. Our research offered a deeper understanding of the underlying processes of immune infiltration in cirrhosis and also suggested potential treatment options for this troublesome condition. Our results demonstrate the effectiveness of WGCNA in uncovering new knowledge regarding the biology of cirrhosis and the function of the immune system in this disease. More studies ought to focus on the validation of the identified hub genes and the determination of their clinical relevance. These results could serve as the basis for the creation of more potent therapies for those with liver cancer linked to cirrhosis.

Ferroptosis-related signature and immune infiltration characterization in acute lung injury/acute respiratory distress syndrome

BackgroundAcute lung injury/acute respiratory distress syndrome (ALI/ARDS) is one of the most life-threatening diseases in the intensive care unit with high mortality and morbidity. Ferroptosis is a newly discovered immune related cell death that is associated with various lung diseases. However, the role of immune-mediated ferroptosis in ALI/ARDS has not been elucidated.MethodWe analyzed two Gene Expression Omnibus (GEO) datasets (GSE2411 and GSE109913) and extracted characteristic ferroptosis-related genes (FRGs) between the control and ALI groups through bioinformatic analysis. Then, we prospectively collected bronchoalveolar lavage fluid (BALF) from patients with ARDS and verified the expression of characteristic FRGs. Lastly, we constructed the ALI/ARDS model induced by LPS and isolated the primary neutrophils of mice. Erastin, an ferroptosis inducer, was used at the cellular level to verify the effect of neutrophils on ferroptosis in lung epithelium cells.ResultWe identified three characteristic FRGs, Cp, Slc39a14 and Slc7a11, by analyzing two gene expression profiling datasets. Immune infiltration analysis showed that the three characteristic genes were significantly positively correlated with the infiltration levels of neutrophils. We collected BALF from 59 ARDS patients to verify the expression of Cp, Slc7a11 and Slc39a14 in humans. The results showed that Cp was elevated in patients with severe ARDS (p = 0.019), Slc7a11 was significantly elevated in patients with moderate ARDS (p = 0.021) relative to patients with mild ARDS. The levels of neutrophils in the peripheral blood of ARDS patients were positively correlated with the expression levels of Slc7a11 (Pearson’s R2 = 0.086, p = 0.033). Three characteristic FRGs were significantly activated after the onset of ferroptosis (6 h) early in LPS induced ALI model, and that ferroptosis was alleviated after the organism compensated within 12 to 48 h. We extracted primary activated neutrophils from mice and co-cultured them with MLE-12 in transwell, Slc7a11, Cp and Slc39a14 in MLE-12 cells were significantly upregulated as the number of neutrophils increased. The results showed that neutrophil infiltration alleviated erastin-induced MDA accumulation, GSH depletion, and divalent iron accumulation, accompanied by upregulation of Slc7a11 and Gpx4, implying the existence of a compensatory effect of lipid oxidation in neutrophils after acute lung injury in the organism.ConclusionWe identified three immune-mediated ferroptosis genes, namely, Cp, Slc7a11 and Slc39a14, which possibly regulated by neutrophils during the development of ALI, and their pathways may be involved in anti-oxidative stress and anti-lipid metabolism. Thus, the present study contributes to the understanding of ALI/ARDS and provide novel targets for future immunotherapeutic.

Bioinformatics Identification of Regulatory Genes and Mechanism Related to Hypoxia-Induced PD-L1 Inhibitor Resistance in Hepatocellular Carcinoma.

The combination of a PD-L1 inhibitor and an anti-angiogenic agent has become the new reference standard in the first-line treatment of non-excisable hepatocellular carcinoma (HCC) due to the survival advantage, but its objective response rate remains low at 36%. Evidence shows that PD-L1 inhibitor resistance is attributed to hypoxic tumor microenvironment. In this study, we performed bioinformatics analysis to identify genes and the underlying mechanisms that improve the efficacy of PD-L1 inhibition. Two public datasets of gene expression profiles, (1) HCC tumor versus adjacent normal tissue (N = 214) and (2) normoxia versus anoxia of HepG2 cells (N = 6), were collected from Gene Expression Omnibus (GEO) database. We identified HCC-signature and hypoxia-related genes, using differential expression analysis, and their 52 overlapping genes. Of these 52 genes, 14 PD-L1 regulator genes were further identified through the multiple regression analysis of TCGA-LIHC dataset (N = 371), and 10 hub genes were indicated in the protein-protein interaction (PPI) network. It was found that POLE2, GABARAPL1, PIK3R1, NDC80, and TPX2 play critical roles in the response and overall survival in cancer patients under PD-L1 inhibitor treatment. Our study provides new insights and potential biomarkers to enhance the immunotherapeutic role of PD-L1 inhibitors in HCC, which can help in exploring new therapeutic strategies.

Histone Demethylation Profiles in Nonalcoholic Fatty Liver Disease and Prognostic Values in Hepatocellular Carcinoma: A Bioinformatic Analysis

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with multifactorial pathogenesis; histone demethylases (HDMs) are emerging as attractive targets. We identified HDM genes (including KDM5C, KDM6B, KDM8, KDM4A, and JMJD7) that were differentially expressed in NAFLD and normal samples by exploring gene expression profiling datasets. There was no significant difference in the expression of genes related to histone demethylation between mild and advanced NAFLD. In vitro and in vivo studies indicated that KDM6B and JMJD7 were upregulated at the mRNA level in NAFLD. We explored the expression levels and prognostic values of the identified HDM genes in hepatocellular carcinoma (HCC). KDM5C and KDM4A were upregulated in HCC compared to normal tissue, while KDM8 showed downregulation. The abnormal expression levels of these HDMs could provide prognostic values. Furthermore, KDM5C and KDM4A were associated with immune cell infiltration in HCC. HDMs were associated with cellular and metabolic processes and may be involved in the regulation of gene expression. Differentially expressed HDM genes identified in NAFLD may provide value to understanding pathogenesis and in the development of epigenetic therapeutic targets. However, on the basis of the inconsistent results of in vitro studies, future in vivo experiments combined with transcriptomic analysis are needed for further validation.

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

BackgroundDetection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.MethodsTo identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein–protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.ResultsWe identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.ConclusionThe findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

Integrative Genomic Analysis of m6a-SNPs Identifies Potential Functional Variants Associated with Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that affects 35 million people worldwide. However, no potential therapeutics currently are available for AD because of the multiple factors involved in it, such as regulatory factors with their candidate genes, factors associated with the expression levels of its corresponding genes, and many others. To date, 29 novel loci from GWAS have been reported for AD by the Psychiatric Genomics Consortium (PGC2). Nevertheless, the main challenge of the post-GWAS era, namely to detect significant variants of the target disease, has not been conducted for AD. N6-methyladenosine (m6a) is reported as the most prevalent mRNA modification that exists in eukaryotes and that influences mRNA nuclear export, translation, splicing, and the stability of mRNA. Furthermore, studies have also reported m6a's association with neurogenesis and brain development. We carried out an integrative genomic analysis of AD variants from GWAS and m6a-SNPs from m6AVAR to identify the effects of m6a-SNPs on AD and identified the significant variants using the statistically significance value (p-value <0.05). The cis-regularity variants with their corresponding genes and their influence on gene expression in the gene expression profiles of AD patients were determined, and showed 1458 potential m6a-SNPs (based on p-value <0.05) associated with AD. eQTL analysis showed that 258 m6a-SNPs had cis-eQTL signals that overlapped with six significant differentially expressed genes based on p-value <0.05 in two datasets of AD gene expression profiles. A follow-up study to elucidate the impact of our identified m6a-SNPs in the experimental study would validate our findings for AD, which would contribute to the etiology of AD.

Identification through machine learning of potential immune- related gene biomarkers associated with immune cell infiltration in myocardial infarction

BackgroundTo investigate the potential role of immune-related genes (IRGs) and immune cells in myocardial infarction (MI) and establish a nomogram model for diagnosing myocardial infarction.MethodsRaw and processed gene expression profiling datasets were archived from the Gene Expression Omnibus (GEO) database. Differentially expressed immune-related genes (DIRGs), which were screened out by four machine learning algorithms-partial least squares (PLS), random forest model (RF), k-nearest neighbor (KNN), and support vector machine model (SVM) were used in the diagnosis of MI.ResultsThe six key DIRGs (PTGER2, LGR6, IL17B, IL13RA1, CCL4, and ADM) were identified by the intersection of the minimal root mean square error (RMSE) of four machine learning algorithms, which were screened out to establish the nomogram model to predict the incidence of MI by using the rms package. The nomogram model exhibited the highest predictive accuracy and better potential clinical utility. The relative distribution of 22 types of immune cells was evaluated using cell type identification, which was done by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. The distribution of four types of immune cells, such as plasma cells, T cells follicular helper, Mast cells resting, and neutrophils, was significantly upregulated in MI, while five types of immune cell dispersion, T cells CD4 naive, macrophages M1, macrophages M2, dendritic cells resting, and mast cells activated in MI patients, were significantly downregulated in MI.ConclusionThis study demonstrated that IRGs were correlated with MI, suggesting that immune cells may be potential therapeutic targets of immunotherapy in MI.

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections.

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

A new hybrid algorithm for three-stage gene selection based on whale optimization

In biomedical data mining, the gene dimension is often much larger than the sample size. To solve this problem, we need to use a feature selection algorithm to select feature gene subsets with a strong correlation with phenotype to ensure the accuracy of subsequent analysis. This paper presents a new three-stage hybrid feature gene selection method, that combines a variance filter, extremely randomized tree, and whale optimization algorithm. First, a variance filter is used to reduce the dimension of the feature gene space, and an extremely randomized tree is used to further reduce the feature gene set. Finally, the whale optimization algorithm is used to select the optimal feature gene subset. We evaluate the proposed method with three different classifiers in seven published gene expression profile datasets and compare it with other advanced feature selection algorithms. The results show that the proposed method has significant advantages in a variety of evaluation indicators.

Identification of key genes and small molecule drugs in osteoarthritis by integrated bioinformatics analysis

BackgroundOsteoarthritis (OA) is a common joint degenerative disease that can affect multiple joints. Genetic events may play an important regulatory role in the early stages of the disease, but the specific mechanisms have not yet been fully elucidated. The main purpose of this study was to screen for disease-causing hub genes and effective small molecule drugs to reveal the pathogenesis of OA and to develop novel drugs for treatment. MethodsTwo gene expression profile datasets, GSE55235 and GSE55457, were integrated and further analyzed. The consistently differentially expressed genes (DEGs) were identified, and functional annotation and pathway analysis of these genes were performed with GO and KEGG. A protein–protein interaction network (PPI) of the DEGs was generated using STRING, and potential small molecule drug screening was performed on the connectivity map (CMap). ResultsA total of 158 consistently differentially expressed genes were identified from the two profile datasets. The functions of these DEGs are mainly related to the TNF signaling pathway, osteoclast differentiation, MAPK signaling pathway and so on. The PPI network contains 127 nodes and 1802 edges, and the ten hub genes were interleukin 6 (IL6), vascular endothelial growth factor A (VEGFA)and so on. 7 small molecule drugs were identified as potential interactors with these hubs. ConclusionsThis study explains the disorder of expression in the pathological process of OA at transcriptome, which will help to understand the pathogenesis of OA.