Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis.

Background: Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis. Methods: Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the "limma" R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes. Results: By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, p = 2e - 19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes. Conclusion: This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis.

PRADclass: Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of Aggressive Prostate Adenocarcinoma.

BACKGROUND: Prostate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality. METHODS: Based on TCGA transcriptomic data pertaining to PRAD and the associated clinical metadata, we determined the sample Gleason grade, and used it to execute: (i) Gleason-grade wise linear modeling, followed by five contrasts against controls and ten contrasts between grades; and (ii) Gleason-grade wise network modeling via weighted gene correlation network analysis (WGCNA). Candidate biomarkers were obtained from the above analysis and the consensus found. The consensus biomarkers were used as the feature space to train ML models for classifying a sample as benign, indolent or aggressive. RESULTS: The statistical modeling yielded 77 Gleason grade-salient genes while the WGCNA algorithm yielded 1003 trait-specific key genes in grade-wise significant modules. Consensus analysis of the two approaches identified two genes in Grade-1 (SLC43A1 and PHGR1), 26 genes in Grade-4 (including LOC100128675, PPP1R3C, NECAB1, UBXN10, SERPINA5, CLU, RASL12, DGKG, FHL1, NCAM1, and CEND1), and seven genes in Grade-5 (CBX2, DPYS, FAM72B, SHCBP1, TMEM132A, TPX2, UBE2C). A RandomForest model trained and optimized on these 35 biomarkers for the ternary classification problem yielded a balanced accuracy ∼ 86% on external validation. CONCLUSIONS: The consensus of multiple parallel computational strategies has unmasked candidate Gleason grade-specific biomarkers. PRADclass, a validated AI model featurizing these biomarkers achieved good performance, and could be trialed to predict the differentiation of prostate cancers. PRADclass is available for academic use at: https://apalania.shinyapps.io/pradclass (online) and https://github.com/apalania/pradclass (command-line interface).

[Key Prediction Genes of Nasopharyngeal Carcinoma:Screening Based on Systematic Bioinformatics and Validation by Cell Experiments].

Objective To screen out the potential prediction genes for nasopharyngeal carcinoma(NPC)from the gene microarray data of NPC samples and then verify the genes by cell experiments.Methods The NPC dataset was downloaded from Gene Expression Omnibus,and limma package was employed to screen out the differentially expressed genes.Weighted correlation network analysis package was used for weighted gene co-expression network analysis,and Venn diagram was drawn to find the common genes.The gene ontology annotation and Kyoto encyclopedia of genes and genomes pathway enrichment were then performed for the common genes.The biomarkers for NPC were further explored by protein-protein interaction network,LASSO regression,and non-parametric tests.Real-time quantitative PCR and Western blotting were employed to determine the mRNA and protein levels of key predictors of NPC,so as to verify the screening results.Results There were 622 up-regulated genes and 351 down-regulated genes in the GSE12452 dataset.A total of 116 common genes were obtained by limma analysis and weighted gene co-expression network analysis.The common genes were mainly involved in the biological processes of cell proliferation and regulation and regulation of intercellular adhesion.They were mainly enriched in Rap1,Ras,and tumor necrosis factor signaling pathways.Six key genes were screened out,encoding angiopoietin-2(ANGPT2),dual oxidase 2(DUOX2),coagulation factor Ⅲ(F3),interleukin-15(IL-15),lipocalin-2,and retinoic acid receptor-related orphan receptor B(RORB).Real-time quantitative PCR and Western blotting showed that the NPC cells had up-regulated mRNA and protein levels of ANGPT2 and IL-15 and down-regulated mRNA and protein levels of DUOX2,F3,and RORB,which was consistent with the results predicted by bioinformatics.Conclusion ANGPT2,DUOX2,F3,IL-15 and RORB are potential predictive molecular markers and therapeutic targets for NPC,which may be involved in Rap1,Ras,tumor necrosis factor and other signaling pathways.

Identification of Key Genes Regulating Sorghum Mesocotyl Elongation through Transcriptome Analysis.

Sorghum with longer mesocotyls is beneficialfor improving its deep tolerance, which is important for the seedling rates. Here, we perform transcriptome analysis between four different sorghum lines, with the aim of identifying the key genes regulating sorghum mesocotyl elongation. According to the mesocotyl length (ML) data, we constructed four comparison groups for the transcriptome analysis and detected 2705 common DEGs. GO and KEGG enrichment analysis showed that the most common category of DEGs were involved in cell wall, microtubule, cell cycle, phytohormone, and energy metabolism-related pathways. In the cell wall biological processes, the expression of SbEXPA9-1, SbEXPA9-2, SbXTH25, SbXTH8-1, and SbXTH27 are increased in the sorghum lines with long ML. In the plant hormone signaling pathway, five auxin-responsive genes and eight cytokinin/zeatin/abscisic acid/salicylic acid-related genes showed a higher expression level in the long ML sorghum lines. In addition, five ERF genes showed a higher expression level in the sorghum lines with long ML, whereas two ERF genes showed a lower expression level in these lines. Furthermore, the expression levels of these genes were further analyzed using real-time PCR (RT-qPCR), which showed similar results. This work identified the candidate gene regulating ML, which may provide additional evidence to understand the regulatory molecular mechanisms of sorghum mesocotyl elongation.

Screening of key genes in childhood asthma based on bioinformatics analysis.

Background: The key genes of pediatric asthma have not yet been identified and there is a lack of serological diagnostic markers. This may be related to the lack of comprehensive exploration of g The study sought to screen the key genes of childhood asthma using a machine-learning algorithm based on transcriptome sequencing results and explore potential diagnostic markers. Methods: The transcriptome sequencing results (GSE188424) of pediatric asthmatic plasma samples were downloaded from the Gene Expression Omnibus database, including 43 controlled pediatric asthma serum samples and 46 uncontrolled pediatric asthma samples. R software (AT&T Bell Laboratories) was used to construct the weighted gene co-expression network and screen the hub genes. The penalty model was established by least absolute shrinkage and selection operator (LASSO) regression analysis to further screen the genes in the hub genes. The receiver operating characteristic curve (ROC) was used to confirm the diagnostic value of key genes. Results: A total of 171 differentially expressed genes were screened from the controlled and uncontrolled samples. Chemokine (C-X-C motif) ligand 12 (CXCL12), matrix metallopeptidase 9 (MMP9), and wingless-type MMTV integration site family member 2 (WNT2) were the key genes, which were upregulated in the uncontrolled samples. The areas under the ROC curve of CXCL12, MMP9, and WNT2 were 0.895, 0.936, and 0.928, respectively. Conclusions: The key genes CXCL12, MMP9, and WNT2 in pediatric asthma were identified by a bioinformatics analysis and machine-learning algorithm, which may be potential diagnostic biomarkers.

Overexpression of Pasteurella multocida OmpA induces transcriptional changes and its possible implications for the macrophage polarization.

Pasteurella multocida (P. multocida) is a highly infectious, zoonotic pathogen. Outer membrane protein A (OmpA) is an important virulence component of the outer membrane of P. multocida. OmpA mediates bacterial biofilm formation, eukaryotic cell infection, and immunomodulation. It is unclear how OmpA affects the host immune response. We estimated the role of OmpA in the pathogenesis of P. multocida by investigating the effect of OmpA on the immune cell transcriptome. Changes in the transcriptome of rat alveolar macrophages (NR8383) upon overexpression of P. multocida OmpA were demonstrated. A model cell line for stable transcription of OmpA was constructed by infecting NR8383 cells with OmpA-expressing lentivirus. RNA was extracted from cells and sequenced on an Illumina HiSeq platform. Key gene analysis of genes in the RNA-seq dataset were performed using various bioinformatics methods, such as gene ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, Gene Set Enrichment Analysis, and Protein-Protein Interaction Analysis. Our findings revealed 1340 differentially expressed genes. Immune-related pathways that were significantly altered in rat alveolar macrophages under the effect of OmpA included focal adhesion, extracellular matrix and vascular endothelial growth factor signaling pathways, antigen processing and presentation, nucleotide oligomerization domain-like receptor and Toll-like receptor signaling pathways, and cytokine-cytokine receptor interaction. The key genes screened were Vegfa, Igf2r, Fabp5, P2rx1, C5ar1, Nedd4l, Gas6, Cxcl1, Pf4, Pdgfb, Thbs1, Col7a1, Vwf, Ccl9, and Arg1. Data of associated pathways and altered gene expression indicated that OmpA might cause the conversion of rat alveolar macrophages to M2-like. The related pathways and key genes can serve as a reference for OmpA of P. multitocida and host interaction mechanism studies.

Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis.

Background: Diabetic nephropathy (DN), the most intractable complication in diabetes patients, can lead to proteinuria and progressive reduction of glomerular filtration rate (GFR), which seriously affects the quality of life of patients and is associated with high mortality. However, the lack of accurate key candidate genes makes diagnosis of DN very difficult. This study aimed to identify new potential candidate genes for DN using bioinformatics, and elucidated the mechanism of DN at the cellular transcriptional level. Methods: The microarray dataset GSE30529 was downloaded from the Gene Expression Omnibus Database (GEO), and the differentially expressed genes (DEGs) were screened by R software. We used Gene Ontology (GO), gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify the signal pathways and genes. Protein-protein interaction (PPI) networks were constructed using the STRING database. The GSE30122 dataset was selected as the validation set. Receiver operating characteristic (ROC) curves were applied to evaluate the predictive value of genes. An area under curve (AUC) greater than 0.85 was considered to be of high diagnostic value. Several online databases were used to predict miRNAs and transcription factors (TFs) capable of binding hub genes. Cytoscape was used for constructing a miRNA-mRNA-TF network. The online database 'nephroseq' predicted the correlation between genes and kidney function. The serum level of creatinine, BUN, and albumin, and the urinary protein/creatinine ratio of the DN rat model were detected. The expression of hub genes was further verified through qPCR. Data were analyzed statistically using Student's t-test by the 'ggpubr' package. Results: A total of 463 DEGs were identified from GSE30529. According to enrichment analysis, DEGs were mainly enriched in the immune response, coagulation cascades, and cytokine signaling pathways. Twenty hub genes with the highest connectivity and several gene cluster modules were ensured using Cytoscape. Five high diagnostic hub genes were selected and verified by GSE30122. The MiRNA-mRNA-TF network suggested a potential RNA regulatory relationship. Hub gene expression was positively correlated with kidney injury. The level of serum creatinine and BUN in the DN group was higher than in the control group (unpaired t test, t = 3.391, df = 4, p = 0.0275, r = 0.861). Meanwhile, the DN group had a higher urinary protein/creatinine ratio (unpaired t test, t = 17.23, df = 16, p < 0.001, r = 0.974). QPCR results showed that the potential candidate genes for DN diagnosis included C1QB, ITGAM, and ITGB2. Conclusions: We identified C1QB, ITGAM and ITGB2 as potential candidate genes for DN diagnosis and therapy and provided insight into the mechanisms of DN development at transcriptome level. We further completed the construction of miRNA-mRNA-TF network to propose potential RNA regulatory pathways adjusting disease progression in DN.

Combined transcriptome and metabolome analysis reveal key regulatory genes and pathways of feed conversion efficiency of oriental river prawn Macrobrachium nipponense.

BACKGROUND: Oriental river prawn Macrobrachium nipponense is an economically important aquaculture species in China, Japan, and Vietnam. In commercial prawn farming, feed cost constitutes about 50 to 65% of the actual variable cost. Improving feed conversion efficiency in prawn culture will not only increase economic benefit, but also save food and protect the environment. The common indicators used for feed conversion efficiency include feed conversion ratio (FCR), feed efficiency ratio (FER), and residual feed intake (RFI). Among these, RFI is much more suitable than FCR and FER during the genetic improvement of feed conversion efficiency for aquaculture species. RESULTS: In this study, the transcriptome and metabolome of hepatopancreas and muscle of M. nipponense from high RFI low RFI groups, which identified after culture for 75 days, were characterized using combined transcriptomic and metabolomic analysis. A total of 4540 differentially expressed genes (DEGs) in hepatopancreas, and 3894 DEGs in muscle were identified, respectively. The DEGs in hepatopancreas were mainly enriched in KEGG pathways including the metabolism of xenobiotics by cytochrome P450 (down-regulated), fat digestion and absorption (down-regulated) and aminoacyl-tRNA biosynthesis (up-regulated), etc. The DEGs in muscle were mainly enriched in KEGG pathways including the protein digestion and absorption (down-regulated), glycolysis/gluconeogenesis (down-regulated), and glutathione metabolism (up-regulated), etc. At the transcriptome level, the RFI of M. nipponense was mainly controlled in biological pathways such as the high immune expression and the reduction of nutrients absorption capacity. A total of 445 and 247 differently expressed metabolites (DEMs) were identified in the hepatopancreas and muscle, respectively. At the metabolome level, the RFI of M. nipponense was affected considerably by amino acid and lipid metabolism. CONCLUSIONS: M. nipponense from higher and lower RFI groups have various physiological and metabolic capability processes. The down-regulated genes, such as carboxypeptidase A1, 6-phosphofructokinase, long-chain-acyl-CoA dehydrogenase, et. al., in digestion and absorption of nutrients, and the up-regulated metabolites, such as aspirin, lysine, et. al., in response to immunity could be potential candidate factors contributed to RFI variation for M. nipponense. Overall, these results would provide new insights into the molecular mechanism of feed conversion efficiency and assist in selective breeding to improve feed conversion efficiency in M. nipponense.

Bioinformatics analysis of potential key ferroptosis-related genes involved in tubulointerstitial injury in patients with diabetic nephropathy.

Diabetic nephropathy (DN) is the primary complication of diabetes mellitus. Ferroptosis is a form of cell death that plays an important role in DN tubulointerstitial injury, but the specific molecular mechanism remains unclear. Here, we downloaded the DN tubulointerstitial datasets GSE104954 and GSE30529 from the Gene Expression Omnibus database. We examined the differentially expressed genes (DEGs) between DN patients and healthy controls, and 36 ferroptosis-related DEGs were selected. Pathway-enrichment analyses showed that many of these genes are involved in metabolic pathways, phosphoinositide 3-kinase/Akt signaling, and hypoxia-inducible factor-1 signaling. Ten of the 36 ferroptosis-related DEGs (CD44, PTEN, CDKN1A, DPP4, DUSP1, CYBB, DDIT3, ALOX5, VEGFA, and NCF2) were identified as key genes. Expression patterns for six of these (CD44, PTEN, DDIT3, ALOX5, VEGFA, and NCF2) were validated in the GSE30529 dataset. Nephroseq data indicated that the mRNA expression levels of CD44, PTEN, ALOX5, and NCF2 were negatively correlated with the glomerular filtration rate (GFR), while VEGFA and DDIT3 mRNA expression levels were positively correlated with GFR. Immune infiltration analysis demonstrated altered immunity in DN patients. Real-time quantitative PCR (qPCR) analysis showed that ALOX5, PTEN, and NCF2 mRNA levels were significantly upregulated in high-glucose-treated human proximal tubular (HK-2) cells, while DDIT3 and VEGFA mRNA levels were significantly downregulated. Immunohistochemistry analysis of human renal biopsies showed positive staining for ALOX5 and NCF2 protein in DN samples but not the controls. These key genes may be involved in the molecular mechanisms underlying ferroptosis in patients with DN, potentially through specific metabolic pathways and immune/inflammatory mechanisms.

IFIH1 was predicted as a key biomarker in primary Sjögren's syndrome based on transcriptome analysis and experimental verification in patients and mice.

OBJECTIVES: To investigate the novel key genes and biological processes that may lead to primary Sjögren' s syndrome (pSS). METHODS: We downloaded datasets about peripheral blood samples of pSS patients and healthy controls (GSE51092, GSE84844, and GSE66795) from Gene Expression Omnibus database. The weighted co-expression network analysis and differential expression analysis first were implemented. After that, protein-protein network interaction and Support Vector Machines were applied in the meantime to take intersection for key genes. Moreover, we conducted immune cell infiltration analysis to explore the relationship between the gene expression and concentration of immune cells in peripheral blood. Lastly, the expression of key genes was verified in pSS patients and murine models by reverse-transcription polymerase chain reaction. Meanwhile, correlation analysis of gene expression and disease activity was also performed. RESULTS: Only 1 key gene, interferon induced with helicase c domain 1 (IFIH1), was identified to be both significantly up-regulated and important for the diagnosis of pSS. The increased expression of IFIH1 in peripheral blood was confirmed in data sets, patients and non-obese diabetic (NOD) mice. Its expression was correlated with disease activity in patients as well. In addition, the IFIH1 expression was also increased in spleen and salivary glands infiltrated with lymphocytes in NOD mice. Furthermore, immune cell infiltration analysis showed that the expression of IFIH1 was positively correlated with the proportion of memory B cells and activated dendritic cells, and negatively correlated with the proportion of macrophage M0. CONCLUSIONS: Here, bioinformatics analyses and experimental assays were performed to provide a new insight for understanding of pSS. IFIH1 may be a new diagnostic marker or therapeutic target for pSS.

[Bioinformatics analysis and validation of key genes in transformation of idiopathic membranous nephropathy to end-stage renal disease and traditional Chinese medicines for prevention and treatment].

This study used bioinformatics analysis to screen out key genes involved in the transformation of idiopathic membranous nephropathy to end-stage renal disease and to predict targeted Chinese herbs and medicines and active ingredients with preventive and curative effects. The GSE108113 microarray of idiopathic membranous nephropathy and GSE37171 microarray of were downloaded from the comprehensive gene expression database, and 8 homozygous differentially expressed genes for the transformation of idiopathic membranous nephropathy into end-stage renal disease of were screened out by R software. GraphPad Prism was used to verify the expression of homozygous differentially expressed genes in GSE115857 microarray of idiopathic membranous nephropathy and GSE66494 microarray of chronic kidney disease, and 7 key genes(FOS, OGT, CLK1, TIA1, TTC14, CHORDC1, and ANKRD36B) were finally obtained. The Gene Ontology(GO) analysis was performed. There were 209 functions of encoded proteins, mainly involved in regulation of RNA splicing, cytoplasmic stress granule, poly(A) binding, etc. Thirteen traditional Chinese medicines with the effect of preventing the transformation of idiopathic membranous nephropathy to end-stage renal disease were screened out from Coremine Medical database, including Ginseng Radix et Rhizoma, Lycopi Herba, and Gardeniae Fructus, which were included in the Chinese Pharmacopoeia(2020 edition). The active ingredient quercetin mined from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP) had ability to dock with the key gene FOS-encoded protein molecule, which provided targets and research ideas for the development of new traditional Chinese medicines.

Exploring the key genes of fucoxanthin biosynthesis in Phaeodactylum tricornutum under phosphorus deficiency, red light and yellow light using WGCNA.

Weighted gene co-expression network analysis (WGCNA) is a method for analysing gene expression patterns across multiple samples, clustering genes with similar expression patterns and identifying key genes associated with specific traits or phenotypes. In this study, we investigated the effects of fucoxanthin accumulation in Phaeodactylum tricornutum in response to abiotic stresses of phosphorus deficiency, red light, and yellow light using transcriptome sequencing and weighted gene co-expression network analysis. The results showed that compared to the control, the fucoxanthin content of P. tricornutum was significantly increased after phosphorus deficiency and red light treatment (P<0.05), but significantly decreased after yellow light treatment (P<0.05). A weighted gene co-expression network was constructed using 10,392 genes obtained from transcriptome sequencing, and ß=18 (R2>0.8) was chosen as a soft threshold in order to ensure a scale-free network. A total of 10 co-expression modules were identified by correlation analysis of fucoxanthin content, with the purple module positively correlated with fucoxanthin content (r=0.9, P=1E-200), and 9 key genes were identified, including five genes in the fucoxanthin biosynthesis pathway (DXR, PSY, PDS1, ZEP2, VDL2) and 4 transcription factors (bHLH5, HOX2, CCHH13, HSF1b). Further qRT-PCR confirmed that key genes were more highly expressed in the phosphorus deficiency treatment and linear regression analysis showed that the relative gene expressions were all highly correlated with the transcriptome data. The results of this study provide a basis for further investigation of the complex regulatory mechanisms of fucoxanthin in P. tricornutum.

Transcriptomic identification of key genes in Pacific oysters Crassostrea gigas responding to major abiotic and biotic stressors.

Oysters are commercially important intertidal filter-feeding species. Mass mortality events of oysters often occur due to environmental stresses, such as exposure to fluctuating temperatures, salinity, and air, as well as to metal pollution and pathogen infection. Here, RNA-seq data were used to identify shared and specific responsive genes by differential gene expression analysis and weighted gene co-expression network analysis. A total of 18 up-regulated and 10 down-regulated shared responsive genes were identified corresponding to five different stressors. Total 27 stressor-specific genes for temperature, 11 for salinity, 80 for air exposure, 51 for metal pollution, and 636 for Vibrio mediterranei pathogen stress were identified in oysters. Elongin-ß was identified as a crucial gene for thermal stress response. Some HSP70s were determined to be shared responsive genes while others were specific to thermal tolerance. The proteins encoded by these stress-related genes should be further investigated to characterize their physiological functions. In addition, the uncharacterized proteins and ncRNAs that were identified may be involved in species-specific stress-response and regulatory mechanisms. This study identified specific genes related to stressors relevant to oyster cultivation. These findings provide useful information for new selective breeding strategies using a data driven method.

[Toxic effects of long-term pesticides exposure and key gene discovery].

Objective: Bioinformatics methods were used to mine differentially expressed genes (DEGs) and enriched signal pathways induced by chronic pesticide exposure, and explore its potential pathogenic mechanisms and key genes. Methods: In July 2021, high-throughput gene expression profile data related to pesticide toxicity was downloaded from Gene Expression Omnibus (GEO) database to obtain DEGs. The samples were from American male farm workers who had been exposed to pesticides for a long time and other industry workers. The functional enrichment analysis of GO, KEGG and Geme Set Enrichment Analysis (GSEA) were performed by R package clusterProfiler. STRING, Cytoscape and other tools were applied to construct and visualize the protein interaction network. With the help of MCODE and Cytohubba plugins, gene function modules were obtained, and hub gene was screened out. Results: 189 DEGs were screened from GSE30335 dataset, including 101 up-regulated genes and 88 down-regulated genes. The results of GO, KEGG and GSEA were mainly enriched in biological functions such as regulation of neuron projection development, regulation of locomotion, ribosomal protein synthesis, and pathways related to complex nervous system diseases such as Parkinson's disease. And the comprehensive screening showed that KLF1 was the hub gene of pesticide exposure, with a fold change of 0.456 (t=-3.82, P=0.021) . Conclusion: Long term exposure to pesticides results in the differential expression of multiple genes in the exposed population, which may be involved in the pathological changes of nervous system by down regulating KLF1 and related biological pathways.

Identification of key genes related to skin burns based on bioinformatics analysis.

To further understand the regulatory network and molecular mechanisms of gene expression after skin burns, we performed bioinformatics analysis of gene expression profiles of skin burn samples and identified key genes associated with skin burns. The GSE8056 dataset and GSE139028 dataset was downloaded from the Gene Expression Omnibus (GEO) database for analysis and validation. The limma package was used to screen for differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses (KEGG) were then performed. Subsequently, LASSO regression analysis was performed on DEGs and a regulatory network map of skin burn-related genes was constructed. Finally, the infiltration of immune cells was calculated and co-expression network maps of immune-related key genes and skin regeneration genes were constructed. Analysis of the GSE8056 dataset showed that 432 genes were upregulated and 351 genes were downregulated. The DEGs were mainly focused on immune response and skin regeneration. Meanwhile, these two groups of pivotal genes were significantly associated with abnormal infiltration of 9 immune cells. GSE139028 validation revealed that 3 hub genes associated with skin burn immunity were differentially expressed, except for S100A8, while only the DPT gene was differentially expressed among the seven hub genes associated with skin regeneration. In short, the effect of skin burn on patients is to regulate the expression of immune-related genes UPP1, MMP1, MMP3 and skin regeneration-related gene DPT, which may be the key target for the treatment of skin burn.

Identification of SLAMF1 as an immune-related key gene associated with rheumatoid arthritis and verified in mice collagen-induced arthritis model.

Background: Rheumatoid arthritis (RA) is the most common inflammatory arthropathy. Immune dysregulation was implicated in the pathogenesis of RA. Thus, the aim of the research was to determine the immune related biomarkers in RA. Methods: We downloaded the gene expression data of RA in GSE89408 and GSE45291 from Gene Expression Omnibus public database (GEO). Differentially expressed genes (DEGs) were identified between RA and control groups. Infiltrating immune cells related genes were obtained by ssGSEA and weighted gene co-expression network analysis (WGCNA). We performed functional enrichment analysis of differentially expressed immunity-related genes (DEIRGs) by "clusterProfiler" R package, key genes screening by protein-protein interaction (PPI) network of DEIRGs. And mice collagen-induced arthritis (CIA) model was employed to verify these key genes. Results: A total of 1,885 up-regulated and 1,899 down-regulated DEGs were identified in RA samples. The ssGSEA analysis showed that the infiltration of 25 cells was significantly different. 603 immune related genes were obtained by WGCNA, and 270 DEIRGs were obtained by taking the intersection of DEGs and immune related genes. Enrichment analyses indicated that DEIRGs were associated with immunity related biological processes. 4 candidate biomarkers (CCR7, KLRK1, TIGIT and SLAMF1) were identified from the PPI network of DEIRGs and literature research.In mice CIA model, the immunohistochemical stain showed SLAMF1 has a significantly high expression in diseased joints. And flow cytometry analysis shows the expression of SLAMF1 on CIA mice-derived CTL cells, Th, NK cells, NKT cells, classical dendritic cell (cDCs) and monocytes/macrophages was also significantly higher than corresponding immune cells from HC mice. Conclusion: Our study identified SMLAF1 as a key biomarker in the development and progression of RA, which might provide new insight for exploring the pathogenesis of RA.

A metabolic reprogramming-related prognostic risk model for clear cell renal cell carcinoma: From construction to preliminary application.

Metabolic reprogramming is one of the characteristics of clear cell renal cell carcinoma (ccRCC). Although some treatments associated with the metabolic reprogramming for ccRCC have been identified, remain still lacking. In this study, we identified the differentially expressed genes (DEGs) associated with clinical traits with a total of 965 samples via DEG analysis and weighted correlation network analysis (WGCNA), screened the prognostic metabolism-related genes, and constructed the risk score prognostic models. We took the intersection of DEGs with significant difference coexpression modules and received two groups of intersection genes that were connected with metabolism via functional enrichment analysis. Then we respectively screened prognostic metabolic-related genes from the genes of the two intersection groups and constructed the risk score prognostic models. Compared with the predicted effect of clinical grade and stage for ccRCC patients, finally, we selected the model constructed with genes of ABAT, ALDH6A1, CHDH, EPHX2, ETNK2, and FBP1. The risk scores of the prognostic model were significantly related to overall survival (OS) and could serve as an independent prognostic factor. The Kaplan-Meier analysis and ROC curves revealed that the model efficiently predicts prognosis in the TCGA-KIRC cohort and the validation cohort. Then we investigated the potential underlying mechanism and sensitive drugs between high- and low-risk groups. The six key genes were significantly linked with worse OS and were downregulated in ccRCC, we confirmed the results in clinical samples. These results demonstrated the efficacy and robustness of the risk score prognostic model, based on the characteristics of metabolic reprogramming in ccRCC, and the key genes used in constructing the model also could develop into targets of molecular therapy for ccRCC.

Upregulation of key genes Eln and Tgfb3 were associated with the severity of cardiac hypertrophy.

BACKGROUND: Hypertension-induced cardiac hypertrophy is one of the most common pre-conditions that accompanies heart failure. This study aimed to identify the key pathogenic genes in the disease process. METHODS: GSE18224 was re-analyzed and differentially expressed genes (DEGs) were obtained. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out. Networks of transcription factor (TF)-mRNA, microRNA (miRNA)-mRNA and Protein-Protein interaction (PPI) were constructed, and a key module was further screened out from PPI network. GSE36074 dataset and our transverse aortic constriction (TAC) mouse model were used to validate gene expression in the module. Finally, the correlation between the genes and biomarkers of cardiac hypertrophy were evaluated. RESULTS: Totally, there were 348 DEGs in GSE18224, which were mainly enriched in biological processes including collagen fibril organization, cellular response to transforming growth factor-beta stimulus and were involved in ECM-receptor interaction and Oxytocin signaling pathway. There were 387 miRNAs targeted by 257 DEGs, while 177 TFs targeted 71 DEGs. The PPI network contained 222 nodes and 770 edges, with 18 genes screened out into the module. After validation, 8 genes, which were also significantly upregulated in the GSE36074 dataset, were selected from the 18 DEGs. 2 of the 8 DEGs, including Eln and Tgfb3 were significantly upregulated in our mouse model of myocardial hypertrophy. Finally, the expression of Eln and Tgfb3 were found to be positively correlated with the level of the disease biomarkers. CONCLUSIONS: Upregulated key genes Eln and Tgfb3 were positively correlated with the severity of cardiac hypertrophy, which may provide potential therapeutic targets for the disease.

Identification and verification of differentially expressed key genes in peripheral blood-derived T cells between chronic immune thrombocytopenia patients and healthy controls.

Immune thrombocytopenia (ITP), characterized by decreased platelet counts, is a complex immune-mediated disorder with unelucidated pathogenesis. Accumulating evidence shows that T cell-mediated platelet destruction is one crucial process during the progression of ITP. Here, we attempted to identify core genes in peripheral blood-derived T-cells of chronic ITP through the analysis of microarray data (GSE43179) and clinical verification, with the aim to further understand the pathogenesis and progression of ITP. Compared with healthy controls, 97 differentially expressed genes (DEGs), including 63 up-regulated and 34 down-regulated were identified in ITP patients. Functional enrichment analysis showed that the DEGs were mainly enriched in innate immune response, inflammatory response, and IL-17 signaling pathway. Among the DEGs, top 15 hub genes ranked by degree score were identified via protein-protein interaction (PPI) network and were further confirmed by quantitative reverse transcription PCR (qRT-PCR). Among top 15 hub genes, the expression levels of 14 DEGs like TLR4, S100A8, S100A9, and S100A12 were significantly up-regulated, while one DEG IFNG was down-regulated in ITP patients. Noticeably, TLR4 exhibited the highest degree score, and S100A8 had the largest fold change in qRT-PCR analysis. Altogether, our results suggested that the pathogenesis and progression of ITP are related with multiple immune-related pathways, and that TLR4 and S100A8 are likely to play crucial roles.

The Four Key Genes Participated in and Maintained Atrial Fibrillation Process via Reprogramming Lipid Metabolism in AF Patients.

Atrial fibrillation (AF) is always in high incidence in the population, which can lead to serious complications. The structural and electrical remodeling of atrial muscle induced by inflammatory reaction or oxidative stress was considered as the major mechanism of AF. The treatment effect is not ideal based on current mechanisms. Recent studies demonstrated that lipid metabolism disorder of atrial muscle played an important role in the occurrence of AF. What key genes are involved is unclear. The purpose of the present study was to explore the lipid metabolism mechanism of AF. With the GEO database and the genomics of AF patients, metabolic related pathways and the key genes were analyzed. At the same time, the rat model of cecal ligation and puncture (CLP) was used to confirm the results. GSE 31821 and GSE 41177 were used as data sources, and the merged differentially expressed genes (DEGs) analysis showed that a total of 272 DEGs were found. GO annotation, KEGG, and gene set enrichment analysis (GSEA) showed that the fatty acid metabolism and the lipid biosynthetic process were involved in AF. Cholesterol biosynthesis, arachidonic acid metabolism, and the lipid droplet pathway were obviously increased in AF. Further analysis showed that four key genes, including ITGB1, HSP90AA1, CCND1, and HSPA8 participated in pathogenesis of AF regulating lipid biosynthesis. In CLP rats, metabolic profiling in the heart showed that the pyrimidine metabolism, the biosynthesis of unsaturated fatty acid metabolism, arginine and proline metabolism, and the fatty acid biosynthesis were involved. The four key genes were confirmed increased in the heart of CLP rats (p < 0.05 or 0.01). The results suggest that the lipid metabolism disorder participates in the occurrence of AF. ITGB1, HSP90AA1, CCND1, and HSPA8 are the key genes involved in the regulation of lipid biosynthesis.