Necroptosis pathway emerged as potential diagnosis markers in spinal cord injury.

The present research focused on identifying necroptosis-related differentially expressed genes (NRDEGs) in spinal cord injury (SCI) to highlight potential therapeutic and prognostic target genes in clinical SCI. Three SCI-related datasets were downloaded, including GSE151371, GSE5296 and GSE47681. MSigDB and KEGG datasets were searched for necroptosis-related genes (NRGs). Differentially expressed genes (DEGs) and NRGs were intersected to obtain NRDEGs. The MCC algorithm was employed to select the first 10 genes as hub genes. A protein-protein interaction (PPI) network related to NRDEGs was developed utilizing STRING. Several databases were searched to predict interactions between hub genes and miRNAs, transcription factors, potential drugs, and small molecules. Immunoassays were performed to identify DEGs using CIBERSORTx. Additionally, qRT-PCR was carried out to verify NRDEGs in an animal model of SCI. Combined analysis of all datasets identified 15 co-expressed DEGs and NRGs. GO and KEGG pathway analyses highlighted DEGs mostly belonged to pathways associated with necroptosis and apoptosis. Hub gene expression analysis showed high accuracy in SCI diagnosis was associated with the expression of CHMP7 and FADD. A total of two hub genes, i.e. CHMP7, FADD, were considered potential targets for SCI therapy.

Mechanism of Xing 9 ling tablet candy for alcoholic liver disease based on network pharmacology.

Xing 9 Ling tablet candy (X9LTC) effectively treats alcoholic liver disease (ALD), but its potential mechanism and molecular targets remain unstudied. We aimed to address this gap using network pharmacology. Furthermore, high-performance liquid chromatography (HPLC) and database analysis revealed a total of 35 active ingredients and 311 corresponding potential targets of X9LTC. Protein interaction analysis revealed PTGS2, JUN, and FOS as its core targets. Enrichment analysis indicated that chemical carcinogenesis-receptor activation, IL-17 and TNF signaling pathway were enriched by multiple core targets, which might be the main pathway of action. Further molecular docking validation showed that the core targets had good binding activities with the identified compounds. Animal experiments showed that X9LTC could reduce the high expression of ALT, AST and TG in the serum of ALD mice, alleviate the lesions in liver tissues, and reverse the high expression of PTGS2, JUN, and FOS proteins in the liver tissues. In this study, we established a method for the determination of X9LTC content for the first time, and predicted its active ingredient and mechanism of action in treating ALD, providing theoretical basis for further research.

Identification of common mechanisms and biomarkers for dermatomyositis and atherosclerosis based on bioinformatics analysis.

BACKGROUND: Dermatomyositis (DM) manifests as an autoimmune and inflammatory condition, clinically characterized by subacute progressive proximal muscle weakness, rashes or both along with extramuscular manifestations. Literature indicates that DM shares common risk factors with atherosclerosis (AS), and they often co-occur, yet the etiology and pathogenesis remain to be fully elucidated. This investigation aims to utilize bioinformatics methods to clarify the crucial genes and pathways that influence the pathophysiology of both DM and AS. METHOD: Microarray datasets for DM (GSE128470, GSE1551, GSE143323) and AS (GSE100927, GSE28829, GSE43292) were retrieved from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was used to reveal their co-expressed modules. Differentially expression genes (DEGs) were identified using the "limma" package in R software, and the functions of common DEGs were determined by functional enrichment analysis. A protein-protein interaction (PPI) network was established using the STRING database, with central genes evaluated by the cytoHubba plugin, and validated through external datasets. Immune infiltration analysis of the hub genes was conducted using the CIBERSORT method, along with Gene Set Enrichment Analysis (GSEA). Finally, the NetworkAnalyst platform was employed to examine the transcription factors (TFs) responsible for regulating pivotal crosstalk genes. RESULTS: Utilizing WGCNA analysis, a total of 271 overlapping genes were pinpointed. Subsequent DEG analysis revealed 34 genes that are commonly found in both DM and AS, including 31 upregulated genes and 3 downregulated genes. The Degree Centrality algorithm was applied separately to the WGCNA and DEG collections to select the 15 genes with the highest connectivity, and crossing the two gene sets yielded 3 hub genes (PTPRC, TYROBP, CXCR4). Validation with external datasets showed their diagnostic value for DM and AS. Analysis of immune infiltration indicates that lymphocytes and macrophages are significantly associated with the pathogenesis of DM and AS. Moreover, GSEA analysis suggested that the shared genes are enriched in various receptor interactions and multiple cytokines and receptor signaling pathways. We coupled the 3 hub genes with their respective predicted genes, identifying a potential key TF, CBFB, which interacts with all 3 hub genes. CONCLUSION: This research utilized comprehensive bioinformatics techniques to explore the shared pathogenesis of DM and AS. The three key genes, including PTPRC, TYROBP, and CXCR4, are related to the pathogenesis of DM and AS. The central genes and their correlations with immune cells may serve as potential diagnostic and therapeutic targets.

Western blot normalization: Time to choose a proper loading control seriously.

Recent research has questioned the validity of housekeeping proteins in Western blot. Our present study proposed new ideas for Western blot normalization that improved the reproducibility of scientific research. We used the Gene Expression Omnibus (GEO) database and the web tool GEO2R to exclude unstable housekeeping genes quickly. In ischemic heart tissues, actin and tubulin changed significantly, whereas no statistically significant changes were observed in the expression of genes relative to glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Besides, the reliability of GAPDH was further examined by Western blot. Additionally, unstable housekeeping genes were found in other animal models of cardiovascular medicine. We also found that sodium dodecyl sulfate and temperature significantly impacted the results of Ponceau S staining. Membranes stained with Ponceau S after immunodetection could avoid this interference, and the coefficients of variation for post-immunodetection staining are lower than those produced by GAPDH immunodetection. Overall, we described a new use of differential gene expression analysis and proposed a modified Ponceau S staining method, which provided researchers with a proper loading control for Western blot and hence could improve reproducibility in research.

Feasibility of olfactomedin 4 as a molecular biomarker for early diagnosis of gastric neoplasia after intestinal metaplasia.

OBJECTIVES: This study discusses whether olfactomedin 4 (OLFM4) could be used as a sensitive and specific biomarker in the early diagnosis of gastric cancer (GC) after gastric intestinal metaplasia (GIM). METHODS: An integrative analysis combining data derived from the Gene Expression Omnibus (GEO) and cBioPortal databases was performed to investigate the potential molecular biomarker. Immunohistochemistry and quantitative real-time polymerase chain reactions were used to measure the expression of messenger ribonucleic acid (mRNA) and protein by OLFM4. In combination with the gastroscopic findings and the OLFM4 expression in GIM-GC, a predictive model was established. The receiver operator characteristic curve (ROC) was applied to assess the diagnostic value of the model for GIM-GC. RESULTS: According to the GEO and cBioPortal databases, OLFM4 was identified as a key gene in the diagnosis of GIM-GC. Higher protein expression of OLFM4 was found in GIM and GIM-GC compared with chronic superficial gastritis (GS) (p < 0.05). The positive expression rate of OLFM4 in paracancerous tissue (GCP) was higher than in GIM (p > 0.05). There was no significant difference between GIM-GC and GCP (p > 0.05). The mRNA expression of OLFM4 was similar to the protein expression, and the positive expression rate was higher in early GIM-GC than in GIM (p < 0.05). CONCLUSION: Olfactomedin 4 could be used as a biomarker for the early diagnosis of GIM-GC, and the logistic predictive model could be an effective tool for increasing the early diagnostic rate.

Long non-coding RNAs MALAT1 and NEAT1 in non-syndromic orofacial clefts.

Long non-coding RNAs (lncRNAs) are thought to play important roles in non-syndromic orofacial clefts (NSOFC). Clinical diagnosis was categorized as either non-syndromic cleft lip with or without cleft palate (NSCL/P), or non-syndromic cleft palate only (NSCPO). Tissues excised from the trimmed wound edge were reserved as experimental samples; adjacent normal control was used as a positive control, and tissue from healthy individuals was used as a blank control. Target lncRNAs in the collected tissues were identified using microarrays and quantitative reverse transcription PCR (RT-qPCR). Immunohistochemical (IHC) staining and RT-qPCR were used to verify the target mRNAs. Pathway, gene ontology (GO) enrichment, and TargetScan predictions were employed to construct competing endogenous RNA networks (ceRNA networks) and explore their potential functions. RNA-Seq revealed 24 upregulated and 43 downregulated lncRNAs; MALAT1 and NEAT1 were screened and validated using RT-qPCR. Common NSOFC risk factors were positively correlated with MALAT1 and NEAT1 expression. Bioinformatics predicted four ceRNA networks; GO enrichment focused on their potential functions. RT-qPCR and IHC data were consistent with respect to expression levels of proteins and the mRNAs that encode them. As MALAT1 and NEAT1 are associated with the severity of NSOFC, they represent potential therapeutic targets and prognostic biomarkers.

Identification and validation of autophagy-related genes in Kawasaki disease.

BACKGROUND: Kawasaki disease (KD) is a systemic vasculitis of unknown etiology affecting mainly children. Studies have shown that the pathogenesis of KD may be related to autophagy. Using bioinformatics analysis, we assessed the significance of autophagy-related genes (ARGs) in KD. METHODS: Common ARGs were identified from the GeneCards Database, the Molecular Signatures Database (MSigDB), and the Gene Expression Omnibus (GEO) database. ARGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network analysis. Furthermore, related microRNAs (miRNAs), transcription factors (TFs), and drug interaction network were predicted. The immune cell infiltration of ARGs in tissues was explored. Finally, we used receiver operating characteristic (ROC) curves and quantitative real-time PCR (qRT-PCR) to validate the diagnostic value and expression levels of ARGs in KD. RESULTS: There were 20 ARGs in total. GO analysis showed that ARGs were mainly rich in autophagy, macro-autophagy, and GTPase activity. KEGG analysis showed that ARGs were mainly rich in autophagy-animal and the collecting duct acid secretion pathway. The expression of WIPI1, WDFY3, ATP6V0E2, RALB, ATP6V1C1, GBA, C9orf72, LRRK2, GNAI3, and PIK3CB is the focus of PPI network. A total of 72 related miRNAs and 130 related TFs were predicted by miRNA and TF targeting network analyses. Ten pairs of gene-drug interaction networks were also predicted; immune infiltration analysis showed that SH3GLB1, ATP6V0E2, PLEKHF1, RALB, KLHL3, and TSPO were closely related to CD8 + T cells and neutrophils. The ROC curve showed that ARGs had good diagnostic value in KD. qRT-PCR showed that WIPI1 and GBA were significantly upregulated. CONCLUSION: Twenty potential ARGs were identified by bioinformatics analysis, and WIPI1 and GBA may be used as potential drug targets and biomarkers.

Identification and validation of ferroptosis-related genes for chronic rhinosinusitis with nasal polyps.

PURPOSE: Even though the great progress in the field of chronic rhinosinusitis with nasal polyps (CRSwNP) has been achieved, ferroptosis and its molecular mechanism in CRSwNP remain blank. We are the first to study the relationship between CRSwNP and ferroptosis, aiming to identify ferroptosis-related genes in the process of CRSwNP. METHODS: Using the GEO database and the FerrDb database, significantly differentially expressed ferroptosis-related genes (DEFGs) were selected between CRSwNP-NP and CRSwNP-IT specimens. Then, the protein-protein interaction (PPI) network of ferroptosis-related genes was constructed. Functional enrichment analyses (GSVA, GO, KEGG, and GeneCodis analyses) were introduced in our study. Besides, based on the GSE136825 data set, DEFGs between CRSwNP-NP and CS-IT specimens were also analyzed. Finally, qRT-PCR was performed to validate the selected ferroptosis-related genes with clinical samples. RESULTS: 31 significantly DEFGs were identified between CRSwNP-NP and CRSwNP-IT specimens. Functional enrichment analyses and the analysis of GeneCodis 4 pointed out that DEFGs may potentially be involved in some related KEGG pathways. 8 DEFGs were selected between CRSwNP-NP and CS-IT specimens. The experimental verification indicated that 4 genes (GPX2, CDO1, CAV1, and TP53) were the important DEFGs of CRSwNP. The Venn diagrams proved that CDO1 and GPX2 were considered as the most important DEFGs genes of CRSwNP, especially GPX2. CONCLUSIONS: Though a comprehensive bioinformatics analysis and the experimental verification, CDO1 and GPX2 were considered as the important ferroptosis-related genes of CRSwNP, especially GPX2. However, further molecular biological experiments would be still required to uncover the underlying mechanism between ferroptosis and CRSwNP.

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.

Exploring the relationship between systemic lupus erythematosus and osteoporosis based on bioinformatics.

OBJECTIVE: This study aimed to explore the relationship between systemic lupus erythematosus (SLE) and osteoporosis (OP) based on bioinformatics. METHODS: The expression profiles of SLE and OP gene chips were searched through the GEO database, and the differentially expressed genes (DEGs) were screened out to obtain the intersection. Then, the Funrich software was used to predict the upstream miRNAs of the intersection genes, and the miRNA-mRNA relationship network was constructed. Afterward, the String database and Cytoscape software were used to construct the protein interaction network of the intersection genes to screen out the key genes. Finally, the functions and related pathways of key genes were analyzed by using the DAVID database. RESULTS: ①A total of 140 intersection genes of SLE and OP were obtained; ②There were 217 miRNAs regulating the intersection genes; ③IL-4, FOS, TLR1, TLR6, CD40LG, CCR1 were the key genes in the protein interaction network; ④The DAVID enrichment analysis mainly covered the positive regulation of cytokine production, the regulation of osteoclast differentiation, macrophage activation and other biological processes, involving Toll-like receptor signaling pathway, T cell receptor signaling pathway, Th1, Th2, and Th17 cells Differentiation, IL-17 signaling pathway. CONCLUSIONS: SLE and OP still have some highly overlapping differential gene expressions under the background of complex gene networks. The gene functions and signaling pathways involved can simultaneously regulate the two diseases, suggesting that there is a close relationship between the molecular mechanisms of the two diseases, and that it may be a target of drugs that interfere with two diseases at the same time.

Construction of lncRNA-miRNA-mRNA network based on ceRNA mechanism reveals the function of lncRNA in the pathogenesis of gout.

OBJECTIVE: To identify differentially expressed lncRNA, miRNA, and mRNA during the pathogenesis of gout, explore the ceRNA network regulatory mechanism of gout, and seek potential therapeutic targets. METHOD: First, gout-related chips were retrieved by GEO database. Then, the analysis of differentially expressed lncRNAs and mRNAs was conducted by R language and other software. Besides, miRNA and its regulated mRNA were predicted based on public databases, the intersection of differentially expressed mRNA and predicated mRNA was taken, and the lncRNA-miRNA-mRNA regulatory relationships were obtained to construct the ceRNA regulatory network. Subsequently, hub genes were screened by the STRING database and Cytoscape software. Then the DAVID database was used to illustrate the gene functions and related pathways of hub genes and to mine key ceRNA networks. RESULTS: Three hundred and eighty-eight lncRNAs and 758 mRNAs were identified with significant differential expression in gout patient, which regulates hub genes in the ceRNA network, such as JUN, FOS, PTGS2, NR4A2, and TNFAIP3. In the ceRNA network, lncRNA competes with mRNA for miRNA, thus affecting the IL-17 signaling pathway, TNF signaling pathway, Oxytocin signaling pathway, and NF-κB signaling pathway through regulating the cell's response to chemical stress. The research indicates that five miRNAs (miR-429, miR-137, miR-139-5p, miR-217, miR-23b-3p) and five lncRNAs (SNHG1, FAM182A, SPAG5-AS1, HNF1A-AS1, UCA1) play an important role in the formation and development of gout. CONCLUSION: The interaction in the ceRNA network can affect the formation and development of gout by regulating the body's inflammatory response as well as proliferation, differentiation, and apoptosis of chondrocytes and osteoclasts. The identification of potential therapeutic targets and signaling pathways through ceRNA network can provide a reference for further research on the pathogenesis of gout.

Construction of Adipogenic ceRNA Network Based on lncRNA Expression Profile of Adipogenic Differentiation of Human MSC Cells.

The Long non-coding RNA (lncRNA) expression profile data of ten samples including human Mesenchymal Stem Cell (MSC) adipogenic differentiation 0, 3, and 6 days from the GEO database, and then perform gene ID conversion, BLAST comparison, and annotation marking. Finally, group A (treatment group on day 3 of differentiation and control group on day 0 of differentiation) obtained a total of 1180 mRNA and 185 lncRNA; group B (treatment group on day 6 of differentiation and control group on day 0 of differentiation). A total of 1376 mRNA and 206 lncRNA were obtained. Finally, we processed the differential lncRNAs and mRNAs obtained in the two groups, and obtained 113 shared differential lncRNAs to further predict the targeted miRNA, a total of 815 lncRNA-miRNA pairs. The targeted mRNA was further predicted, and the grouped differential mRNAs were combined to obtain 64 differential mRNAs. In the end, we obtained 216 ceRNAs containing 26 lncRNAs, 27 miRNAs and 64 mRNAs. We found that the mRNAs in the ceRNA network were mainly enriched with 45 Gene Ontology (GO) terms, mainly including glucose homeostasis mechanism and insulin stimulation response. 69 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were mainly enriched. It mainly includes many pathways related to lipid metabolism such as Adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK), Rap1, cAMP, mitogen-activated protein kinase (MAPK), Ras, hypoxia inducible factor-1 (HIF-1), PI3K-Akt, insulin signaling and so on. In the end, we identified 216 ceRNA regulatory relationships related to obesity research. Our research provides a clearer direction for understanding the molecular mechanism of obesity, the screening and determination of drug targets biomarkers in the future.

[Mining of Differentially Expressed Genes in Diabetic Cardiomyopathy Based on GEO Database].

Objective To screen out the key genes leading to diabetic cardiomyopathy by analyzing the mRNA array associated with diabetic cardiomyopathy in the GEO database. Methods The online tool GEO2R of GEO was used to mine the differentially expressed genes (DEG) in the datasets GSE4745 and GSE5606.R was used to draw the volcano map of the DEG,and the Venn diagram was established online to identify the common DEG shared by the two datasets.The clusterProfile package in R was used for gene ontology annotation and Kyoto encyclopedia of genes and genomes pathway enrichment of the DEG.GSEA was used for gene set enrichment analysis,and STRING for the construction of a protein-protein interaction network.The maximal clique centrality algorithm in the plug-in Cytohubba of Cytoscape was used to determine the top 10 key genes. The expression of key genes was studied in the primary cardiomyocytes of rats and compared between the normal control group and high glucose group. Results The expression of Pdk4,Ucp3,Hmgcs2,Asl6,and Slc2a4 was consistent with the array analysis results.The expression of Pdk4,Ucp3,and Hmgcs2 was up-regulated while that of Acsl6 and Slc2a4 was down-regulated in the cardiomyocytes stimulated by high glucose (25 mmol/L) for 72 h. Conclusion Pdk4,Ucp3,Hmgcs2,Asl6,and Slc2a4 may be associated with the occurrence and development of diabetic cardiomyopathy,and may serve as the potential biomarkers of diabetic cardiomyopathy.

Integrated bioinformatics-based identification of potential diagnostic biomarkers associated with atopic dermatitis.

Introduction: In-depth analysis of the rambling genes of atopic dermatitis may help to identify the pathologic mechanism of this disease. However, this has seldom been performed. Aim: Using bioinformatics approaches, we analysed 3 gene expression profiles in the gene expression omnibus (GEO) database, identified the differentially expressed genes (DEGs), and found out the overlapping DEGs (common DEGs, cDEGs) in the above 3 profiles. Material and methods: We identified 91 upregulated cDEGs, which were then arranged into a protein-protein interaction (PPI) network, and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) term enrichment analyses were performed to explore the functional roles of these genes. Results: GO analyses revealed these DEGs to be significantly enriched in biological processes including immune system process, immune response, defence response, leukocyte activation, and response to the biotic stimulus. These DEGs were also enriched in the KEGG pathway, including influenza A, amoebiasis, primary immunodeficiency, cytokine-cytokine receptor interaction, and IL-17 signalling pathway. PPI analysis showed that 9 genes (PTPRC-CTLA4-CD274-CD1C-IL7R-GZMB-CCL5-CD83, and CCL22) were probably the novel hub genes of atopic dermatitis. Conclusions: Together, the findings of these bioinformatics analyses thus identified key hub genes associated with AD development.

Identification of key genes for HNSCC from public databases using bioinformatics analysis.

BACKGROUND: The cause and underlying molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) are unclear. Our study aims to identify the key genes associated with HNSCC and reveal potential biomarkers. METHODS: In this study, the expression profile dataset GSE83519 of the Gene Expression Omnibus database and the RNA sequencing dataset of HNSCC of The Cancer Genome Atlas were included for analysis. Sixteen differentially expressed genes were screened from these two datasets using R software. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) was then adopted for survival analysis, and finally, three key genes related to the overall survival of HNSCC patients were identified. Furthermore, we verified these three genes using the Oncomine database and from real-time PCR and immunohistochemistry results from HNSCC tissues. RESULTS: The expression data of 44 samples from GSE83519 and 545 samples from TCGA-HNSC were collected. Using bioinformatics, the two databases were integrated, and 16 DEGs were screened out. Gene Ontology (GO) enrichment analysis showed that the biological functions of DEGs focused primarily on the apical plasma membrane and regulation of anoikis. Kyoto Encyclopedia of Genes and Genomes (KEGG) signalling pathway analysis showed that these DEGs were mainly involved in drug metabolism-cytochrome P450 and serotonergic synapses. Survival analysis identified three key genes, CEACAM5, CEACAM6 and CLCA4, that were closely related to HNSCC prognosis. The Oncomine database, qRT-PCR and IHC verified that all 3 key genes were downregulated in most HNSCC tissues compared to adjacent normal tissues. CONCLUSIONS: This study indicates that integrated bioinformatics analyses play an important role in screening for differentially expressed genes and pathways in HNSCC, helping us better understand the biomarkers and molecular mechanism of HNSCC.

IMUP and GPRC5A: two newly identified risk score indicators in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic cancer has been a threateningly lethal malignant tumor worldwide. Despite the promising survival improvement in other cancer types attributing to the fast development of molecular precise medicine, the current treatment situation of pancreatic cancer is still woefully challenging since its limited response to neither traditional radiotherapy and chemotherapy nor emerging immunotherapy. The study is to explore potential responsible genes during the development of pancreatic cancer, thus identifying promising gene indicators and probable drug targets. METHODS: Different bioinformatic analysis were used to interpret the genetic events in pancreatic cancer development. Firstly, based on multiple cDNA microarray profiles from Gene Expression Omnibus (GEO) database, the genes with differently mRNA expression in cancer comparing to normal pancreatic tissues were identified, followed by being grouped based on the difference level. Then, GO and KEGG were performed to separately interpret the multiple groups of genes, and further Kaplan-Meier survival and Cox Regression analysis assisted us to scale down the candidate genes and select the potential key genes. Further, the basic physicochemical properties, the association with immune cells infiltration, mutation or other types variations besides expression gap in pancreatic cancer comparing to normal tissues of the selected key genes were analyzed. Moreover, the aberrant changed expression of key genes was validated by immunohistochemistry (IHC) experiment using local hospital tissue microarray samples and the clinical significance was explored based on TCGA clinical data. RESULTS: Firstly, a total of 22,491 genes were identified to express differently in cancer comparing to normal pancreatic tissues based on 5 cDNA expression profiles, and the difference of 487/22491 genes was over eightfold, and 55/487 genes were shared in multi profiles. Moreover, after genes interpretation which showed the > eightfold genes were mainly related to extracellular matrix structural constituent regulation, Kaplan-Meier survival and Cox-regression analysis were performed continually, and the result indicated that of the 55 extracellular locating genes, GPRC5A and IMUP were the only two independent prognostic indicators of pancreatic cancer. Further, detailed information of IMUP and GPRC5A were analyzed including their physicochemical properties, their expression and variation ratio and their association with immune cells infiltration in cancer, as well as the probable signaling pathways of genes regulation on pancreatic cancer development. Lastly, local IHC experiment performed on PAAD tissue array which was produced with 62 local hospital patients samples confirmed that GPRC5A and IMUP were abnormally up-regulated in pancreatic cancer, which directly associated with worse patients both overall (OS) and recurrence free survival (RFS). CONCLUSIONS: Using multiple bioinformatic analysis as well as local hospital samples validation, we revealed that GPRC5A and IMUP expression were abnormally up-regulated in pancreatic cancer which associated statistical significantly with patients survival, and the genes' biological features and clinical significance were also explored. However, more detailed experiments and clinical trials are obligatory to support their further potential drug-target role in clinical medical treatment.

Bioinformatic analysis identifying FGF1 gene as a new prognostic indicator in clear cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) has been the commonest renal cell carcinoma (RCC). Although the disease classification, diagnosis and targeted therapy of RCC has been increasingly evolving attributing to the rapid development of current molecular pathology, the current clinical treatment situation is still challenging considering the comprehensive and progressively developing nature of malignant cancer. The study is to identify more potential responsible genes during the development of ccRCC using bioinformatic analysis, thus aiding more precise interpretation of the disease METHODS: Firstly, different cDNA expression profiles from Gene Expression Omnibus (GEO) online database were used to screen the abnormal differently expressed genes (DEGs) between ccRCC and normal renal tissues. Then, based on the protein-protein interaction network (PPI) of all DEGs, the module analysis was performed to scale down the potential genes, and further survival analysis assisted our proceeding to the next step for selecting a credible key gene. Thirdly, immunohistochemistry (IHC) and quantitative real-time PCR (QPCR) were conducted to validate the expression change of the key gene in ccRCC comparing to normal tissues, meanwhile the prognostic value was verified using TCGA clinical data. Lastly, the potential biological function of the gene and signaling mechanism of gene regulating ccRCC development was preliminary explored. RESULTS: Four cDNA expression profiles were picked from GEO database based on the number of containing sample cases, and a total of 192 DEGs, including 39 up-regulated and 153 down-regulated genes were shared in four profiles. Based on the DEGs PPI network, four function modules were identified highlighting a FGF1 gene involving PI3K-AKT signaling pathway which was shared in 3/4 modules. Further, both the IHC performed with ccRCC tissue microarray which contained 104 local samples and QPCR conducted using 30 different samples confirmed that FGF1 was aberrant lost in ccRCC. And Kaplan-Meier overall survival analysis revealed that FGF1 gene loss was related to worse ccRCC patients survival. Lastly, the pathological clinical features of FGF1 gene and the probable biological functions and signaling pathways it involved were analyzed using TCGA clinical data. CONCLUSIONS: Using bioinformatic analysis, we revealed that FGF1 expression was aberrant lost in ccRCC which statistical significantly correlated with patients overall survival, and the gene's clinical features and potential biological functions were also explored. However, more detailed experiments and clinical trials are needed to support its potential drug-target role in clinical medical use.

Glucocorticoid resistance induced by ANXA5 overexpression in B-cell acute lymphoblastic leukemia.

Development of chemo­resistance is ultimately responsible for treatment failure and relapse in B-cell acute lymphoblastic leukemia (B-ALL). However, the mechanism underlying glucocorticoid (GC) resistance remains unclear. This study was performed to identify GC resistance-related genes using the transcriptome chip from the GEO database, and preliminarily analyze drug resistance mechanism in B-ALL. Here, we found that ANXA5 expression was upregulated in B-ALL cells and high-level ANXA5 was associated with dexamethasone (DEX) resistance. Then, small interfering RNA (siRNA) was designed to silence ANXA5 expression in the B-ALL cell lines, and the apoptotic rate of cells treated with DEX was detected by flow cytometry. As a result, cell apoptosis was dramatically promoted in B-ALL cells following silencing of ANXA5 and DEX administration versus that in ANXA5-silenced alone or DEX-treated alone cells. It was further found that down-regulation of ANXA5 in B-ALL cells significantly increased the relative amount of cleaved Caspase 3 and Caspase 9 induced by DEX. Collectively, inhibition of ANXA5 gene expression may represent a novel method to restore the sensitivity of treatment-resistant B-ALL tumors to GC-induced cell death, which is of important clinical significance to overcome drug resistance associated with B-ALL.

Development and validation of a novel epigenetic signature for predicting prognosis in colon cancer.

Epigenetic factors play a critical role in carcinogenesis by imparting a distinct feature to the chromatin architecture. The present study aimed to develop a novel epigenetic signature for evaluating the relapse-free survival of colon cancer patients. Public microarray datasets were acquired from the Gene Expression Omnibus databases: GSE39582, GSE17538, GSE33113, and GSE37892 set. Patients from GSE39582 set were randomized 1:1 into training and internal validation series. Patients were divided into high-risk and low-risk groups in training series based on a set of 11 epigenetic factors (p < .001). The good reproducibility for the prognostic value of the epigenetic signature was confirmed in the internal validation series (p < .001), external validation series (a combination of GSE17538 set, GSE33113 set, and GSE37892 set; p = .018), and entire series (p < .001). Furthermore, a nomogram, which integrated the epigenetic signature, pathological stage, and postoperative chemotherapy, was developed based on the GSE39582 set. The time-dependent receiver operating characteristic curve at 1 year demonstrated that the comprehensive signature presented superior prognostic value than the pathological stage. In conclusion, an epigenetic signature, which could be utilized to divide colon cancer patients into two groups with significantly different risk of relapse, was established. This biomarker would aid in identifying patients who require an intensive follow-up and aggressive therapeutic intervention.

Discovering novel hub genes and pathways associated with the pathogenesis of psoriasis.

In-depth analysis on the rambling genes of psoriasis may help to identify the pathologic mechanism of this disease. However, this has seldom been performed. Using bioinformatic approaches, we analyzed four gene expression profiles in gene expression omnibus (GEO) database, identified the differentially expressed genes (DEGs), and found out the overlapping DEGs (common DEGs, CDEGs) in the above four profiles. The CDEGs were further subjected to Gene Ontology (GO) enrichment analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network analysis, and hub genes were ranked. We identified 139 CDEGs associated with a variety of GO processes including keratinization, immune and inflammatory responses, and type 1 interferon signaling pathway. These CDEGs were enriched in a variety of KEGG processes, including cytokine-cytokine receptor interaction and chemokine signaling. PPI analysis showed that seven genes (HERC6, ISG15, MX1, RSAD2, OAS2, OASL, and OAS3) were likely the novel hub genes of psoriasis. RT-qPCR identified that five (ISG15, MX1, OAS2, OASL, and OAS3) of the seven predicted hub genes were overexpressed in TNF-α stimulated HaCaT cell lines, a result quite consistent with the predictions. The study provides new information in exploring the mechanisms and therapeutic targets of psoriasis.