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Objective To establish a prognostic risk scoring model for patients with pancreatic ductal adenocarcinoma (PDAC) based on cell ferroptosis-related genes, and to investigate its application value. Methods The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus database, and Genotype-Tissue Expression database were used to obtain the transcriptome sequencing data of PDAC tissue and normal pancreatic tissues and the clinical data of PDAC patients including overall survival (OS), and the GeneCards database was used to obtain the data on cell ferroptosis-related genes. R software was used to identify the differentially expressed genes (DEGs) between PDAC tissue and normal pancreatic tissue. The univariate Cox regression analysis and LASSO regression were used to establish a prognostic risk scoring model for PDAC patients, and based on this risk scoring model, the PDAC cases in TCGA database were divided into low- and high-risk groups. The Kaplan-Meier survival curves were plotted to compare OS between the two groups. CCK-8 assay and Western blotting were used to further validate the association between the key genes of cell ferroptosis in the risk scoring model and cell ferroptosis in PDAC. Results A prognostic risk scoring model for PDAC patients was successfully established based on seven cell ferroptosis-related genes, i.e., SLC6A14, DKK1, KRT19, AURKA, EMP1, ANXA2, and LGALS3, and the Kaplan-Meier survival curve showed that the low-risk group had a significantly longer OS than the high-risk group (P

Objective To identify the lipid metabolism-related metastasis risk genes for hepatocellular carcinoma (HCC) based on related databases, and to construct a predictive model for the prognosis of HCC patients in combination with other clinical risk factors. Methods R software was used to obtain the differentially expressed genes (DEGs) between the patients with primary HCC and those with metastatic HCC from the GEO database, and the DEGs associated with the prognosis of patients were identified. The HCC patients in TCGA database were divided into two groups based on hierarchical clustering, and the two groups were assessed in terms of epithelial-mesenchymal transition (EMT), lipid metabolism, and prognosis. The data in the ICGC database were used for validation of the above analysis. The LASSO regression model was used to obtain the lipid metabolism-related metastasis risk genes and determine their risk scores, and according to the median of risk scores, HCC patients in both TCGA and ICGC databases were divided into high and low risk groups to analyze the prognosis of patients. Univariate and multivariate Cox regression analyses were used to obtain independent risk factors for the prognosis of HCC patients, and a nomogram prognostic model was constructed. Western blot and oil red O staining were used to detect the lipid metabolism of Huh7 cells after treatment with the lipid metabolism inhibitor Fatostatin, and qPCR was used to measure the expression levels of lipid metabolism-related metastasis risk genes in Huh7 cells. Results A total of 159 DEGs were obtained from the patients with primary HCC and those with metastatic HCC in the GEO database, among which 65 DEGs were significantly associated with the overall survival (OS) of HCC patients. Based on the EMT score, the two groups of HCC patients obtained by clustering from the TCGA database were defined as high and low metastasis risk groups, respectively, and the patients in the high metastasis risk group tended to have a higher lipid metabolism score and a shorter OS. The validation results in the ICGC database were consistent with the results based on the TCGA database. The LASSO regression model was used to identify the lipid metabolism-related metastasis risk genes, and the high-risk group had a shorter OS. The lipid metabolism-related metastasis risk genes were combined with the independent risk factors for the prognosis of patients with HCC to construct a nomogram prognostic model. Cell experiments confirmed that after the treatment with Fatostatin, there were reductions in the expression of fatty acid synthase and the content of lipid droplets in Huh7 cells, as well as changes in the expression of a variety of lipid metabolism-related metastasis risk genes. Conclusion A total of 13 lipid metabolism-related metastasis risk genes are obtained based on related databases, which are combined with the clinical risk factors to construct a prognostic predictive model for HCC patients, and cell experiments are conducted to confirm that the lipid metabolism-related metastasis risk genes are closely associated with lipid metabolism.

Objective To investigate the expression of the growth factor receptor-bound protein-2 (GRB2) gene in renal carcinoma and its influence on prognosis. Methods The ONCOMINE database was used to obtain the data on the expression of the GRB2 gene in renal carcinoma, and the TCGA database was used to analyze the difference in the expression of the GRB2 gene between renal carcinoma tissue and normal tissue, as well as the association between the expression of the GRB2 gene and the prognosis of patients. The STRING database was used to construct the protein-protein interaction network of the GRB2 gene. Quantitative real-time PCR was used to measure the relative expression level of the GRB2 gene in six renal carcinoma tissue samples and corresponding adjacent normal tissue samples. Results The expression of the GRB2 gene in renal carcinoma tissue was significantly higher than that in normal renal tissue (t=8.21,P

Objectives: This study aims to investigate possible hub genes, associated pathways, and transcription factors between chronic periodontitis (CP) and Parkinson's disease (PD)., Methods: Gene expression profiles of CP (GSE16134, GSE23586, and GSE10334) and PD (GSE20141 and GSE49036) were downloaded from the gene expression omnibus (GEO) database for differential expression analysis and functional clustering analysis. The protein-protein interaction (PPI) network was constructed, and hub genes were screened by four topological analysis algorithms and modular segmentation. Functional clustering analysis was performed. The hub genes were validated by external datasets of CP and PD, and causal relation was further assessed by Mendelian randomization (MR)., Results: After merging the data, 1 211 differentially expressed genes (DEGs) were screened in the CP datasets; of which, 551 were upregulated and 660 were downregulated. A total of 2 407 DEGs were screened in the PD dataset, of which, 1 438 were upregulated and 969 were downregulated. The PPI network included 145 nodes and 126 edges. Four hub genes (FCGR3B, PRF1, IL18, and CD33) and three transcription factors (HSF1, HSF2, and HSF4) were finally screened. The relevant pathway was predominantly natural killer (NK) cell-mediated toxic effects. The MR results suggest a possible positive causal relationship between CP and the risk of developing PD., Conclusions: This study indicated the probably shared pathophysiology and possible causal relationship between CP and PD and may offer novel concepts and therapeutic targets for future mechanistic investigations.

Objective: Exploring gene-age interactions associated with breast cancer prognosis based on epigenomic data. Methods: Differential expression analysis of DNA methylation was conducted using multiple independent epigenomic datasets of breast cancer from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The false discovery rate (FDR) method was used for multiple corrections, retaining differentially methylated sites with q -FDR≤0.05. A three-stage analytic strategy was implemented, using a multivariable Cox proportional hazards regression model to examine gene-age interactions. In the discovery phase, signals with q -FDR ≤ 0.05 were screened out using TCGA-BRCA database. In validation phaseⅠ, the interaction was validated using GSE72245 data, with criteria of P ≤0.05 and consistent effect direction. In validation phaseⅡ, the signals were further validated using GSE37754 and GSE75067 data. A prognostic prediction model was constructed by incorporating clinical indicators and interaction signals. Results: The three-stage analytic strategy identified a methylation site (cg16126280 EBF1 ), which interacted with age to jointly affect the overall survival time of patients (interaction HR = 1.001 1,95% CI :1.000 7-1.001 5, P <0.001). Stratified analysis by age showed that the effect of hypermethylation of cg16126280 EBF1 was completely opposite in younger patients ( HR =0.550 5, 95% CI : 0.383 8-0.789 6, P =0.001) and older patients ( HR =2.166 5, 95% CI : 1.285 2-3.652 2, P =0.004). Conclusions: The DNA methylation site cg16126280 EBF1 exhibits an interaction with age, jointly influencing the prognosis of breast cancer in a complex association pattern. This finding contributes new population-based evidence for the development of age-specific targeted drugs.

Objective: To identify the biomarkers for early rheumatoid arthritis (RA) diagnosis and explore the possible immune regulatory mechanisms., Methods: The differentially expressed genesin RA were screened and functionally annotated using the limma, RRA, batch correction, and clusterProfiler. The protein-protein interaction network was retrieved from the STRING database, and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms. ROC curves was used to validate the accuracy of diagnostic models based on the key genes. The disease-specific immune cells were selected via machine learning, and their correlation with the key genes were analyzed using Corrplot package. Biological functions of the key genes were explored using GSEA method. The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis (CIA)., Results: We identified 9 core key genes in RA (CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL), which regulate synovial inflammation primarily through cytokines-related pathways. ROC curve analysis showed a high predictive accuracy of the 9 core genes, among which STAT1 had the highest AUC (0.909). Correlation analysis revealed strong correlations of CD3G, ITGAL, LCK, CD8A, and STAT1 with disease-specific immune cells, and STAT1 showed the strongest correlation with M1-type macrophages ( R =0.68, P =2.9e-08). The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts. The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment., Conclusion: CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL may serve as biomarkers for early diagnosis of RA. Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells, ITGAL-Tfh cells, and STAT1-M1-type macrophages may be closely related with the development of RA.

Objectives: Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis (WGCNA) and machine learning., Methods: This study downloaded the gene chip data from the Gene Expression Omnibus (GEO) database, and used WGCNA to screen out important gene modules related to Parkinson's disease. Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the the functions of immune-related genes and signaling pathways involved. Immune cell infiltration analysis was performed using the CIBERSORT package of R language. Using bioinformatics method and 3 machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM)], the immune-related genes of Parkinson's disease were further screened. A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes (hub) gene. The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn., Results: A total of 218 immune genes related to Parkinson's disease were identified, including 45 upregulated genes and 50 downregulated genes. Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways. The results of immune infiltration analysis showed that the infiltration percentages of CD4 + T cells, NK cells, CD8 + T cells, and B cells were higher in the samples of Parkinson's disease patients, while resting NK cells and resting CD4 + T cells were significantly infiltrated in the samples of Parkinson's disease patients. ANK1 was screened out as the hub gene. The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction, iron homeostasis regulation, and immune system activation., Conclusions: This study identifies the Parkinson's disease immune-related key gene ANK1 via WGCNA and machine learning methods, suggesting its potential as a candidate therapeutic target for Parkinson's disease.

The present study aimed to explore the main active components and underlying mechanisms of Marsdenia tenacissima in the treatment of ovarian cancer(OC) through network pharmacology, molecular docking, and in vitro cell experiments. The active components of M. tenacissima were obtained from the literature search, and their potential targets were obtained from SwissTargetPrediction. The OC-related targets were retrieved from Therapeutic Target Database(TTD), Online Mendelian Inheritance in Man(OMIM), GeneCards, and PharmGKB. The common targets of the drug and the disease were screened out by Venn diagram. Cytoscape was used to construct an &quot;active component-target-disease&quot; network, and the core components were screened out according to the node degree. The protein-protein interaction(PPI) network of the common targets was constructed by STRING and Cytoscape, and the core targets were screened out according to the node degree. GO and KEGG enrichment analyses of potential therapeutic targets were carried out with DAVID database. Molecular docking was used to determine the binding activity of some active components to key targets by AutoDock. Finally, the anti-OC activity of M. tenacissima extract was verified based on SKOV3 cells in vitro. The PI3K/AKT signaling pathway was selected for in vitro experimental verification according to the results of GO function and KEGG pathway analyses. Network pharmacology results showed that 39 active components, such as kaempferol, 11α-O-benzoyl-12β-O-acetyltenacigenin B, and drevogenin Q, were screened out, involving 25 core targets such as AKT1, VEGFA, and EGFR, and the PI3K-AKT signaling pathway was the main pathway of target protein enrichment. The results of molecular docking also showed that the top ten core components showed good binding affinity to the top ten core targets. The results of in vitro experiments showed that M. tenacissima extract could significantly inhibit the proliferation of OC cells, induce apoptosis of OC cells through the mitochondrial pathway, and down-regulate the expression of proteins related to the PI3K/AKT signaling pathway. This study shows that M. tenacissima has the characteristics of multi-component, multi-target, and multi-pathway synergistic effect in the treatment of OC, which provides a theoretical basis for in-depth research on the material basis, mechanism, and clinical application.

Metabolic reprogramming is a common phenomenon in cancer, with aerobic glycolysis being one of its important characteristics. Hypoxia-inducible factor-1α ( HIF1Α ) is thought to play an important role in aerobic glycolysis. Meanwhile, naringin is a natural flavanone glycoside derived from grapefruits and many other citrus fruits. In this work, we identified glycolytic genes related to HIF1Α by analyzing the colon cancer database. The analysis of extracellular acidification rate and cell function verified the regulatory effects of HIF1Α overexpression on glycolysis, and the proliferation and migration of colon cancer cells. Moreover, naringin was used as an inhibitor of colon cancer cells to illustrate its effect on HIF1Α function. The results showed that the HIF1Α and enolase 2 ( ENO2 ) levels in colon cancer tissues were highly correlated, and their high expression indicated a poor prognosis for colon cancer patients. Mechanistically, HIF1Α directly binds to the DNA promoter region and upregulates the transcription of ENO2 ; ectopic expression of ENO2 increased aerobic glycolysis in colon cancer cells. Most importantly, we found that the appropriate concentration of naringin inhibited the transcriptional activity of HIF1Α , which in turn decreased aerobic glycolysis in colon cancer cells. Generally, naringin reduces glycolysis in colon cancer cells by reducing the transcriptional activity of HIF1Α and the proliferation and invasion of colon cancer cells. This study helps to elucidate the relationship between colon cancer progression and glucose metabolism, and demonstrates the efficacy of naringin in the treatment of colon cancer.

This paper aimed to explore the antidepressant effect of the essential oil from Schizonepeta tenuifolia Briq.(EOST) on the treatment of depression and its mechanism by using a combination of network pharmacology and the mouse model of lipopolysaccharide(LPS)-induced depression. The chemical components in EOST were identified using gas chromatography-mass spectrometer(GC-MS), and 12 active components were selected as the study objects. The targets related to EOST were obtained by Traditional Chinese Medicines Systems Pharmacology(TCMSP) and SwissTargetPrediction database. The targets related to depression were screened out through GeneCards, Therapeutic Target Database(TTD), and Online Mendelian Inheritance in Man(OMIM) database. The Venny 2.1 was applied to screen out the common targets of EOST and depression. The targets were imported into Cytoscape 3.7.2 to generate &quot;drug-active component-diease-target&quot; network diagram. The protein-protein interaction(PPI) network was constructed using STRING 11.5 database and Cytoscape 3.7.2, and the core targets were screened out. DAVID 6.8 database was used for Gene Ontology(GO) func-tional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis, and subsequently the enrichment results were visualized through the bioinformatics platform. The mouse model of depression was induced by intraperitoneally injecting with LPS in mice. Before modeling, mice were administrated orally with EOST. The antidepressant effect of EOST was evalua-ted by tail suspension test(TST), forced swimming test(FST), and novelty suppressed feeding test(NSFT) after modeling. The content of interleukin(IL)-1β was determined by enzyme-linked immunosorbent assay(ELISA), and the protein expression levels of IL-1β and pro IL-1β in the hippocampus were determined by Western blot. There were 12 main components and 179 targets in EOAT, of which, 116 targets were related to depression, mainly involved in neuroactive ligand-receptor interaction, calcium signaling pathway, and cyclic adenosine monophosphate(cAMP) signaling pathway. Biological processes such as synaptic signal transduction, G-protein coupled receptor signaling pathway, and chemical synaptic transmission were involved. Molecular functions such as neurotransmitter receptor activity, RNA polymerase Ⅱ transcription factor activity, and heme binding were involved. In mice experiments, the results showed that EOST at 100 mg·kg~(-1) and 50 mg·kg~(-1) significantly shortened the immobility time in TST and FST as well as the feeding latency in NSFT compared with the model group, decreased the levels of serum IL-1β and NO, and reduced the protein expression levels of IL-1β and pro IL-1β in the hippocampus. In conclusion, EOST shows a good antidepressant effect in a multi-component, multi-target, and multi-pathway manner. The mechanism may be attributed to the fact that EOST can down-regulate the protein expression levels of IL-1β and pro IL-1β, decrease the release of inflammatory factors, and reduce neuroinflammation response.

The present study aimed to explore the main active components and potential mechanisms of Panax notoginseng saponins(PNS) and osteopractic total flavone(OTF) in the treatment of osteoporosis(OP) through network pharmacology, molecular docking and in vitro cell experiments, which was expected to provide a theoretical basis for clinical applications. The blood-entering components of PNS and OTF were obtained from literature search and online database, and their potential targets were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP) and SwissTargetPrediction. The OP targets were obtained by means of searching Online Mendelian Inheritance in Man(OMIM) and GeneCards. The common targets of the drug and disease were screened by Venn. Cytoscape was used to construct a &quot;drug-component-target-disease&quot; network, and the core components were screened according to the node degree. The protein-protein interaction(PPI) network of the common targets was constructed by STRING and Cytoscape, and the core targets were screened according to the node degree. GO and KEGG enrichment analysis of potential therapeutic targets were carried out by R language. Molecular docking was used to determine the binding activity of some active components to key targets by AutoDock Vina. Finally, HIF-1 signaling pathway was selected for in vitro experimental verification according to the results of KEGG pathway analysis. Network pharmacology showed that there were 45 active components such as leachianone A, kurarinone, 20(R)-protopanaxatriol, 20(S)-protopanaxatriol, and kaempferol, and 103 therapeutic targets such as IL6, AKT1, TNF, VEGFA and MAPK3 involved. PI3K-AKT, HIF-1, TNF and other signaling pathways were enriched. Molecular docking revealed that the core components had good binding ability to the core targets. In vitro experiments found that PNS-OTF could up-regulate the mRNA expression levels of HIF-1α, VEGFA and Runx2, indicating that the mechanism of PNS-OTF in treating OP may be related to the activation of HIF-1 signaling pathway, and thus PNS-OTF played a role in promoting angiogenesis and osteogenic differentiation. In conclusion, this study predicted the core targets and pathways of PNS-OTF in treating OP based on network pharmacology and carried out in vitro experimental verification, which reflected the characteristics of multi-component, multi-target and multi-pathway synergy of PNS-OTF, and provided new ideas for the future clinical treatment of OP.

As one of the most commonly diagnosed cancers in the world, female breast cancer is induced by the high level of estrogen. Saussureae Involucratae Herba(SIH), a gynecological medicinal, regulates estrogen-induced diseases. However, the therapeutic effect of SIH on breast cancer has not been reported. Therefore, this study aims to explore the potential efficacy of SIH on breast cancer based on in vitro experiment and network pharmacology. The inhibitory effect of SIH water extract on proliferation and migration of breast cancer MDA-MB-231 cells was examined. The result demonstrated SIH water extract significantly suppressed the proliferation of breast cancer cells(IC&#95;(50)=6.47 mg·mL~(-1)) and also restricted the migration. A total of 39 components of SIH were retrieved from traditional Chinese medicine database(TCMD) and 160 targets of SIH were screened by target fishing with the PharmaDB database. The Online Mendelian Inheritance in Man(OMIM) was used to establish a 1 001-targets data set of breast cancer. Based on the overlaps(45) of targets between SIH and breast cancer, a protein-protein interaction(PPI) network was built to analyze the interactions among these targets with STRING platform and Cytoscape. Finally, through topology and GO and KEGG analysis, 8 targets, 101 pathways and 85 biological processes were found to involve the treatment of breast cancer by SIH. SIH may exert the anti-breast cancer effect by regulating cell cycle, inhibiting proliferation, migration and adhesion of cancer cells, and modulating estrogen receptor. This study clarified the mechanism of SIH in treating breast cancer, which lays a foundation for the further development of SIH.

Background: Lung cancer still has the highest incidence rate and mortality rate nowadays. In recent years, with the emergence of new drugs and the optimization of treatment mode, especially the clinical application of immunotherapy, the prognosis of lung cancer patients has been improved. However, the benefits of immunotherapy are still limited. Therefore, it is necessary to find new biomarkers to predict the prognosis of lung adenocarcinoma patients and explore its impact on the immune microenvironment., Methods: The Cancer Genome Atlas (TCGA) database was used to analyze the gene sequencing and clinical data of patients with lung adenocarcinoma. The distribution of RASGRP2 in lung adenocarcinoma was determined by using the human protein mapping database. The Kaplan-Meier plotter database was used to explore the relationship between the expression of RASGRP2 and the prognosis of patients with lung adenocarcinoma. KEGG and GO gene enrichment analysis was performed in patients with high and low expression of RASGRP2. TCGA database was used to analyze the co-expression genes of RASGRP2 and TIMER database was used to calculate the immune related lymphoid infiltration of RASGRP2 and its coexpression genes. The relationship between RASGRP2 expression and immune checkpoint expression was analyzed by using TIMER 2.0 database., Results: We found that RASGRP2 was low expressed in lung adenocarcinoma, and its expression level was related to the prognosis of patients. The high expression of RASGRP2 was involved in the process of hematopoietic cell formation and cell adhesion, and RASGRP2 played an important role in the process of T cell activation. Through TCGA database analysis, ZAP70, TBC1D10C, RASAL3, FGD2, CD37 and ACAP1 were significantly correlated with RASGRP2. The high expression of these genes leaded to the increase of the proportion of CD8+ T cells, memory CD4+ T cells, and the decrease of the proportion of neutrophils and Treg cells. Finally, we found that the expression of RASGRP2 was significantly correlated with the expression of CD274, CTLA4, LAG3 and TIGIT., Conclusions: RASGRP2 was abnormally expressed in lung adenocarcinoma and correlated with the infiltration level of immune related cells, which might influence the efficacy of immunotherapy.

The acupoint application of Euodiae Fructus at Yongquan(KI1) can significantly improve the sleep quality of patients with insomnia with berberine as the main effective component for the efficacy. Nineteen active compounds and 203 drug targets were screened out from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). After comparison with GeneCards and Online Mendelian Inheritance in Man(OMIM), 24 common genes of diseases and drugs were obtained. STRING 11.0 was used to construct a protein-protein interaction(PPI) network of the overlapping genes, and Matthews correlation coefficient(MCC) was employed to screen the core genes, which were then subjected to enrichment analysis with gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG). The results revealed that the main compounds of Euodiae Fructus, such as berberine and rutaecarpine, participated in the biological processes(such as neurotransmitter receptor activity) by regulating C-reactive protein(CRP), estrogen receptor 1(ESR1), 5-hydroxytryptamine(5-HT) receptor, and interleukin-6(IL-6) to exert sedative, anxiolytic, and antidepressant effects. Sixty 4-week-old SPF mice were randomly divided into a control group, a model group, a positive drug(diazepam tablets) group, and low-, medium-, and high-dose berberine groups. Medication with corresponding drugs was performed for one week. The results demonstrated that berberine was potent in reducing the activities and standing times of mice, down-regulating the levels of CRP and IL-6 mRNA in the hypothalamus, and up-regulating the expression of 5-HT(P&lt;0.01); however, no significant effect on ESR1 was observed. The network of Euodiae Fructus in treating insomnia was constructed by network pharmacology and verified by tests. The findings indicated that the therapeutic efficacy of Euodiae Fructus in treating insomnia was achieved by participating in multiple biological processes, such as neurotransmitter receptor activity, which provided a scientific basis for its clinical application.

Background: Lung cancer is a malignant with high incidence and mortality and adenocarcinoma is among the most popular subtypes. Epidermal growth factor receptor (EGFR) mutation is one of the most important driver mutations for lung adenocarcinoma and EGFR-tyrosine kinase inhibitor (TKI) will benefit those patients with sensitive EGFR mutations. Recently, immune checkpoint inhibitor (ICI) therapy, provide a new breakthrough treatment for lung cancer patients. Whereas immunotherapy as an emerging treatment does not benefit patients with EGFR mutations, for which mechanistic studies are poorly defined and focused on the link of EGFR mutations and programmed cell death-ligand 1 (PD-L1) expression, we speculate that the different immune microenvironment associated with the two classes of patients., Methods: Lung adenocarcinoma datasets were collected from the Cancer Genome Atlas (TCGA) database, and clinical information and gene expression profiles were downloaded. The immune related lymphocyte infiltration in TCGA database were generated through timer 2.0 GSEA was used to analyze the difference of pathway expression between EGFR mutant patients and wild type patients., Results: EGFR mutation was more frequently among women and never smokers. Immunoinfiltration analysis showed that patients with EGFR mutation tends to have more tumor associated fibroblasts, common myeloid progenitor cells, hematopoietic stem cells, effector CD4⁺ T cells and natural killer T cells infiltration, and less memory B cells, naïve B cells, plasma B cells, plasmacytoid dendritic cells, memory CD4⁺ T cells, CD4⁺ helper T cells 2, naive CD8⁺ T cells, CD8⁺ T cells and central memory CD8⁺ T cells infiltration. Moreover, patients with more infiltration of CD8⁺ T cells, natural killer T cells, memory B cells and hematopoietic stem cells, tends have better prognosis (Log-rank test, P=0.017, 0.0093, 0.018, 0.016). However, the patients with more CD4⁺ T th2 infiltration in the tumor tends to have worse prognosis (Log-rank test, P=0.016). Furthermore, the results of gene set enrichment analysis showed that compared with the lung adenocarcinoma patients with EGFR wild type, the three pathways positive regulation of natural killer (NK) cell-mediated immune response to tumor cells, NK cell activation involved in immune response, and NK cell-mediated immune response to tumor cells related to natural killer cells in patients with EGFR mutation were down regulated, while the pathway the positive regulation of cytokine secretion involved in immune response was up-regulated in EGFR mutation patients., Conclusions: The tumour microenvironment of patients with EGFR mutations lacks potent tumour killing effector cells and appears dysfunctional with effector cells. This may be a potential reason for the poor efficacy of immunotherapy in patients with EGFR mutations.

Objective: To obtain biomarkers of allergic rhinitis (AR) by performing bioinformatics analysis on gene chips related to allergic rhinitis in the Gene Expression Database (GEO). Methods: From June 2018 to December 2019, we downloaded data (GSE46171) involving 3 control individuals and 6 AR patients from the publicallyavailable Gene Expression Omnibus database (GEO,http://www.ncbi.nlm.nih.gov/geo), and differentially expressed genes (DEGs) were screened between AR and normal tissues by using the GEO2R online tool comprehensively. Then, we used the bioinformatics methods, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene, Genome (KEGG) pathway analysis and protein-protein interaction (PPI) network construction to identify key genes in AR. In the same period, the inferior turbinate mucosa tissues of 15 AR patients and 15 healthy controls were collected during operationinthe Department of Otolaryngology Head and Neck Surgery of the People 's Hospital of Wuhan Universityto further verify important genes and pathways and perform real-time quantitative PCR.SPSS9.0 statistical software was used for statistical analysis. Results: Two hundred and seventeen DEGs genes were selected, of which 112 were down-regulated genes and 105 were up-regulated genes. Among them, the five up-regulated genes with the most significant differences were KLK7, TMPRSS11A, SPRR2C, TPSAB1, and TXLNGY; the five down-regulated genes with the most significant differences were: XIST, CTAG1A, PRB1, CXCL11 and PRB2. By constructing a PPI network among 217 DEGs, the 15 hub genes obtained were IFIH1, CCR2, CD80, TLR7, EIF1AY, DDX3Y, RSAD2, RPS4Y2, RPS4Y1, XAF1, KDM5D, ZFY, NLGN4Y, IFIT5 and DDX60L, these Genes were at a hub in a gene network. We collected inferior turbinate mucosa tissue during surgery,and these 15 genes were verified, and the expressions of IFIH1, CCR2, CD80, TLR7, RSAD2, XAF1, IFIT5 and DDX60L were reduced, wherea the expressions of EIF1AY, DDX3Y, RPS4Y2, RPS4Y1, KDM5D, ZFY and NLGN4Y were increased, differences were statistically significant (all P< 0.05). Conclusions: The study finds 217 genes closely related to allergic rhinitis and obtains 15 hub genes through the PPI network. These genes may be involved in the pathogenesis of allergic rhinitis and are expected to become new biomarkers for allergic rhinitis.

The aim of this paper was to screen the active targets of Schizonepetae Herba and Saposhnikoviae Radix in the treatment of ulcerative colitis by means of network pharmacology,and to investigate their mechanism of action. The effective components of Schizonepetae Herba and Saposhnikoviae Radix were screened out by traditional Chinese medicine systematic pharmacological( TCMSP)database,with oral bioavilability( OB) ≥30% and drug-like( DL) ≥18% selected as the thresholds. Target PPI network was built between the main components and their corresponding targets. One hundred and eighty-two human genes corresponding to the medicine target sites were obtained from Uniprot database; 3 874 genes corresponding to ulcerative colitis were obtained from Genecard database.A total of 115 intersection genes were screened from disease genes and medicine genes,and the PPI interaction analysis was conducted by using String tool. Disease-target PPI network was drawn by using Cytoscape software,and component-target-disease network was constructed. One hundred and eight nodes and 1 882 connections were found,and then Cytoscape software was used to merge the networks and filter the core network for gene GO function analysis and KEGG pathway enrichment analysis. The mechanism of Schizonepetae Herba and Saposhnikoviae Radix was then verified by animal experiment. Gene GO functional analysis suggested that biological process,molecular functions and cell components were involved,and it was found that ulcerative colitis might be related to transcription factor activity,and cytokine receptor binding,etc. Gene KEGG pathway enrichment analysis showed that the mechanism of ulcerative colitis might be associated with TNF and Toll-like receptors( TLRs) signaling pathway-mediated cytoinflammatory factors interleukin-1( IL-1) and interleukin-6( IL6). The possible mechanism of the effective components of Schizonepetae Herba and Saposhnikoviae Radix in treating ulcerative colitis might be related to intervening the cytokine receptor binding of TNF and TLRs signaling pathways,reducing the transcription of nuclear factor-kappaB( NF-κB),and inhibiting the secretion of intestinal inflammatory factors IL-1 and IL-6.

Personal genomic information benefits from accumulated big data and its application is no longer limited to scientific research. Presently, it is undergoing the transformation to daily medical practice. Systematic arrangement, archiving and rational utilization of disease-related genomic information is an important foundation of future precision medicine. Hemoglobinopathy is prevalent in southern China, but its molecular pathological basis has racial specificity. To facilitate clinical diagnosis and genetic screening of hemoglobinopathy in southern China, we established the LOVD gene data management system for the variation and phenotype spectrum of hemoglobinopathy. Then we designed an integrated and efficient on-line auxiliary accurate diagnosis and risk assessment system in order to assist clinicians to make comprehensive diagnosis and genetic counseling in a short time based on cloud standardized annotated library of specific hemoglobinopathy variants and diagnostic repository. The methodology and experience of improving the clinical decision-making efficiency of diseases with big data and artificial intelligence technology can be used as an example in the clinical and preventive application of other diseases.

Genetic resources are important national strategic resources. Their preservation, protection and rational utilization form a solid foundation to guarantee national security and to build national competitiveness for the future. Due to a relatively late starting point, China is actively catching up with global peers in storing genetic samples and data. In view of this, in 2011 China approved a plan to build its first nation-level comprehensive gene bank, the China National GeneBank (CNGB), and entrusted BGI-Research to implement its construction and operation. It is China's first gene bank for "reading, writing and storing" bioresources. In this paper, we summarize the development of influential platforms at home and abroad, and focus on CNGB's position, mission, and its structure of "Three Banks and Two Platforms". CNGB launched its official operation in September 2016 and aims to develop a world-class, non-profit and strategic platform that supports science and technology development. It has built capacities to store tens of millions of traceable samples and to analyze handreds of thousanda of WGS each year. It has also set up China's first Pb-level digitalization platform and a high-efficient synthesis platform with a production rate of ten million bases per year. Based on such capacities, CNGB has established its open sharing mechanism for biological samples and data, provided public platform services for life science research, and achieved initial results in supporting innovation and development of the bio-industry.

Objective: To explore an effective long non-coding RNA (lncRNA) signature in predicting the prognosis of hepatocellular carcinoma through the analysis on RNA sequencing data of hepatocellular carcinoma patients and peritumoral tissues in the Cancer Genome Atlas (TCGA) database. Methods: The clinical characteristics and RNA sequencing data of 377 hepatocellular carcinoma patients were obtained from TCGA database by the end of February 2018. Then, differentially expressed lncRNAs between 50 pairs of tumor and peritumoral tissues were explored using student's t -test. Next, a lncRNA signature was established through LASSO Cox regression analysis. All the patients were divided into four groups (< P(25) , P(25) -, P(50) -, ≥ P(75) ) based on the cut-off quartiles signature. Finally, compared with the control group (< P(25) ), the hazard ratios ( HR s) of three groups ( P(25) -, P(50) -, ≥ P(75) ) were calculated by using Cox regression. The survival outcomes of patients in the four groups were compared to evaluate the capacity of the lncRNA signature model. Results: A total of 951 differentially expressed lncRNAs were identified between tumor and peritumoral tissues. A three-lncRNA signature, including LNCSRLR, MKLN1-AS and ZFPM2-AS1, was established to predict the prognosis of hepatocellular carcinoma patients. The outcome suggested that the death risk of the ≥ P(75) group was 1.57 times larger than that of the < P(25) group (95 %CI : 1.06-2.31, P <0.05). Conclusion: The three-lncRNA signature, which established by LNCSRLR, MKLN1-AS and ZFPM2-AS1, was significantly associated with the prognosis of hepatocellular carcinoma patients based on TCGA database data.

Objective: Deep learning models, including recurrent neural network (RNN) and gated recurrent unit (GRU), were used to construct the clinical prognostic prediction models for peritoneal dialysis (PD) patients based on routine clinical data. The performance of the RNN and GRU were compared with logistic regression (LR), which is commonly used in medical researches. The possible underlining clinical implications based on the result from the GRU model were also investigated., Methods: We used the clinical data from the PD center of Peking University Third Hospital as the data source. Both the baseline data at the beginning of dialysis, and the follow-up and prognostic data of the patients were used by the RNN and GRU prediction models. The hyper-parameters were tuned based on the 10-fold cross-validation. The risk prediction performance of each model was evaluated via area under the receiver operation characteristic curve (AUROC), recall rate and F1-score on the testset., Results: A total of 656 patients with the 261 occurrences of death were included in the experiment. The total number of all diagnostic records were 13 091. The results on the testset showed that the AUROC of the LR model, RNN model, and GRU model was 0.701 4, 0.786 0, and 0.814 7, respectively. The predictive performances of the GRU and RNN models were significantly better than that of the LR model. The performances of the GRU and RNN models assessed by recall rate and F1-score were also significantly better than that of the LR model, in which the GRU model reached the best performance. In addition, the recall rates were different among different causes of death or by different prediction time windows., Conclusion: The recurrent neural network model, especially the GRU model, is more effective in predicting PD patients' prognosis as compared with the LR model. This new model may be helpful for clinicians to provide timely intervention, thus improving the quality of care of PD.

Background: Lung cancer is a malignant tumor disease with high morbidity and high mortality. The non-small cell lung cancer (NSCLC) is the most common type, among them, lung squamous cell carcinoma own special pathological type and specific treatment, is a subtype of non-small cell lung cancer and can be divided into peripheral type and central type according to clinical phenotype. This study explores the differences in gene levels and their potential values based on clinical differences between central and peripheral in lung squamous cell carcinoma., Methods: The lung squamous cell carcinoma dataset was collected from The Cancer Genome Atlas (TCGA) database, clinical information and the corresponding gene expression profiles were downloaded. Then we further sort and analyze all these data., Results: In clinical characteristics analysis, result showed that central lung squamous cell carcinoma was more likely to metastasis with lymph node than peripheral lung squamous cell carcinoma (46.2%, 67/145 vs 28.9%, 26/90; P=0.019), while there were no significant differences in gender, age, tumor size, distant metastasis, tumor node metastasis (TNM) stage, and EGFR mutation. Gene expression analysis showed 1,031 differentially expressed genes between central and peripheral lung squamous cell carcinoma, of which 629 genes were up-regulated and 402 genes were down-regulated (peripheral vs central). Further enrichment analysis showed differentially expressed genes were mainly riched in 6 signaling pathways. Among them, the neuroactive ligand-receptor interaction pathway was the main enrichment pathway of differentially expressed genes, and other differential expressed genes were mainly involved in lipid metabolism and glucose metabolism. The analysis of interaction network showed that hepatocyte nuclear factor 1 homeobox A (HNF1A) and cytochrome p450 family, Cytochrome P450 3A4 (CYP3A4) own widely effect in up-regulated genes, while ALB and APOA1 at the key positions of the network in down-regulated genes were CONCLUSIONS: Central and peripheral lung squamous cell carcinoma showed clinical phenotype difference not only reflected in the incidence of lymph node metastasis, but also in gene expression profiles. Among them, HNF1A, CYP3A4, ALB, APOA1 at the key position of the differential gene interaction network and maybe as regulatory factors in the phenotypic difference.

Objective: To investigate the differentially expressed genes between gastric cancer and normal gastric mucosa by bioinformatics analysis, identify the important gene participating in the occurrence and progression of gastric cancer, and predict the functions of these genes., Methods: The gene expression microarray data GSE100935 (including 18 gastric cancer samples and normal gastric mucosal tissues) downloaded from the GEO expression profile database were analyzed using Morpheus to obtain the differentially expressed genes in gastric cancer, and a cluster analysis heat map was constructed. The online database UALCAN was used to obtain the expression levels of these differentially expressed genes in gastric cancer and normal gastric mucosa. The prognostic value of the differentially expressed genes in gastric cancer was evaluated with Kaplan-Meier survival analysis. GO functional enrichment analysis was performed using Fun-Rich software, and the STRING database was exploited to establish a PPI network for the differentially expressed genes., Results: A total of 45119 differentially expressed genes were identified from GSE100935 microarray data. Analysis with UALCAN showed an obvious high expression of EXD3 gene in gastric cancer, and survival analysis suggested that a high expression level of EXD3 was associated with a poorer prognosis of the patients with gastric cancer. GO functional enrichment analysis found that the differentially expressed genes in gastric cancer were involved mainly in the regulation of nucleotide metabolism and the activity of transcription factors in the cancer cells., Conclusions: EXD3 may be a potential oncogene in gastric cancer possibly in relation to DNA damage repair. The up-regulation of EXD3 plays an important role in the development and prognosis of gastric cancer, and may serve as an important indicator for prognostic evaluation of the patients.

The advancement and popularization of molecular diagnostic techniques has challenged and redefined the traditional concept of genetic metabolic disease. Regardless of disease origin, all genetic defects that lead to hepatobiliary dysfunction or structural abnormalities are termed as genetic liver disorders. Online Mendelian Inheritance in Man (OMIM) is a database consisting 693 genetic diseases with clear molecular mechanism of liver related phenotypes. Moreover, the effective measures to control infectious liver disease have strengthened the importance of research in the field of (adult and children) genetic liver disorders at home and abroad by well-recognized hepatologists. Notably, all patients with unexplained hepatopathy and multiple system diseases involving liver and gallbladder needs screening for genetic liver disorders, except for factors such as infection, immunity, drug-related, and anatomical abnormalities. We hope more patients with complicated liver disorders will benefit from definitive diagnosis and effective treatment in the near future with clear explanation of clinical phenotype, genotype, and metabolomics.

Omics data in life and health sciences are of fundamental significance for scientific research and biomedical technology development. However, there is yet to be a platform for biological data management and sharing in China, making it difficult to meet the development needs of biomedical and related fields and consequently leading to severe issues in big data management, sharing and translation. To address these issues, Beijing Institute of Genomics (BIG) of Chinese Academy of Sciences founded the BIG Data Center (BIGD) in 2016, which is dedicated to establish a biological big data management platform and multi-omics databases, with a particular focus on national population healthcare and important strategic biological resources. In this paper, we describe core database resources in BIGD, including GSA (Genome Sequence Archive), GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas), MethBank (Methylation Bank), BioCode and Science Wikis. Taken together, all these resources provide a series of services for data deposition, integration and sharing, laying solid foundations for enhancing national biological science data management and further promoting the construction of national bioinformatics center.

With the implementation of the international human genome project and 1000 genome project, hundreds of Chinese individual genome sequences have been published. Establishing a high-precision Chinese population reference genome and identifying the unique genome variations are fundamental for future precision medicine research in China. To further meet the needs of scientific management and deep mining on the rapidly growing Chinese genomic data, Beijing Institute of Genomics, Chinese Academy of Sciences, has developed a Virtual Chinese Genome Database (VCGDB, http://bigd.big.ac.cn/vcg/) and Genome Variation Map (GVM, http://bigd.big.ac.cn/gvm/) based on the public whole genome sequencing data, which provides the worldwide services of data retrieval, sharing, downloading and online analysis. This paper presents the brief introduction of characteristics and functions of the two databases, as well as their future development and application prospects, aiming to provide useful information for the promotion and development of the reference genome and genome variation map database in China.

Omics plays an important role in life sciences, which studies all the components and their interrelations as a whole. The omics databases collected and sorted out the relevant information to support omics research. The crop-omics database, based on the major food crop information, especially on the sequencing information and data mining, is set up to improve the basic research level of crop sciences and production, which potentially benefit our grain stock. Here, we summarize crop genomics, transcriptomics, proteomics, metabolomics and phenomics, explore the development and utilization status of crop-omics, and reveal that the comprehensive utilization of multiple omics databases has become an important research method for crop sciences in the era of high throughput information.

Objective: To explore the biological effects of amino acid transporter gene SLC7A5 (solute carrier family 7, member 5) on tumor cells and the regulatory mechanism at transcriptional level.
 Methods: The expression of SLC7A5 was examined in human normal tissues and corresponding tumor tissues by Gene Expression Omnibus (GEO) database. The recombinant plasmid of SLC7A5 gene was constructed, and the effect of the SLC7A5 gene on tumor cell proliferation was investigated by methylthiazolyldiphenyl-tetrazolium bromide (MTT) and flow cytometry. SLC7A5 gene promoter and transcription factor binding sites were predicted through bioinformatics analysis, and the gene promoter recombinant plasmid was constructed. Then the dual luciferase reporter gene assay and reverse transcription polymerase chain reaction (RT-PCR) were used to explore the regulation of transforming growth factor-β1 (TGF-β1) signal on SLC7A5 gene expression.
 Results: The GEO database analysis showed that the distribution of SLC7A5 was tissue specific, and its expression level was significantly higher in the tumor tissues than that in the corresponding normal tissues. The results of MTT and flow cytometry showed that SLC7A5 could promote cell proliferation. Results from the promoter analysis, reporter gene assay and RT-PCR confirmed that TGF-β1 could up-regulate the activity of SLC7A5 promoter and promote the expression of the SLC7A5 gene.
 Conclusion: SLC7A5 gene plays a role in promoting tumor development, which is regulated by the TGF-β1 signaling pathway.

Traditional classifiers, such as support vector machine and Bayesian classifier, require data normalization for removing experimental batch effects, which limit their applications at the individual level. In this paper,we aim to build a classifier to distinguish lung cancer and non-cancer lung tissues(pneumonia and normal lung tissues).We identified gene pairs as signatures to build a classifier based on the within-sample relative expression orderings of gene pairs in a particular type of tissues(cancer or non-cancer). Using multiple independent datasets as the training data,including a total of 197 lung cancer cases and 189 non-cancer cases, we identified three gene pairs. Classifying a sample by the majority voting rule, the average accuracy reached 95.34% in the training data. Using multiple independent validation datasets, including a total of 251 lung cancer samples and 141 non-cancer samples without data normalization, the average accuracy was as high as 96.78%. The rank-based signature is robust against experimental batch effects and can be used to diagnose lung cancer using samples measured by different laboratories at the individual level.

Gene is the base of in vivo metabolism and effectiveness for traditional Chinese medicines (TCM), and the gene expression, regulation and modification are used as the research directions to perform the TCM multi-component, multi-link and multi-target in vivo metabolism studies, which will improve the research on TCM metabolic proecess, effect target and molecular mechanism. Humans are superorganisms with 1% genes inherited from parents and 99% genes from various parts of the human body, mainly coming from the microorganisms in intestinal flora. These indicate that genetically inherited human genome and "second genome" could affect the TCM in vivo metabolism from inheritance and "environmental" aspects respectively. In the present paper, typical case study was used to discuss related TCM in vivo metabolic genomics research, mainly including TCM genomics research and gut metagenomics research, as well as the personalized medicine evoked from the individual difference of above genomics (metagenomics)., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Chinese Pharmaceutical Association.)

In recent years, the antigen recognition mechanism based on variable lymphocyte receptors (VLRs) was found in agnathan lamprey. To illuminate the genetic basis of immune response of lymphocyte-like cells in the mucosal immune system of lamprey and explore the evolutionary relationship of adaptive immune responses between the jawless and jawed vertebrates, we constructed cDNA libraries of lamprey (Lampetra japonica) gills before and after stimulation, and then performed high-throughput transcriptome sequencing and analysis. Through functional annotation of 88 525 assembled unigenes, 21 704 and 9769 unigenes were annotated in Gene Ontology (GO) and Kyto Encyclopedia of Genes and Genomes (KEGG) databases, respectively. Among 999 unigenes involved in multiple pathways of immune system, 184 unigenes were highly homologous to 51 TCR (T cell receptor) and BCR (B cell receptor) signalling molecules in higher vertebrates, indicating that molecules involved in adaptive immune signalling pathways in higher vertebrates also exist in lampreys. In addition, identification of five VLRA, seven VLRB and four VLRC molecules suggest that at least three types of lymphocyte subsets are distributed in lamprey gill mucosal immune tissues. The results of real-time fluorescence quantitative PCR showed that the expression levels of Lck, Fyn and Zap70 were up-regulated after immune stimulation while those of Syk, Btk and Blnk were not changed significantly, indicating the activation of TCR-like signal transduction pathway after antigen stimulation in lamprey gill tissues. Our studies preliminaryly proved that two parallel adaptive immune systems in jawless and jawed vertebrates have common genetic basis, and also provided valuable clues to the exploration of signalling processes of VLRA⁺, VLRB⁺, and VLRC⁺ lymphocyte-like cells in response to antigens.

Objective: By collecting and analyzing the laryngeal cancer related genes and the miRNAs, to build a comprehensive laryngeal cancer-related gene database, which differs from the current biological information database with complex and clumsy structure and focuses on the theme of gene and miRNA, and it could make the research and teaching more convenient and efficient., Methods: Based on the B/S architecture, using Apache as a Web server, MySQL as coding language of database design and PHP as coding language of web design, a comprehensive database for laryngeal cancer-related genes was established, providing with the gene tables, protein tables, miRNA tables and clinical information tables of the patients with laryngeal cancer., Results: The established database containsed 207 laryngeal cancer related genes, 243 proteins, 26 miRNAs, and their particular information such as mutations, methylations, diversified expressions, and the empirical references of laryngeal cancer relevant molecules. The database could be accessed and operated via the Internet, by which browsing and retrieval of the information were performed. The database were maintained and updated regularly., Conclusion: The database for laryngeal cancer related genes is resource-integrated and user-friendly, providing a genetic information query tool for the study of laryngeal cancer.

To wished to characterize the hemagglutinin (H) gene of the measles virus epidemic strain H1a in Liaoning Province (China) from 1997-2014 to provide a basis for the control and elimination of measles. All 63 measles virus strains were the H1a genotype. Fragments of the H gene (1854 nucleotides) and nucleoprotein (N) gene (450 nucleotides) were amplified by reverse transcription-polymerase chain reaction (RT-PCR) and the PCR products sequenced and analyzed. Phylogenetic-trees were constructed with reference strains of the genotype-H measles virus downloaded from GenBank, including Chinese measles virus strains isolated in 1993-1994 and the vaccine reference strains S-191 and C-47. Sixty-three strains of the measles virus in 1997-2014 belonged to genotype H1a. The mean evolutionary rate for gene N-450 was higher than that for the H gene. All 63 strains of the measles virus were mutated from: serine (Ser S) to asparagine (Asn N) in the 240th amino acid; arginine (Arg R) to glycine (Gly G) in the 243th; and tyrosine (Tyr Y) to Asn N in the 481th amino acid. All measles virus strains in cluster 2 were mutated from proline (Pro P) to leucine (Leu L) in the 397th amino acid. The other neutralization sites showed no apparent difference when comparing the nucleotides/amino acids of the H gene of S191 vaccine strains.

Cancer can exhibit different forms depending on the site of origin, cell types, the different forms of genetic mutations which also affect cancer therapeutic effect. Although many genes have been demonstrated to change a direct result of the change in phenotype, however, many cancers lineage complex molecular mechanisms are still not fully elucidated. Therefore, The Cancer Genome Atlas (TCGA) Research Network analyzed a large human tumors, in order to find the molecular changes in DNA, RNA, protein and epigenetic level, The results contain a wealth of data provides us with an opportunity for common, personality and new ideas throughout the cancer lineages form a whole description. Pan-cancer genome program first compares the 12 kinds of cancer types. Analysis of different tumor molecular changes and their functions, will tell us how effective treatment method is applied to a similar phenotype of the tumor.

Online Mendelian Inheritance in Man (OMIM) is a knowledge source and data base for human genetic diseases and related genes. Each OMIM entry includes clinical synopsis, linkage analysis for candidate genes, chromosomal localization and animal models, which has become an authoritative source of information for the study of the relationship between genes and diseases. As overlap of disease symptoms may reflect interactions at the molecular level, comparison of phenotypic similarity may indicate candidate genes and help to discover functional connections between genes and proteins. However, the OMIM has used free text to describe disease phenotypes, which does not suit computer analysis. Standardization of OMIM data therefore has important implications for large-scale comparison of disease phenotypes and prediction of phenotype-genotype correlations. Recently, standard medical language systems, term frequency-inverse document frequency and the law of cosines for document classification have been introduced for mining of OMIM data. Combined with Gene Ontology and various comparison methods, this has achieved substantial successes. In this article, we have reviewed various methods for standardization and similarity comparison of OMIM data. We also predicted the trend for research in this direction.

The development of next-generation sequencing has generated high demand for data processing and analysis. Although there are a lot of software for analyzing next-generation sequencing data, most of them are designed for one specific function (e.g., alignment, variant calling or annotation). Therefore, it is necessary to combine them together for data analysis and to generate interpretable results for biologists. This study designed a pipeline to process Illumina sequencing data based on Perl programming language and SGE system. The pipeline takes original sequence data (fastq format) as input, calls the standard data processing software (e.g., BWA, Samtools, GATK, and Annovar), and finally outputs a list of annotated variants that researchers can further analyze. The pipeline simplifies the manual operation and improves the efficiency by automatization and parallel computation. Users can easily run the pipeline by editing the configuration file or clicking the graphical interface. Our work will facilitate the research projects using the sequencing technology.

With the current accumulation of metagenome data, it is possible to build an integrated platform for processing of rigorously selected metagenomic samples (also referred as "metagenomic communities" here) of interests. Any metagenomic samples could then be searched against this database to find the most similar sample(s). However, on one hand, current databases with a large number of metagenomic samples mostly serve as data repositories but not well annotated database, and only offer few functions for analysis. On the other hand, the few available methods to measure the similarity of metagenomic data could only compare a few pre-defined set of metagenome. It has long been intriguing scientists to effectively calculate similarities between microbial communities in a large repository, to examine how similar these samples are and to find the correlation of the meta-information of these samples. In this work we propose a novel system, Meta-Mesh, which includes a metagenomic database and its companion analysis platform that could systematically and efficiently analyze, compare and search similar metagenomic samples. In the database part, we have collected more than 7 000 high quality and well annotated metagenomic samples from the public domain and in-house facilities. The analysis platform supplies a list of online tools which could accept metagenomic samples, build taxonomical annotations, compare sample in multiple angle, and then search for similar samples against its database by a fast indexing strategy and scoring function. We also used case studies of "database search for identification" and "samples clustering based on similarity matrix" using human-associated habitat samples to demonstrate the performance of Meta-Mesh in metagenomic analysis. Therefore, Meta-Mesh would serve as a database and data analysis system to quickly parse and identify similar

To investigate the epidemic and evolutionary trends of enterovirus (EV) in the external environment of Yunnan Province, China, molecular typing was performed on 4 EV strains that were isolated from environmental sewage in Yunnan. The VP1 region of isolates was amplified by RT-PCR using universal enterovirus primers, and the amplified VP1 region was sequenced for GenBank BLAST search and genotype analysis. The 4 EV strains were identified as ECHO7. Their nucleotide and amino acid homologies with the VP1 sequences of 68 ECHO7 strains retrieved from GenBank were measured by Mega software analysis. Our findings showed that ECHO7 strains from environmental sewage and population samples were in different evolutionary branches. These strains showed typical geographical and temporal differences; In addition, there were different transmission chains at the same time and in the same area. ECHO7 strains isolated from sewage water and patients with acute flaccid paralysis during the same period in Yunnan belonged to different clusters and evolved at different speeds. Special concerns are needed for this problem. Continuous molecular biological surveillance of human EV in the external environment of Yunnan will provide strong support for early warning of EV diseases.

In 2013, the World Health Organization reported the first case of human infection with a new influenza A (H7N9) virus in China. This has caused damage and panic within certain areas in China. Therefore, analysis of this virus with bioinformatics technology is very necessary. Neuraminidase (NA) is one of the most important antigens of the influenza virus and an important target for anti-flu drugs. In this study, the nucleotide and protein sequences of NA gene of A/H7N9 influenza viruses were retrieved from the NCBI database, and MEGA 5.0 software was employed to construct a phylogenetic tree based on the nucleotide coding sequence; BioEdit software was used to align the nucleotide and protein sequences of NA and calculate the homologies of nucleotides and amino acids and then to analyze the important mutation sites of NA gene. The results demonstrated that the spread of influenza virus H7N9 showed certain geographical and temporal relations. The H7N9 virus isolated from China in 2013 belonged to Euroasiatic serotype, and its NA stalk region hadobvious variation, which may be one of the reasons that this virus infects human. These analyses may be very helpful for understanding the evolutionary relationship and mutation trend of A/H7N9 influenza viruses.

MicroRNAs (miRNAs) are a class of small endogenously RNA with an approximate length of 22nt which are known to be ubiquitous in eukaryotes. More importantly, miRNAs are key regulators of gene expression in eukaryotic cells through the degradation of target mRNA. The investigation on miRNAs in tomato that is an important model plant has made a great progress in recent years. Herein, by collecting the reported literature and miRBase, we found that 34 miRNAs in tomato are closely associated with pathogenicity. Subsequently, we predicted their target genes through bioinformatics approaches and built a disease-related regulatory network of miRNA and its target genes using Cytoscope program. This has led us to identify 13 miRNAs that are closely associated with pathogenicity in tomato from which we selected miR169, miR482, miR5300, miR6024, miR6026 and miR6027 for further analysis based on the association between miRNAs and the number of target genes. Lastly, we performed the analysis of target gene, promoter and real time quantitative PCR verification for these 6 miRNAs. Our study may pave the way for future in-depth analysis of biological action of miRNAs.

Coronary heart disease (CHD) related genes and targets, as well as drug targets for preventing and treating CHD were taken as the study objects to build a CHD disease network and a drug action network preventing and treating CHD. Such topological characteristic parameters of the networks as degree distribution, characteristic path length, connectivity and heterogeneity were analyzed to verify the reliability of the networks. On that basis, the intersection calculation was conducted for both networks to analyze the drug action mechanism of their sub-networks. The disease network are composed of 15,221 nodes and 31,177 sides, while the drug action network preventing and treating CHD has 15,073 nodes and 32,376 sides. Both of their topological characteristic parameters showed scale-free small world structural characteristics. Two reaction pathways in the sub-networks-calcitonin gene-related peptide and IL-6 activated JAK/STAT were taken as examples to discuss the indirect action mechanism for preventing and treating CHD. The results showed that the biological network analysis method combining the disease network and the drug action network is helpful to further studies on the action mechanism of the drugs, and significant to the prevention and treatment of diseases.

Objective: We evaluated the accuracy and efficiency of computational inference methods for haplotype on estimate HLA-A-B-C haplotype frequencies by compared with the haplotypes manually defined in a family-base dataset., Methods: 558 individuals with pedigree information were selected, and their haplotyps were compared with the data obtained by the following three method: the Expectation-Maximization (EM) and Excoffier-Laval-Balding (ELB)algorithms using the AELEQUIN software, and the SAS/Genetics PROC HAPLOTYPE method., Results: After performing the SAS/Genetics method, and the Expectation-Maximization (EM) and Excoffier-Laval-Balding (ELB) algorithms using the AELEQUIN software, 248, 247, and 238 different haplotypes were obtained respectively. The accuracy rates of these three methods were 88.5%, 89.1%, and 90.3% respectively. There are no significant different in the accuracy and estimated haplotype frequency comparisons among any two of these computational inference methods., Conclusion: High accuracy haplotype frequency estimate rates could be obtained by these three computational inference methods, and there are no significant difference in the comparison of haplotypes estimated by SAS/Genetics, the EM and ELB algorithms using the AELEQUIN software. However, ELB algorithm shows better performance than EM algorithm and SAS/Genetics PROC HAPLOTYPE method for haplotype frequencies estimation in general.

Objective: In order to screen out important genes from large gene data of gene microarray after nerve injury, we combine gene ontology (GO) method and computer pattern recognition technology to find key genes responding to nerve injury, and then verify one of these screened-out genes., Methods: Data mining and gene ontology analysis of gene chip data GSE26350 was carried out through MATLAB software. Cd44 was selected from screened-out key gene molecular spectrum by comparing genes' different GO terms and positions on score map of principal component. Function interferences were employed to influence the normal binding of Cd44 and one of its ligands, chondroitin sulfate C (CSC), to observe neurite extension., Results: Gene ontology analysis showed that the first genes on score map (marked by red *) mainly distributed in molecular transducer activity, receptor activity, protein binding et al molecular function GO terms. Cd44 is one of six effector protein genes, and attracted us with its function diversity. After adding different reagents into the medium to interfere the normal binding of CSC and Cd44, varying-degree remissions of CSC's inhibition on neurite extension were observed., Conclusion: CSC can inhibit neurite extension through binding Cd44 on the neuron membrane. This verifies that important genes in given physiological processes can be identified by gene ontology analysis of gene chip data.

This study was aimed to explore the distribution characteristics of the human platelet antigen (HPA) gene of human platelet donors and its polymorphism in Mudanjiang area of Heilongjiang Province in China, to determine platelet antigen system with clinical significance by judging the rate of incompatibility of HPA, as well as to establish a database of donors' HPA. The genotyping of 154 unrelated platelet donors was performed by means of PCR-SSP. The frequencies of gene and genotype were calculated and compared with that in other areas. The results showed that the genes 1a-17a of HPA-a were all expressed in the 154 healthy and unrelated platelet donors. Only genes 1b, 2b, 3b, 5b, 6b and 15b of HPA-b were expressed while genes 4b, 7b-14b, 16b were not expressed. Among the genotypes, aa homozygosity was predominant and HPA15 had the greatest heterozygosity, while HPA3 had lower heterozygosity. There were 23 combined types of HPA, 5 of them had a rate higher than 10%, and the frequencies of the other 18 were lower than 8%. HPA genotype frequencies showed a good consistency to Hardy-Weinberg equilibrium. It is concluded that the distribution of the allele polymorphism of HPA1-HPA17 in Mudanjiang area has its own characteristics, compared with other areas and some countries, the local HPA genotype database of platelet donors is established in Mudanjiang area, which can provide the matching donors for clinical use with immunological significance.

Objective: To study the differentially expressed genes in asthenospermia to gain a deeper insight into the molecular mechanisms of the disease., Methods: We analyzed the differentially expressed genes in asthenospermia using GATHER, PANTHER and ToppGene online bioinformatics tools., Results: Our bioinformatics mining and analyses revealed that the differentially expressed genes in asthenospermia played important roles in the cellular protein and macromolecular metabolism, protein modification, cell death, cell apoptosis and apoptosis induction., Conclusion: Asthenospermia patients experience a decline in sperm activity and the basic life activities of sperm simultaneously, and are also prone to cell apoptosis or death. Such differentially expressed genes as KIF3B, MYO15A, KIF6, KIF26B, KIF3A, DNHD2, DMN, DYNC2H1, STARD9, MYOHD1, and TPM1, which are involved in cytoskeletal structure, microtubule movement and cell movement, may be associated with asthenospermia, and therefore deserve further studies.

Based on the Phytophthora infestans genome sequence, we used bioinformatics and computer-based prediction algorithms to predict the secreted proteins of P infestans in detail, which would help us to elucidate the molecular mechanism underlying the interaction between the host plants and the P infestans. In this study, the signal peptide prediction algorithms SignalP v3.0 and PSORT, transmembrane helix prediction algorithms TMHMM-2.0 and THUMBUP, GPI-anchoring site prediction algorithm big-PI Predictor, and subcellular protein location distribution algorithm TargetP v1.01 were used to analyze the 22658 protein sequences of P infestans published. Our results suggested that there might be 671 secreted pro- teins, accounting for 3.0% of the total proteins. Among them, the functions of the 45 secreted proteins had been described previously. Their functions involved cellular metabolism and signal transduction etc. In addition, some of the secreted proteins were functionally similar to elicitin, which were likely to be associated with the virulence of P. infestans.

Online Mendelian Inheritance in Man (OMIM, http://omim. org/) is a comprehensive, authoritative, practical and timely knowledgebase of human genes and genetic disorders. OMIM, as a genetic encyclopedia, provides an easy and straightforward access to information on human genetics to students, researchers and clinicians. This article presents an overview on the contents of OMIM and its application to medical genetics.

Researches on the next generation sequencing (NGS) and the comparative genome analysis have recently been concerned. The analyses on transposable element composition and abundance are important parts for genome studies. Generally, the analyses of transposable element system were based on the complete spliced genomes; however, the post-processing and sequence splicing of the huge amount of short sequences from the 454 sequencer always encounter problems. Moreover, the occasion that large amount of repeat elements made up by transposable elements were incorrectly splicing or lost, leading to uncertain results. This study aimed at the construction of a framework to automatically analyze the insert sequence (IS) abundance and their composition based on a stimulated Roche 454 deep-sequencing data set, which was a 33-fold coverage of Microcystis aeruginosa NIES 843 genome. The result from the examination under the setting of three classes of division on the IS element candidates and a separated transposase examination thresholds is the most reliable. It showed that the abundance of IS element in this stimulated dataset was 10.38%, including 14 IS families and 66 IS subfamilies, which demonstrated no significant difference with the two sets of previous analysis results based on the spliced M. aeruginosa NIES 843 genome and a high percentage of IS element sequence overlap, indicating the reliability of this framework.

We applied public databases of single nucleotide polymorphism (SNP) to screen complex disease-related SNPs and assessed the potential functions of selected SNPs through SNP function prediction software, including FastSNP, SNP Function Prediction, F-SNP. We selected Tag SNP in HapMap database and compared all results with above software. With above strategies we screened IGFBP7 gene and obtained total 47 SNPs, including 11 TFBS SNPs, 31 intronic enhancer SNPs, 4 intronic enhancer and TFBS SNPs and 1 splicing sites SNPs.