Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study.METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes.The function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma.CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

Identification of key genes involved in axon regeneration and Wallerian degeneration by weighted gene co-expression network analysis

Peripheral nerve injury repair requires a certain degree of cooperation between axon regeneration and Wallerian degeneration.Therefore,investigating how axon regeneration and degeneration work together to repair peripheral nerve injury may uncover the molecular mechanisms and signal cascades underlying peripheral nerve repair and provide potential strategies for improving the low axon regeneration capacity of the central nervous system.In this study,we applied weighted gene co-expression network analysis to identify differentially expressed genes in proximal and distal sciatic nerve segments from rats with sciatic nerve injury.We identified 31 and 15 co-expression modules from the proximal and distal sciatic nerve segments,respectively.Functional enrichment analysis revealed that the differentially expressed genes in proximal modules promoted regeneration,while the differentially expressed genes in distal modules promoted neurodegeneration.Next,we constructed hub gene networks for selected modules and identified a key hub gene,Kif22,which was up-regulated in both nerve segments.In vitro experiments confirmed that Kif22 knockdown inhibited proliferation and migration of Schwann cells by modulating the activity of the extracellular signal-regulated kinase signaling pathway.Collectively,our findings provide a comparative framework of gene modules that are co-expressed in injured proximal and distal sciatic nerve segments,and identify Kif22 as a potential therapeutic target for promoting peripheral nerve injury repair via Schwann cell proliferation and migration.All animal experiments were approved by the Institutional Animal Ethics Committee of Nantong University,China(approval No.S20210322-008)on March 22,2021.

Weighted Gene Co-expression Network Analysis of Gene Modules for the Prognosis of Esophageal Cancer

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas(TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival(PFS) or overall survival(OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that 'glycoprotein binding' was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor(PTAFR) and feline Gardner-Rasheed(FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.

Identification of Potential Therapeutic Targets of Alzheimer's Disease By Weighted Gene Co-Expression Network Analysis

Objective Alzheimer's disease(AD)is the most common cause of dementia.The pathophysiology of the disease mostly remains unearthed,thereby challenging drug development for AD.This study aims to screen high throughput gene expression data using weighted co-expression network analysis(WGCNA)to explore the potential therapeutic targets.Methods The dataset of GSE36980 was obtained from the Gene Expression Omnibus(GEO)database.Normalization,quality control,filtration,and soft-threshold calculation were carried out before clustering the co-expressed genes into different modules.Furthermore,the correlation coefiidents between the modules and clinical traits were computed to identify the key modules.Gene ontology and pathway enrichment analyses were performed on the key module genes.The STRING database was used to construct the protein-protein interaction(PPI)networks,which were further analyzed by Cytoscape app(MCODE).Finally,validation of hub genes was conducted by external GEO datasets of GSE 1297 and GSE 28146.Results Co-expressed genes were clustered into 27 modules,among which 6 modules were identified as the key module relating to AD occurrence.These key modules are primarily involved in chemical synaptic transmission(G0:0007268),the tricarboxylic acid(TCA)cycle and respiratory electron transport(R-HSA-1428517).WDR47,OXCT1,C3orfl4,ATP6V1A,SLC25A14,NAPB were found as the hub genes and their expression were validated by external datasets.Conclusions Through modules co-expression network analyses and PPI network analyses,we identified the hub genes of AD,including WDR47,0XCT1,C3orfl4i ATP6V1A,SLC25A14 and NAPB.Among them,three hub genes(ATP6V1A,SLC25A14,OXCT1)might contribute to AD pathogenesis through pathway of TCA cycle.

Weighted gene co-expression network analysis reveals similarities and differences of molecular features between dilated and ischemic cardiomyopathies

Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function.Though progress has been made to elucidate the process,molecular mechanisms of different classes of cardiomyopathies remain elusive.This paper aims to describe the similarities and differences in molecular features of dilated cardiomyopathy(DCM)and ischemic cardiomyopathy(ICM).We firstly detected the co-expressed modules using the weighted gene co-expression network analysis(WGCNA).Significant modules associated with DCM/ICM were identified by the Pearson correlation coefficient(PCC)between the modules and the phenotype of DCM/ICM.The differentially expressed genes in the modules were selected to perform functional enrichment.The potential transcription factors(TFs)prediction was conducted for transcription regulation of hub genes.Apoptosis and cardiac conduction were perturbed in DCM and ICM,respectively.TFs demonstrated that the biomarkers and the transcription regulations in DCM and ICM were different,which helps make more accurate discrimination between them at molecular levels.In conclusion,comprehensive analyses of the molecular features may advance our understanding of DCM and ICM causes and progression.Thus,this understanding may promote the development of innovative diagnoses and treatments.

Identification of Potential Zinc Deficiency Responsive Genes and Regulatory Pathways in Rice by Weighted Gene Co-expression Network Analysis

Zinc(Zn)malnutrition is a major public health issue.Genetic biofortification of Zn in rice grain can alleviate global Zn malnutrition.Therefore,elucidating the genetic mechanisms regulating Zn deprivation response in rice is essential to identify elite genes useful for breeding high grain Zn rice varieties.Here,a meta-analysis of previous RNA-Seq studies involving Zn deficient conditions was conducted using the weighted gene co-expression network analysis(WGCNA)and other in silico prediction tools to identify modules(denoting cluster of genes with related expression pattern)of co-expressed genes,modular genes which are conserved differentially expressed genes(DEGs)across independent RNA-Seq studies,and the molecular pathways of the conserved modular DEGs.WGCNA identified 16 modules of co-expressed genes.Twenty-eight and five modular DEGs were conserved in leaf and crown,and root tissues across two independent RNA-Seq studies.Functional enrichment analysis showed that 24 of the 28 conserved modular DEGs from leaf and crown tissues significantly up-regulated 2 Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways and 15 Gene Ontology(GO)terms,including the substrate-specific transmembrane transporter and the small molecule metabolic process.Further,the well-studied transcription factors(OsWOX11 and OsbHLH120),protein kinase(OsCDPK20 and OsMPK17),and miRNAs(OSA-MIR397A and OSA-MIR397B)were predicted to target some of the identified conserved modular DEGs.Out of the 24 conserved and up-regulated modular DEGs,19 were yet to be experimentally validated as Zn deficiency responsive genes.Findings from this study provide a comprehensive insight on the molecular mechanisms of Zn deficiency response and may facilitate gene and pathway prioritization for improving Zn use efficiency and Zn biofortification in rice.

Identification of potential key molecules and signaling pathways for psoriasis based on weighted gene co-expression network analysis

BACKGROUND Psoriasis is a chronic inflammatory skin disease,the pathogenesis of which is more complicated and often requires long-term treatment.In particular,moderate to severe psoriasis usually requires systemic treatment.Psoriasis is also associated with many diseases,such as cardiometabolic diseases,malignant tumors,infections,and mood disorders.Psoriasis can appear at any age,and lead to a substantial burden for individuals and society.At present,psoriasis is still a treatable,but incurable,disease.Previous studies have found that micro RNAs(mi RNAs)play an important regulatory role in the progression of various diseases.Currently,mi RNAs studies in psoriasis and dermatology are relatively new.Therefore,the identification of key mi RNAs in psoriasis is helpful to elucidate the molecular mechanism of psoriasis.AIM To identify key molecular markers and signaling pathways to provide potential basis for the treatment and management of psoriasis.METHODS The mi RNA and m RNA data were obtained from the Gene Expression Omnibus database.Then,differentially expressed m RNAs(DEm RNAs)and differentially expressed mi RNAs(DEmi RNAs)were screened out by limma R package.Subsequently,DEm RNAs were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomics functional enrichment.The“WGCNA”R package was used to analyze the co-expression network of all mi RNAs.In addition,we constructed mi RNA-m RNA regulatory networks based on identified hub mi RNAs.Finally,in vitro validation was performed.All experimental procedures were approved by the ethics committee of Chinese PLA General Hospital(S2021-012-01).RESULTS A total of 639 DEm RNAs and 84 DEmi RNAs were identified.DEm RNAs screening criteria were adjusted P(adj.P)value<0.01 and|log Fold Change|(|log FC|)>1.DEmi RNAs screening criteria were adj.P value<0.01 and|logFC|>1.5.KEGG functional analysis demonstrated that DEm RNAs were significantly enriched in immune-related biological functions,for example,tolllike receptor signaling pathway,cytokine-cytokine receptor interaction,and chemokine signaling pathway.In weighted gene co-expression network analysis,turquoise module was the hub module.Moreover,10 hub mi RNAs were identified.Among these 10 hub mi RNAs,only 8 hub mi RNAs predicted the corresponding target m RNAs.97 negatively regulated mi RNA-m RNA pairs were involved in the mi RNA-m RNA regulatory network,for example,hsa-mi R-21-5 pclaudin 8(CLDN8),hsa-mi R-30 a-3 p-interleukin-1 B(IL-1 B),and hsa-mi R-181 a-5 p/hsa-mi R-30 c-2-3 p-C-X-C motif chemokine ligand 9(CXCL9).Real-time polymerase chain reaction results showed that IL-1 B and CXCL9 were up-regulated and CLDN8 was down-regulated in psoriasis with statistically significant differences.CONCLUSION The identification of potential key molecular markers and signaling pathways provides potential research directions for further understanding the molecular mechanisms of psoriasis.This may also provide new research ideas for the prevention and treatment of psoriasis in the future.

3-80 Identify the Signature Genes for Diagnose of Uveal Melanoma by Weight Gene Co-expression Network Analysis

Weighted Gene Co-expression Network Analysis (WGCNA) is a powerful tool which is applied to investigate the relationship between gene expression levels and patient clinic traits[1;2]. In this study, we identified four co-expression modules significantly correlated with clinic traits. Module blue positively correlated with radiotherapy treatment;module purple positively correlates with tumor location (sclera) and negatively correlates with patient age;

Co-expression Network Analysis Identifies Fourteen Hub Genes Associated with Prognosis in Clear Cell Renal Cell Carcinoma

Summary:Renal cancer is a common genitourinary malignance,of which clear cell renal cell carcinoma(ccRCC)has high aggressiveness and leads to most cancer-related deaths.Identification of sensitive and reliable biomarkers for predicting tumorigenesis and progression has great significance in guiding the diagnosis and treatment of ccRCC.Here,we identified 2397 common difTerentially expressed genes(DEGs)using paired normal and tumor ccRCC tissues from GSE53757 and The Cancer Genome Atlas(TCGA).Then,we performed weighted gene co-expression network analysis and protein-protein interaction network analysis,17 candidate hub genes were identified.These candidate hub genes were further validated in GSE36895 and Oncomine database and 14 real hub genes were identified.All the hub genes were up-regulated and significantly positively correlated with pathological stage and histologic grade of ccRCC.Survival analysis showed that the higher expression level of each hub gene tended to predict a worse clinical outcome.ROC analysis showed that all the hub genes can accurately distinguish between tumor and normal samples,and between early stage and advanced stage ccRCC.Moreover,all the hub genes were positively associated with distant metastasis,lymph node infiltration,tumor recurrence and the expression of MKi67,suggesting these genes might promote tumor proliferation,invasion and metastasis.Furthermore,the functional annotation demonstrated that most genes were enriched in cell-cycle related biological function.In summary,our study identified 14 potential biomarkers for predicting tumorigenesis and progression,which might contribute to early diagnosis,prognosis prediction and therapeutic intervention.

Transcriptome analysis reveals the genetic basis of crest cushion formation in duck

The Chinese crested duck is a unique duck breed having a bulbous feather shape on its duck head.However,the mechanisms involved in its formation and development are unclear.In the present study,RNA sequencing analysis was performed on the crested tissues of 6 Chinese crested ducks and the scalp tissues of 6 cherry valley ducks(CVs)from 2 developmental stages.This study identified 261 differentially expressed genes(DEGs),122 upregulated and 139 downregulated,in the E28 stage and 361 DEGs,154 upregulated and 207 downregulated in the D42 stage between CC and CV ducks.The subsequent results of weighted gene co-expression network analysis(WGCNA)revealed that the turquoise and cyan modules were associated with the crest trait in the D42 stage,meanwhile,the green,brown,and pink modules were associated with the crest trait in the E28 stage.Venn analysis of the DEGs and WGCNA showed that 145 and 45 genes are associated between the D42 and E28 stages,respectively.The expression of WNT16,BMP2,SLC35F2,SLC6A15,APOBEC2,ABHD6,TNNC2,MYL1,and TNNI2 were verified by real-time quantitative PCR.This study provides an approach to reveal the molecular mechanisms underlying the crested trait development.

Transglutaminase 2 serves as a pathogenic hub gene of KRAS mutant colon cancer based on integrated analysis

BACKGROUND Colon cancer is acknowledged as one of the most common malignancies worldwide,ranking third in United States regarding incidence and mortality.Notably,approximately 40%of colon cancer cases harbor oncogenic KRAS mutations,resulting in the continuous activation of epidermal growth factor receptor signaling.AIM To investigate the key pathogenic genes in KRAS mutant colon cancer holds considerable importance.METHODS Weighted gene co-expression network analysis,in combination with additional bioinformatics analysis,were conducted to screen the key factors driving the progression of KRAS mutant colon cancer.Meanwhile,various in vitro experiments were also conducted to explore the biological function of transglutaminase 2(TGM2).RESULTS Integrated analysis demonstrated that TGM2 acted as an independent prognostic factor for progression-free survival.Immunohistochemical analysis on tissue microarrays revealed that TGM2 was associated with an elevated probability of perineural invasion in patients with KRAS mutant colon cancer.Additionally,biological roles of the key gene TGM2 was also assessed,suggesting that the downregulation of TGM2 attenuated the proliferation,invasion,and migration of the KRAS mutant colon cancer cell line.CONCLUSION This study underscores the potential significance of TGM2 in the progression of KRAS mutant colon cancer.This insight not only offers a theoretical foundation for therapeutic approaches but also highlights the need for additional clinical trials and fundamental research to support our preliminary findings.

Discovering hidden information of gene ontology based on complex networks analysis

To resolve the ontology understanding problem, the structural features and the potential important terms of a large-scale ontology are investigated from the perspective of complex networks analysis. Through the empirical studies of the gene ontology with various perspectives, this paper shows that the whole gene ontology displays the same topological features as complex networks including ＂small world＂ and ＂scale-free＂,while some sub-ontologies have the ＂scale-free＂ property but no ＂small world＂ effect.The potential important terms in an ontology are discovered by some famous complex network centralization methods.An evaluation method based on information retrieval in MEDLINE is designed to measure the effectiveness of the discovered important terms.According to the relevant literature of the gene ontology terms,the suitability of these centralization methods for ontology important concepts discovering is quantitatively evaluated.The experimental results indicate that the betweenness centrality is the most appropriate method among all the evaluated centralization measures.

Identification of Prognostic Genes for Colon Cancer through Gene Coexpression Network Analysis

Objective:The present study was aimed to identify novel key genes,prognostic biomarkers and molecular pathways implicated in tumorigenesis of colon cancer.Methods:The microarray data GSE41328 containing 10 colon cancer samples and 10 adjacent normal tissues was analyzed to identify 4763 differentially expressed genes.Meanwhile,another microarray data GSE17536 was performed for weighted gene co-expression network analysis(WGCNA).Results:In present study,12 co-expressed gene modules associated with tumor progression were identified for further studies.The red module showed the highest association with pathological stage by Pearson's correlation analysis.Functional enrichment analysis revealed that genes in red module focused on cell division,cell proliferation,cell cycle and metabolic related pathway.Then,a total of 26 key hub genes were identified,and GEPIA database was subsequently selected for validation.Holliday junction-recognizing protein(HJURP)and cell division cycle 25 homolog C(CDC25C)were identified as effective prognosis biomarkers,which were all detrimental to prognosis.Gene set enrichment analyses(GSEA)found the two hub genes were enriched in“oocyte meiosis”,“oocyte maturation that are progesterone-mediated”,“p53 signaling pathway”,and“cell cycle”.Furthermore,the immunohistochemistry and western blotting showed that HJURP was highly expressed in colon cancer tissue.Conclusion:HJURP was identified as a key gene associated with colon cancer progression and prognosis by WGCNA,which might influence the prognosis by regulating cell cycle pathways.

Prognostic value of sorting nexin 10 weak expression in stomach adenocarcinoma revealed by weighted gene coexpression network analysis

AIM TO detect significant clusters of co-expressed genes associated with tumorigenesis that might help to predict stomach adenocarcinoma （SA） prognosis.METHODS The Cancer Genome Atlas database was used to obtain RNA sequences as well as complete clinical data of SA and adjacent normal tissues from patients. Weighted gene co-expression network analysis （WGCNA） was used to investigate the meaningful module along with hub genes. Expression of hub genes was analyzed in 362 paraffin-embedded SA biopsy tissues by immunohistochemical staining. Patients were classified into two groups （according to expression of hub genes）： Weak expression and over-expression groups. Correlation of biomarkers with clinicopathological factors indicated patient survival.RESULTS Whole genome expression level screening identified 6,231 differentially expressed genes. Twenty-four co- expressed gene modules were identified using WGCNA. Pearson＇s correlation analysis showed that the tan module was the most relevant to tumor stage （r = 0.24, P = 7 × 10 -6）. In addition, we detected sorting nexin （SNX）10 as the hub gene of the tan module. SNX10 expression was linked to T category （P = 0.042, x2= 8.708）, N category （P = 0.000, x2= 18.778）, TNM stage （P = 0.001, x2 = 16.744） as well as tumor differentiation （P = 0.000,x2= 251.930）. Patients with high SNX10 expression tended to have longer diseasefree survival （DFS; 44.97 mo vs 33.85 mo, P = 0.000） as well as overall survival （OS; 49.95 vs 40.84 mo, P = 0.000） in univariate analysis. Multivariate analysis showed that dismal prognosis could be precisely predicted clinicopathologically using SNX10 [DFS： P = 0.014, hazard ratio （HR） = 0.698, 95% confidence interval （CI）： 0.524-0.930, OS： P = 0.017, HR = 0.704, 95%CI： 0.528-0.940].CONCLUSION This study provides a new technique for screening prognostic biomarkers of SA. Weak expression of SNX10 is linked to poor prognosis, and is a suitable prognostic biomarker of SA.

Identification of key genes underlying clinical features of hepatocellular carcinoma based on weighted gene co‑expression network analysis and bioinformatics analysis

Objective: To identify module genes that are closely related to clinical features of hepatocellular carcinoma (HCC) by weighted gene co‑expression network analysis, and to provide a reference for early clinical diagnosis and treatment. Methods: GSE84598 chip data were downloaded from the GEO database, and module genes closely related to the clinical features of HCC were extracted by comprehensive weighted gene co‑expression network analysis. Hub genes were identified through protein interaction network analysis by the maximum clique centrality (MCC) algorithm;Finally, the expression of hub genes was validated by TCGA database and the Kaplan Meier plotter online database was used to evaluate the prognostic relationship between hub genes and HCC patients. Results: By comparing the gene expression data between HCC tissue samples and normal liver tissue samples, a total of 6 262 differentially expressed genes were obtained, of which 2 207 were upregulated and 4 055 were downregulated. Weighted gene co‑expression network analysis was applied to identify 120 genes of key modules. By intersecting with the differentially expressed genes, 115 candidate hub genes were obtained. The results of enrichment analysis showed that the candidate hub genes were closely related to cell mitosis, p53 signaling pathway and so on. Further application of the MCC algorithm to the protein interaction network of 115 candidate hub genes identified five hub genes, namely NUF2, RRM2, UBE2C, CDC20 and MAD2L1. Validation of hub genes by TCGA database revealed that all five hub genes were significantly upregulated in HCC tissues compared to normal liver tissues;Moreover, survival analysis revealed that high expression of hub genes was closely associated with poor prognosis in HCC patients. Conclusions: This study identifies five hub genes by combining multiple databases, which may provide directions for the clinical diagnosis and treatment of HCC.

Transcriptome analysis reveals potential genes associated with plant height in rice

Plant height(PH)is a complex trait regulated by the environment and multiple genes.PH directly affects crop yield,harvest index,and lodging resistance.From plant dwarf mutants,many genes related to PH have been identified and described.Nonetheless,the molecular mechanism of height regulation in high-culm rice mutants has not been well studied.By using transcriptome and weighted gene co-expression network analysis(WGCNA),we identified the differentially expressed genes(DEGs)between high-culm rice mutants(MUT)and wild-type(WT)and explored the key pathways and potential candidate genes involved in PH regulation.Transcriptome analysis identified a total of 2,184 DEGs,of which 1,317 were identified at the jointing stage and 1,512 were identified at the heading stage.Kyoto Encyclopedia of Genes and Genomes enrichment showed that the enrichment pathways were mainly involved in plant hormone signal transduction,ABC transportation,and steroid hormone biosynthesis.Among these metabolic pathways,LOC_Os05g43910 and LOC_Os01g35030 were auxin(IAA)-related genes,up-regulated in MUT and LOC_Os02g08500(LEPTO1),LOC_Os11g04720,and LOC_Os12g04500 were cytokinin(CK)-related genes,downregulated in MUT.The WGCNA identified four modules(light cyan,dark grey,grey,and pale turquoise)closely related to PH,and seven key genes were screened from these modules,of which two were up-regulated cell wallrelated genes(LOC_Os01g26174(OsWAK5),LOC_Os06g05050)in MUT,and one gibberellic acid(GA)gene(LOC_Os06g37364,OsKO2)was also up-regulated.These genes might be closely related to PH regulation.These findings help us better understand the transcriptional regulation of rice plant growth and development and provide a theoretical basis for mapping and cloning the PH regulatory genes.

Constrain-based analysis of gene deletion on the metabolic flux redistribution of Saccharomyces Cerevisiae

Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.

Identification of differentially expressed metastatic genes and their signatures to predict the overall survival of uveal melanoma patients by bioinformatics analysis

AIM:To identify metastatic genes and mi RNAs and to investigate the metastatic mechanism of uveal melanoma(UVM).METHODS:GSE27831,GSE39717,and GSE73652 gene expression profiles were downloaded from the Gene Expression Omnibus(GEO)database,and the limma R package was used to identify differentially expressed genes(DEGs).Gene Ontology(GO)term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis were performed using the DAVID online tool.A comprehensive list of interacting DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes(STRING)database and Cytoscape software.The Cytoscape MCODE plug-in was used to identify clustered sub-networks and modules of hub genes from the proteinprotein interaction network.GEPIA online software was used for survival analysis of UVM patients(n=80)from the The Cancer Genome Atlas(TCGA)cohort.Oncomi R online software was used to find that the mi RNAs were associated with UVM prognosis from the TCGA cohort.Target Scan Human 7.2 software was then used to identify the mi RNAs targeting the genes.RESULTS:There were 1600 up-regulated genes and 1399 down-regulated genes.The up-regulated genes were mainly involved in protein translation in the cytosol,whereas the down-regulated genes were correlated with extracellular matrix organization and cell adhesion in the extracellular space.Among the 2999 DEGs,five genes,Znf391,Mrps11,Htra3,Sulf2,and Smarcd3 were potential predictors of UVM prognosis.Otherwise,three mi RNAs,hsa-mi R-509-3-5 p,hsa-mi R-513 a-5 p,and hsa-mi R-1269 a were associated with UVM prognosis.CONCLUSION:After analyzing the metastasis-related enriched terms and signaling pathways,the up-regulated DEGs are mainly involved in protein synthesis and cell proliferation by ribosome and mitogen-activated protein kinase(MAPK)pathways.However,the down-regulated DEGs are mainly involved in processes that reduced cell-cell adhesion and promoted cell migration in the extracellular matrix through PI3 K-Akt signaling pathway,focal adhesion,and extracellular matrix-receptor interactions.Bioinformatics and interaction analysis may provide new insights on the events leading up to the development and progression of UVM.

Systemic omics analysis of the hub genes, proteins, metabolites and metabolic pathways related to the hypoxia preconditioning in mice

Hypoxia preconditioning （HPC） is associated with many complicated pathophysiological and biochemical processes that integrated and regulated via molecular levels. HPC could protect cells, tissues, organs and systems from hypoxia injury, but up to date, the molecular mechanism still remained unclear. The acute and repetitive hy- poxia preconditioning model was constructed and the related parameters were observed. The high-throughput mi- croarray analysis and multiple bioinformatics were used to explore the differentially expressed genes in HPC mice brain and the related gene network, pathways and biological processes related to HPC. The 2D-DIGE coupled with MALDI-TOF/TOF-MS was performed to identify these proteins that were differentially expressed during HPC. The UPLC-HRMS based metabolomics method was utilized to explore the key endogenous metabolites and metabolic pathways related to HPC. The results showed that （1） 1175 differentially expressed genes in HPC mice brain were identified. Fourteen of these genes were the related hub genes for HPC, including Cacna2dl, Grin2a, Npylr, Mef2c, Epha4, Rxfpl, Chrm3, Pdela, Atp2b4, Glral, Idil , Fgfl, Grin2b and Cda. The change trends of all the detected genes by RT-PCR were consistent with the data of gene chips. There were 113 significant functions up- regulated and 138 significant functions down-regulated in HPC mice. （2） About 2100 proteins were revealed via the gel imaging and spot detection. 66, 45 and 70 of proteins were found to have significantly difference between the control group and three times of HPC group, the control and six times of HPC, and the three times of HPC and six times of HPC group. （3）Some endogenous metabolites such as phenylalanine, valine, proline, leucine and glu- tamine were increased, while ereatine was decreased, both in HPC brain and heart; in addition, y-aminobutyric acid was markedly decreased in brain. The sphingolipid metabolic pathways were noticed due to the low p-value and high pathway impact. Especially, the sphingolipid compound sphingomyelin, ceramide, glucosyleeramide, galactosylceramide and laetosylceramide were mapping in this metabolic pathway. Interestingly, these sphingolipid metabolites with olefinic bond in the long fatty chain were up-regulated, while those sphingolipids without olefinic bond were down-regulated. The functions of these differentially expressed genes mainly involved the cellular proces- ses including MAPK pathway, ion transport, neurotransmitter transport and neuropeptide signal pathway. The pro- tein levels related the ATP synthesis and citric acid cycle decreased while the proteins with the glycolysis and oxy- gen-binding increased. Glutathione, GNBP-1 and GPD1L were related to preventing hypoxic damage. The results indicated that C24：l-Cers played a critical role in HPC and had potential in endogenous protective mechanism. The combinations of the system omies data of the different molecules were sufficient to give a further understanding of the molecular pathways affected by HPC. Our data provided an important insight to reveal the protection mechanism of HPC.