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).

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 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.

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.

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 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.

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.

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.

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 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.

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.

Transcriptome-based analysis of key genes and pathways affecting the linoleic acid content in chickens

Linoleic acid is an essential polyunsaturated fatty acid that cannot be synthesized by humans or animals themselves and can only be obtained externally.The amount of linoleic acid present has an impact on the quality and flavour of meat and indirectly affects consumer preference.However,the molecular mechanisms influencing the deposition of linoleic acid in organisms are not clear.As the molecular mechanisms of linoleic acid deposition are not well understood,to investigate the main effector genes affecting the linoleic acid content,this study aimed to screen for hub genes in slow-type yellow-feathered chickens by transcriptome sequencing(RNA-Seq)and weighted gene coexpression network analysis(WGCNA).We screened for candidate genes associated with the linoleic acid content in slow-type yellow-feathered broilers.A total of 399 Tiannong partridge chickens were slaughtered at 126 days of age,fatty acid levels were measured in pectoral muscle,and pectoral muscle tissue was collected for transcriptome sequencing.Transcriptome sequencing results were combined with phenotypes for WGCNA to screen for candidate genes.KEGG enrichment analysis was also performed on the genes that were significantly enriched in the modules with the highest correlation.A total of 13310 genes were identified after quality control of transcriptomic data from 399 pectoral muscle tissues.WGCNA was performed,and a total of 26 modules were obtained,eight of which were highly correlated with the linoleic acid content.Four key genes,namely,MDH2,ATP5B,RPL7A and PDGFRA,were screened according to the criteria|GS|>0.2 and|MM|>0.8.The functional enrichment results showed that the genes within the target modules were mainly enriched in metabolic pathways.In this study,a large-sample-size transcriptome analysis revealed that metabolic pathways play an important role in the regulation of the linoleic acid content in Tiannong partridge chickens,and MDH2,ATP5B,RPL7A and PDGFRA were screened as important candidate genes affecting the linoleic acid content.The results of this study provide a theoretical basis for selecting molecular markers and comprehensively understanding the molecular mechanism affecting the linoleic acid content in muscle,providing an important reference for the breeding of slow-type yellowfeathered broiler chickens.

Identification of key genes regulating the synthesis of quercetin derivatives in Rosa roxburghii through integrated transcriptomics and metabolomics

Rosa roxburghii fruit is rich in flavonoids, but little is known about their biosynthetic pathways. In this study, we employed transcriptomics and metabolomics to study changes related to the flavonoids at five different stages of R. roxburghii fruit development. Flavonoids and the genes related to their biosynthesis were found to undergo significant changes in abundance across different developmental stages, and numerous quercetin derivatives were identified. We found three gene expression modules that were significantly associated with the abundances of the different flavonoids in R. roxburghii and identified three structural UDP-glycosyltransferase genes directly involved in the synthesis of quercetin derivatives within these modules. In addition, we found that RrBEH4, RrLBD1 and RrPIF8could significantly increase the expression of downstream quercetin derivative biosynthesis genes. Taken together,these results provide new insights into the metabolism of flavonoids and the accumulation of quercetin derivatives in R. roxburghii.

Functional investigation and two-sample Mendelian randomization study of primary biliary cholangitis hub genes

BACKGROUND The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis(PBC)and finding relevant biomarkers for diagnosis and therapeutic evaluation.AIM To determine PBC-associated hub genes and assess their clinical utility for disease prediction.METHODS PBC expression data were obtained from the Gene Expression Omnibus database.Overlapping genes from differential expression analysis and weighted gene coexpression network analysis(WGCNA)were identified as key genes for PBC.Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes.Hub genes were identified in protein-protein interaction(PPI)networks using the Degree algorithm in Cytoscape software.The relationship between hub genes and immune cells was investigated.Finally,a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC.RESULTS We identified 71 overlapping key genes using differential expression analysis and WGCNA.These genes were primarily enriched in pathways related to cytokinecytokine receptor interaction,and Th1,Th2,and Th17 cell differentiation.We utilized Cytoscape software and identified five hub genes(CD247,IL10,CCL5,CCL3,and STAT3)in PPI networks.These hub genes showed a strong correlation with immune cell infiltration in PBC.However,inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk.CONCLUSION Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment,thereby offering significant clinical utility.

Identification of the key genes and mechanisms associated with transcatheter arterial chemoembolisation refractoriness in hepatocellular carcinoma

BACKGROUND Transcatheter arterial embolisation(TACE)is the primary treatment for intermediate-stage hepatocellular carcinoma(HCC)patients while some HCC cases have shown resistance to TACE.AIM To investigate the key genes and potential mechanisms correlated with TACE refractoriness in HCC.METHODS The microarray datasets of TACE-treated HCC tissues,HCC and non-HCC tissues were collected by searching multiple public databases.The respective differentially expressed genes(DEGs)were attained via limma R package.Weighted gene co-expression network analysis was employed for identifying the significant modules related to TACE non-response.TACE refractoriness-related genes were obtained by intersecting up-regulated TACE-associated and HCC-associated DEGs together with the genes in significant modules related to TACE nonresponse.The key genes expression in the above two pairs of samples was compared respectively via Wilcoxon tests and standard mean differences model.The prognostic value of the key genes was evaluated by Kaplan-Meier curve.Multivariate analysis was utilised to investigate the independent prognostic factor in key genes.Single-cell RNA(scRNA)sequencing analysis was conducted to explore the cell types in HCC.TACE refractoriness-related genes activity was calculated via AUCell packages.The CellChat R package was used for the investigation of the cell–cell communication between the identified cell types.RESULTS HCC tissues of TACE non-responders(n=66)and TACE responders(n=81),HCC(n=3941)and non-HCC(n=3443)tissues were obtained.The five key genes,DLG associated protein 5(DLGAP5),Kinesin family member 20A(KIF20A),Assembly factor for spindle microtubules(ASPM),Kinesin family member 11(KIF11)and TPX2 microtubule nucleation factor(TPX2)in TACE refractoriness-related genes,were identified.The five key genes were all up-regulated in the TACE non-responders group and the HCC group.High expression of the five key genes predicted poor prognosis in HCC.Among the key genes,TPX2 was an independent prognostic factor.Four cell types,hepatocytes,embryonic stem cells,T cells and B cells,were identified in the HCC tissues.The TACE refractoriness-related genes expressed primarily in hepatocytes and embryonic stem cells.Hepatocytes,as the providers of ligands,had the strongest interaction with embryonic stem cells that provided receptors.CONCLUSION Five key genes(DLGAP5,KIF20A,ASPM,KIF11 and TPX2)were identified as promoting refractory TACE.Hepatocytes and embryonic stem cells were likely to boost TACE refractoriness.

Identification of immune cell-related prognostic genes characterized by a distinct microenvironment in hepatocellular carcinoma

BACKGROUND The development and progression of hepatocellular carcinoma(HCC)have been reported to be associated with immune-related genes and the tumor microenvir-onment.Nevertheless,there are not enough prognostic biomarkers and models available for clinical use.Based on seven prognostic genes,this study calculated overall survival in patients with HCC using a prognostic survival model and revealed the immune status of the tumor microenvironment(TME).AIM To develop a novel immune cell-related prognostic model of HCC and depict the basic profile of the immune response in HCC.METHODS We obtained clinical information and gene expression data of HCC from The Cancer Genome Atlas(TCGA)and International Cancer Genome Consortium(ICGC)datasets.TCGA and ICGC datasets were used for screening prognostic genes along with developing and validating a seven-gene prognostic survival model by weighted gene coexpression network analysis and least absolute shrinkage and selection operator regression with Cox regression.The relative analysis of tumor mutation burden(TMB),TME cell infiltration,immune check-points,immune therapy,and functional pathways was also performed based on prognostic genes.RESULTS Seven prognostic genes were identified for signature construction.Survival receiver operating characteristic curve analysis showed the good performance of survival prediction.TMB could be regarded as an independent factor in HCC survival prediction.There was a significant difference in stromal score,immune score,and estimate score between the high-risk and low-risk groups stratified based on the risk score derived from the seven-gene prognostic model.Several immune checkpoints,including VTCN1 and TNFSF9,were found to be associated with the seven prognostic genes and risk score.Different combinations of checkpoint blockade targeting inhibitory CTLA4 and PD1 receptors and potential chemotherapy drugs hold great promise for specific HCC therapies.Potential pathways,such as cell cycle regulation and metabolism of some amino acids,were also identified and analyzed.CONCLUSION The novel seven-gene(CYTH3,ENG,HTRA3,PDZD4,SAMD14,PGF,and PLN)prognostic model showed high predictive efficiency.The TMB analysis based on the seven genes could depict the basic profile of the immune response in HCC,which might be worthy of clinical application.

Bioinformatics Analysis Revealed Potential Tumor Suppressors (KLF4/CGN), Oncogenes (SHH/LIF) and Biomarkers of Asian Stomach Adenocarcinoma

Stomach adenocarcinoma (STAD) is the fifth most prevalent cancer and the third leading cause of cancer-related death in the world and is more common in Asia than in most Western countries. There is an urgent need to identify potential novel oncogenes and tumor suppressor genes, and biomarkers for STAD. 6652 differentially expressed genes were identified between STAD and normal samples based on the transcriptome data analysis of the TCGA and GEO databases. 13 key modules were identified in STAD by WGCNA analysis. 293 potential STAD associated genes were identified from intersection by Venn Diagram. The 293 intersected genes were enriched in cell cortex and infection by GO and KEGG analysis. 10 hub genes were identified from PPI and Cytoscape analyses of the intersected genes. KLF4/CGN low and SHH/LIF high expression were associated with short overall survival of Asian STAD patients. Bioinformatics analysis revealed potential novel tumor suppressors (KLF4/CGN), oncogenes (SHH/LIF) and biomarkers for diagnosis, therapy and prognosis of STAD, specifically for Asian patients.