Identifying the key genes and functional enrichment pathways associated with feed efficiency in cattle

Gene ◽  
2022 ◽  
Vol 807 ◽  
pp. 145934
Author(s):  
Chaoyun Yang ◽  
Yun Zhu ◽  
Yanling Ding ◽  
Zengwen Huang ◽  
Xingang Dan ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Functional Enrichment ◽  
Key Genes

scholarly journals HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhixin Wu ◽  
Yinxian Wen ◽  
Guanlan Fan ◽  
Hangyuan He ◽  
Siqi Zhou ◽  
...  
Keyword(s):  
Femoral Head ◽  
Roc Curve ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Key Genes

Abstract Background Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. Methods The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. Results Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. Conclusions Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

scholarly journals Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

2020 ◽  
Vol 19 ◽  
pp. 153303382098417
Author(s):  
Ting-ting Liu ◽  
Shu-min Liu
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Small Gtpase ◽  
Functional Enrichment ◽  
Clinical Correlation ◽  
Cox Regression Analysis ◽  
Prognostic Analysis ◽  
Key Genes

Objective: The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC). Methods: This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes. Results: Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation. Conclusions: In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

scholarly journals Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11496
Author(s):  
Yutao Jia ◽  
Yang Liu ◽  
Zhihua Han ◽  
Rong Tian
Keyword(s):  
Roc Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Gene Signatures ◽  
Expression Levels ◽  
Link Type ◽  
Diagnostic Values ◽  
Key Genes ◽  
Integration Analysis ◽  
Geo Database

Background Osteosarcoma (OS) is the most primary malignant bone cancer in children and adolescents with a high mortality rate. This work aims to screen novel potential gene signatures associated with OS by integrated microarray analysis of the Gene Expression Omnibus (GEO) database. Material and Methods The OS microarray datasets were searched and downloaded from GEO database to identify differentially expressed genes (DEGs) between OS and normal samples. Afterwards, the functional enrichment analysis, protein–protein interaction (PPI) network analysis and transcription factor (TF)-target gene regulatory network were applied to uncover the biological function of DEGs. Finally, two published OS datasets (GSE39262 and GSE126209) were obtained from GEO database for evaluating the expression level and diagnostic values of key genes. Results  In total 1,059 DEGs (569 up-regulated DEGs and 490 down-regulated DEGs) between OS and normal samples were screened. Functional analysis showed that these DEGs were markedly enriched in 214 GO terms and 54 KEGG pathways such as pathways in cancer. Five genes (CAMP, METTL7A, TCN1, LTF and CXCL12) acted as hub genes in PPI network. Besides, METTL7A, CYP4F3, TCN1, LTF and NETO2 were key genes in TF-gene network. Moreover, Pax-6 regulated four key genes (TCN1, CYP4F3, NETO2 and CXCL12). The expression levels of four genes (METTL7A, TCN1, CXCL12 and NETO2) in GSE39262 set were consistent with our integration analysis. The expression levels of two genes (CXCL12 and NETO2) in GSE126209 set were consistent with our integration analysis. ROC analysis of GSE39262 set revealed that CYP4F3, CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients. ROC analysis of GSE126209 set revealed that CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients.

scholarly journals Integrative analyses of biomarkers and pathways for heart failure

2021 ◽  
Author(s):  
Shaowei Fan ◽  
Yuanhui Hu
Keyword(s):  
Heart Failure ◽  
Extracellular Matrix ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Signal Pathway ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Ppi Networks ◽  
Key Genes

Abstract Background: Heart failure (HF) is the most common potential cause of death, causing a huge health and economic burden all over the world. So far, some impressive progress has been made in the study of pathogenesis. However, the underlying molecular mechanisms leading to this disease remain to be fully elucidated. Methods: The microarray data sets of GSE76701, GSE21610 and GSE8331 were retrieved from the gene expression comprehensive database (GEO). After merging all microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA), response pathway database and Disease Ontology (DO). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. The comparative toxicological genomics database (CTD) is used to explore the interaction between potential key genes and HF. Results: We identified 38 patients with heart failure and 16 normal controls. There were 315 DEGs among HF samples, including 278 up-regulated genes and 37 down-regulated genes. Pathway enrichment analysis showed that most DEGs were significantly enriched in BMP signal pathway, transmembrane receptor protein serine / threonine kinase signal pathway, extracellular matrix, basement membrane, glycosaminoglycan binding, sulfur compound binding and so on. Similarly, GSEA enrichment analysis showed that DEGs were mainly enriched in extracellular matrix and extracellular matrix related proteins. BBS9, CHRD, BMP4, MYH6, NPPA and CCL5 are central genes in PPI networks and modules. Conclusions: the enrichment pathway of DEGs and go ontology may reveal the molecular mechanism of HF. Among them, target genes EIF1AY, RPS4Y1, USP9Y, KDM5D, DDX3Y, NPPA, HBB, TSIX, LOC28556 and XIST are expected to become new targets for heart failure. Our findings provide potential biomarkers or therapeutic targets for the further study of heart failure and contribute to the development of advanced prediction, diagnosis and treatment strategies.

scholarly journals Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8831 ◽  
Author(s):  
Xiaojiao Guan ◽  
Yao Yao ◽  
Guangyao Bao ◽  
Yue Wang ◽  
Aimeng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Esophageal Cancer ◽  
Esophageal Carcinoma ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Diagnostic Model ◽  
Link Type ◽  
Key Genes

Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

scholarly journals Identification of Hypoxia-Related Differentially Expressed Genes and Construction of the Clinical Prognostic Predictor in Hepatocellular Carcinoma by Bioinformatic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hao Guo ◽  
Jing Zhou ◽  
Yanjun Zhang ◽  
Zhi Wang ◽  
Likun Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Model ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Prognostic Predictor ◽  
Key Genes

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.

scholarly journals Identification of key genes and biological processes contributing to colitis associated dysplasia in ulcerative colitis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11321
Author(s):  
Di Zhang ◽  
Pengguang Yan ◽  
Taotao Han ◽  
Xiaoyun Cheng ◽  
Jingnan Li
Keyword(s):  
Ulcerative Colitis ◽  
Mitochondrial Dysfunction ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Junction ◽  
Biological Processes ◽  
Hub Genes ◽  
Key Genes

Background Ulcerative colitis-associated colorectal cancer (UC-CRC) is a life-threatening complication of ulcerative colitis (UC). The mechanisms underlying UC-CRC remain to be elucidated. The purpose of this study was to explore the key genes and biological processes contributing to colitis-associated dysplasia (CAD) or carcinogenesis in UC via database mining, thus offering opportunities for early prediction and intervention of UC-CRC. Methods Microarray datasets (GSE47908 and GSE87466) were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between groups of GSE47908 were identified using the “limma” R package. Weighted gene co-expression network analysis (WGCNA) based on DEGs between the CAD and control groups was conducted subsequently. Functional enrichment analysis was performed, and hub genes of selected modules were identified using the “clusterProfiler” R package. Single-gene gene set enrichment analysis (GSEA) was conducted to predict significant biological processes and pathways associated with the specified gene. Results Six functional modules were identified based on 4929 DEGs. Green and blue modules were selected because of their consistent correlation with UC and CAD, and the highest correlation coefficient with the progress of UC-associated carcinogenesis. Functional enrichment analysis revealed that genes of these two modules were significantly enriched in biological processes, including mitochondrial dysfunction, cell-cell junction, and immune responses. However, GSEA based on differential expression analysis between sporadic colorectal cancer (CRC) and normal controls from The Cancer Genome Atlas (TCGA) indicated that mitochondrial dysfunction may not be the major carcinogenic mechanism underlying sporadic CRC. Thirteen hub genes (SLC25A3, ACO2, AIFM1, ATP5A1, DLD, TFE3, UQCRC1, ADIPOR2, SLC35D1, TOR1AIP1, PRR5L, ATOX1, and DTX3) were identified. Their expression trends were validated in UC patients of GSE87466, and their potential carcinogenic effects in UC were supported by their known functions and other relevant studies reported in the literature. Single-gene GSEA indicated that biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to angiogenesis and immune response were positively correlated with the upregulation of TFE3, whereas those related to mitochondrial function and energy metabolism were negatively correlated with the upregulation of TFE3. Conclusions Using WGCNA, this study found two gene modules that were significantly correlated with CAD, of which 13 hub genes were identified as the potential key genes. The critical biological processes in which the genes of these two modules were significantly enriched include mitochondrial dysfunction, cell-cell junction, and immune responses. TFE3, a transcription factor related to mitochondrial function and cancers, may play a central role in UC-associated carcinogenesis.

scholarly journals Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

2020 ◽  
Vol 9 (2) ◽  
pp. LMT30
Author(s):  
Chuanli Ren ◽  
Weixiu Sun ◽  
Xu Lian ◽  
Chongxu Han
Keyword(s):  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Kaplan Meier ◽  
Key Genes ◽  
Core Genes

Aim: To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD). Materials & methods: We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID. Results: Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD. Conclusion: 23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

scholarly journals Identification of Key Biomarkers and Immune Infiltration in Sciatic Nerve of Diabetic Neuropathy BKS-db/db Mice by Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Yixuan Lin ◽  
Fanjing Wang ◽  
Lianzhi Cheng ◽  
Zhaohui Fang ◽  
Guoming Shen
Keyword(s):  
Diabetic Neuropathy ◽  
Signaling Pathway ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Data Set ◽  
Immune Infiltration ◽  
P53 Signaling ◽  
Ppi Networks ◽  
Key Genes ◽  
P53 Signaling Pathway

Diabetic neuropathy (DN) is one of the chronic complications of diabetes which can cause severe harm to patients. In order to determine the key genes and pathways related to the pathogenesis of DN, we downloaded the microarray data set GSE27382 from Gene Expression Omnibus (GEO) and adopted bioinformatics methods for comprehensive analysis, including functional enrichment, construction of PPI networks, central genes screening, TFs-target interaction analysis, and evaluation of immune infiltration characteristics. Finally, we examined quantitative real- time PCR (qPCR) to validate the expression of hub genes. A total of 318 differentially expressed genes (DEGs) were identified, among which 125 upregulated DEGs were enriched in the mitotic nuclear division, extracellular region, immunoglobulin receptor binding, and p53 signaling pathway, while 193 downregulated DEGs were enriched in ion transport, membrane, synapse, sodium channel activity, and retrograde endocannabinoid signaling. GSEA plots showed that condensed nuclear chromosome kinetochore were the most significant enriched gene set positively correlated with the DN group. Importantly, we identified five central genes (Birc5, Bub1, Cdk1, Ccnb2, and Ccnb1), and KEGG pathway analysis showed that the five hub genes were focused on progesterone-mediated oocyte maturation, cell cycle, and p53 signaling pathway. The proportion of immune cells from DN tissue and normal group showed significant individual differences. In DN samples, T cells CD4 memory resting and dendritic cells resting accounted for a higher proportion, and macrophage M2 accounted for a lower proportion. In addition, all five central genes showed consistent correlation with immune cell infiltration levels. qPCR showed the same expression trend of five central genes as in our analysis. Our research identified key genes related to differential genes and immune infiltration related to the pathogenesis of DN and provided new diagnostic and potential therapeutic targets for DN.

scholarly journals Characterization and Duodenal Transcriptome Analysis of Chinese Beef Cattle With Divergent Feed Efficiency Using RNA-Seq

2021 ◽  
Vol 12 ◽  
Author(s):  
Chaoyun Yang ◽  
Liyun Han ◽  
Peng Li ◽  
Yanling Ding ◽  
Yun Zhu ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Differentially Expressed Genes ◽  
Feed Intake ◽  
Feed Efficiency ◽  
Muscle Development ◽  
Dietary Factors ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Biological Regulation ◽  
Key Genes

Residual feed intake (RFI) is an important measure of feed efficiency for agricultural animals. Factors associated with cattle RFI include physiology, dietary factors, and the environment. However, a precise genetic mechanism underlying cattle RFI variations in duodenal tissue is currently unavailable. The present study aimed to identify the key genes and functional pathways contributing to variance in cattle RFI phenotypes using RNA sequencing (RNA-seq). Six bulls with extremely high or low RFIs were selected for detecting differentially expressed genes (DEGs) by RNA-seq, followed by conducting GO, KEGG enrichment, protein-protein interaction (PPI), and co-expression network (WGCNA, n = 10) analysis. A total of 380 differentially expressed genes was obtained from high and low RFI groups, including genes related to energy metabolism (ALDOA, HADHB, INPPL1), mitochondrial function (NDUFS1, RFN4, CUL1), and feed intake behavior (CCK). Two key sub-networks and 26 key genes were detected using GO analysis of DEGs and PPI analysis, such as TPM1 and TPM2, which are involved in mitochondrial pathways and protein synthesis. Through WGCNA, a gene network was built, and genes were sorted into 27 modules, among which the blue (r = 0.72, p = 0.03) and salmon modules (r = −0.87, p = 0.002) were most closely related with RFI. DEGs and genes from the main sub-networks and closely related modules were largely involved in metabolism; oxidative phosphorylation; glucagon, ribosome, and N-glycan biosynthesis, and the MAPK and PI3K-Akt signaling pathways. Through WGCNA, five key genes, including FN1 and TPM2, associated with the biological regulation of oxidative processes and skeletal muscle development were identified. Taken together, our data suggest that the duodenum has specific biological functions in regulating feed intake. Our findings provide broad-scale perspectives for identifying potential pathways and key genes involved in the regulation of feed efficiency in beef cattle.

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