scholarly journals Co-expression network and comparative transcriptome analysis for fiber initiation and elongation reveal genetic differences in two lines from upland cotton CCRI70 RIL population

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11812
Author(s):  
Xiao Jiang ◽  
Liqiang Fan ◽  
Pengtao Li ◽  
Xianyan Zou ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Cotton Fiber ◽  
Upland Cotton ◽  
Molecular Mechanisms ◽  
Natural Fiber ◽  
Expression Profiles ◽  
Fiber Quality ◽  
Auxin Signaling ◽  
Hub Genes ◽  
Fiber Initiation ◽  
Ril Population

Upland cotton is the most widely planted for natural fiber around the world, and either lint percentage (LP) or fiber length (FL) is the crucial component tremendously affecting cotton yield and fiber quality, respectively. In this study, two lines MBZ70-053 and MBZ70-236 derived from G. hirsutum CCRI70 recombinant inbred line (RIL) population presenting different phenotypes in LP and FL traits were chosen to conduct RNA sequencing on ovule and fiber samples, aiming at exploring the differences of molecular and genetic mechanisms during cotton fiber initiation and elongation stages. As a result, 249/128, 369/206, 4296/1198 and 3547/2129 up-/down- regulated differentially expressed genes (DGEs) in L2 were obtained at −3, 0, 5 and 10 days post-anthesis (DPA), respectively. Seven gene expression profiles were discriminated using Short Time-series Expression Miner (STEM) analysis; seven modules and hub genes were identified using weighted gene co-expression network analysis. The DEGs were mainly enriched into energetic metabolism and accumulating as well as auxin signaling pathway in initiation and elongation stages, respectively. Meanwhile, 29 hub genes were identified as 14-3-3ω, TBL35, GhACS, PME3, GAMMA-TIP, PUM-7, etc., where the DEGs and hub genes revealed the genetic and molecular mechanisms and differences during cotton fiber development.

scholarly journals Identification of Novel Hub Genes Through Expression Profiles Analysis in Adrenocortical Carcinoma

2020 ◽  
Author(s):  
Chenhe Yao ◽  
Xiaoling Zhao ◽  
Xuemeng Shang ◽  
Binghan Jia ◽  
Shuaijie Dou ◽  
...  
Keyword(s):  
Adrenocortical Carcinoma ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Cross Validation ◽  
Expression Profiles ◽  
Ppi Network ◽  
Hub Genes ◽  
Tumor Association ◽  
Prediction Of Prognosis ◽  
Geo Database

Abstract Background: Adrenocortical carcinoma (ACC) is a heterogeneous and rare malignant tumor associated with a poor prognosis. The molecular mechanisms of ACC remain elusive and more accurate biomarkers for the prediction of prognosis are needed.Methods: In this study, integrative profiling analyses were performed to identify novel hub genes in ACC to provide promising targets for future investigation. Three gene expression profiling datasets in the GEO database were used for the identification of overlapped differentially expressed genes (DEGs) following the criteria of adj.P.Value<0.05 and |log2 FC|>0.5 in ACC. Novel hub genes were screened out following a series of processes: the retrieval of DEGs with no known associations with ACC on Pubmed, then the cross-validation of expression values and significant associations with overall survival in the GEPIA2 and starBase databases, and finally the prediction of gene-tumor association in the GeneCards database.Results: Four novel hub genes were identified and two of them, TPX2 and RACGAP1, were positively correlated with the staging. Interestingly, co-expression analysis revealed that the association between TPX2 and RACGAP1 was the strongest and that the expression of HOXA5 was almost completely independent of that of RACGAP1 and TPX2. Furthermore, the PPI network consisting of four novel genes and seed genes in ACC revealed that HOXA5, TPX2, and RACGAP1 were all associated with TP53. Conclusions: This study identified four novel hub genes (TPX2, RACHAP1, HXOA5 and FMO2) that may play crucial roles in the tumorigenesis and the prediction of prognosis of ACC.

scholarly journals SMC4, CCNB1 and CKS1B as potential targets and new critical biomarkers for the prognosis of human bladder cancer

2021 ◽  
Author(s):  
Hongpeng Fang ◽  
Zhansen Huang ◽  
Xianzi Zeng ◽  
Jiaming Wan ◽  
Jieying Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Transcriptional Level ◽  
Hub Genes ◽  
Kaplan Meier ◽  
High Form

Abstract Background As a common malignant cancer of the urinary system, the precise molecular mechanisms of bladder cancer remain to be illuminated. The purpose of this study was to identify core genes with prognostic value as potential oncogenes for the diagnosis, prognosis or novel therapeutic targets of bladder cancer. Methods The gene expression profiles GSE3167 and GSE7476 were available from the Gene Expression Omnibus (GEO) database. Next, PPI network was built to filter the hub gene through the STRING database and Cytoscape software and GEPIA and Kaplan-Meier plotter were implemented. Frequency and type of hub genes and sub groups analysis were performed in cBioportal and ULCAN database. Finally,We used RT-qPCR to confirm our results. Results Totally, 251 DEGs were excavated from two datasets in our study. We only founded high expression of SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 was associated with worse outcomes in bladder cancer patients and no matter from the type of mutation or at the transcriptional level of hub genes, the tumor showed a high form of expression. However, only the expression of SMC4,CCNB1and CKS1B remained changed between the cancer and the normal samples in our results of RT-qPCR. Conclusion In conclusion,These findings indicate that the SMC4,CCNB1 and CKS1B may serve as critical biomarkers in the development and poor prognosis.

A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

2020 ◽  
Author(s):  
Yanjie Han ◽  
Xinxin Li ◽  
Jiliang Yan ◽  
Chunyan Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Growth Factor Receptor ◽  
Principal Component ◽  
Distant Metastases ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

scholarly journals Identification of Hub Genes and Potential Molecular Mechanisms in Patients with HBV-Associated Acute Liver Failure

2020 ◽  
Vol 16 ◽  
pp. 117693432094390
Author(s):  
Ying Sun ◽  
Haitao Yu ◽  
Fangfang Li ◽  
Liqiang Lan ◽  
Daxin He ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Division ◽  
Liver Failure ◽  
Acute Liver Failure ◽  
Antigen Processing ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Killer Cell ◽  
Cyclin B1 ◽  
Hub Genes

Hepatitis B virus (HBV) infection is a major cause of acute liver failure (ALF) in China, and mortality rates are high among patients who do not receive a matched liver transplant. This study aimed to determine potential mechanisms involved in HBV-ALF pathogenesis. Gene expression profiles under access numbers GSE38941 and GSE14668 were downloaded from the Gene Expression Omnibus database, including cohorts of HBV-ALF liver tissue and normal samples. Differentially expressed genes (DEGs) with false discovery rates (FDR) <0.05 and |log2(fold change)| >1 as thresholds were screened using the Limma package. Gene modules associated with stable disease were mined using weighed gene co-expression network analysis (WGCNA). A co-expression network was constructed and DEGs were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A gene-based network was constructed to explore major factors associated with disease progression. We identified 2238 overlapping DEGs as crucial gene cohorts in ALF development. Based on a WGCNA algorithm, 10 modules (modules 1-10) were obtained that ranged from 75 to 1078 genes per module. Cyclin-dependent kinase 1 ( CDK1), cyclin B1 ( CCNB1), and cell-division cycle protein 20 ( CDC20) hub genes were screened using the co-expression network. Furthermore, 17 GO terms and 6 KEGG pathways were identified, such as cell division, immune response process, and antigen processing and presentation. Two overlapping signaling pathways that are crucial factors in HBV-ALF were screened using the Comprehensive Toxicogenomics Database (CTD). Several candidate genes including HLA-E, B2M, HLA-DPA1, and SYK were associated with HBV-ALF progression. Natural killer cell-mediated cytotoxicity and antigen presentation contributed to the progression of HBV-ALF. The HLA-E, B2M, HLA-DPA1, and SYK genes play critical roles in the pathogenesis and development of HBV-ALF.

scholarly journals Genome-Wide Analysis of Cotton Auxin Early Response Gene Families and Their Roles in Somatic Embryogenesis

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 730 ◽  
Author(s):  
Sun ◽  
Wang ◽  
Ma ◽  
Li ◽  
Liu
Keyword(s):  
Somatic Embryogenesis ◽  
Upland Cotton ◽  
Expression Profiles ◽  
Gene Families ◽  
Early Response ◽  
Auxin Signaling ◽  
Evolutionary Divergence ◽  
Genome Wide ◽  
A Genome ◽  
Early Response Genes

Auxin is well known to regulate growth and development processes. Auxin early response genes serve as a critical component of auxin signaling and mediate auxin regulation of diverse physiological processes. In the present study, a genome-wide identification and comprehensive analysis of auxin early response genes were conducted in upland cotton. A total of 71 auxin response factor (ARF), 86 Auxin/Indole-3-Acetic Acid (Aux/IAA), 63 Gretchen Hagen3 (GH3), and 194 small auxin upregulated RNA (SAUR) genes were identified in upland cotton, respectively. Phylogenetic analysis revealed that the ARF, GH3, and SAUR families were likely subject to extensive evolutionary divergence between Arabidopsis and upland cotton, while the Aux/IAA family was evolutionary conserved. Expression profiles showed that the ARF, Aux/IAA, GH3, and SAUR family genes were extensively involved in embryogenic competence acquisition of upland cotton callus. The Aux/IAA family genes generally showed a higher expression level in the non-embryogenic callus (NEC) of highly embryogenic cultivar CCRI24 than that of recalcitrant cultivar CCRI12, which may be conducive to initializing the embryogenic transformation. Auxin early response genes were tightly co-expressed with most of the known somatic embryogenesis (SE) related genes, indicating that these genes may regulate upland cotton SE by interacting with auxin early response genes.

scholarly journals Co-Expression Network Analysis and Hub Gene Selection for High-Quality Fiber in Upland Cotton (Gossypium hirsutum) Using RNA Sequencing Analysis

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 119 ◽  
Author(s):  
Xianyan Zou ◽  
Aiying Liu ◽  
Zhen Zhang ◽  
Qun Ge ◽  
Senmiao Fan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Gossypium Hirsutum ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Upland Cotton ◽  
Fiber Quality ◽  
Fiber Development ◽  
Hub Genes ◽  
High Quality ◽  
Development Stages

Upland cotton (Gossypium hirsutum) is grown for its elite fiber. Understanding differential gene expression patterns during fiber development will help to identify genes associated with fiber quality. In this study, we used two recombinant inbred lines (RILs) differing in fiber quality derived from an intra-hirsutum population to explore expression profiling differences and identify genes associated with high-quality fiber or specific fiber-development stages using RNA sequencing. Overall, 72/27, 1137/1584, 437/393, 1019/184, and 2555/1479 differentially expressed genes were up-/down-regulated in an elite fiber line (L1) relative to a poor-quality fiber line (L2) at 10, 15, 20, 25, and 30 days post-anthesis, respectively. Three-hundred sixty-three differentially expressed genes (DEGs) between two lines were colocalized in fiber strength (FS) quantitative trait loci (QTL). Short Time-series Expression Miner (STEM) analysis discriminated seven expression profiles; gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation were performed to identify difference in function between genes unique to L1 and L2. Co-expression network analysis detected five modules highly associated with specific fiber-development stages, especially for high-quality fiber tissues. The hub genes in each module were identified by weighted gene co-expression network analysis. Hub genes encoding actin 1, Rho GTPase-activating protein with PAK-box, TPX2 protein, bHLH transcription factor, and leucine-rich repeat receptor-like protein kinase were identified. Correlation networks revealed considerable interaction among the hub genes, transcription factors, and other genes.

scholarly journals QTL and genetic analysis controlling fiber quality traits using paternal backcross population in Upland Cotton

2019 ◽  
Author(s):  
Lingling Ma ◽  
Ying Su ◽  
Yumei Wang ◽  
Hushai Nie ◽  
Yupeng Cui ◽  
...  
Keyword(s):  
Upland Cotton ◽  
Fiber Strength ◽  
Fiber Quality ◽  
Field Trials ◽  
Quality Traits ◽  
Backcross Population ◽  
Fiber Quality Traits ◽  
Ril Population ◽  
Cotton Hybrid ◽  
Novel Alleles

ABSTRACTIn present study, F14 recombinant inbred line (RIL) population was backcrossed to paternal parent for a paternal backcross (BC/P) population, deriving from one Upland cotton hybrid. Three repetitive BC/P field trials and one BC/M field trial were performed including both two BC populations and the original RIL population. Totally, for fiber quality traits, 24 novel QTLs were detected and 13 QTLs validated previous results. And 19 quantitative trait loci (QTL) in BC/P populations explained 5.01% - 22.09% of phenotype variation (PV). Among the 19 QTLs, three QTLs were detected simultaneously in BC/M population. The present study provided novel alleles of male parent for fiber quality traits with positive genetic effects. Particularly, qFS-Chr3-1 controlling fiber strength explained 22.09% of PV in BC/P population, which increased 0.48 cN/tex for fiber strength. A total of seven, two, eight, two and six QTLs explained over 10.00% of PV for fiber length, fiber uniformity, fiber strength, fiber elongation and fiber micronaire, respectively. In the RIL population, six common QTLs detected in more than one environment such as qFL-Chr1-2, qFS-Chr5-1, qFS-Chr9-1, qFS-Chr21-1, qFM-Chr9-1 and qFM-Chr9-2. Two common QTLs of qFE-Chr2-2 (TMB2386-SWU12343) and qFM-Chr9-1 (NAU2873-CGR6771) explained 22.42% and 21.91% of PV. In addition, a total of 142 and 46 epistatic QTLs and QTL × environments (E-QTLs and QQEs) were identified in RIL-P and BC/P populations, respectively.

scholarly journals Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Md. Rakibul Islam ◽  
Lway Faisal Abdulrazak ◽  
Mohammad Khursheed Alam ◽  
Bikash Kumar Paul ◽  
Kawsar Ahmed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log   fold   change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

scholarly journals Identification of Candidate Genes Controlling Fiber Quality Traits in Upland Cotton Through Integration of Meta-QTL, Significant SNP and Transcriptomic Data

2020 ◽  
Author(s):  
XU Shudi ◽  
Zhenyuan Pan ◽  
Feifan Yin ◽  
Qingyong Yang ◽  
Zhongxu Lin ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Cotton Fiber ◽  
Molecular Mechanisms ◽  
Fiber Strength ◽  
Fiber Quality ◽  
Significant Snps ◽  
Fiber Development ◽  
Computational Technique ◽  
Quality Traits ◽  
Fiber Quality Traits

Abstract Background Meta-analysis of quantitative trait locus (QTL) is a computational technique to identify consensus QTL and refine QTL positions on the consensus map from multiple mapping studies. The combination of meta-QTL intervals, significant SNPs and transcriptome analysis has been widely used to identify candidate genes in various plants. Results In our study, 884 QTL associated with cotton fiber quality traits from 12 studies were used for meta-QTL analysis based on reference genome TM-1, as a result, 74 meta-QTL were identified, including 19 meta-QTL for fiber length (FL), 18 meta-QTL for fiber strength (FS), 11 meta-QTL for fiber uniformity (FU), 11 meta-QTL for fiber elongation (FE), and 15 meta-QTL for micronaire (MIC). Combined with 8589 significant SNPs associated with fiber quality traits collected from 15 studies, 297 candidate genes were identified in the meta-QTL intervals, 20 of which showed high expression specifically in the developing fibers. According to the function annotations, some of the 20 key candidate genes are associated with the fiber development. Conclusions This study provides not only stable QTLs used for marker-assisted selection (MAS), but also candidate genes to uncover the molecular mechanisms for cotton fiber development.

scholarly journals Screening and identification of significant genes in esophageal squamous cell carcinoma by bioinformatical analysis

2020 ◽  
Author(s):  
Weirui Ren ◽  
Chuang Zhang ◽  
Lei Pan ◽  
Weijing Wang ◽  
Wenjuan Zhao ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Disease Free Survival ◽  
Hub Genes ◽  
Incidence And Mortality ◽  
Bioinformatical Analysis

Abstract Background: Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers with notably high incidence and mortality rates. However, the molecular mechanism underlying ESCC pathogenesis and prognosis is very complicated. The main objective of our investigation has been to obtain some knowledge of significant genes with poor outcome and their underlying mechanisms.Methods: Gene expression profiles of GSE26886, GSE23400, GSE20347 and GSE17351 were available from GEO database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape software.Results: A total of 105 DEGs were identified between normal esophagus and ESCC bioinformatical analysis samples. Functional annotations of the common DEGs indicate that extracellular matrix (ECM) remodeling plays a key role in tumor formation and progression.18 hub genes were identified and disease free survival analysis showed that CDKN3, RAD51AP1, KIF4A may be involved in poor prognosis in ESCC patients.Conclusions: DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of ESCC, and provide candidate targets for diagnosis and treatment of ESCC.

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