scholarly journals Identification of potential key genes and high-frequency mutant genes in prostate cancer by using RNA-Seq data

2018 ◽  
Author(s):  
Ze Zhang ◽  
He Wu ◽  
Hong Zhou ◽  
Yunhe Gu ◽  
Yufeng Bai ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Frequency ◽  
Rna Seq ◽  
Key Genes ◽  
Mutant Genes

scholarly journals Identification of open chromosomal regions and key genes in prostate cancer via integrated analysis of DNase‑seq and RNA‑seq data

2018 ◽  
Author(s):  
Xin Wei ◽  
Lili Yu ◽  
Xuefei Jin ◽  
Lide Song ◽  
Yanting Lv ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Key Genes

Investigation of Key Genes associated with Prostate Cancer using RNA-Seq Data

2014 ◽  
Vol 29 (1) ◽  
pp. e86-e92 ◽  
Author(s):  
Jitao Wu ◽  
Fan Feng ◽  
Diandong Yang ◽  
Shengqiang Yu ◽  
Jianqiu Liu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Normal Sample ◽  
Pathway Enrichment Analysis ◽  
Rna Seq ◽  
Cancer Sample ◽  
Key Genes

We aimed to identify key genes associated with prostate cancer using RNA-sequencing (RNA-seq) data. RNA-seq data, including 1 cancer sample and 1 adjacent normal sample, were downloaded from the NCBI SRA database and the differentially expressed genes (DEGs) were identified with the software Cufflinks. Functional enrichment analysis was performed to uncover the biological functions of DEGs. Regulatory information was retrieved from the IPA database and a network was established. A total of 147 DEGs were obtained, including 96 downregulated and 51 upregulated DEGs. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that metabolism and signal transduction were the 2 major functions that were significantly influenced. Moreover, an interaction network was built. In conclusion, a number of DEGs was identified and their roles in the pathogenesis of cancer were supported by previous studies. More studies are necessary to further validate their usefulness in the diagnosis and treatment of prostate cancer.

scholarly journals Bioinformatics-based Screening of Key Genes for Saponin Metabolism in Quinoa

2021 ◽  
Author(s):  
Chengang Guo ◽  
Zhimin wei ◽  
Wei Lyu ◽  
Yanlou Geng
Keyword(s):  
Differentially Expressed Genes ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Hierarchical Cluster ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Rna Seq ◽  
Protein Coding ◽  
Key Genes

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.

scholarly journals Identification of Key Genes Affecting Porcine Fat Deposition Based on Co-Expression Network Analysis of Weighted Genes

2021 ◽  
Author(s):  
Kai Xing ◽  
Huatao Liu ◽  
Fengxia Zhang ◽  
Yibing Liu ◽  
Yong Shi ◽  
...  
Keyword(s):  
Network Analysis ◽  
Production Efficiency ◽  
Fat Deposition ◽  
Rna Seq ◽  
Genetic Studies ◽  
Gene Expression Matrix ◽  
Differential Gene ◽  
Key Genes ◽  
Strong Candidate ◽  
Molecular Regulatory Mechanisms

Abstract Background: Fat deposition is an important economic consideration for pig production. The amount of fat deposition in pigs seriously affects production efficiency, quality, and reproductive performance, while also affecting consumers' choice of pork. Weighted gene co-expression network analysis (WGCNA) has been shown to be effective in pig genetic studies. Therefore, this study aimed to identify modules that co-express genes associated with fat deposition in pigs (Songliao black and Landrace breeds) with extreme levels of backfat (high and low), and to identify the central genes in each of these modules. Results: We used RNA-seq of different pig tissues to construct a gene expression matrix consisting of 12 862 genes from 36 samples. Eleven co-expression modules were identified using WGCNA; the number of genes in these modules ranged from 39 to 3363. We found four co-expression modules were significantly correlated with backfat thickness. A total of 14 genes ( RAD9A , IGF2R , SCAP , TCAP , DGAT1 , GPS2 , IGF1 , MAPK8 , FABP , FABP5 , LEPR , UCP3 , APOF , and FASN ) were found to be related to fat deposition. Conclusions: RAD9A , TCAP , GPS2 , and APOF were found to be the key genes in the four modules according to the degree of gene connectivity. Combining the results of differential gene analysis, APOF was proposed as a strong candidate gene for body size traits. This study explores the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms behind porcine fat deposition.

RNA-seq analysis revealed key genes associated with salt tolerance in rapeseed germination through carbohydrate metabolism, hormone, and MAPK signaling pathways

2022 ◽  
Vol 176 ◽  
pp. 114262
Author(s):  
Ibrahim A.A. Mohamed ◽  
Nesma Shalby ◽  
Ali Mahmoud El-Badri ◽  
Maria Batool ◽  
Chunyun Wang ◽  
...  
Keyword(s):  
Salt Tolerance ◽  
Signaling Pathways ◽  
Carbohydrate Metabolism ◽  
Mapk Signaling ◽  
Rna Seq ◽  
Key Genes ◽  
Mapk Signaling Pathways

scholarly journals Transcriptome Analysis Reveals Key Seed-Development Genes in Common Buckwheat (Fagopyrum esculentum)

2019 ◽  
Vol 20 (17) ◽  
pp. 4303 ◽  
Author(s):  
Hongyou Li ◽  
Qiuyu Lv ◽  
Jiao Deng ◽  
Juan Huang ◽  
Fang Cai ◽  
...  
Keyword(s):  
Signal Transduction ◽  
Seed Development ◽  
Seed Size ◽  
Molecular Mechanisms ◽  
Signal Transduction Pathway ◽  
Fagopyrum Esculentum ◽  
Rna Seq ◽  
Common Buckwheat ◽  
Size Change ◽  
Key Genes

Seed development is an essential and complex process, which is involved in seed size change and various nutrients accumulation, and determines crop yield and quality. Common buckwheat (Fagopyrum esculentum Moench) is a widely cultivated minor crop with excellent economic and nutritional value in temperate zones. However, little is known about the molecular mechanisms of seed development in common buckwheat (Fagopyrum esculentum). In this study, we performed RNA-Seq to investigate the transcriptional dynamics and identify the key genes involved in common buckwheat seed development at three different developmental stages. A total of 4619 differentially expressed genes (DEGs) were identified. Based on the results of Gene Ontology (GO) and KEGG analysis of DEGs, many key genes involved in the seed development, including the Ca2+ signal transduction pathway, the hormone signal transduction pathways, transcription factors (TFs), and starch biosynthesis-related genes, were identified. More importantly, 18 DEGs were identified as the key candidate genes for seed size through homologous query using the known seed size-related genes from different seed plants. Furthermore, 15 DEGs from these identified as the key genes of seed development were selected to confirm the validity of the data by using quantitative real-time PCR (qRT-PCR), and the results show high consistency with the RNA-Seq results. Taken together, our results revealed the underlying molecular mechanisms of common buckwheat seed development and could provide valuable information for further studies, especially for common buckwheat seed improvement.

scholarly journals Identification of key genes and specific pathways potentially involved in androgen-independent, mitoxantrone-resistant prostate cancer

2019 ◽  
Vol Volume 11 ◽  
pp. 419-430
Author(s):  
Sha Zhu ◽  
Lili Jiang ◽  
Liuyan Wang ◽  
Lingli Wang ◽  
Cong Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Key Genes ◽  
Androgen Independent

scholarly journals Identifying the key genes and microRNAs in prostate cancer bone metastasis by bioinformatics analysis

FEBS Open Bio ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 674-688 ◽  
Author(s):  
Zhiguo Zhu ◽  
Yaoan Wen ◽  
Chunxiang Xuan ◽  
Qingping Chen ◽  
Qian Xiang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Bone Metastasis ◽  
Bioinformatics Analysis ◽  
Key Genes

scholarly journals Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data

2019 ◽  
Vol 15 (11) ◽  
pp. e1007435 ◽  
Author(s):  
Jiajun Zhang ◽  
Wenbo Zhu ◽  
Qianliang Wang ◽  
Jiayu Gu ◽  
L. Frank Huang ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Regulatory Network ◽  
Time Course ◽  
Cancer Drug ◽  
Rna Seq ◽  
Cancer Drug Resistance ◽  
Key Genes
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