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

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.

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

10.21203/rs.3.rs-139481/v1 ◽

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.

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

10.21203/rs.3.rs-148726/v1 ◽

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.

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

Industrial Crops and Products ◽

10.1016/j.indcrop.2021.114262 ◽

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

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

International Journal of Molecular Sciences ◽

10.3390/ijms20174303 ◽

2019 ◽

Vol 20 (17) ◽

pp. 4303 ◽

Cited By ~ 3

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.

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