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ABSTRACT: Genome doubled HCT-116 cells

PROVIDER: PRJNA595067 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR10673517_1.fastq.gz	Fastqsanger.gz
	SRR10673517_2.fastq.gz	Fastqsanger.gz
	SRR10673518_1.fastq.gz	Fastqsanger.gz
	SRR10673518_2.fastq.gz	Fastqsanger.gz
	SRR10673519_1.fastq.gz	Fastqsanger.gz

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Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived HCT-116 cell transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis. Methods: HCT-116 cell mRNA profiles of HCT-116-GOLPH3-Vector and HCT-116-GOLPH3-Overepression were generated by deep sequencing, in triplicate, using Illumina GAIIx. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays. Results: RNA sequencing (RNA-seq) was used to investigate gene expression in HCT-116-PMSCV-Vector and HCT-116-PMSCV GOLPH3 cells, while Gene Ontology (GO) was used to annotate the various functional genes. By comparing the differentially expressed genes, we noticed that GOLPH3 was associated with EMT. Gene set enrichment analysis (GSEA) analysis of the RNA-seq results based on the GSE77953 dataset was performed to investigate the biological functions of HCT-116-PMSCV-Vector and HCT-116-PMSCV-GOLPH3 cells. The findings suggested that GOLPH3 expression was positively associated with colon cancer cell autophagy. Signal pathway enrichment was next analyzed based on the differential expression of genes between HCT-116-PMSCV-Vector and HCT-116-PMSCV-GOLPH3 cells, as examined by RNA-seq. Notably, GOLPH3 has correlated with the PI3K/Akt signaling pathway. Conclusions: Our study represents the first detailed analysis of HCT-116 cell transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

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