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Identification and characterization of new pancreatic cancer stem cells marker

ABSTRACT: To identify gene transcripts that were responsible for the stemness formation in pancreatic cancer cells, spheroid PANC1 and BXPC3 cells from primary sphere formation were enriched and then used for RNA-seq analysis. A gemcitabine-resistant SW1990 cell line was also established and used for RNA-seq analysis. In order to ensure the data quality, raw reads were preprocessed by removing low quality sequences, dejunction contamination(Trimmomatic), A/T/G/C Content Distribution(RSeQC), rRNA removal(bowtie2), etc to obtain high quality sequences (clean reads) and all subsequent analyses were based on clean reads. Clean Data was aligned to the reference genome by HISAT, and gene expression quantification was done via software FeatureCounts. DESseq2 package in the R software was introduced to analysis differentially expressed genes between comparisons using read counts estimated by FeatureCounts. Genes with adjusted pvalue< 0.05 and |log2(Fold Change) | > 1 were defined as differentially expressed genes. Totally, we identified 108 DEGs whose expression changes were almost similar in all the data sets.

ORGANISM(S): Homo sapiens

PROVIDER: GSE172303 | GEO | 2023/04/19

REPOSITORIES: GEO

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Project description:Purpose: Cucumber (Cucumis sativus L.) is an economically important vegetable crop worldwide, and cucumber fruit spine density has an important impact on the commercial value. However, little is known about the regulatory mechanism for the fruit spine formation.In this study, the transcriptome analyses of ovaries and pericarps from numerous-spine parent and few-spine parent were conducted to identify the gene regulatory networks involved in the formation and development of numerous fruit spines in cucumber. Methods: Cucumber mRNA profiles of ovaries and pericarps from numerous-spine parent and few-spine parent were generated by deep sequencing, in triplicate, using Illumina HiSeq 4000. Then, clean data (clean reads) were obtained by removing reads containing adapters, reads containing poly-N sequences and low-quality reads from the raw data. Simultaneously, the Q20, Q30 and GC contents of the clean data were calculated. All of the downstream analyses were based on the high-quality clean data. Clean paired-end reads were mapped to the reference genome using TopHat v2.0.12 (Trapnell et al. 2012). Then, the FPKM (fragments per kilobase of transcript sequence per million base pairs sequenced) value of each gene was calculated to estimate gene expression levels (Trapnell et al. 2010). Genes with an adjusted P-value < 0.05 identified by DESeq were assigned as differentially expressed genes(DEGs). Results: We generated 42.96-57.53 million raw reads from each library, and 39.85-54.02 million clean reads were obtained after the removal of low-quality reads and adapter sequences. Among the clean reads, 79.03-80.94% were mapped to the gene database . Based on the KEGG database, pathway enrichment analysis was performed to identify significantly enriched metabolic pathways or signal transduction pathways in DEGs. Plant hormone signal transduction was significantly enriched in up-regulated genes in both F_6DBF compared with M_6DBF and F_0DAA compared with M_0DAA. Conclusions: Based on the transcriptome analysis, we excavated possible biological regulatory networks involved in the formation and development of numerous fruit spines in cucumber. This work will promote the exploration of molecular mechanisms that regulate cucumber fruit spine density.

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Identification and characterization of new pancreatic cancer stem cells marker

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