DOI: 10.5061/dryad.7h44j102r
title: The transcriptomes in RGS10-depleted SKBR3 cells

description: RGS10 plays an important role in cell survival, polarization, adhesion,
 chemotaxis, and differentiation in various cancers. However, the mechanism
 underlying the function of RGS10 in breast cancer remains unclear. We
 compared the gene expression differences between the RGS silencing group
 and the wild group using RNA seq. The shRNA-RGS10 and shRNA negative
 control (NC) are transfected in SKBR3 cells. We utilized a series of
 functional experiments to verify the silencing of RGS10. The
 transcriptomes in RGS10-depleted SKBR3 cells and shRNA-NC SKBR3 cells were
 performed by second-generation high-throughput sequencing. This project
 measured a total of 6 samples using the BGISEQ platform, with an average
 output of 6.70G of data per sample. The average comparison rate of the
 sample compared to the genome is 90.38%, and the average comparison rate
 of the gene set is 70.96%; A total of 16606 genes were detected. By KEGG
 enrichment analysis, upregulated KEGG pathways were found to be associated
 with cytokine-cytokine receptor interactions and extracellular
 matrix-receptor interactions. The biomarkers expression of EMT was
 upregulated in RGS10-depleted SKBR3 cells compared to NC in Western
 blotting. LCN2 and vimentin protein levels were higher and E-cadherin
 protein levels were lower in RGS10-depleted SKBR3 cells compared to NC.
 These findings show that RGS10 deficiency induces EMT by activating the
 LCN2, supporting the potential of RGS10 as a prognostic biomarker in
 breast cancer.
description: The dataset was collected by RNA sequencing. The raw sequencing
 data contains low-quality, contaminated joints, and reads with high levels
 of unknown base N. These reads need to be removed before data analysis to
 ensure the reliability of the results by SOAPnuke (v1.5.6). The
 high-quality reads were mapped and aligned to the human reference genome
 using HISAT2. The reference genome source is NCBI, and the reference
 genome version is GCF_000001405.39_GRCh38.p13. Alignment and
 quantification of reads were performed using Bowtie. The expression levels
 of genes were quantified to identify differentially expressed genes by
 RSEM (v1.3.1). To gain insight into the change of phenotype, GO
 (http://www.geneontology.org/) and KEGG (https://www.kegg.jp/) enrichment
 analysis of annotated different expression genes was performed by Phyper
 based on a Hypergeometric test. The data uploaded here has not undergone
 any processing. RNA sequencing was performed by the BGI
 (Shenzhen, China). Briefly, RNA from the RGS silencing group and the wild
 group(NC group) was extracted using TRIzol reagent. RNA samples were
 sequenced on the BGISEQ platform. The sequencing data was filtered with
 SOAPnuke by (1) Removing reads containing sequencing adapter; (2) Removing
 reads whose low-quality base ratio (base quality less than or equal to 15)
 is more than 20%; (3) Removing reads whose unknown base ('N'
 base) ratio is more than 5%, afterwards clean reads were obtained and
 stored in FASTQ format. Then, the clean reads were mapped to the reference
 genome using HISAT, and Bowtie2 was used to align the clean reads to the
 reference genes. The reference genome source is NCBI, and the reference
 genome version is GCF\_000001405.39\_GRCh38.p13. The expression levels of
 genes were quantified to identify differentially expressed genes by RSEM.
 The subsequent analysis and data mining was performed on Dr. Tom's
 Multi-omics Data mining system (https://biosys.bgi.com). The analysis
 results show that multiple genes in MSCs from patients with diabetes were
 up- or down-regulated compared to healthy controls. By KEGG enrichment
 analysis, upregulated KEGG pathways were found to be associated with
 cytokine-cytokine receptor interactions and extracellular matrix-receptor
 interactions. The biomarkers expression of EMT was upregulated in
 RGS10-depleted SKBR3 cells compared to the negative control group in
 Western blotting. LCN2 and vimentin protein levels were higher and
 E-cadherin protein levels were lower in RGS10-depleted SKBR3 cells
 compared to the negative control group.
description: # The transcriptomes in RGS10-depleted SKBR3 cells Dataset DOI link:
 [https://doi.org/10.5061/dryad.7h44j102r](https://doi.org/10.5061/dryad.7h44j102r) ## Description of the data and file structure Here are twelve groups of data for analysis. The group labeled shRNA-RGS10 represents breast cancer cell line SKBR3 after silencing the RGS10 gene. The shRNA-NC group serves as the negative control. Each set of sequencing was performed with three biological replicates. \_1 indicates the forward sequencing results, and \_2 indicates the reverse sequencing results. All data are presented in fastq format. For a more detailed explanation: * "shRNA" stands for short hairpin RNA, which is used to silence specific genes through RNA interference. * "RGS10" refers to the Regulator of G protein Signaling 10, the gene that is being silenced. * "NC" typically stands for "Negative Control," meaning this group does not receive the gene silencing treatment.