The transcriptomes in RGS10-depleted SKBR3 cells

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.

Methods
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.