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Construction of rat aortic smooth muscle cell line A7r5-specific gene expression matrix

ABSTRACT: The purpose of our study is to construct rat aortic smooth cell line A7r5-specific interactome by aggregating publically available microarray samples, and provided a global view of biological process and identified transcription factors that regulate the transcriptomic changes. We collected 30 samples of A7r5 cell line microarray from 2 prior studies and our own study data, GSE87439. We performed normalization for Affymetrix microarray platform using SCAN.UPC R package (Release 3.9, PMID: 22959562, https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). After normallizatin, we performed batch effect adjusting because datasets came from different laboratoriesand, and combined the datasets . The matrix data we deposited in GEO has normalized log2 signal intensity for 14,004 genes which are common across datasets. The genes were mapped to Rattus norvegicus Entrez Gene IDs. A7r5-specific interactome was assembled by Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe, PMID: 16723010). Then, we used Master Regulator Analysis-Fisher’s Extact Test (MRA-FET, PMID: 20531406) to infer transcription factors which control a gene signature changes by the effect of SCE and SchB on the TGFβ1-treated A7r5 cells (PMID: 29423034). We discovered Junb, Srebf2, Foxk2, and Irf9 as a master regulator to modulate the gene signatures on the TGFβ1-treated A7r5 cells. Our findings will provide biological insights into the pharmaceutical effect of SCE and SchB in A7r5 cells.

ORGANISM(S): Rattus norvegicus

PROVIDER: GSE134932 | GEO | 2020/02/13

REPOSITORIES: GEO

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Project description:The purpose of our study was to construct prostate cancer cell-specific interactome by aggregating microarray samples available in public, and identify novel transcriptional regulators that control prostate cancer cell cycle progression. We collected 121 samples of PC-3 cell microarray data from 11 prior studies and our own study data, GSE45567, and performed pre-processing steps, such as quality control test, normalization, and batch effect adjusting. In quality control test, 10 low-quality samples were removed. The matrix data we deposited in GEO has normalized log2 signal intensity of 11,877 genes common in 110 samples, as the 110 samples came from different Affymetrix. The Gene IDs were identified by Entrez Gene IDs that overlapped across different Affymetrix platforms (File: GSE67157_Matrix_PC-3cell_Interactome.txt). In GSE45567 dataset, which is one of the 12 data sets, we selected microarray samples of vehicle-, cineol-, linalool-, and geraniol-treated PC-3 cells, and performed unsupervised clustering analysis to establish dependable clusters of a clear phenotypic transition. Then, the gene set specifically enriched or depleted in the geraniol cluster against vehicle cluster was used for gene set enrichment analysis (GSEA). We found cell cycle-related gene signatures were specifically down-regulated in the geraniol cluster. PC-3 cell-specific interactome was assembled from 110 microarray samples by Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe, PMID: 16723010). We used Master Regulator Analysis-Fisher’s Extact Test (MRA-FET, PMID: 20531406) to infer transcription factors which are geraniol target molecules and control cell cycle gene signatures. We discovered E2F8 is as a master regulator to modulate geraniol-specific genes toward G2/M cell cycle arrest. Our findings will provide biological insights into the role of E2F8 in prostate cancer for transcriptional regulation leading to the development of cell cycle-targeting chemotherapeutic reagent. In addition, our approach serves as an example to decide the most potent anti-cancer regime in other cancer types and identify the target molecules of cancer-preventive reagents through computational analyses.

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Construction of rat aortic smooth muscle cell line A7r5-specific gene expression matrix

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