Dataset Information

RNA-seq data of invasive ductal carcinoma and adjacent normal tissues from a Korean patient with breast cancer.

ABSTRACT: Invasive ductal carcinoma is the most common type of breast cancer. Here, we provide a whole transcriptome shotgun sequencing (called RNA-seq) dataset conducted with ten samples of invasive ductal carcinoma tissue and three samples of adjacent normal tissue from a single Korean breast cancer patient (luminal B subtype). Differentially expressed genes (DEGs) were identified with a false discovery rate (FDR)-adjusted p-value of 0.05. Gene ontology analysis identified several key pathways, including lymphocyte activation. A list of differentially expressed genes is provided. The raw data was uploaded to the sequence read archive (SRA) database and the BioProject ID is PRJNA432903.

SUBMITTER: Hong JH

PROVIDER: S-EPMC5996721 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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RNA-seq data of invasive ductal carcinoma and adjacent normal tissues from a Korean patient with breast cancer.

Hong Ji Hyung JH Ko Yoon Ho YH Kang Keunsoo K

Data in brief 20180321

Invasive ductal carcinoma is the most common type of breast cancer. Here, we provide a whole transcriptome shotgun sequencing (called RNA-seq) dataset conducted with ten samples of invasive ductal carcinoma tissue and three samples of adjacent normal tissue from a single Korean breast cancer patient (luminal B subtype). Differentially expressed genes (DEGs) were identified with a false discovery rate (FDR)-adjusted <i>p</i>-value of 0.05. Gene ontology analysis identified several key pathways, i ...[more]

PMID: 29900229

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Project description:BackgroundDuctal carcinoma in situ (DCIS) accounts for approximately 20% of mammographically detected breast cancers. Although DCIS is generally highly curable, some women with DCIS will develop life-threatening invasive breast cancer, but the determinants of progression to infiltrating ductal cancer (IDC) are largely unknown.MethodsIn the current study, we used multiplex ligation-dependent probe amplification (MLPA), a multiplex PCR-based test, to compare copy numbers of 21 breast cancer related genes between laser-microdissected DCIS and adjacent IDC lesions in 39 patients. Genes included in this study were ESR1, EGFR, FGFR1, ADAM9, IKBKB, PRDM14, MTDH, MYC, CCND1, EMSY, CDH1, TRAF4, CPD, MED1, HER2, CDC6, TOP2A, MAPT, BIRC5, CCNE1 and AURKA.ResultsThere were no significant differences in copy number for the 21 genes between DCIS and adjacent IDC. Low/intermediate-grade DCIS showed on average 6 gains/amplifications versus 8 in high-grade DCIS (p?=?0.158). Furthermore, alterations of AURKA and CCNE1 were exclusively found in high-grade DCIS, and HER2, PRDM14 and EMSY amplification was more frequent in high-grade DCIS than in low/intermediate-grade DCIS. In contrast, the average number of alterations in low/intermediate and high grade IDC was similar, and although EGFR alterations were exclusively found in high grade IDC compared to low/intermediate-grade IDC, there were generally fewer differences between low/intermediate-grade and high-grade IDC than between low/intermediate-grade and high-grade DCIS.ConclusionIn conclusion, there were no significant differences in copy number for 21 breast cancer related genes between DCIS and adjacent IDC, indicating that DCIS is genetically as advanced as its invasive counterpart. However, high grade DCIS showed more copy number changes than low/intermediate grade DCIS with specifically involved genes, supporting a model in which different histological grades of DCIS are associated with distinct genomic changes that progress to IDC in different routes. These high grade DCIS specific genes may be potential targets for treatment and/or predict progression.

Project description:A comprehensive meta-analysis of publicly available gene expression microarray data obtained from human-derived pancreatic ductal adenocarcinoma (PDAC) tissues and their histologically matched adjacent tissue samples was performed to provide diagnostic and prognostic biomarkers, and molecular targets for PDAC. An integrative meta-analysis of four submissions (GSE62452, GSE15471, GSE62165, and GSE56560) containing 105 eligible tumor-adjacent tissue pairs revealed 344 differentially over-expressed and 168 repressed genes in PDAC compared to the adjacent-to-tumor samples. The validation analysis using TCGA combined GTEx data confirmed 98.24% of the identified up-regulated and 73.88% of the down-regulated protein-coding genes in PDAC. Pathway enrichment analysis showed that "ECM-receptor interaction", "PI3K-Akt signaling pathway", and "focal adhesion" are the most enriched KEGG pathways in PDAC. Protein-protein interaction analysis identified FN1, TIMP1, and MSLN as the most highly ranked hub genes among the DEGs. Transcription factor enrichment analysis revealed that TCF7, CTNNB1, SMAD3, and JUN are significantly activated in PDAC, while SMAD7 is inhibited. The prognostic significance of the identified and validated differentially expressed genes in PDAC was evaluated via survival analysis of TCGA Pan-Cancer pancreatic ductal adenocarcinoma data. The identified candidate prognostic biomarkers were then validated in four external validation datasets (GSE21501, GSE50827, GSE57495, and GSE71729) to further improve reliability. A total of 28 up-regulated genes were found to be significantly correlated with worse overall survival in patients with PDAC. Twenty-one of the identified prognostic genes (ITGB6, LAMC2, KRT7, SERPINB5, IGF2BP3, IL1RN, MPZL2, SFTA2, MET, LAMA3, ARNTL2, SLC2A1, LAMB3, COL17A1, EPSTI1, IL1RAP, AK4, ANXA2, S100A16, KRT19, and GPRC5A) were also found to be significantly correlated with the pathological stages of the disease. The results of this study provided promising prognostic biomarkers that have the potential to differentiate PDAC from both healthy and adjacent-to-tumor pancreatic tissues. Several novel dysregulated genes merit further study as potentially promising candidates for the development of more effective treatment strategies for PDAC.

Dataset Information

RNA-seq data of invasive ductal carcinoma and adjacent normal tissues from a Korean patient with breast cancer.

Publications

RNA-seq data of invasive ductal carcinoma and adjacent normal tissues from a Korean patient with breast cancer.

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