Project description:microRNA expression signatures can differentiate normal and breast cancer tissues and can define specific clinico-pathological phenotypes in breast tumors. In order to further evaluate the microRNA expression profile in breast cancer, we analyzed the expression of 667 microRNAs in 29 tumors and 21 adjacent normal tissues using TaqMan Low-density arrays. 130 miRNAs showed significant differential expression (adjusted P value=0.05, Fold Change=2) in breast tumors compared to the normal adjacent tissue. Importantly, the role of 43 of these microRNAs has not been previously reported in breast cancer, including several evolutionary conserved microRNA*, showing similar expression rates to that of their corresponding leading strand. The procedure begins with the retro-transcription of 70ng of total RNA with stem-loop primers to obtain a cDNA template. A pre-amplification step was included in order to increase the concentration of the original material and to detect microRNAs that are expressed at low levels. The pre-amplified product was loaded into the TaqManM-BM-. Low Density Arrays and amplification signal detection was carried out using the 7900 FAST real time thermal cycler (ABI). A total of 29 tumor and 21 normal samples (two pools: one containing five samples, other containing 12 samples, plus 4 independent normal samples) were analyzed. 23 tumors and the two normal pools were processed by triplicate, representing 82% of the total samples.
Project description:Here, we analyse bulk mRNA-seq data derived from normal tissues adjacent to tumors (NATs) and tumour tissues of colorectal cancer (CRC), including 80 NAT samples and 80 tumour samples.
Project description:In this study, we employed an integrated proteomics and acetyl-proteomics strategy to characterize of 8 tumors and 8 matched normal adjacent tissues (NATs) with 4D proteomics technology. Proteomics analysis identified a total of 6551 proteins, of which 5411 proteins were quantified across all samples. A total of 8789 acetylation sites on 2348 proteins were identified, of which 5112 sites of 1620 proteins contained quantitative information.
Project description:microRNA expression signatures can differentiate normal and breast cancer tissues and can define specific clinico-pathological phenotypes in breast tumors. In order to further evaluate the microRNA expression profile in breast cancer, we analyzed the expression of 667 microRNAs in 29 tumors and 21 adjacent normal tissues using TaqMan Low-density arrays. 130 miRNAs showed significant differential expression (adjusted P value=0.05, Fold Change=2) in breast tumors compared to the normal adjacent tissue. Importantly, the role of 43 of these microRNAs has not been previously reported in breast cancer, including several evolutionary conserved microRNA*, showing similar expression rates to that of their corresponding leading strand.
Project description:Circulating transcriptional landscapes between breast cancer tissues and adjacent normal tissues were compared using the Affymetrix Human OE LncRNA Microarray with probes for profiling of 63542 human lncRNAs. Goal was to investigate potential lncRNAs involved in breast cancer progression.
Project description:Cancer tissues and noncancerous adjacent tissues (NATs) with the luminal A subtype (ER- and PR-positive, HER2-negative) were obtained from paired IDC and ILC patients respectively. Label-free quantitative proteomics and phosphoproteomics methods were used to detect differential proteins and the phosphorylation status between 10 paired breast cancer and NATs. Then, the difference in protein expression and its phosphorylation between IDC and ILC subtypes were explored. Meanwhile, the activation of kinases and their substrates was also revealed by Kinase-Substrate Enrichment Analysis (KSEA).
Project description:Although multi-omics studies of glioblastoma (GBM) have improved understanding of its biology nature and accelerated targeted therapy, data for paired adjacent normal tissues (NAT) remains limited. Here, we report proteomes from 3 paired of tumor tissues and NATs of glioblastoma (GBM) patients using liquid chromatography with tandem mass spectrometry (LC-MS/MS)-based label-free quantification. This dataset provides a resource of paired GBM and normal tissues to identify novel tumor-specific oncogenes or tumor-suppressor genes.