Browse
Submit Data
Databases
API
Help

Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Differentially expressed genes after miRNA or siRNA transfection in human cancer cell lines Panc-1, FaDu and PC3

ABSTRACT: To identify differentially expressed genes by anti cancer treatments (microRNAs or siRNAs) in human cancer, several cell lines (pancreatic cancer, hypopharyngeal squamous cell carcinoma and prostate cancer) were subjected to Agilent whole genome microarrays. Human cell lines (Panc-1, FaDu and PC3) were treated with miRNAs (miR99a-5p, miR-99a-3p, miR-100-3p, miR-150-5p and miR-150-3p), siRNAs (si-FOXQ1).

ORGANISM(S): Homo sapiens

SUBMITTER: Naohiko Seki

PROVIDER: E-GEOD-85614 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

Analysis of differentially expressed lncRNA, circRNA and mRNA between FaDu and cisplatin-resistant FaDu cell

Project description:Cisplatin is a common chemotherapeutic drug for hypopharyngeal cancer. But cisplatin-resistance of hypopharyngeal cancer is rarely explored. We cultured hypopharyngeal cancer cell (FaDu) and induced its cisplatin-resistant cell (FaDu/DDP4). The resistance index (RI) of FaDu/DDP4 was 2.828. Then we tested the differentially expressed genes (DEGs) between FaDu and FaDu/DDP4. DEGs contain 2388 lncRNAs, 1932 circRNAs, 745 mRNAs and 202 miRNAs. We used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzed the DEGs. The differentially expressed 745 mRNAs were classified into 3 domains and 47 secondary GO terms. In KEGG pathway enrichment, “TNF signaling pathway”, “IL-17 signaling pathway” and “JAK-STAT signaling pathway” have greater enrich factors. And we drew the ceRNA networks of DEGs. 52 lncRNAs, 148 circRNAs, 155 mRNAs and 18 miRNAs were selected to draw the network. We noticed several potential targets (as miR-197-5p, miR-6808-5p, APOE, MMP1, S100A9 and CYP24A1). At last, we chose 8 miRNAs and 6 mRNAs for qRT-PCR to verify our microarray. In them, miR-197-5p, miR-6808-5p, APOE, MMP1, S100A9 and CYP24A1 might be potential genes inducing resistance.

2021-06-02 | GSE167396 | GEO

Analysis of differentially expressed miRNA in cisplatin-resistant FaDu cell

2021-06-02 | GSE167398 | GEO

Five microRNAs in serum are able to differentiate breast cancer patients from healthy individuals

Project description:Breast Cancer is the cancer with most incidence and mortality in women. microRNAs are emerging as novel prognosis/diagnostic tools. Our aim was to identify a serum microRNA signature useful to predict cancer development. We focused on studying the expression levels of 30 microRNAs in the serum of 96 breast cancer patients versus 92 control individuals. Bioinformatic studies provide a microRNA signature, designated as a predictor, based upon the expression levels of 5 microRNAs. Then, we tested the predictor in a group of 60 randomly chosen women. Lastly, a proteomic study unveiled the over-expression and down-regulation of proteins differently expressed in the serum of breast cancer patients versus that of control individuals. Twenty-six microRNAs differentiate cancer tissue from healthy tissue and 16 microRNAs differentiate the serum of cancer patients from that of the control group. The tissue expression of miR-99a-5p, mir-497-5p, miR-362, and miR-1274, and the serum levels of miR-141 correlated with patient survival. Moreover, the predictor consisting of mir-125b-5p, miR-29c-3p, mir-16-5p, miR-1260, and miR-451a was able to differentiate breast cancer patients from controls. The predictor was validated in 20 new cases of breast cancer patients and tested in 60 volunteer women, assigning 11 out of 60 women to the cancer group. An association of low levels of mir-16-5p with a high content of CD44 protein in serum was found. Circulating microRNAs in serum can represent biomarkers for cancer prediction. Their clinical relevance and use of the predictor here described might be of potential importance for breast cancer prediction.

2020-09-18 | PXD020795 | Pride

Differentially expressed genes after miRNA or siRNA transfection in human cancer cell lines (II)

Project description:To identify differentially expressed genes by anti cancer treatments (microRNAs or siRNAs) in human cancer, several cell lines (bladder cancer, prostate cancer, renal cell carcinoma, oral squamous cell carcinoma and lung squamous cell carcinoma) were subjected to Agilent whole genome microarrays. Human cancer cell lines (SAS, HSC3, BOY, T24, PC3, PC3M, DU145, C4-2, 786-O, A-498 and EBC-1) were treated with miRNAs (miR-205, miR-29a, miR-144-3p, miR-144-5p, miR-451, miR-210, miR-145-5p, miR-145-3p, miR-23b cluster, miR-221, miR-222 and miR-223), siRNAs (si-GOLM1, si-HMGB3, si-CENPF, si-LOXL2, si-TMEM184B and si-CORO1C).

2016-04-13 | E-GEOD-66498 | biostudies-arrayexpress

5b763f6e-2031-4d46-913d-ee7491e7e50c - samples

Project description:iPSC-derived neurons were treated with mimics and inhibitors of the miRNAs miR-150-5p, hsa-mir-193a-3p and hsa-miR-19b-3p. RNA-sequencing was then performed to examine the effects of miRNA up-regulation and inhibition.

| EGAD00001006844 | EGA

56afc434-ec9b-4228-9939-fc0a3871593d - samples

Project description:Microglia were derived from iPSCs and treated with mimics and inhibitors of the miRNAs hsa-miR-150-5p, hsa-miR-193a-3p and hsa-miR-19b-3p. RNA-sequencing was then performed to examine the effects of up- and down-regulation of the respective miRNAs.

| EGAD00001006842 | EGA

Differentially expressed genes after miRNA or siRNA transfection in human cancer cell lines II

Project description:To identify differentially expressed genes by anti cancer treatments (microRNAs or siRNAs) in human cancer, several cell lines (pancreatic cancer, esophageal cancer, bladder cancer, prostate cancer, renal cell carcinoma and lung squamous cell carcinoma) were subjected to Agilent whole genome microarrays. Human cell lines (Panc-1, sw1990, TE8, TE9, A549, MRC-5, BOY, T24, PC3, C4-2, 786-O, A-498 and EBC-1) were treated with miRNAs (miR-375, miR-29a, miR-26a, miR-145-5p, miR-145-3p, miR-218, miR-320a), siRNAs (si-MMP11, si-LAMP1, si-LOXL2, si-PLOD2, si-UHRF1, and si-FOXM1).

2016-04-13 | E-GEOD-77790 | biostudies-arrayexpress

Differentially expressed genes after miRNA or siRNA transfection in human cancer cell lines Panc-1, FaDu and PC3

2016-08-16 | GSE85614 | GEO

miRNA transcriptome profiling in early-stage luminal breast cancer

Project description:Oxysterols, oxidized derivatives of cholesterol, act in breast cancer (BC) as selective estrogen receptor modulators and affect cholesterol homeostasis, drug transport, nuclear and cell receptors, and other signaling proteins. Using overlapping data from patients with early-stage estrogen receptor-positive BC—high-coverage targeted DNA sequencing (99 patients, 113 genes), mRNA sequencing (67 patients), and full miRNome by microarrays (123 patients)—we describe complex mRNA-miRNA and miRNA-miRNA interaction (correlation) networks, with validation in two carefully curated public datasets (n=538 in total) and 11 databases. The ESR1-CH25H-INSIG1-ABCA9 axis was the most prominent, being interconnected through hsa-miR-125b-5p, but also hsa-miR-99a-5p, hsa-miR-100-5p, hsa miR 143 3p, hsa-199b-5p, hsa-miR-376a-3p, and hsa-miR-376c-3p. Mutations in SC5D, CYP46A1, and its functionally linked gene set were associated with multiple differentially expressed genes. STARD5 was upregulated in patients with positive lymph node status. High expression of miR-19b-3p was weakly associated with poor survival in multiple datasets. This is the first detailed dedicated study of interactions between DNA variation and mRNA expression of oxysterol-related genes, the miRNA transcriptome, and clinical factors in BC.

2023-08-02 | GSE225292 | GEO

Integrative analysis of mRNA-miRNA expression profiles and somatic variants in oxysterol signaling in early-stage luminal breast cancer

2023-08-02 | GSE225327 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data