Reference gene for primary culture of prostate cancer cells.

Mol Biol Rep

Laboratório de Biologia Molecular Endócrina e Tumoral, Departamento de Fisiologia, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Rua Sarmento Leite 500, 2º andar, laboratório 09, Porto Alegre, RS 90050-170, Brazil.

Published: April 2013

Selection of reference genes to normalize mRNA levels between samples is critical for gene expression studies because their expression can vary depending on the tissues or cells used and the experimental conditions. We performed ten cell cultures from samples of prostate cancer. Cells were divided into three groups: control (with no transfection protocol), cells transfected with siRNA specific to knockdown the androgen receptor and cells transfected with inespecific siRNAs. After 24 h, mRNA was extracted and gene expression was analyzed by Real-time qPCR. Nine candidates to reference genes for gene expression studies in this model were analyzed (aminolevulinate, delta-, synthase 1 (ALAS1); beta-actin (ACTB); beta-2-microglobulin (B2M); glyceraldehyde-3-phosphate dehydrogenase (GAPDH); hypoxanthine phosphoribosyltransferase 1 (HPRT1); succinate dehydrogenase complex, subunit A, flavoprotein (Fp) (SDHA); TATA box binding protein (TBP); ubiquitin C (UBC); tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ)). Expression stability was calculated NormFinder algorithm to find the most stable genes. NormFinder calculated SDHA as the most stable gene and the gene with the lowest intergroup and intragroup variation, and indicated GAPDH and SDHA as the best combination of two genes for the purpose of normalization. Androgen receptor mRNA expression was evaluated after normalization by each candidate gene and showed statistical difference in the transfected group compared to control group only when normalized by combination of GAPDH and SDHA. Based on the algorithm analysis, the combination of SDHA and GAPDH should be used to normalize target genes mRNA levels in primary culture of prostate cancer cells submitted to transfection with siRNAs.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11033-012-2366-5DOI Listing

Publication Analysis

Top Keywords

prostate cancer
12
cancer cells
12
gene expression
12
primary culture
8
culture prostate
8
reference genes
8
mrna levels
8
expression studies
8
cells transfected
8
androgen receptor
8

Similar Publications

Importance: Incidence of distant stage prostate cancer is increasing in the United States. Research is needed to understand trends by social and geographic factors.

Objective: To examine trends in prostate cancer incidence and mortality rates in California by stage, age, race and ethnicity, and region.

View Article and Find Full Text PDF

The word "cancer" evokes myriad emotions, ranging from fear and despair to hope and determination. Cancer is aptly defined as a complex and multifaceted group of diseases that has unapologetically led to the loss of countless lives and affected innumerable families across the globe. The battle with cancer is not only a physical battle, but also an emotional, as well as a psychological skirmish for patients and for their loved ones.

View Article and Find Full Text PDF

Due to the emergence of drug resistance, androgen receptor (AR)-targeted drugs still pose great challenges in the treatment of prostate cancer, and it is urgent to explore an innovative therapeutic strategy. MK-1775, a highly selective WEE1 inhibitor, is shown to have favorable therapeutic benefits in several solid tumor models. Recent evidence suggests that the combination of MK-1775 with DNA-damaging agents could lead to enhanced antitumor efficacy.

View Article and Find Full Text PDF

Harnessing machine learning to predict prostate cancer survival: a review.

Front Oncol

January 2025

Department of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.

The prediction of survival outcomes is a key factor in making decisions for prostate cancer (PCa) treatment. Advances in computer-based technologies have increased the role of machine learning (ML) methods in predicting cancer prognosis. Due to the various effective treatments available for each non-linear landscape of PCa, the integration of ML can help offer tailored treatment strategies and precision medicine approaches, thus improving survival in patients with PCa.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!