ArticleAsian Pacific journal of cancer prevention : APJCP2016
Evaluation of Combined Quantification of PCA3 and AMACR Gene Expression for Molecular Diagnosis of Prostate Cancer in Moroccan Patients by RT-qPCR
Article in Asian Pacific journal of cancer prevention : APJCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Diagnosis accuracy of PCA3 level in patients with prostate cancer: a systematic review with meta-analysis.International braz j urol : official journal of the Brazilian Society of UrologyPooled it
- Morphomechanically-Informed Spatial Curvature Sequencing in Prostate Cancer.bioRxiv : the preprint server for biology · 2026Article
- Advanced breast cancer diagnosis: Multiplex RT-qPCR for precise typing and angiogenesis profiling.Biochemistry and biophysics reports · 2024Article
- Surface-enhanced Raman spectroscopy of serum accurately detects prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL.International journal of nanomedicine · 2017Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) remains one of the most widespread and perplexing of all human malignancies. Assessment of gene expression is thought to have an important impact on cancer diagnosis, prognosis and therapeutic decisions. In this context, we explored combined expression of PCa related target genes AMACR and PCA3 in 126 formalin fixed paraffin embedded prostate tissues (FFPE) from Moroccan patients, using quantitative real time reverse transcription-PCR (RT-qPCR). This quantification required data normalization accomplished using stably expressed reference genes (RGs). A panel of twelve RG was assessed, data being analyzed using GenEx V6 based on geNorm, NormFinder and statistical methods. Accordingly, the hnRNP A1 gene was identified and selected as the most stably expressed RG for reliable and accurate gene expression quantification in prostate tissues. The ratios of both PCA3 and AMACR gene expression relative to that of the hnRNP A1 gene were calculated and the performance of each target gene for PCa diagnosis was evaluated using receiver-operating characteristics. PCA3 and AMACR mRNA quantification based on RT-qPCR may prove useful in PCa diagnosis. Of particular interesting, combining PCA3 and AMACR quantification improved PCa prediction by increasing sensitivity with retention of good specificity.
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