ArticleMolecular neurobiology2018
Validation of Reference Genes for Expression Studies in Human Meningiomas under Different Experimental Settings.
Article in Molecular neurobiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Determination of the most suitable reference gene for transcription analysis of synovial tissue from osteoarthritis patients.Scientific reports · 2026Article
- Application of the Mass Spectrometry-High-Throughput Technique Over the Immunohistochemical Analysis for Human Brain Tumor Diagnosis and Prognosis: Insights Into Biomarkers' Identification for the Case Study of Grade IV Astrocytomas and Meningiomas.Biomedical chromatography : BMC · 2025Article
- Reference gene evaluation for normalization of gene expression studies with lymph tissue and node‑derived stromal cells of patients with oral squamous cell carcinoma.Oncology letters · 2024Article
- Optimization of internal reference genes for qPCR in human pancreatic cancer research.Translational cancer research · 2020Article
- Selection and Validation of Appropriate Reference Genes for Quantitative RT-PCR Analysis inBioMed research international · 2020Article
- Optimal use of statistical methods to validate reference gene stability in longitudinal studies.PloS one · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Quantitative polymerase chain reaction (qPCR) is a sensitive technique for the quantitative analysis of gene expression levels. To compare mRNA transcripts across tumour and non-pathological tissue, appropriate reference genes are required for internal standardisation. Validation of these reference genes in meningiomas has not yet been reported. After mRNA transcription of meningioma (WHO grade I-III) and meningeal tissue from three different experimental sample types (fresh tissue, primary cell cultures and FFPE tissue), 13 candidate reference genes (ACTB, B2M, HPRT, VIM, GAPDH, YWHAZ, EIF4A2, MUC1, ATP5B, GNB2L, TUBB, CYC1, RPL13A) were chosen for quantitative expression analysis. Two statistical algorithms (GeNorm and NormFinder) were used for validation of gene expression stability. All candidate housekeepers tested for stability were checked within and across the three tissue analysis groups. Pearson correlation, the ΔC
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