ArticleFrontiers in endocrinology2022
Whole Transcriptome Profiling of Adrenocortical Tumors Using Formalin-Fixed Paraffin-Embedded Samples.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Molecular pathology of primary aldosteronism and hypercortisolism: Impact on adrenal surgery.iScience · 2026Review
- The Evolving Landscape of GEP-NENs in the Era of Precision Oncology: Molecular Insights into Tumor Heterogeneity.Cancers · 2025Review
- Identification and validation of susceptibility modules and hub genes of adrenocortical carcinoma through WGCNA and machine learning.Discover oncology · 2025Article
- Skull base chordoma and spinal chordoma exhibit consistency in terms of endoplasmic reticulum stress aspects.Neuro-oncology · 2024Article
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole transcriptome profiling is a promising technique in adrenal studies; however, whole transcriptome profiling of adrenal disease using formalin-fixed paraffin-embedded (FFPE) samples has to be further explored. The aim of this study was to evaluate the utility of transcriptome data from FFPE samples of adrenocortical tumors. We performed whole transcriptome profiling of FFPE and fresh frozen samples of adrenocortical carcinoma (ACC, n = 3), aldosterone-producing adenoma (APA, n = 3), and cortisol-producing adenoma (CPA, n = 3), and examined the similarity between the transcriptome data. We further examined whether the transcriptome data of FFPE samples could be used to distinguish tumor types and detect marker genes. The number of read counts was smaller in FFPE samples than in fresh frozen samples (P < 0.01), while the number of genes detected was similar (P = 0.39). The gene expression profiles of FFPE and fresh frozen samples were highly correlated (r = 0.93, P < 0.01). Tumor types could be distinguished by consensus clustering and principal component analysis using transcriptome data from FFPE samples. In the differential expression analysis between ACC and APA-CPA, known marker genes of ACC (e.g.,
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Registered trials
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