ArticleIntractable & rare diseases research2022
Identification of potential core genes and miRNAs in pediatric ACC
Article in Intractable & rare diseases research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Histopathological and Molecular Characteristics of Pediatric Adrenocortical Tumors: where Do we Stand?Endocrine pathology · 2026Review
- Case Report: Adrenocortical carcinoma in children-symptoms, diagnosis, and treatment.Frontiers in endocrinology · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pediatric adrenocortical carcinomas (ACC) are rare aggressive neoplasms with heterogeneous prognosis, and often produce a most lethal malignant tumor, whereas its aetiology is still unclear. The aim of the present study was to identify the factors responsible for the development of pediatric ACC, a better understanding of the disease, and investigate new molecular biomarkers and therapeutic targets. To identify the key genes and miRNAs linked to pediatric ACC, as well as their potential molecular mechanisms, the GSEGSE75415 and GSE169253 microarray datasets were analyzed. A total of 329 differentially produced genes (DEGs) and 187 differentially produced miRNAs (DEMs) were obtained after analyzing the GSEGSE75415 and GSE169253 datasets, respectively. Next, 3,359 genes were obtained by overlapping the target mRNAs of DEMs. Following protein-protein interaction network and Gene Ontology analysis, the ten nodes with the highest degrees were screened as hub genes. Among them, the highly expressed hub genes,
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Registered trials
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