Identification of Differential Transcriptional Patterns in Primary and Secondary Hyperparathyroidism
Bibliographic record
Abstract
Context: Hyperparathyroidism is associated with hypercalcemia and the excess of parathyroid hormone secretion; however, the alterations in molecular pattern of functional genes during parathyroid tumorigenesis have not been unraveled. We aimed at establishing transcriptional patterns of normal and pathological parathyroid glands (PGs) in sporadic primary (HPT1) and secondary hyperparathyroidism (HPT2). Objective: To evaluate dynamic alterations in molecular patterns as a function of the type of PG pathology, a comparative transcript analysis was conducted in subgroups of healthy samples, sporadic HPT1 adenoma and hyperplasia, and HPT2. Design: Normal, adenomatous, HPT1, and HPT2 hyperplastic PG formalin-fixed paraffin-embedded samples were subjected to NanoString analysis. In silico microRNA (miRNA) analyses and messenger RNA-miRNA network in PG pathologies were conducted. Individual messenger RNA and miRNA levels were assessed in snap-frozen PG samples. Results: The expression levels of c-MET, MYC, TIMP1, and clock genes NFIL3 and PER1 were significantly altered in HPT1 adenoma compared with normal PG tissue when assessed by NanoString and quantitative reverse transcription polymerase chain reaction. RET was affected in HPT1 hyperplasia, whereas CaSR and VDR transcripts were downregulated in HPT2 hyperplastic PG tissue. CDH1, c-MET, MYC, and CaSR were altered in adenoma compared with hyperplasia. Correlation analyses suggest that c-MET, MYC, and NFIL3 exhibit collective expression level changes associated with HPT1 adenoma development. miRNAs, predicted in silico to target these genes, did not exhibit a clear tendency upon experimental validation. Conclusions: The presented gene expression analysis provides a differential molecular characterization of PG adenoma and hyperplasia pathologies, advancing our understanding of their etiology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".