Daily Endocrinology Research Analysis
Analyzed 103 papers and selected 3 impactful papers.
Summary
Three clinically impactful endocrinology studies stood out today. Multi-omics profiling of medullary thyroid cancer uncovered metabolically defined subtypes with poor-prognosis GAG biosynthesis driven by CHSY1, while a meta-analysis of randomized trials found that mineralocorticoid receptor antagonists reduce all-cause mortality in predominantly hypertensive populations. A multi-platform proteomics study identified circulating APOA4 as a consistent, downregulated biomarker that differentiates adrenocortical carcinoma from adenoma, supporting preoperative risk stratification.
Research Themes
- Metabolic reprogramming and prognostic subtyping in endocrine cancers
- Evidence synthesis on mineralocorticoid receptor antagonists and mortality
- Serum proteomics biomarkers for adrenal malignancy differentiation
Selected Articles
1. Integrated multi-omics and single-cell analyses identify metabolic heterogeneity and therapeutic vulnerabilities in medullary thyroid cancer.
By integrating bulk RNA-seq, untargeted metabolomics, tissue validation, and single-cell data, the authors define three metabolic subtypes of medullary thyroid cancer. A poor-prognosis subtype (M3) is driven by glycosaminoglycan biosynthesis (notably chondroitin sulfate) and CHSY1, linked to EMT and myofibroblast interactions, and supports metabolite- and gene-based prognostication.
Impact: It links a tractable metabolic pathway (GAG biosynthesis/CHSY1) to prognosis and tumor-stroma interactions in MTC and delivers validated prognostic classifiers that can inform precision management.
Clinical Implications: Metabolic subtyping and CHSY1-associated signatures may guide risk stratification and suggest CHSY1/GAG biosynthesis as therapeutic targets in poor-prognosis MTC, complementing current genotype-based care.
Key Findings
- Three metabolic MTC subtypes were identified; the M3 subtype had poor prognosis with upregulated GAG biosynthesis and CHSY1 overexpression.
- Multi-omics and tissue validation linked CHSY1 to EMT and myofibroblast interactions.
- Prognostic models (8-metabolite and 28-gene panels) stratified recurrence risk with performance driven by GAG-associated metabolism.
Methodological Strengths
- Integrated multi-omics discovery with orthogonal validation (IHC, multiplex IF, single-cell datasets).
- Development of prognostic classifiers with deep-learning support and external datasets.
Limitations
- Retrospective analyses with moderate sample sizes and limited prospective external validation.
- Functional causality of CHSY1 in vivo remains to be fully established and therapeutically tested.
Future Directions: Prospective, multi-center validation of the classifiers, functional targeting of CHSY1/GAG pathways, and integration with genomic drivers to tailor therapy.
BACKGROUND: Medullary thyroid cancer (MTC) is a heterogeneous and aggressive malignancy with limited therapeutic options. Metabolic reprogramming, a hallmark of cancer, may offer a promising avenue for understanding and managing MTC. METHODS: RNA sequencing data of 101 MTC samples were obtained from a published dataset PRJCA008783, and untargeted metabolomic profiling was performed on 51 paired samples. Metabolic subtypes were identified using clustering analyses and validated using immunohistochemistry (47 cases), multiplex immunofluorescence (12 cases), and a previously published single-cell RNA sequencing dataset (7 cases derived from PRJCA021386). Deep learning-based approaches were applied to develop prognostic models. RESULTS: Three metabolic subtypes were identified. The M3 subtype, associated with poor prognosis, was characterised by upregulated glycosaminoglycan (GAGs) biosynthesis, particularly chondroitin sulfate, and elevated expression of CHSY1, a key GAGs biosynthetic enzyme. M3 tumours displayed enhanced epithelial-mesenchymal transition (EMT) signatures. Multi-omic analyses implicated CHSY1 may promote EMT through interactions with myofibroblasts, which was supported by immunohistochemistry and immunofluorescence. Two prognostic classifiers, the 8 Metabolites Model and the 28 Metabolic Genes Model, effectively stratified patients by recurrence risk, with predictive power largely driven by GAGs-associated metabolism. CONCLUSIONS: Our study reveals substantial metabolic heterogeneity in MTC and proposes a novel metabolic classification system, offering mechanistic insights and supporting metabolite-driven prognostication for precision management of MTC.
2. Mineralocorticoid receptor antagonists and mortality in hypertension: a systematic review and meta-analysis.
Across 17 randomized trials (n=25,498; 12 reporting mortality), mineralocorticoid receptor antagonists were associated with a modest but significant reduction in all-cause mortality (OR 0.91; I2=0%). The analysis was PROSPERO-registered and showed consistent effects across heterogeneous comorbid hypertensive populations.
Impact: This synthesis clarifies mortality benefits of MRAs in real trial populations with hypertension, informing guideline harmonization and patient selection.
Clinical Implications: MRAs should be more strongly considered in hypertensive patients with appropriate indications, with attention to monitoring (e.g., potassium, renal function) and comorbidities.
Key Findings
- Seventeen RCTs (n=25,498) identified; 12 reported mortality (n=24,426).
- MRA use reduced all-cause mortality versus control (OR 0.91, 95% CI 0.84–0.99) with no heterogeneity (I2=0%).
- Absolute risk reduction was 0.88% over a median 20.5 months; median follow-up across trials was 12 months (IQR 9–32).
Methodological Strengths
- Trial-level meta-analysis of RCTs with random-effects modeling and dual independent review.
- Pre-registered protocol (PROSPERO) and low statistical heterogeneity (I2=0%).
Limitations
- Relatively short follow-up; mortality not a primary endpoint in many included trials.
- Predominantly hypertensive with comorbidities; dedicated hypertension mortality trials are lacking.
Future Directions: Design and conduct large, long-duration RCTs in hypertensive populations with mortality as a primary endpoint, and define subgroups maximizing net benefit.
BACKGROUND: International guidelines differ in their recommendations for mineralocorticoid receptor antagonist (MRA) use in hypertension, as the effect on mortality is unclear. We meta-analyzed trial-level data to determine the association of MRA use with all-cause mortality in hypertension. METHODS: MEDLINE and EMBASE were searched for randomized clinical trials published from database inception through March 14, 2025, evaluating MRA use in trial populations with at least 85% prevalence of hypertension. The control groups consisted of placebo or usual care. Two reviewers independently screened and extracted data. Pooled estimates were calculated using random-effects and inverse variance models. The primary outcome was all-cause mortality. RESULTS: Seventeen randomized clinical trials were eligible for inclusion (n = 25 498), of which 12 trials reported mortality events (n = 24 426). The mean (±SD) baseline prevalence of hypertension was 95.0% (±5.0), mean baseline SBP was 134.8 (±9.0) mmHg, mean age was 64.1 (±5.3) years and 43.3% (n = 11 047) were female. The median duration of follow-up was 12 months (interquartile range 9-32 months). MRA use versus control was significantly associated with lower odds of all-cause mortality (12 trials, n = 24 426) (10.7 versus 11.6% over a median follow-up of 20.5 months; odds ratio (OR), 0.91 [95% confidence interval, 95% CI, 0.84-0.99]; absolute risk reduction, 0.88% [95% CI, 0.1-1.7]; I2 = 0.0%). CONCLUSION: In this meta-analysis of randomized clinical trials in predominantly hypertensive populations with substantial comorbidity, MRA use was significantly associated with lower all-cause mortality. Further dedicated trials in hypertensive populations, with longer follow-up and mortality as primary outcome, are warranted to confirm these findings. (PROSPERO; CRD420250601811).
3. Plasma proteomic profiling identifies APOA4 as a downregulated biomarker of adrenocortical carcinoma: a multi-platform discovery and validation study.
Using discovery/verification LC-MS/MS, targeted PRM, and orthogonal Olink assays across multiple cohorts, APOA4 consistently appeared lower in ACC than in ACA. This multi-platform convergence supports APOA4 as a promising circulating biomarker for preoperative ACC–ACA differentiation.
Impact: Addresses a major diagnostic gap in adrenal oncology with convergent proteomic evidence and practical translational potential.
Clinical Implications: APOA4 measurement could inform preoperative differentiation of ACC vs. ACA, potentially guiding surgical planning and referral; development of clinically deployable assays and threshold calibration is the next step.
Key Findings
- Discovery and verification cohorts identified APOA4 as consistently under-expressed in ACC compared with ACA.
- Targeted PRM in an expanded, two-center cohort confirmed significant reductions of APOA4 (and CD44) in ACC.
- Orthogonal Olink panel analysis replicated APOA4 downregulation in ACC after FDR correction.
Methodological Strengths
- Stepwise, multi-platform proteomics with discovery, targeted, and orthogonal validation.
- Independent, multi-center expansion improves generalizability and reduces platform bias.
Limitations
- ACC sample sizes remain modest; case-control design may be susceptible to selection bias.
- Clinical thresholds, assay standardization, and prospective validation are not yet established.
Future Directions: Prospective, multi-ethnic validation with predefined thresholds, integration into multi-marker panels, and development of clinical-grade assays.
OBJECTIVES: Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy associated with heterogeneous prognosis. Preoperative differentiation from adrenocortical adenoma (ACA) remains challenging, and no serum tumor marker has been established. We aimed to identify circulating protein biomarkers that distinguish ACC from ACA using a stepwise, multi-platform proteomics strategy. METHODS: We assembled discovery (ACC=10, ACA=67) and verification (ACC=7, ACA=11) cohorts from a tertiary center and profiled fasting plasma using LC-MS/MS with data-independent acquisition. Differentially expressed proteins (DEPs) were defined by t-tests with P<0.05 and |fold-change|>1.2; DEPs common to both cohorts were prioritized. Targeted validation by parallel reaction monitoring (PRM) used an expanded, two-center cohort including additional cases from Asan Medical Center (ACC=31; ACA=78). Orthogonal validation employed the Olink Explore 384 Inflammation II panel in an independent set (ACC=15; ACA=24). RESULTS: The discovery cohort yielded 67 DEPs (22 up-regulated and 45 down-regulated in ACC), and the verification cohort identified 17 DEPs. Three proteins; CD44, PRG4, and APOA4 were common to both analyses and were under-expressed in ACC compared to ACA. In PRM, CD44 and APOA4 showed directionally concordant, significant decreases in ACC, prioritizing these markers for further evaluation. In the Olink analysis, 40 proteins differed between ACC and ACA after FDR correction; APOA4 remained significantly lower in ACC. CONCLUSION: Across discovery, targeted, and orthogonal platforms, APOA4 consistently exhibited lower circulating levels in ACC, supporting its potential as a serum biomarker for the preoperative differentiation of ACC from ACA. External, multi-ethnic validation and clinically deployable assays, alone or within multi-marker panels are warranted.