Serum long non-coding RNA SNHG9 as a diagnostic biomarker for acute coronary syndrome and a predictor of prognosis following percutaneous coronary intervention: a clinical evaluation.

Authors

Jinhe Chen, Yujuan Gao, Fan Liu, Yuan Li, Li Liu, Xiaona Jia

Published in

Journal of cardiothoracic surgery. Jul 18, 2026. Epub Jul 18, 2026.

Abstract

With the change of lifestyle and the aging of the population, the incidence of Acute coronary syndrome (ACS) is increasing and showing a trend of younger patients. Therefore, the study of risk factors for ACS has important clinical implications for the diagnosis and prognosis of patients. The purpose of this study was to explore the role of long non-coding RNA (lncRNA) SNHG9 in the diagnosis and prognosis of ACS, with a view to developing new biomarkers for the diagnosis and prognosis of ACS.
A total of 130 ACS patients and 99 healthy subjects were included in this study, and their serum SNHG9 levels were measured by RT-qPCR. The diagnostic value of SNHG9 in ACS was evaluated by ROC curve and binary Logistic analysis. Risk factors for major adverse cardiovascular events (MACE) in patients with ACS were analyzed using Kaplan-Meier curves and multivariate Cox regression. Correlation of SNHG9 levels with clinical indicators was analyzed using Pearson and Spearman methods.
SNHG9 was highly expressed in ACS patients compared to healthy subjects. Logistic analysis and ROC results showed high diagnostic accuracy of SNHG9 in ACS patients (OR = 7.106, P < 0.001; AUC = 0.929). Furthermore, SNHG9 expression was upregulated in the MACE events group compared to the group without MACE events. Kaplan-Meier curves indicated lower survival in ACS patients with high SNHG9 expression (P = 0.002). Cox results showed that SNHG9 is a risk factor for MACE events in ACS patients after treatment. Besides, SNHG9 was positively and significantly correlated with cTnI, NT-proBNP, hs-CRP, Gensini score, and the number of diseased vessels.
SNHG9 has high predictive value for the diagnosis and prognosis of ACS patients, which may become a biomarker for clinical diagnosis and prediction of survival outcome in ACS patients.

PMID:
42471680
Bibliographic data and abstract were imported from PubMed on 19 Jul 2026.

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