Scholarlink

Background <p>Acute kidney injury (AKI) represents a critical complication in patients with acute coronary syndrome (ACS), particularly in patients undergoing percutaneous coronary intervention (PCI). Current tests for detecting early AKI, such as creatinine and cystatin C, have modest sensitivity. This study explores the role of bioinformatics in creating an implementable predictive key gene in cardiorenal pathogenesis and evaluates the diagnostic potential of <i>GTF2I</i>,<i> ANGPTL4</i>, and <i>MMP14</i> in predicting AKI in ACS patients.</p> Methods <p>This is a single-center prospective observational cohort study that enrolled 167 participants: healthy controls (<i>n</i> = 60), ACS patients (<i>n</i> = 56), and ACS patients with AKI (<i>n</i> = 51). Baseline clinical and renal markers were measured 24&#xa0;h pre-PCI, with RNA biomarkers (<i>GTF2I</i>, <i>ANGPTL4</i>, <i>MMP14</i>) assessed at 48&#xa0;h post-PCI, with daily creatinine monitoring performed for up to 6 days after PCI.</p> Results <p><i>GTF2I</i>,<i> ANGPTL4</i>, and <i>MMP14</i> were significantly elevated in ACS patients, with further increases in ACS-AKI patients. <i>GTF2I</i> showed the strongest predictive accuracy for AKI (AUC = 0.98), outperforming creatinine and cystatin. <i>ANGPTL4</i> correlated negatively with serum creatinine (<i>r</i> = − 0.213, <i>P</i> = 0.006), suggesting a modulatory role in renal injury, while <i>MMP14</i> elevations were pronounced in ACS-AKI. Notably, the combined biomarker panel achieved the highest diagnostic accuracy (AUC = 0.99), underscoring its potential as a robust early predictor of AKI. Subgroup analysis in diabetic patients revealed differential biomarker expression between AKI and non-AKI cases. Logistic regression confirmed albuminuria, <i>ANGPTL4</i>, <i>GTF2I</i>, and <i>MMP14</i>, in addition to several metabolic biomarkers, as significant predictors of AKI.</p> Conclusion <p><i>GTF2I</i>, <i>ANGPTL4</i>, and <i>MMP14</i> are promising biomarkers for predicting AKI in ACS patients and could be integrated into clinical practice for early AKI detection, improving patient management and outcomes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

RNA biomarker signatures for prediction of acute kidney injury in acute coronary syndrome patients undergoing PCI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

查看全文
  • Radwa Khaled,
  • Emad El-Zayat,
  • Mohamed A. Ragheb,
  • Sara Elsayed Abdelrahman,
  • Marwa Matboli

摘要

Background

Acute kidney injury (AKI) represents a critical complication in patients with acute coronary syndrome (ACS), particularly in patients undergoing percutaneous coronary intervention (PCI). Current tests for detecting early AKI, such as creatinine and cystatin C, have modest sensitivity. This study explores the role of bioinformatics in creating an implementable predictive key gene in cardiorenal pathogenesis and evaluates the diagnostic potential of GTF2I, ANGPTL4, and MMP14 in predicting AKI in ACS patients.

Methods

This is a single-center prospective observational cohort study that enrolled 167 participants: healthy controls (n = 60), ACS patients (n = 56), and ACS patients with AKI (n = 51). Baseline clinical and renal markers were measured 24 h pre-PCI, with RNA biomarkers (GTF2I, ANGPTL4, MMP14) assessed at 48 h post-PCI, with daily creatinine monitoring performed for up to 6 days after PCI.

Results

GTF2I, ANGPTL4, and MMP14 were significantly elevated in ACS patients, with further increases in ACS-AKI patients. GTF2I showed the strongest predictive accuracy for AKI (AUC = 0.98), outperforming creatinine and cystatin. ANGPTL4 correlated negatively with serum creatinine (r = − 0.213, P = 0.006), suggesting a modulatory role in renal injury, while MMP14 elevations were pronounced in ACS-AKI. Notably, the combined biomarker panel achieved the highest diagnostic accuracy (AUC = 0.99), underscoring its potential as a robust early predictor of AKI. Subgroup analysis in diabetic patients revealed differential biomarker expression between AKI and non-AKI cases. Logistic regression confirmed albuminuria, ANGPTL4, GTF2I, and MMP14, in addition to several metabolic biomarkers, as significant predictors of AKI.

Conclusion

GTF2I, ANGPTL4, and MMP14 are promising biomarkers for predicting AKI in ACS patients and could be integrated into clinical practice for early AKI detection, improving patient management and outcomes.