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Background <p>Screening tests, particularly liquid biopsy with circulating miRNAs, hold significant potential for non-invasive cancer detection before symptoms manifest.</p> Methods <p>This study aimed to identify biomarkers with high sensitivity and specificity for multiple and specific cancer screening. 972 Serum miRNA profiles were compared across thirteen cancer types and healthy individuals using weighted miRNA co-expression network analysis. To prioritize miRNAs, module membership measure and miRNA trait significance were employed. Subsequently, for specific cancer screening, gastric cancer was focused on, using a similar strategy and a further step of preservation analysis. Machine learning techniques were then applied to evaluate two distinct miRNA panels: one for multi-cancer screening and another for gastric cancer classification.</p> Results <p>The first panel (hsa-miR-8073, hsa-miR-614, hsa-miR-548ah-5p, hsa-miR-1258) achieved 96.1% accuracy, 96% specificity, and 98.6% sensitivity in multi-cancer screening. The second panel (hsa-miR-1228-5p, hsa-miR-1343-3p, hsa-miR-6765-5p, hsa-miR-6787-5p) showed promise in detecting gastric cancer with 87% accuracy, 90% specificity, and 89% sensitivity.</p> Conclusions <p>Both panels exhibit potential for patient classification in diagnostic and prognostic applications, highlighting the significance of liquid biopsy in advancing cancer screening methodologies.</p>

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Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月6日

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  • Leila Kamkar,
  • Samaneh Saberi,
  • Mehdi Totonchi,
  • Kaveh Kavousi

摘要

Background

Screening tests, particularly liquid biopsy with circulating miRNAs, hold significant potential for non-invasive cancer detection before symptoms manifest.

Methods

This study aimed to identify biomarkers with high sensitivity and specificity for multiple and specific cancer screening. 972 Serum miRNA profiles were compared across thirteen cancer types and healthy individuals using weighted miRNA co-expression network analysis. To prioritize miRNAs, module membership measure and miRNA trait significance were employed. Subsequently, for specific cancer screening, gastric cancer was focused on, using a similar strategy and a further step of preservation analysis. Machine learning techniques were then applied to evaluate two distinct miRNA panels: one for multi-cancer screening and another for gastric cancer classification.

Results

The first panel (hsa-miR-8073, hsa-miR-614, hsa-miR-548ah-5p, hsa-miR-1258) achieved 96.1% accuracy, 96% specificity, and 98.6% sensitivity in multi-cancer screening. The second panel (hsa-miR-1228-5p, hsa-miR-1343-3p, hsa-miR-6765-5p, hsa-miR-6787-5p) showed promise in detecting gastric cancer with 87% accuracy, 90% specificity, and 89% sensitivity.

Conclusions

Both panels exhibit potential for patient classification in diagnostic and prognostic applications, highlighting the significance of liquid biopsy in advancing cancer screening methodologies.