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    <responseDate>2026-10-15T10:23:25Z</responseDate>
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                <identifier>oai:data.mendeley.com/k7psdf4n3c.1</identifier>
                <datestamp>2026-08-12T16:27:41Z</datestamp>
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            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>管, 丹萍</dc:creator>
    <dc:title>OM-sqPCR enables one-Step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>Accurate and multiplexed quantification of circulating small RNAs (sRNAs) remains technically challenging due to their structural diversity and low abundance. Here we present OM-sqPCR, an AI-assisted one-step multiplex quantitative PCR platform that enables simultaneous detection of microRNAs, transfer RNA–derived sRNAs (tsRNAs), and ribosomal RNA–derived sRNAs (rsRNAs) in a single closed-tube reaction. The system integrates optimized enzyme–buffer chemistry for short RNA templates with machine-learning–guided primer–probe design, enabling synchronized amplification across multiple optical channels. Applied to a multicentre clinical cohort (n = 651), OM-sqPCR identified a four-sRNA signature that discriminated colorectal cancer from controls with an area under the ROC curve (AUC) = 0.97, 85.9% sensitivity, and 93.1% specificity, outperforming serum carcinoembryonic antigen (CEA)  and maintaining high accuracy in early-stage disease (stage I–II, sensitivity: 84.6%). The workflow also validated biomarker panels for renal and thyroid cancers with comparable accuracy (AUC &gt; 0.98). This generalizable, low-cost platform provides a scalable and automation-ready solution for unified small-RNA quantification across diverse clinical contexts.</dc:description>
    <dc:subject>Publication</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/k7psdf4n3c.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/k7psdf4n3c.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
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    <dc:date>2026-08-12T16:27:41Z</dc:date>
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