{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE342nnn/GSE342306/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Genomics"],"species":["Homo sapiens"],"gds_type":["Non-coding RNA profiling by high throughput sequencing"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE342306"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"OM-sqPCR enables one-Step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification","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 > 0.98). This generalizable, low-cost platform provides a scalable and automation-ready solution for unified small-RNA quantification across diverse clinical contexts.","dates":{"publication":"2026/08/12"},"accession":"GSE342306","cross_references":{"GSM":["GSM9927698","GSM9927709","GSM9927707","GSM9927708","GSM9927712","GSM9927701","GSM9927702","GSM9927713","GSM9927710","GSM9927699","GSM9927700","GSM9927711","GSM9927705","GSM9927716","GSM9927706","GSM9927717","GSM9927703","GSM9927714","GSM9927715","GSM9927704"],"GPL":["24676"],"GSE":["342306"],"taxon":["Homo sapiens"]}}