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Herein, a novel photoelectrochemical (PEC) biosensor based on dual-signal copper oxide nanoparticles is developed for the combined detection of dual lncRNAs, utilizing machine learning to enhance the intelligent screening efficiency of lung cancer. The as-synthesized copper oxide NPs exhibit a high photoelectric conversion efficiency and dual-photocurrent signals. Through lncRNA-triggered catalytic hairpin assembly signal amplification, abundant tungsten disulfide quantum dots are introduced into the photoelectrode to reduce the photocurrent response. The proposed PEC biosensing platform has high sensitivity with low detection limits of 72 aM for HOTAIR and 78 aM for MALAT1. Furthermore, this PEC platform for dual lncRNAs in whole blood can successfully distinguish cancer patients from healthy individuals. Moreover, machine learning is employed to explore the hidden potential patterns in the established PEC method, and the accuracy rate of intelligent screening for lung cancer reaches 93%. The integrated detection system of machine learning and PEC biosensors offers a new method for dual lncRNA analysis and cancer early screening.<\/jats:p>","DOI":"10.1021\/acs.analchem.6c02014","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T19:49:01Z","timestamp":1783712941000},"page":"21466-21474","source":"Crossref","is-referenced-by-count":3,"title":["A Machine Learning-Assisted\nHighly Sensitive Photoelectrochemical\nBiosensor Based on Dual-Signal CuO Nanoparticles for the Combined\nDetection of Dual LncRNAs"],"prefix":"10.1021","volume":"98","author":[{"given":"Mengjiao","family":"Mei","sequence":"first","affiliation":[{"name":"Zhengzhou University , , ,","place":["Zhengzhou, China, 450001"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhengzhou University , , ,","place":["Zhengzhou, China, 450001"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sirong","family":"Li","sequence":"additional","affiliation":[{"name":"Zhengzhou University , , ,","place":["Zhengzhou, China, 450001"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhengzhou University , , ,","place":["Zhengzhou, China, 450001"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlin","family":"Shang","sequence":"additional","affiliation":[{"name":"Zhengzhou University , , ,","place":["Zhengzhou, China, 450001"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Ma","sequence":"additional","affiliation":[{"name":"Zhengzhou University , , ,","place":["Zhengzhou, China, 450001"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhengzhou University , , ,","place":["Zhengzhou, China, 450001"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenchao","family":"Geng","sequence":"additional","affiliation":[{"name":"Henan University of Engineering , , ,","place":["Zhengzhou, China, 451191"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5720-1271","authenticated-orcid":true,"given":"Ruiying","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhengzhou University , , ,","place":["Zhengzhou, China, 450001"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"2026072808074962100_cit1","doi-asserted-by":"publisher","first-page":"36397","DOI":"10.1021\/acsnano.5c10251","article-title":"Endogenously Activated\nMultiple DNAzyme-Encoded Nanoflowers for High-Contrast Imaging of\nLong Noncoding RNA and Cancer Therapy","volume":"19","author":"Yuan","year":"2025","journal-title":"ACS Nano"},{"key":"2026072808074962100_cit2","doi-asserted-by":"publisher","first-page":"23609","DOI":"10.1021\/acs.analchem.5c05377","article-title":"Detection\nof m6 A-Modified\nRNA Using CRISPR\/Cas12a-Integrated Iontronic Biosensor with Hydrophobized\nNanochannels: Toward Early Cancer Diagnosis by Machine Learning","volume":"97","author":"Gao","year":"2025","journal-title":"Anal. 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