A robust machine learning model based on ribosomal‐subunit‐derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large‐scale of sequencing data
Abstract Nonsmall cell lung cancer (NSCLC) is a lethal cancer and lacks robust biomarkers for noninvasive clinical diagnosis. Detecting NSCLC at the early stage can decrease the mortality rate and minimise harm caused by various treatments. We curated 2050 samples from public tissue and plasma datas...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Clinical and Translational Medicine |
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| Online Access: | https://doi.org/10.1002/ctm2.70418 |
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| author | Zitong Gao Masaki Nasu Gehan Devendra Ayman A. Abdul‐Ghani Anthony J. Herrera Jeffrey A. Borgia Christopher W. Seder Donna Lee Kuehu Zhuokun Feng Yu Chen Ting Gong Zao Zhang Owen Chan Hua Yang Jianhua Yu Yuanyuan Fu Lang Wu Youping Deng |
| author_facet | Zitong Gao Masaki Nasu Gehan Devendra Ayman A. Abdul‐Ghani Anthony J. Herrera Jeffrey A. Borgia Christopher W. Seder Donna Lee Kuehu Zhuokun Feng Yu Chen Ting Gong Zao Zhang Owen Chan Hua Yang Jianhua Yu Yuanyuan Fu Lang Wu Youping Deng |
| author_sort | Zitong Gao |
| collection | DOAJ |
| description | Abstract Nonsmall cell lung cancer (NSCLC) is a lethal cancer and lacks robust biomarkers for noninvasive clinical diagnosis. Detecting NSCLC at the early stage can decrease the mortality rate and minimise harm caused by various treatments. We curated 2050 samples from public tissue and plasma datasets including both invasive and noninvasive types, then supplemented with in‐house pooled plasma and exosome samples. Eleven independent transcriptome datasets were utilised to develop a new machine learning model by integrating PIWI‐interacting RNA (piRNA) to predict NSCLC. Five piRNA signatures derived from ribosomal subunits identified to be tumour‐specific exhibited robust diagnostic ability and were combined into a piRNA‐Based Tumour Probability Index (pi‐TPI) risk evaluation model. pi‐TPI effectively distinguished NSCLC patients from healthy individuals and showed efficacy in identifying early‐stage cancers with Area under the ROC Curve (AUC) values over .80. Plasma cohorts exhibited the diagnosis efficacy of pi‐TPI with an AUC value of .85. Experimental exosomal data enhances the accuracy of diagnosing noncancerous, benign, and cancer cases. The pi‐TPI marker in the noncancer/cancer subgroup exhibited superior predictive performance with an AUC value of .96. These findings underscore the significant clinical potential of the five piRNA signatures as a powerful diagnostic tool for NSCLC, particularly of noninvasive cancer diagnostics. |
| format | Article |
| id | doaj-art-2c128a9f05b340fca55c09b6cceed79e |
| institution | Kabale University |
| issn | 2001-1326 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Medicine |
| spelling | doaj-art-2c128a9f05b340fca55c09b6cceed79e2025-08-25T18:28:44ZengWileyClinical and Translational Medicine2001-13262025-08-01158n/an/a10.1002/ctm2.70418A robust machine learning model based on ribosomal‐subunit‐derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large‐scale of sequencing dataZitong Gao0Masaki Nasu1Gehan Devendra2Ayman A. Abdul‐Ghani3Anthony J. Herrera4Jeffrey A. Borgia5Christopher W. Seder6Donna Lee Kuehu7Zhuokun Feng8Yu Chen9Ting Gong10Zao Zhang11Owen Chan12Hua Yang13Jianhua Yu14Yuanyuan Fu15Lang Wu16Youping Deng17Department of Quantitative Health Sciences John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USADepartment of Quantitative Health Sciences John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USADepartment of Medicine John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USACardiothoracic Surgery The Queen's Medical Center, Honolulu Honolulu Hawaiʻi USAInterventional Radiology The Queen's Medical Center Honolulu Hawaiʻi USADepartments of Anatomy & Cell Biology and Pathology RUSH University Cancer Center Chicago Illinois USACardiothoracic Residency Program RUSH University Chicago Illinois USADepartment of Quantitative Health Sciences John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USADepartment of Quantitative Health Sciences John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USADepartment of Quantitative Health Sciences John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USADepartment of Quantitative Health Sciences John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USAHospitalist Medicine The Queen's Medical Center Honolulu Hawaiʻi USAPathology Core Shared Resource University of Hawaii Cancer Center Honolulu Hawaiʻi USADepartment of Quantitative Health Sciences John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USAInstitute for Precision Cancer Therapeutics and Immuno‐Oncology Chao Family Comprehensive Cancer Center University of California Irvine California USADepartment of Quantitative Health Sciences John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USAPacific Center for Genome Research University of Hawaii Cancer Center Honolulu Hawaiʻi USADepartment of Quantitative Health Sciences John A. Burns School of Medicine University of Hawaii at Manoa Honolulu Hawaiʻi USAAbstract Nonsmall cell lung cancer (NSCLC) is a lethal cancer and lacks robust biomarkers for noninvasive clinical diagnosis. Detecting NSCLC at the early stage can decrease the mortality rate and minimise harm caused by various treatments. We curated 2050 samples from public tissue and plasma datasets including both invasive and noninvasive types, then supplemented with in‐house pooled plasma and exosome samples. Eleven independent transcriptome datasets were utilised to develop a new machine learning model by integrating PIWI‐interacting RNA (piRNA) to predict NSCLC. Five piRNA signatures derived from ribosomal subunits identified to be tumour‐specific exhibited robust diagnostic ability and were combined into a piRNA‐Based Tumour Probability Index (pi‐TPI) risk evaluation model. pi‐TPI effectively distinguished NSCLC patients from healthy individuals and showed efficacy in identifying early‐stage cancers with Area under the ROC Curve (AUC) values over .80. Plasma cohorts exhibited the diagnosis efficacy of pi‐TPI with an AUC value of .85. Experimental exosomal data enhances the accuracy of diagnosing noncancerous, benign, and cancer cases. The pi‐TPI marker in the noncancer/cancer subgroup exhibited superior predictive performance with an AUC value of .96. These findings underscore the significant clinical potential of the five piRNA signatures as a powerful diagnostic tool for NSCLC, particularly of noninvasive cancer diagnostics.https://doi.org/10.1002/ctm2.70418machine learningnoninvasive diagnosisnonsmall cell lung cancerPIWI‐interacting RNAsmall noncoding RNA |
| spellingShingle | Zitong Gao Masaki Nasu Gehan Devendra Ayman A. Abdul‐Ghani Anthony J. Herrera Jeffrey A. Borgia Christopher W. Seder Donna Lee Kuehu Zhuokun Feng Yu Chen Ting Gong Zao Zhang Owen Chan Hua Yang Jianhua Yu Yuanyuan Fu Lang Wu Youping Deng A robust machine learning model based on ribosomal‐subunit‐derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large‐scale of sequencing data Clinical and Translational Medicine machine learning noninvasive diagnosis nonsmall cell lung cancer PIWI‐interacting RNA small noncoding RNA |
| title | A robust machine learning model based on ribosomal‐subunit‐derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large‐scale of sequencing data |
| title_full | A robust machine learning model based on ribosomal‐subunit‐derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large‐scale of sequencing data |
| title_fullStr | A robust machine learning model based on ribosomal‐subunit‐derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large‐scale of sequencing data |
| title_full_unstemmed | A robust machine learning model based on ribosomal‐subunit‐derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large‐scale of sequencing data |
| title_short | A robust machine learning model based on ribosomal‐subunit‐derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large‐scale of sequencing data |
| title_sort | robust machine learning model based on ribosomal subunit derived pirnas for diagnostic potential of nonsmall cell lung cancer across multicentre large scale of sequencing data |
| topic | machine learning noninvasive diagnosis nonsmall cell lung cancer PIWI‐interacting RNA small noncoding RNA |
| url | https://doi.org/10.1002/ctm2.70418 |
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