Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach
Abstract 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 scree...
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BMC
2025-02-01
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| Series: | BMC Medical Genomics |
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| Online Access: | https://doi.org/10.1186/s12920-025-02091-x |
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| author | Leila Kamkar Samaneh Saberi Mehdi Totonchi Kaveh Kavousi |
| author_facet | Leila Kamkar Samaneh Saberi Mehdi Totonchi Kaveh Kavousi |
| author_sort | Leila Kamkar |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-f11c20be476540e0a0ed26a89df6c9cd |
| institution | Kabale University |
| issn | 1755-8794 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj-art-f11c20be476540e0a0ed26a89df6c9cd2025-02-09T12:58:55ZengBMCBMC Medical Genomics1755-87942025-02-0118111910.1186/s12920-025-02091-xCirculating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approachLeila Kamkar0Samaneh Saberi1Mehdi Totonchi2Kaveh Kavousi3Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of TehranHPGC Research Group, Department of Medical Biotechnology, Biotechnology Research Center, Pasteur Institute of IranDepartment of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECRLaboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of TehranAbstract 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.https://doi.org/10.1186/s12920-025-02091-xMultiple cancer detectionLiquid biopsymiRNABiomarkerNetwork biologyWGCNA |
| spellingShingle | Leila Kamkar Samaneh Saberi Mehdi Totonchi Kaveh Kavousi Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach BMC Medical Genomics Multiple cancer detection Liquid biopsy miRNA Biomarker Network biology WGCNA |
| title | Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach |
| title_full | Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach |
| title_fullStr | Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach |
| title_full_unstemmed | Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach |
| title_short | Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach |
| title_sort | circulating microrna panels for multi cancer detection and gastric cancer screening leveraging a network biology approach |
| topic | Multiple cancer detection Liquid biopsy miRNA Biomarker Network biology WGCNA |
| url | https://doi.org/10.1186/s12920-025-02091-x |
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