Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer
Abstract Background No residual disease (R0 resection) after debulking surgery is the most critical independent prognostic factor for advanced ovarian cancer (AOC). There is an unmet clinical need for selecting primary or interval debulking surgery in AOC patients using existing prediction models. M...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-12-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-023-04774-4 |
| _version_ | 1797376829599776768 |
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| author | Xiaofang Zhou Mu Liu Lijuan Sun Yumei Cao Shanmei Tan Guangxia Luo Tingting Liu Ying Yao Wangli Xiao Ziqing Wan Jie Tang |
| author_facet | Xiaofang Zhou Mu Liu Lijuan Sun Yumei Cao Shanmei Tan Guangxia Luo Tingting Liu Ying Yao Wangli Xiao Ziqing Wan Jie Tang |
| author_sort | Xiaofang Zhou |
| collection | DOAJ |
| description | Abstract Background No residual disease (R0 resection) after debulking surgery is the most critical independent prognostic factor for advanced ovarian cancer (AOC). There is an unmet clinical need for selecting primary or interval debulking surgery in AOC patients using existing prediction models. Methods RNA sequencing of circulating small extracellular vesicles (sEVs) was used to discover the differential expression microRNAs (DEMs) profile between any residual disease (R0, n = 17) and no residual disease (non-R0, n = 20) in AOC patients. We further analyzed plasma samples of AOC patients collected before surgery or neoadjuvant chemotherapy via TaqMan qRT-PCR. The combined risk model of residual disease was developed by logistic regression analysis based on the discovery-validation sets. Results Using a comprehensive plasma small extracellular vesicles (sEVs) microRNAs (miRNAs) profile in AOC, we identified and optimized a risk prediction model consisting of plasma sEVs-derived 4-miRNA and CA-125 with better performance in predicting R0 resection. Based on 360 clinical human samples, this model was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression analysis, and it has favorable calibration and discrimination ability (AUC:0.903; sensitivity:0.897; specificity:0.910; PPV:0.926; NPV:0.871). The quantitative evaluation of Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) suggested that the additional predictive power of the combined model was significantly improved contrasted with CA-125 or 4-miRNA alone (NRI = 0.471, IDI = 0.538, p < 0.001; NRI = 0.122, IDI = 0.185, p < 0.01). Conclusion Overall, we established a reliable, non-invasive, and objective detection method composed of circulating tumor-derived sEVs 4-miRNA plus CA-125 to preoperatively anticipate the high-risk AOC patients of residual disease to optimize clinical therapy. |
| first_indexed | 2024-03-08T19:44:15Z |
| format | Article |
| id | doaj.art-41dc2eb9e3e34b55a9e476725eb91b42 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-03-08T19:44:15Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-41dc2eb9e3e34b55a9e476725eb91b422023-12-24T12:27:56ZengBMCJournal of Translational Medicine1479-58762023-12-0121111910.1186/s12967-023-04774-4Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancerXiaofang Zhou0Mu Liu1Lijuan Sun2Yumei Cao3Shanmei Tan4Guangxia Luo5Tingting Liu6Ying Yao7Wangli Xiao8Ziqing Wan9Jie Tang10Department of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South UniversityDepartment of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South UniversityDepartment of Gynecology and Obstetrics, The Central Hospital of ShaoyangDepartment of Gynecology and Obstetrics, The Central Hospital of ShaoyangDepartment of Gynecology and Obstetrics, The First People’s Hospital of Huaihua, The Affiliated Huaihua Hospital of University of South ChinaDepartment of Gynecology and Obstetrics, The First People’s Hospital of Huaihua, The Affiliated Huaihua Hospital of University of South ChinaDepartment of Gynecology and Obstetrics, The First People’s Hospital of ChangdeDepartment of Gynecology and Obstetrics, The First People’s Hospital of YueyangDepartment of Gynecology and Obstetrics, The First People’s Hospital of YueyangDepartment of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South UniversityDepartment of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South UniversityAbstract Background No residual disease (R0 resection) after debulking surgery is the most critical independent prognostic factor for advanced ovarian cancer (AOC). There is an unmet clinical need for selecting primary or interval debulking surgery in AOC patients using existing prediction models. Methods RNA sequencing of circulating small extracellular vesicles (sEVs) was used to discover the differential expression microRNAs (DEMs) profile between any residual disease (R0, n = 17) and no residual disease (non-R0, n = 20) in AOC patients. We further analyzed plasma samples of AOC patients collected before surgery or neoadjuvant chemotherapy via TaqMan qRT-PCR. The combined risk model of residual disease was developed by logistic regression analysis based on the discovery-validation sets. Results Using a comprehensive plasma small extracellular vesicles (sEVs) microRNAs (miRNAs) profile in AOC, we identified and optimized a risk prediction model consisting of plasma sEVs-derived 4-miRNA and CA-125 with better performance in predicting R0 resection. Based on 360 clinical human samples, this model was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression analysis, and it has favorable calibration and discrimination ability (AUC:0.903; sensitivity:0.897; specificity:0.910; PPV:0.926; NPV:0.871). The quantitative evaluation of Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) suggested that the additional predictive power of the combined model was significantly improved contrasted with CA-125 or 4-miRNA alone (NRI = 0.471, IDI = 0.538, p < 0.001; NRI = 0.122, IDI = 0.185, p < 0.01). Conclusion Overall, we established a reliable, non-invasive, and objective detection method composed of circulating tumor-derived sEVs 4-miRNA plus CA-125 to preoperatively anticipate the high-risk AOC patients of residual disease to optimize clinical therapy.https://doi.org/10.1186/s12967-023-04774-4Ovarian cancerResidual diseaseSmall extracellular vesiclesmicroRNAPrediction model |
| spellingShingle | Xiaofang Zhou Mu Liu Lijuan Sun Yumei Cao Shanmei Tan Guangxia Luo Tingting Liu Ying Yao Wangli Xiao Ziqing Wan Jie Tang Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer Journal of Translational Medicine Ovarian cancer Residual disease Small extracellular vesicles microRNA Prediction model |
| title | Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer |
| title_full | Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer |
| title_fullStr | Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer |
| title_full_unstemmed | Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer |
| title_short | Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer |
| title_sort | circulating small extracellular vesicles micrornas plus ca 125 for treatment stratification in advanced ovarian cancer |
| topic | Ovarian cancer Residual disease Small extracellular vesicles microRNA Prediction model |
| url | https://doi.org/10.1186/s12967-023-04774-4 |
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