Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling
Abstract Background Endometrial cancer (EMC) is the most common female genital tract malignancy with an increasing prevalence in many countries including Japan, a fact that renders early detection and treatment necessary to protect health and fertility. Although early detection and treatment are nec...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-10-01
|
| Series: | Cancer & Metabolism |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40170-023-00317-z |
| _version_ | 1827708745796288512 |
|---|---|
| author | Eiji Hishinuma Muneaki Shimada Naomi Matsukawa Yoshiko Shima Bin Li Ikuko N. Motoike Yusuke Shibuya Tatsuya Hagihara Shogo Shigeta Hideki Tokunaga Daisuke Saigusa Kengo Kinoshita Seizo Koshiba Nobuo Yaegashi |
| author_facet | Eiji Hishinuma Muneaki Shimada Naomi Matsukawa Yoshiko Shima Bin Li Ikuko N. Motoike Yusuke Shibuya Tatsuya Hagihara Shogo Shigeta Hideki Tokunaga Daisuke Saigusa Kengo Kinoshita Seizo Koshiba Nobuo Yaegashi |
| author_sort | Eiji Hishinuma |
| collection | DOAJ |
| description | Abstract Background Endometrial cancer (EMC) is the most common female genital tract malignancy with an increasing prevalence in many countries including Japan, a fact that renders early detection and treatment necessary to protect health and fertility. Although early detection and treatment are necessary to further improve the prognosis of women with endometrial cancer, biomarkers that accurately reflect the pathophysiology of EMC patients are still unclear. Therefore, it is clinically critical to identify biomarkers to assess diagnosis and treatment efficacy to facilitate appropriate treatment and development of new therapies for EMC. Methods In this study, wide-targeted plasma metabolome analysis was performed to identify biomarkers for EMC diagnosis and the prediction of treatment responses. The absolute quantification of 628 metabolites in plasma samples from 142 patients with EMC was performed using ultra-high-performance liquid chromatography with tandem mass spectrometry. Results The concentrations of 111 metabolites increased significantly, while the concentrations of 148 metabolites decreased significantly in patients with EMC compared to healthy controls. Specifically, LysoPC and TGs, including unsaturated fatty acids, were reduced in patients with stage IA EMC compared to healthy controls, indicating that these metabolic profiles could be used as early diagnostic markers of EMC. In contrast, blood levels of amino acids such as histidine and tryptophan decreased as the risk of recurrence increased and the stages of EMC advanced. Furthermore, a marked increase in total TG and a decrease in specific TGs and free fatty acids including polyunsaturated fatty acids levels were observed in patients with EMC. These results suggest that the polyunsaturated fatty acids in patients with EMC are crucial for disease progression. Conclusions Our data identified specific metabolite profiles that reflect the pathogenesis of EMC and showed that these metabolites correlate with the risk of recurrence and disease stage. Analysis of changes in plasma metabolite profiles could be applied for the early diagnosis and monitoring of the course of treatment of EMC patients. |
| first_indexed | 2024-03-10T17:10:11Z |
| format | Article |
| id | doaj.art-77c306c70fdb4ce8be4a3a78799f70a4 |
| institution | Directory Open Access Journal |
| issn | 2049-3002 |
| language | English |
| last_indexed | 2024-03-10T17:10:11Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer & Metabolism |
| spelling | doaj.art-77c306c70fdb4ce8be4a3a78799f70a42023-11-20T10:41:07ZengBMCCancer & Metabolism2049-30022023-10-0111111210.1186/s40170-023-00317-zIdentification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profilingEiji Hishinuma0Muneaki Shimada1Naomi Matsukawa2Yoshiko Shima3Bin Li4Ikuko N. Motoike5Yusuke Shibuya6Tatsuya Hagihara7Shogo Shigeta8Hideki Tokunaga9Daisuke Saigusa10Kengo Kinoshita11Seizo Koshiba12Nobuo Yaegashi13Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku UniversityAdvanced Research Center for Innovations in Next-Generation Medicine, Tohoku UniversityTohoku Medical Megabank Organization, Tohoku UniversityTohoku Medical Megabank Organization, Tohoku UniversityAdvanced Research Center for Innovations in Next-Generation Medicine, Tohoku UniversityTohoku Medical Megabank Organization, Tohoku UniversityDepartment of Gynecology and Obstetrics, Graduate School of Medicine, Tohoku UniversityDepartment of Gynecology and Obstetrics, Graduate School of Medicine, Tohoku UniversityDepartment of Gynecology and Obstetrics, Graduate School of Medicine, Tohoku UniversityAdvanced Research Center for Innovations in Next-Generation Medicine, Tohoku UniversityTohoku Medical Megabank Organization, Tohoku UniversityAdvanced Research Center for Innovations in Next-Generation Medicine, Tohoku UniversityAdvanced Research Center for Innovations in Next-Generation Medicine, Tohoku UniversityAdvanced Research Center for Innovations in Next-Generation Medicine, Tohoku UniversityAbstract Background Endometrial cancer (EMC) is the most common female genital tract malignancy with an increasing prevalence in many countries including Japan, a fact that renders early detection and treatment necessary to protect health and fertility. Although early detection and treatment are necessary to further improve the prognosis of women with endometrial cancer, biomarkers that accurately reflect the pathophysiology of EMC patients are still unclear. Therefore, it is clinically critical to identify biomarkers to assess diagnosis and treatment efficacy to facilitate appropriate treatment and development of new therapies for EMC. Methods In this study, wide-targeted plasma metabolome analysis was performed to identify biomarkers for EMC diagnosis and the prediction of treatment responses. The absolute quantification of 628 metabolites in plasma samples from 142 patients with EMC was performed using ultra-high-performance liquid chromatography with tandem mass spectrometry. Results The concentrations of 111 metabolites increased significantly, while the concentrations of 148 metabolites decreased significantly in patients with EMC compared to healthy controls. Specifically, LysoPC and TGs, including unsaturated fatty acids, were reduced in patients with stage IA EMC compared to healthy controls, indicating that these metabolic profiles could be used as early diagnostic markers of EMC. In contrast, blood levels of amino acids such as histidine and tryptophan decreased as the risk of recurrence increased and the stages of EMC advanced. Furthermore, a marked increase in total TG and a decrease in specific TGs and free fatty acids including polyunsaturated fatty acids levels were observed in patients with EMC. These results suggest that the polyunsaturated fatty acids in patients with EMC are crucial for disease progression. Conclusions Our data identified specific metabolite profiles that reflect the pathogenesis of EMC and showed that these metabolites correlate with the risk of recurrence and disease stage. Analysis of changes in plasma metabolite profiles could be applied for the early diagnosis and monitoring of the course of treatment of EMC patients.https://doi.org/10.1186/s40170-023-00317-zEndometrial cancerMetabolome analysisBiomarkerMass spectrometry |
| spellingShingle | Eiji Hishinuma Muneaki Shimada Naomi Matsukawa Yoshiko Shima Bin Li Ikuko N. Motoike Yusuke Shibuya Tatsuya Hagihara Shogo Shigeta Hideki Tokunaga Daisuke Saigusa Kengo Kinoshita Seizo Koshiba Nobuo Yaegashi Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling Cancer & Metabolism Endometrial cancer Metabolome analysis Biomarker Mass spectrometry |
| title | Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling |
| title_full | Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling |
| title_fullStr | Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling |
| title_full_unstemmed | Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling |
| title_short | Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling |
| title_sort | identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling |
| topic | Endometrial cancer Metabolome analysis Biomarker Mass spectrometry |
| url | https://doi.org/10.1186/s40170-023-00317-z |
| work_keys_str_mv | AT eijihishinuma identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT muneakishimada identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT naomimatsukawa identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT yoshikoshima identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT binli identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT ikukonmotoike identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT yusukeshibuya identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT tatsuyahagihara identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT shogoshigeta identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT hidekitokunaga identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT daisukesaigusa identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT kengokinoshita identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT seizokoshiba identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling AT nobuoyaegashi identificationofpredictivebiomarkersforendometrialcancerdiagnosisandtreatmentresponsemonitoringusingplasmametabolomeprofiling |