Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms
Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model based on mitochondria-related genes(MRGs) for preec...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1304165/full |
| _version_ | 1797362469753061376 |
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| author | Pu Huang Yuchun Song Yu Yang Feiyue Bai Na Li Dan Liu Chunfang Li Xuelan Li Wenli Gou Lu Zong |
| author_facet | Pu Huang Yuchun Song Yu Yang Feiyue Bai Na Li Dan Liu Chunfang Li Xuelan Li Wenli Gou Lu Zong |
| author_sort | Pu Huang |
| collection | DOAJ |
| description | Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model based on mitochondria-related genes(MRGs) for preeclampsia using machine learning and further investigate the association of the MRGs and immune infiltration landscape in preeclampsia. In this research, we analyzed GSE75010 database and screened 552 DE-MRGs between preeclampsia samples and normal samples. Enrichment assays indicated that 552 DE-MRGs were mainly related to energy metabolism pathway and several different diseases. Then, we performed LASSO and SVM-RFE and identified three critical diagnostic genes for preeclampsia, including CPOX, DEGS1 and SH3BP5. In addition, we developed a novel diagnostic model using the above three genes and its diagnostic value was confirmed in GSE44711, GSE75010 datasets and our cohorts. Importantly, the results of RT-PCR confirmed the expressions of CPOX, DEGS1 and SH3BP5 were distinctly increased in preeclampsia samples compared with normal samples. The results of the CIBERSORT algorithm revealed a striking dissimilarity between the immune cells found in preeclampsia samples and those found in normal samples. In addition, we found that the levels of SH3BP5 were closely associated with several immune cells, highlighting its potential involved in immune microenvironment of preeclampsia. Overall, this study has provided a novel diagnostic model and diagnostic genes for preeclampsia while also revealing the association between MRGs and immune infiltration. These findings offer valuable insights for further research and treatment of preeclampsia. |
| first_indexed | 2024-03-08T16:07:24Z |
| format | Article |
| id | doaj.art-6e456fa538ab4b8d8a41f0326f860b41 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-08T16:07:24Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-6e456fa538ab4b8d8a41f0326f860b412024-01-08T04:44:32ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-01-011410.3389/fimmu.2023.13041651304165Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithmsPu Huang0Yuchun Song1Yu Yang2Feiyue Bai3Na Li4Dan Liu5Chunfang Li6Xuelan Li7Wenli Gou8Lu Zong9Department of Obstetrics & Gynecology, the First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, ChinaDepartment of Gynecology and Obstetrics, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, ChinaDepartment of Obstetrics & Gynecology, the First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, ChinaDepartment of Obstetrics & Gynecology, the First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, ChinaDepartment of Obstetrics & Gynecology, the First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, ChinaDepartment of Obstetrics & Gynecology, the First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, ChinaDepartment of Obstetrics & Gynecology, the First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, ChinaDepartment of Obstetrics & Gynecology, the First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, ChinaDepartment of Obstetrics & Gynecology, the First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, ChinaDepartment of Obstetrics & Gynecology, the First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, ChinaPreeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model based on mitochondria-related genes(MRGs) for preeclampsia using machine learning and further investigate the association of the MRGs and immune infiltration landscape in preeclampsia. In this research, we analyzed GSE75010 database and screened 552 DE-MRGs between preeclampsia samples and normal samples. Enrichment assays indicated that 552 DE-MRGs were mainly related to energy metabolism pathway and several different diseases. Then, we performed LASSO and SVM-RFE and identified three critical diagnostic genes for preeclampsia, including CPOX, DEGS1 and SH3BP5. In addition, we developed a novel diagnostic model using the above three genes and its diagnostic value was confirmed in GSE44711, GSE75010 datasets and our cohorts. Importantly, the results of RT-PCR confirmed the expressions of CPOX, DEGS1 and SH3BP5 were distinctly increased in preeclampsia samples compared with normal samples. The results of the CIBERSORT algorithm revealed a striking dissimilarity between the immune cells found in preeclampsia samples and those found in normal samples. In addition, we found that the levels of SH3BP5 were closely associated with several immune cells, highlighting its potential involved in immune microenvironment of preeclampsia. Overall, this study has provided a novel diagnostic model and diagnostic genes for preeclampsia while also revealing the association between MRGs and immune infiltration. These findings offer valuable insights for further research and treatment of preeclampsia.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1304165/fullpreeclampsiamitochondria-related genesimmune microenvironmentmachine learningdiagnostic model |
| spellingShingle | Pu Huang Yuchun Song Yu Yang Feiyue Bai Na Li Dan Liu Chunfang Li Xuelan Li Wenli Gou Lu Zong Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms Frontiers in Immunology preeclampsia mitochondria-related genes immune microenvironment machine learning diagnostic model |
| title | Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms |
| title_full | Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms |
| title_fullStr | Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms |
| title_full_unstemmed | Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms |
| title_short | Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms |
| title_sort | identification and verification of diagnostic biomarkers based on mitochondria related genes related to immune microenvironment for preeclampsia using machine learning algorithms |
| topic | preeclampsia mitochondria-related genes immune microenvironment machine learning diagnostic model |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1304165/full |
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