Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study
Ovarian serous cancer (OSC) is one of the leading causes of death across the world. The role of the tumor microenvironment (TME) in OSC has received increasing attention. Targeted maximum likelihood estimation (TMLE) is developed under a counterfactual framework to produce effect estimation for both...
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.625145/full |
| _version_ | 1829514232188633088 |
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| author | Lu Wang Lu Wang Xiaoru Sun Xiaoru Sun Chuandi Jin Chuandi Jin Yue Fan Fuzhong Xue Fuzhong Xue |
| author_facet | Lu Wang Lu Wang Xiaoru Sun Xiaoru Sun Chuandi Jin Chuandi Jin Yue Fan Fuzhong Xue Fuzhong Xue |
| author_sort | Lu Wang |
| collection | DOAJ |
| description | Ovarian serous cancer (OSC) is one of the leading causes of death across the world. The role of the tumor microenvironment (TME) in OSC has received increasing attention. Targeted maximum likelihood estimation (TMLE) is developed under a counterfactual framework to produce effect estimation for both the population level and individual level. In this study, we aim to identify TME-related genes and using the TMLE method to estimate their effects on the 3-year mortality of OSC. In total, 285 OSC patients from the TCGA database constituted the studying population. ESTIMATE algorithm was implemented to evaluate immune and stromal components in TME. Differential analysis between high-score and low-score groups regarding ImmuneScore and StromalScore was performed to select shared differential expressed genes (DEGs). Univariate logistic regression analysis was followed to evaluate associations between DEGs and clinical pathologic factors with 3-year mortality. TMLE analysis was conducted to estimate the average effect (AE), individual effect (IE), and marginal odds ratio (MOR). The validation was performed using three datasets from Gene Expression Omnibus (GEO) database. Additionally, 355 DEGs were selected after differential analysis, and 12 genes from DEGs were significant after univariate logistic regression. Four genes remained significant after TMLE analysis. In specific, ARID3C and FREM2 were negatively correlated with OSC 3-year mortality. CROCC2 and PTF1A were positively correlated with OSC 3-year mortality. Combining of ESTIMATE algorithm and TMLE algorithm, we identified four TME-related genes in OSC. AEs were estimated to provide averaged effects based on the population level, while IEs were estimated to provide individualized effects and may be helpful for precision medicine. |
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| language | English |
| last_indexed | 2024-12-16T13:14:01Z |
| publishDate | 2021-06-01 |
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| series | Frontiers in Genetics |
| spelling | doaj.art-80341136b1f34a4c879d908b8f8b87d72022-12-21T22:30:32ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-06-011210.3389/fgene.2021.625145625145Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining StudyLu Wang0Lu Wang1Xiaoru Sun2Xiaoru Sun3Chuandi Jin4Chuandi Jin5Yue Fan6Fuzhong Xue7Fuzhong Xue8Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaInstitute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaInstitute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaKey Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Xi’an Jiaotong University, Xi’an, ChinaInstitute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaOvarian serous cancer (OSC) is one of the leading causes of death across the world. The role of the tumor microenvironment (TME) in OSC has received increasing attention. Targeted maximum likelihood estimation (TMLE) is developed under a counterfactual framework to produce effect estimation for both the population level and individual level. In this study, we aim to identify TME-related genes and using the TMLE method to estimate their effects on the 3-year mortality of OSC. In total, 285 OSC patients from the TCGA database constituted the studying population. ESTIMATE algorithm was implemented to evaluate immune and stromal components in TME. Differential analysis between high-score and low-score groups regarding ImmuneScore and StromalScore was performed to select shared differential expressed genes (DEGs). Univariate logistic regression analysis was followed to evaluate associations between DEGs and clinical pathologic factors with 3-year mortality. TMLE analysis was conducted to estimate the average effect (AE), individual effect (IE), and marginal odds ratio (MOR). The validation was performed using three datasets from Gene Expression Omnibus (GEO) database. Additionally, 355 DEGs were selected after differential analysis, and 12 genes from DEGs were significant after univariate logistic regression. Four genes remained significant after TMLE analysis. In specific, ARID3C and FREM2 were negatively correlated with OSC 3-year mortality. CROCC2 and PTF1A were positively correlated with OSC 3-year mortality. Combining of ESTIMATE algorithm and TMLE algorithm, we identified four TME-related genes in OSC. AEs were estimated to provide averaged effects based on the population level, while IEs were estimated to provide individualized effects and may be helpful for precision medicine.https://www.frontiersin.org/articles/10.3389/fgene.2021.625145/fulltargeted maximum likelihood estimationovarian cancertumor microenvironmentgene expressionprognostic biomarker |
| spellingShingle | Lu Wang Lu Wang Xiaoru Sun Xiaoru Sun Chuandi Jin Chuandi Jin Yue Fan Fuzhong Xue Fuzhong Xue Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study Frontiers in Genetics targeted maximum likelihood estimation ovarian cancer tumor microenvironment gene expression prognostic biomarker |
| title | Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study |
| title_full | Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study |
| title_fullStr | Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study |
| title_full_unstemmed | Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study |
| title_short | Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study |
| title_sort | identification of tumor microenvironment related prognostic biomarkers for ovarian serous cancer 3 year mortality using targeted maximum likelihood estimation a tcga data mining study |
| topic | targeted maximum likelihood estimation ovarian cancer tumor microenvironment gene expression prognostic biomarker |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.625145/full |
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