A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patients
Abstract Ovarian cancer is the deadliest gynecologic cancer due to its high rate of recurrence and limited early diagnosis. For certain patients, particularly those with recurring disorders, standard treatment alone is insufficient in the majority of cases. Ferroptosis, an iron- and ROS (reactive ox...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | Diagnostic Pathology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13000-023-01414-9 |
| _version_ | 1827388845183729664 |
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| author | Tingchuan Xiong Yinghong Wang Changjun Zhu |
| author_facet | Tingchuan Xiong Yinghong Wang Changjun Zhu |
| author_sort | Tingchuan Xiong |
| collection | DOAJ |
| description | Abstract Ovarian cancer is the deadliest gynecologic cancer due to its high rate of recurrence and limited early diagnosis. For certain patients, particularly those with recurring disorders, standard treatment alone is insufficient in the majority of cases. Ferroptosis, an iron- and ROS (reactive oxygen species)-reliant cell death, plays a vital role in the occurrence of ovarian cancer. Herein, subjects from TCGA-OV were calculated for immune scores using the ESTIMATE algorithm and assigned into high- (N = 185) or low-immune (N = 193) score groups; 259 ferroptosis regulators and markers were analyzed for expression, and 64 were significantly differentially expressed between two groups. These 64 differentially expressed genes were applied for LASSO-regularized linear Cox regression for establishing ferroptosis regulators and a markers-based risk model, and a 10-gene signature was established. The ROC curve indicated that the risk score-based curve showed satisfactory predictive efficiency. Univariate and multivariate Cox risk regression analyses showed that age and risk score were risk factors for ovarian cancer patients’ overall survival; patients in the high-risk score group obtained lower immune scores. The Nomogram analysis indicated that the model has a good prognostic performance. GO functional enrichment annotation confirmed again the involvement of these 10 genes in ferroptosis and immune activities. TIMER online analysis showed that risk factors and immune cells were significantly correlated. In conclusion, the risk model based on 10 ferroptosis regulators and markers has a good prognostic value for ovarian cancer patients. |
| first_indexed | 2024-03-08T16:24:50Z |
| format | Article |
| id | doaj.art-f5bf27de2c264995927cdc09a862c19c |
| institution | Directory Open Access Journal |
| issn | 1746-1596 |
| language | English |
| last_indexed | 2024-03-08T16:24:50Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Diagnostic Pathology |
| spelling | doaj.art-f5bf27de2c264995927cdc09a862c19c2024-01-07T12:06:09ZengBMCDiagnostic Pathology1746-15962024-01-0119111610.1186/s13000-023-01414-9A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patientsTingchuan Xiong0Yinghong Wang1Changjun Zhu2Department of Gynecologic Surgery, The 3rd Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Cancer Hospital)Center of Heath Management, The First Affiliated Hospital of Xinjiang Medical UniversityTianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal UniversityAbstract Ovarian cancer is the deadliest gynecologic cancer due to its high rate of recurrence and limited early diagnosis. For certain patients, particularly those with recurring disorders, standard treatment alone is insufficient in the majority of cases. Ferroptosis, an iron- and ROS (reactive oxygen species)-reliant cell death, plays a vital role in the occurrence of ovarian cancer. Herein, subjects from TCGA-OV were calculated for immune scores using the ESTIMATE algorithm and assigned into high- (N = 185) or low-immune (N = 193) score groups; 259 ferroptosis regulators and markers were analyzed for expression, and 64 were significantly differentially expressed between two groups. These 64 differentially expressed genes were applied for LASSO-regularized linear Cox regression for establishing ferroptosis regulators and a markers-based risk model, and a 10-gene signature was established. The ROC curve indicated that the risk score-based curve showed satisfactory predictive efficiency. Univariate and multivariate Cox risk regression analyses showed that age and risk score were risk factors for ovarian cancer patients’ overall survival; patients in the high-risk score group obtained lower immune scores. The Nomogram analysis indicated that the model has a good prognostic performance. GO functional enrichment annotation confirmed again the involvement of these 10 genes in ferroptosis and immune activities. TIMER online analysis showed that risk factors and immune cells were significantly correlated. In conclusion, the risk model based on 10 ferroptosis regulators and markers has a good prognostic value for ovarian cancer patients.https://doi.org/10.1186/s13000-023-01414-9Ovarian cancerESTIMATE algorithmImmune score10-ferroptosis regulator and marker signatureLASSO-regularized linear Cox regression |
| spellingShingle | Tingchuan Xiong Yinghong Wang Changjun Zhu A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patients Diagnostic Pathology Ovarian cancer ESTIMATE algorithm Immune score 10-ferroptosis regulator and marker signature LASSO-regularized linear Cox regression |
| title | A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patients |
| title_full | A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patients |
| title_fullStr | A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patients |
| title_full_unstemmed | A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patients |
| title_short | A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patients |
| title_sort | risk model based on 10 ferroptosis regulators and markers established by lasso regularized linear cox regression has a good prognostic value for ovarian cancer patients |
| topic | Ovarian cancer ESTIMATE algorithm Immune score 10-ferroptosis regulator and marker signature LASSO-regularized linear Cox regression |
| url | https://doi.org/10.1186/s13000-023-01414-9 |
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