A multi-variable predictive warning model for cervical cancer using clinical and SNPs data
IntroductionCervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms...
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| Format: | Article |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1294230/full |
| _version_ | 1797300135941636096 |
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| author | Xiangqin Li Xiangqin Li Ruoqi Ning Ruoqi Ning Bing Xiao Bing Xiao Silu Meng Silu Meng Haiying Sun Haiying Sun Xinran Fan Xinran Fan Shuang Li Shuang Li |
| author_facet | Xiangqin Li Xiangqin Li Ruoqi Ning Ruoqi Ning Bing Xiao Bing Xiao Silu Meng Silu Meng Haiying Sun Haiying Sun Xinran Fan Xinran Fan Shuang Li Shuang Li |
| author_sort | Xiangqin Li |
| collection | DOAJ |
| description | IntroductionCervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).MethodsClinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models’ efficiencies. The performance of models was validated using decision curve analysis (DCA).ResultsThe LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.DiscussionThe predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management. |
| first_indexed | 2024-03-07T23:01:50Z |
| format | Article |
| id | doaj.art-1adaec4d50ff4a6dbeb53b02fd420e84 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-07T23:01:50Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-1adaec4d50ff4a6dbeb53b02fd420e842024-02-22T11:37:28ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2024-02-011110.3389/fmed.2024.12942301294230A multi-variable predictive warning model for cervical cancer using clinical and SNPs dataXiangqin Li0Xiangqin Li1Ruoqi Ning2Ruoqi Ning3Bing Xiao4Bing Xiao5Silu Meng6Silu Meng7Haiying Sun8Haiying Sun9Xinran Fan10Xinran Fan11Shuang Li12Shuang Li13Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaCancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaCancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaCancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaCancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaCancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaCancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaCancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaIntroductionCervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).MethodsClinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models’ efficiencies. The performance of models was validated using decision curve analysis (DCA).ResultsThe LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.DiscussionThe predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management.https://www.frontiersin.org/articles/10.3389/fmed.2024.1294230/fullcervical cancerpredictive modelgermline mutationSNPsclinical features |
| spellingShingle | Xiangqin Li Xiangqin Li Ruoqi Ning Ruoqi Ning Bing Xiao Bing Xiao Silu Meng Silu Meng Haiying Sun Haiying Sun Xinran Fan Xinran Fan Shuang Li Shuang Li A multi-variable predictive warning model for cervical cancer using clinical and SNPs data Frontiers in Medicine cervical cancer predictive model germline mutation SNPs clinical features |
| title | A multi-variable predictive warning model for cervical cancer using clinical and SNPs data |
| title_full | A multi-variable predictive warning model for cervical cancer using clinical and SNPs data |
| title_fullStr | A multi-variable predictive warning model for cervical cancer using clinical and SNPs data |
| title_full_unstemmed | A multi-variable predictive warning model for cervical cancer using clinical and SNPs data |
| title_short | A multi-variable predictive warning model for cervical cancer using clinical and SNPs data |
| title_sort | multi variable predictive warning model for cervical cancer using clinical and snps data |
| topic | cervical cancer predictive model germline mutation SNPs clinical features |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1294230/full |
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