Identifying the Role of Disulfidptosis in Endometrial Cancer via Machine Learning Methods
Uterine corpus endometrial carcinoma (UCEC) is the second most common gynecological cancer in the world. With the increased occurrence of UCEC and the stagnation of research in the field, there is a pressing need to identify novel UCEC biomarkers. Disulfidptosis is a novel form of cell death, but it...
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MDPI AG
2023-10-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/3/4/56 |
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| author | Fei Fu Xuesong Lu Zhushanying Zhang Zhi Li Qinlan Xie |
| author_facet | Fei Fu Xuesong Lu Zhushanying Zhang Zhi Li Qinlan Xie |
| author_sort | Fei Fu |
| collection | DOAJ |
| description | Uterine corpus endometrial carcinoma (UCEC) is the second most common gynecological cancer in the world. With the increased occurrence of UCEC and the stagnation of research in the field, there is a pressing need to identify novel UCEC biomarkers. Disulfidptosis is a novel form of cell death, but its role in UCEC is unclear. We integrate differential analysis and the XGBoost algorithm to determine a disulfidptosis-related characteristic gene (DRCG), namely LRPPRC. By prediction and verification based on online databases, we construct a regulatory network of ceRNA in line with the scientific hypothesis, including a ceRNA regulatory axis and two mRNA-miRNA regulatory axes, i.e., mRNA LRPPRC/miRNA hsa-miR-616-5p/lncRNA TSPEAR-AS2, mRNA LRPPRC/miRNA hsa-miR-4658, and mRNA LRPPRC/miRNA hsa-miR-6783-5p. We use machine learning methods such as GBM to screen out seven disulfidptosis-related characteristic lncRNAs (DRCLs) as predictors, and build a risk prediction model with good prediction ability. SCORE = (1.136*LINC02449) + (−2.173*KIF9-AS1) + <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><mo>−</mo></mrow></semantics></math></inline-formula>0.235*ACBD3-AS1) + (1.830*AL354892.3) + (−1.314*AC093677.2) + (0.636*AC113361.1) + (−0.589*CDC37L1-DT). The ROC curve shows that in the training set samples, the AUCs for predicting 1-, 3-, 6-, and 10-year OS are 0.804, 0.724, 0.719, and 0.846, respectively. In the test set samples, the AUCs for predicting 1-, 3-, 6-, and 10-year OS are 0.615, 0.657, 0.687, and 0.702, respectively. In all samples, the AUCs for predicting 1-, 3-, 6-, and 10-year OS are 0.752, 0.706, 0.705, and 0.834, respectively. CP724714 has been screened as a potential therapy option for individuals who have a high risk of developing UCEC. Two subtypes of disulfidptosis-related genes (DRGs) and two subtypes of DRCLs are obtained by NMF method. We find that subtype N1 of DRGs is mainly enriched in various metabolic pathways, and subtype N1 may play a significant role in the process of disulfidptosis. Our study confirms for the first time that disulfidptosis plays a role in UCEC. Our findings help improve the prognosis and treatment of UCEC. |
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| spelling | doaj.art-b2292254c8e549bc802a14f5e3c16efe2023-12-22T13:55:25ZengMDPI AGBioMedInformatics2673-74262023-10-013490892510.3390/biomedinformatics3040056Identifying the Role of Disulfidptosis in Endometrial Cancer via Machine Learning MethodsFei Fu0Xuesong Lu1Zhushanying Zhang2Zhi Li3Qinlan Xie4College of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, ChinaCollege of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, ChinaCollege of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, ChinaCollege of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, ChinaCollege of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, ChinaUterine corpus endometrial carcinoma (UCEC) is the second most common gynecological cancer in the world. With the increased occurrence of UCEC and the stagnation of research in the field, there is a pressing need to identify novel UCEC biomarkers. Disulfidptosis is a novel form of cell death, but its role in UCEC is unclear. We integrate differential analysis and the XGBoost algorithm to determine a disulfidptosis-related characteristic gene (DRCG), namely LRPPRC. By prediction and verification based on online databases, we construct a regulatory network of ceRNA in line with the scientific hypothesis, including a ceRNA regulatory axis and two mRNA-miRNA regulatory axes, i.e., mRNA LRPPRC/miRNA hsa-miR-616-5p/lncRNA TSPEAR-AS2, mRNA LRPPRC/miRNA hsa-miR-4658, and mRNA LRPPRC/miRNA hsa-miR-6783-5p. We use machine learning methods such as GBM to screen out seven disulfidptosis-related characteristic lncRNAs (DRCLs) as predictors, and build a risk prediction model with good prediction ability. SCORE = (1.136*LINC02449) + (−2.173*KIF9-AS1) + <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><mo>−</mo></mrow></semantics></math></inline-formula>0.235*ACBD3-AS1) + (1.830*AL354892.3) + (−1.314*AC093677.2) + (0.636*AC113361.1) + (−0.589*CDC37L1-DT). The ROC curve shows that in the training set samples, the AUCs for predicting 1-, 3-, 6-, and 10-year OS are 0.804, 0.724, 0.719, and 0.846, respectively. In the test set samples, the AUCs for predicting 1-, 3-, 6-, and 10-year OS are 0.615, 0.657, 0.687, and 0.702, respectively. In all samples, the AUCs for predicting 1-, 3-, 6-, and 10-year OS are 0.752, 0.706, 0.705, and 0.834, respectively. CP724714 has been screened as a potential therapy option for individuals who have a high risk of developing UCEC. Two subtypes of disulfidptosis-related genes (DRGs) and two subtypes of DRCLs are obtained by NMF method. We find that subtype N1 of DRGs is mainly enriched in various metabolic pathways, and subtype N1 may play a significant role in the process of disulfidptosis. Our study confirms for the first time that disulfidptosis plays a role in UCEC. Our findings help improve the prognosis and treatment of UCEC.https://www.mdpi.com/2673-7426/3/4/56disulfidptosisendometrial cancermachine learningLRPPRCceRNArisk prediction model |
| spellingShingle | Fei Fu Xuesong Lu Zhushanying Zhang Zhi Li Qinlan Xie Identifying the Role of Disulfidptosis in Endometrial Cancer via Machine Learning Methods BioMedInformatics disulfidptosis endometrial cancer machine learning LRPPRC ceRNA risk prediction model |
| title | Identifying the Role of Disulfidptosis in Endometrial Cancer via Machine Learning Methods |
| title_full | Identifying the Role of Disulfidptosis in Endometrial Cancer via Machine Learning Methods |
| title_fullStr | Identifying the Role of Disulfidptosis in Endometrial Cancer via Machine Learning Methods |
| title_full_unstemmed | Identifying the Role of Disulfidptosis in Endometrial Cancer via Machine Learning Methods |
| title_short | Identifying the Role of Disulfidptosis in Endometrial Cancer via Machine Learning Methods |
| title_sort | identifying the role of disulfidptosis in endometrial cancer via machine learning methods |
| topic | disulfidptosis endometrial cancer machine learning LRPPRC ceRNA risk prediction model |
| url | https://www.mdpi.com/2673-7426/3/4/56 |
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