RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model
Abstract Purpose This study aims to utilize bioinformatics methods to systematically screen and identify susceptibility genes for cervical cancer, as well as to construct and validate an mitophagy-related genes (MRGs) diagnostic model. The objective is to increase the understanding of the disease’s...
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| Format: | Article |
| Language: | English |
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Springer
2024-09-01
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| Series: | Journal of Cancer Research and Clinical Oncology |
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| Online Access: | https://doi.org/10.1007/s00432-024-05952-7 |
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| _version_ | 1832586079672205312 |
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| author | Zhang Zhang Fangfang Chen Xiaoxiao Deng |
| author_facet | Zhang Zhang Fangfang Chen Xiaoxiao Deng |
| author_sort | Zhang Zhang |
| collection | DOAJ |
| description | Abstract Purpose This study aims to utilize bioinformatics methods to systematically screen and identify susceptibility genes for cervical cancer, as well as to construct and validate an mitophagy-related genes (MRGs) diagnostic model. The objective is to increase the understanding of the disease’s pathogenesis and improve early diagnosis and treatment. Method We initially collected a large amount of genomic data, including gene expression profile and single nucleotide polymorphism (SNP) data, from the control group and Cervical cancer (CC) patients. Through bioinformatics analysis, which employs methods such as differential gene expression analysis and pathway enrichment analysis, we identified a set of candidate susceptibility genes associated with cervical cancer. Results MRGs were extracted from single-cell RNA sequencing data, and a network graph was constructed on the basis of intercellular interaction data. Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. Through bioinformatics analysis, we successfully identified a group of genes whose expression significantly differed during the development of CC and revealed the biological pathways in which these genes are involved. Moreover, our constructed clinical prognostic model demonstrated excellent performance in the validation phase, accurately predicting the clinical prognosis of patients. Conclusion This study delves into the susceptibility genes of cervical cancer through bioinformatics approaches and successfully builds a reliable clinical prognostic model. This study not only helps uncover potential pathogenic mechanisms of cervical cancer but also provides new directions for early diagnosis and treatment of the disease. |
| format | Article |
| id | doaj-art-aa5a8fb12a45412386ceec00214d6d6d |
| institution | Kabale University |
| issn | 1432-1335 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of Cancer Research and Clinical Oncology |
| spelling | doaj-art-aa5a8fb12a45412386ceec00214d6d6d2025-01-26T12:13:28ZengSpringerJournal of Cancer Research and Clinical Oncology1432-13352024-09-01150911710.1007/s00432-024-05952-7RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic modelZhang Zhang0Fangfang Chen1Xiaoxiao Deng2Department of Gynecology, The People’s Hospital of PingyangDepartment of Gynecology, The People’s Hospital of PingyangDepartment of Gynecology, The People’s Hospital of PingyangAbstract Purpose This study aims to utilize bioinformatics methods to systematically screen and identify susceptibility genes for cervical cancer, as well as to construct and validate an mitophagy-related genes (MRGs) diagnostic model. The objective is to increase the understanding of the disease’s pathogenesis and improve early diagnosis and treatment. Method We initially collected a large amount of genomic data, including gene expression profile and single nucleotide polymorphism (SNP) data, from the control group and Cervical cancer (CC) patients. Through bioinformatics analysis, which employs methods such as differential gene expression analysis and pathway enrichment analysis, we identified a set of candidate susceptibility genes associated with cervical cancer. Results MRGs were extracted from single-cell RNA sequencing data, and a network graph was constructed on the basis of intercellular interaction data. Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. Through bioinformatics analysis, we successfully identified a group of genes whose expression significantly differed during the development of CC and revealed the biological pathways in which these genes are involved. Moreover, our constructed clinical prognostic model demonstrated excellent performance in the validation phase, accurately predicting the clinical prognosis of patients. Conclusion This study delves into the susceptibility genes of cervical cancer through bioinformatics approaches and successfully builds a reliable clinical prognostic model. This study not only helps uncover potential pathogenic mechanisms of cervical cancer but also provides new directions for early diagnosis and treatment of the disease.https://doi.org/10.1007/s00432-024-05952-7Susceptibility genesCervical cancerBioinformatics analysisClinical prognosis model |
| spellingShingle | Zhang Zhang Fangfang Chen Xiaoxiao Deng RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model Journal of Cancer Research and Clinical Oncology Susceptibility genes Cervical cancer Bioinformatics analysis Clinical prognosis model |
| title | RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model |
| title_full | RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model |
| title_fullStr | RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model |
| title_full_unstemmed | RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model |
| title_short | RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model |
| title_sort | retracted article screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy related genes diagnostic model |
| topic | Susceptibility genes Cervical cancer Bioinformatics analysis Clinical prognosis model |
| url | https://doi.org/10.1007/s00432-024-05952-7 |
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