Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma
Abstract Objective Neuroendocrine cervical carcinoma (NECC) is a rare but highly aggressive tumor. The clinical management of NECC follows neuroendocrine neoplasms and cervical cancer in general. However, the diagnosis and prognosis of NECC remain dismal. The aim of this study was to identify a spec...
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2025-01-01
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| Online Access: | https://doi.org/10.1186/s12885-025-13454-z |
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| author | Tao Shen Tingting Dong Haiyang Wang Yi Ding Jianuo Zhang Xinyi Zhu Yeping Ding Wen Cai Yalan Wei Qiao Wang Sufen Wang Feiyun Jiang Bin Tang |
| author_facet | Tao Shen Tingting Dong Haiyang Wang Yi Ding Jianuo Zhang Xinyi Zhu Yeping Ding Wen Cai Yalan Wei Qiao Wang Sufen Wang Feiyun Jiang Bin Tang |
| author_sort | Tao Shen |
| collection | DOAJ |
| description | Abstract Objective Neuroendocrine cervical carcinoma (NECC) is a rare but highly aggressive tumor. The clinical management of NECC follows neuroendocrine neoplasms and cervical cancer in general. However, the diagnosis and prognosis of NECC remain dismal. The aim of this study was to identify a specific protein signature for the diagnosis of NECC. Methods Protein and gene expression data for NECC and other cervical cancers were retrieved or downloaded from self-collected samples or public resources. Eleven machine-learning algorithms were packaged into 66 combinations, of which we selected the optimal algorithm, including randomForest, SVM-RFE, and LASSO, to select key NECC specific dysregulated proteins (kNsDEPs). The diagnostic effect of kNsDEPs was validated by a set of predictive models and immunohistochemical staining method. The dysregulation patterns of kNsDEPs were further investigated in other neuroendocrine carcinomas. Results Our results showed that NECC displays distinctive biological characteristics, such as HPV18 infection, and exhibits unique molecular features, particularly an enrichment in cytoskeleton-related functions. Furthermore, secretagogin (SCGN), adenylyl cyclase-associated protein 2 (CAP2), and calcyclin-binding protein (CACYBP) were identified as kNsDEPs. These kNsDEPs play a central role in cytoskeleton protein binding and showcase robust diagnostic ability and specificity for NECC. Moreover, the concurrent upregulation of SCGN and CACYBP, along with the downregulation of CAP2, represents a unique feature of NECC, distinguishing it from other neuroendocrine carcinomas. Conclusions This study uncovers the significance of kNsDEPs and elucidates their regulated networks in the context of NECC. It highlights the pivotal role of kNsDEPs in NECC diagnosis, thus offering promising prospects for the development of diagnostic biomarkers for NECC. |
| format | Article |
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| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | BMC Cancer |
| spelling | doaj-art-41f30cb2418649569e86180e5e0b28682025-01-12T12:27:22ZengBMCBMC Cancer1471-24072025-01-0125111610.1186/s12885-025-13454-zIntegrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinomaTao Shen0Tingting Dong1Haiyang Wang2Yi Ding3Jianuo Zhang4Xinyi Zhu5Yeping Ding6Wen Cai7Yalan Wei8Qiao Wang9Sufen Wang10Feiyun Jiang11Bin Tang12Anhui Provincial Key Laboratory of Molecular Enzymology and Mechanism of Major Metabolic Diseases, Anhui Provincial Engineering Research Centre for Molecular Detection and Diagnostics, College of Life Sciences, Anhui Normal UniversityAnhui Provincial Key Laboratory of Molecular Enzymology and Mechanism of Major Metabolic Diseases, Anhui Provincial Engineering Research Centre for Molecular Detection and Diagnostics, College of Life Sciences, Anhui Normal UniversityAnhui Provincial Key Laboratory of Molecular Enzymology and Mechanism of Major Metabolic Diseases, Anhui Provincial Engineering Research Centre for Molecular Detection and Diagnostics, College of Life Sciences, Anhui Normal UniversityDepartment of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)Anhui Provincial Key Laboratory of Molecular Enzymology and Mechanism of Major Metabolic Diseases, Anhui Provincial Engineering Research Centre for Molecular Detection and Diagnostics, College of Life Sciences, Anhui Normal UniversityAnhui Provincial Key Laboratory of Molecular Enzymology and Mechanism of Major Metabolic Diseases, Anhui Provincial Engineering Research Centre for Molecular Detection and Diagnostics, College of Life Sciences, Anhui Normal UniversityDepartment of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)Department of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)Department of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)Department of Pathology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)Department of Pathology, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College)Department of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)Department of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)Abstract Objective Neuroendocrine cervical carcinoma (NECC) is a rare but highly aggressive tumor. The clinical management of NECC follows neuroendocrine neoplasms and cervical cancer in general. However, the diagnosis and prognosis of NECC remain dismal. The aim of this study was to identify a specific protein signature for the diagnosis of NECC. Methods Protein and gene expression data for NECC and other cervical cancers were retrieved or downloaded from self-collected samples or public resources. Eleven machine-learning algorithms were packaged into 66 combinations, of which we selected the optimal algorithm, including randomForest, SVM-RFE, and LASSO, to select key NECC specific dysregulated proteins (kNsDEPs). The diagnostic effect of kNsDEPs was validated by a set of predictive models and immunohistochemical staining method. The dysregulation patterns of kNsDEPs were further investigated in other neuroendocrine carcinomas. Results Our results showed that NECC displays distinctive biological characteristics, such as HPV18 infection, and exhibits unique molecular features, particularly an enrichment in cytoskeleton-related functions. Furthermore, secretagogin (SCGN), adenylyl cyclase-associated protein 2 (CAP2), and calcyclin-binding protein (CACYBP) were identified as kNsDEPs. These kNsDEPs play a central role in cytoskeleton protein binding and showcase robust diagnostic ability and specificity for NECC. Moreover, the concurrent upregulation of SCGN and CACYBP, along with the downregulation of CAP2, represents a unique feature of NECC, distinguishing it from other neuroendocrine carcinomas. Conclusions This study uncovers the significance of kNsDEPs and elucidates their regulated networks in the context of NECC. It highlights the pivotal role of kNsDEPs in NECC diagnosis, thus offering promising prospects for the development of diagnostic biomarkers for NECC.https://doi.org/10.1186/s12885-025-13454-zNeuroendocrine cervical carcinomaCervical cancerProteomicsMachine learning algorithmsPredictive model |
| spellingShingle | Tao Shen Tingting Dong Haiyang Wang Yi Ding Jianuo Zhang Xinyi Zhu Yeping Ding Wen Cai Yalan Wei Qiao Wang Sufen Wang Feiyun Jiang Bin Tang Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma BMC Cancer Neuroendocrine cervical carcinoma Cervical cancer Proteomics Machine learning algorithms Predictive model |
| title | Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma |
| title_full | Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma |
| title_fullStr | Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma |
| title_full_unstemmed | Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma |
| title_short | Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma |
| title_sort | integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma |
| topic | Neuroendocrine cervical carcinoma Cervical cancer Proteomics Machine learning algorithms Predictive model |
| url | https://doi.org/10.1186/s12885-025-13454-z |
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