Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma
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
Affiliations
Tao Shen
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 University
Tingting Dong
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 University
Haiyang Wang
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 University
Yi Ding
Department of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)
Jianuo Zhang
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 University
Xinyi Zhu
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 University
Yeping Ding
Department of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)
Wen Cai
Department of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)
Yalan Wei
Department of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)
Qiao Wang
Department of Pathology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)
Sufen Wang
Department of Pathology, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College)
Feiyun Jiang
Department of Gynecology, East China Normal University Wuhu Affiliated Hospital (The Second People’s Hospital of Wuhu City)
Bin Tang
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.
