Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma

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.

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.

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.

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.