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

Tao Shen1, Tingting Dong2, Haiyang Wang2

  • 1Anhui 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, Wuhu, China. stao@ahnu.edu.cn.

BMC Cancer
|January 10, 2025
PubMed
Summary

This study identified key proteins (kNsDEPs) like SCGN, CAP2, and CACYBP that can diagnose neuroendocrine cervical carcinoma (NECC). These proteins, involved in cytoskeleton function, offer new hope for early NECC detection.

Keywords:
Cervical cancerMachine learning algorithmsNeuroendocrine cervical carcinomaPredictive modelProteomics

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Area of Science:

  • Oncology
  • Molecular Biology
  • Biomarker Discovery

Background:

  • Neuroendocrine cervical carcinoma (NECC) is a rare, aggressive cancer with poor prognosis.
  • Current diagnosis and management of NECC lack specific biomarkers.
  • Identifying a distinct protein signature is crucial for improving NECC diagnosis.

Purpose of the Study:

  • To identify a specific protein signature for the diagnosis of NECC.
  • To validate the diagnostic and prognostic potential of identified proteins.
  • To investigate the role of these proteins in NECC pathogenesis.

Main Methods:

  • Utilized machine learning algorithms (randomForest, SVM-RFE, LASSO) on NECC and cervical cancer gene/protein expression data.
  • Selected key NECC-specific dysregulated proteins (kNsDEPs) using an optimal algorithm combination.
  • Validated kNsDEPs diagnostic effect via predictive models and immunohistochemistry; investigated patterns in other neuroendocrine carcinomas.

Main Results:

  • NECC exhibits unique molecular features, including HPV18 infection and cytoskeleton-related functions.
  • Identified secretagogin (SCGN), adenylyl cyclase-associated protein 2 (CAP2), and calcyclin-binding protein (CACYBP) as diagnostic kNsDEPs.
  • Demonstrated robust diagnostic ability and specificity of kNsDEPs; found unique upregulation/downregulation patterns distinguishing NECC.

Conclusions:

  • Key dysregulated proteins (kNsDEPs) are significant in NECC diagnosis.
  • These proteins and their networks offer promising diagnostic biomarker development for NECC.
  • Elucidating kNsDEP roles enhances understanding of NECC pathogenesis.