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Related Experiment Video

Updated: Jan 11, 2026

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Determining a Stability Prognostic Panel for 636 Patients With Melanoma Using a Machine Learning Computational

Hewen Guan1,2, Yuankuan Jiang1,2, Yuying Cui2

  • 1Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

Experimental Dermatology
|November 13, 2025
PubMed
Summary

A new 14-gene signature improves melanoma prognosis prediction, outperforming existing models. This machine learning approach identifies key biomarkers, including CUL2, for personalized melanoma treatment strategies.

Keywords:
CUL2 genebulk RNA sequencingmelanomasingle‐cell RNA sequencing

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Current melanoma prognosis relies on limited histopathological and clinical staging.
  • Individual patient heterogeneity is not adequately addressed by conventional methods.
  • There is a need for improved prognostic accuracy and personalized treatment frameworks in melanoma.

Purpose of the Study:

  • To develop a machine learning-driven prognostic signature for melanoma.
  • To identify pivotal biomarkers for melanoma prognosis.
  • To establish a precision medicine framework for melanoma management.

Main Methods:

  • Utilized bulk RNA-seq data from TCGA and GEO databases (636 patients).
  • Applied univariate Cox regression and machine learning algorithms (LASSO, RSF) to identify prognostic genes and develop a signature.
  • Validated the signature across multiple independent cohorts and assessed hub gene relevance through molecular experiments.

Main Results:

  • Identified 53 protective prognosis-related genes (PRGs), with higher activity in primary melanoma.
  • Developed a 14-gene consensus prognostic signature with high predictive accuracy (C-index 0.908 in TCGA-SKCM, 0.758 across validation cohorts).
  • The signature outperformed 19 existing prognostic models and identified CUL2 as a key tumor-suppressive biomarker.

Conclusions:

  • A robust 14-gene prognostic signature for melanoma has been developed and validated.
  • This signature offers improved prognostic performance and supports precision medicine in melanoma.
  • CUL2 is a significant protective biomarker with demonstrated tumor-suppressive functions in melanoma.