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Related Experiment Videos

An Exploratory Transcriptomic Classification Model for Psoriasis Based on Apoptosis-Associated and

Xinhao Liu1, Wenqing Fu2, Jiachen Li1

  • 1Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.

International Journal of Molecular Sciences
|June 26, 2026
PubMed
Summary

Related Concept Videos

The Intrinsic Apoptotic Pathway01:31

The Intrinsic Apoptotic Pathway

Internal cellular stress, such as cellular injury or hypoxia, triggers intrinsic apoptosis. The B-cell lymphoma 2 (Bcl-2) family of proteins are the primary regulators of the intrinsic apoptotic pathway. For example, during DNA damage, checkpoint proteins, such as Ataxia Telangiectasia Mutated (ATM protein) and Checkpoints Factor-2 (Chk2) proteins, are activated. These proteins phosphorylate p53 which further activates pro-apoptotic proteins, such as Bax, Bak, PUMA, and Noxa, and inhibits...
EPS and iPS Cells in Disease Research01:21

EPS and iPS Cells in Disease Research

Embryonic and induced pluripotent stem cells are excellent models for disease research because of their ability to self-renew and differentiate into most cell types. Somatic cells from a patient are isolated and reprogrammed into induced pluripotent stem cells or iPSCs. These iPSCs are later differentiated into the desired cell type, which mirrors the diseased cell of the patient. In this way, disease models have been created for investigating diseases such as Down syndrome, type I diabetes,...

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This summary is machine-generated.

This study developed a five-gene molecular signature using machine learning to classify psoriasis. This transcriptomic model shows promise for distinguishing psoriasis from healthy controls across different tissue types.

Area of Science:

  • Genomics
  • Computational Biology
  • Dermatology

Background:

  • Psoriasis classification relies on clinical presentation, lacking objective molecular markers.
  • Transcriptomic signatures offer potential for objective disease classification.
  • Integrating apoptosis and proliferation data may reveal key molecular drivers.

Purpose of the Study:

  • To construct an exploratory molecular classification model for psoriasis using transcriptomic data.
  • To identify a robust gene signature associated with apoptosis and proliferation in psoriasis.
  • To validate the model's generalizability across different tissue types.

Main Methods:

  • Merged skin transcriptomic datasets (GSE30999, GSE53552) for training and whole-blood dataset (GSE55201) for external validation.
Keywords:
apoptosisexplainable machine learninggradient boosting machinepsoriasistranscriptomic classification

Related Experiment Videos

  • Differential expression analysis and GeneCards database intersection to identify apoptosis-related genes.
  • Protein-protein interaction network analysis for hub gene selection and machine learning algorithms for model construction.
  • DALEX-based permutation feature importance to identify a five-gene signature (CCNB1, KIF11, HDAC1, TPX2, MELK).

    • Main Results:

    • A five-gene signature comprising CCNB1, KIF11, HDAC1, TPX2, and MELK was identified.
    • The model achieved high accuracy (AUC 0.966) in the training cohort and moderate cross-tissue generalizability (AUC 0.811) in the whole-blood validation cohort.
    • The developed framework provides an interpretable molecular classification for psoriasis versus healthy controls.

    Conclusions:

    • An explainable, five-gene transcriptomic signature can classify psoriasis from healthy controls.
    • The model demonstrates moderate cross-tissue generalizability, suggesting potential for broader application.
    • Further validation is needed to assess its utility in differentiating psoriasis from similar dermatoses.