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Updated: Jul 15, 2026

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning.

Weixiang Wang¹, Qiang Zhang¹, Suting Xu¹

¹Department of Clinical laboratory, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, Lishui, Zhejiang, China.

|July 13, 2026

Summary

This study identifies four key genes (DEFB4A, GJB2, SERPINB3, SERPINB13) driving psoriasis by analyzing gene expression in skin lesions. These findings offer potential new therapeutic targets for psoriasis treatment.

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

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Last Updated: Jul 15, 2026

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Dermatology
Immunology
Genetics

Background:

Psoriasis is a complex skin disorder influenced by genetics, immunity, and environment.
The IL-17/IL-23 immune pathway is a known factor, but other drivers are under investigation.
Identifying novel genetic and molecular contributors is crucial for understanding psoriasis pathogenesis.

Purpose of the Study:

To identify key psoriasis-associated genes using advanced computational methods.
To investigate the lesional regulatory roles of identified genes.
To propose targeted therapeutic strategies based on these genetic findings.

Main Methods:

Utilized single-cell RNA sequencing (scRNA-seq) data from psoriatic and healthy skin samples.
Employed Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and weighted gene co-expression network analysis (WGCNA).
Applied machine learning algorithms to pinpoint four critical psoriasis-associated genes: DEFB4A, GJB2, SERPINB3, and SERPINB13, validating their expression.

Main Results:

Identified 271 hub genes linked to psoriasis lesions and basal cells.
Machine learning refined the list to four key psoriasis genes: DEFB4A, GJB2, SERPINB3, and SERPINB13.
These four genes were found to be upregulated in lesional psoriatic skin.

Conclusions:

DEFB4A, GJB2, SERPINB3, and SERPINB13 are significantly associated with psoriasis pathogenesis.
These genes are upregulated in lesional skin, indicating their role in disease activity.
Small-molecule compounds targeting these genes present promising therapeutic avenues for psoriasis.