Single-cell and bulk RNAseq unveils the immune infiltration landscape and targeted therapeutic biomarkers of psoriasis.

ABSTRACT:

Background

Psoriasis represents a multifaceted and debilitating immune-mediated systemic ailment afflicting millions globally. Despite the continuous discovery of biomarkers associated with psoriasis, identifying lysosomal biomarkers, pivotal as cellular metabolic hubs, remains elusive.

Methods

We employed a combination of differential expression analysis and weighted gene co-expression network analysis (WGCNA) to initially identify lysosomal genes. Subsequently, to mitigate overfitting and eliminate collinear genes, we applied 12 machine learning algorithms to screen robust lysosomal genes. These genes underwent further refinement through random forest (RF) and Lasso algorithms to ascertain the final hub lysosomal genes. To assess their predictive efficacy, we conducted rec

SUBMITTER: Deng W 

PROVIDER: S-EPMC11063342 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature