Introduction

The pathogenesis of Cutaneous T-cell lymphoma (CTCL) remains poorly understood and the histologic diagnosis of early MF is one of the most vexing problems in dermatopathology. Our aim was to apply state-of-the-art bioinformatic tools in order to unravel the mechanisms of CTCL pathobiology, to address new therapeutic possibilities as well as to identify potential biomarkers.

Materials & Methods

Datasets Selection: we searched the GEO (http://www.ncbi.nlm.nih.gov/geo/) for gene (GSE143382) and miRNA (GSE109421) expression datasets containing early-stage MF samples and reactive skin lesions (inflammatory dermatosis) as control.

Differential expression data analysis: was performed in R programming language using the LIMMA R package. Differentially expressed genes (DEGs) were selected by applying selection thresholds of adjusted p-value < 0.05 and absolute (log2FC) >= 1 (i.e. FC>=2 or FC<=0.5). For the case of differentially expressed miRNAs (DEMs) we used as selection criteria the p-value < 0.05 and the FC≥1.2 for the overexpressed miRNAs and FC≤0.83 for the under-expressed ones.

Pathway Enrichment Analysis using DEGs: was performed using the Enrichr, a web-based tool for analyzing gene sets that returns any enrichment of common annotated biological features (https://maayanlab.cloud/Enrichr/).
Pathway Enrichment Analysis using DEMs: was performed using the DIANA-mirPath which is a miRNA pathway analysis web-server, which can utilize experimentally validated miRNA interactions derived from DIANA-TarBase v6.0.

The enrichment was obtained using information from KEGG Human and the most significantly enriched pathways were selected based on the p-value <0.05.

Results

Our bioinformatics analysis detected:

A list of DEGs in CTCL which are associated with increased cell proliferation decreased apoptosis, increased Th2 differentiation and immune activation leading to generation of malignant CD4+ T-cells clones, propagating the disease.

A list of DEMs in CTCL. A more restricted set of overexpressed miRNAs (miR-26a, miR-92a, miR-106b, miR-142, miR-146a, miR-155, miR-181a, miR-222 and miR-494) were selected and could be therefore used as biomarkers.

A number of pathways that have been extracted from the genes and miRNAs implicated in the pathogenesis of CTCL and can enlighten its etiopathogenesis. Fourteen percent (14%) of the pathways were common in both analyses including TGF-beta signaling pathway, Pathways in cancer, PI3K-Akt signaling pathway and p53 signaling pathway.

Conclusion

In this project, the tools of bioinformatics were used as a stepping-stone in a combined approach highlighting differentially expressed genes and miRNAs as well as the implicated pathways involved in CTCL development. These findings formed a resource of genes and miRNAs that will be further tested in vitro and in vivo in order to identify the etiopathogenesis of the disease, discover biomarkers for diagnosis and highlight new therapeutic targets.