Jiayue Qi,1,2 Qingqing Guo,1,2 Jia Bai,1 Xiaoqiang Liang,1 Wenwei Zhu,1 Chengxin Li,1,2,* Fang Xie1,* 

1Department of Dermatology, First Medical Center, Chinese PLA General Hospital, Beijing, People’s Republic of China; 2School of Medicine, Nankai University, Tianjin, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Chengxin Li; Fang Xie, Department of Dermatology, First Medical Center, Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China, Email chengxinderm@163.com; xiefang@301hospital.com.cn

Purpose: Cutaneous squamous cell carcinoma (cSCC) ranks as the second most common malignancy in clinical practice and poses a significant threat to public health due to its high malignancy. In this study, we aimed to explore potential biomarkers and molecular mechanisms of cSCC.
Methods: Differentially expressed genes (DEGs) from GSE66359 and GSE117247 datasets were identified using R software. We conducted enrichment analyses and screened hub genes through protein-protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA). To assess the diagnostic performance of these genes, we generated ROC curves using both internal and external datasets (GSE45164) and validated the expression levels of these genes in cSCC tissues through immunohistochemistry. Subsequently, we predicted the target miRNAs and lncRNAs for hub genes using online databases and constructed competing endogenous RNA (ceRNA) networks.
Results: In total, we identified 505 upregulated DEGs and 522 downregulated DEGs. Through PPI and WGCNA analyses, we identified four hub genes exhibiting robust diagnostic performance in internal and external datasets (AUC > 0.9) and selected three previously unreported genes for further analysis. Immunohistochemistry demonstrated significantly elevated CCNA2, CCNB2, and UBE2C expression in cSCC tissues compared to normal skin tissues. Finally, we constructed three ceRNA networks, namely NEAT1/H19-hsa-miR-148a-3p-CCNA2 and NEAT1-hsa-miR-140-3p-UBE2C.
Conclusion: In conclusion, we have identified CCNA2, CCNB2, and UBE2C as novel biomarkers for cSCC, and the NEAT1/H19-hsa-miR-148a-3p-CCNA2 and NEAT1-hsa-miR-140-3p-UBE2C ceRNA networks may represent molecular mechanisms under-lying cSCC progression. The findings of this study offer new diagnostic and therapeutic options for cSCC patients.

Keywords: weighted gene co-expression network analysis, protein-protein interaction analysis, competitive endogenous RNA, pathogenesis