Single-cell sequencing highlights heterogeneity and malignant progression in actinic keratosis and cutaneous squamous cell carcinoma.
Dan-Dan ZouYa-Zhou SunXin-Jie LiWen-Juan WuDan XuYu-Tong HeJue QiYing TuYang TangYun-Hua TuXiao-Li WangXing LiFeng-Yan LuLing HuangHeng LongLi HeXin LiPublished in: eLife (2023)
Cutaneous squamous cell carcinoma (cSCC) is the second most frequent of the keratinocyte-derived malignancies with actinic keratosis (AK) as a precancerous lesion. To comprehensively delineate the underlying mechanisms for the whole progression from normal skin to AK to invasive cSCC, we performed single-cell RNA sequencing (scRNA-seq) to acquire the transcriptomes of 138,982 cells from 13 samples of six patients including AK, squamous cell carcinoma in situ (SCCIS), cSCC, and their matched normal tissues, covering comprehensive clinical courses of cSCC. We identified diverse cell types, including important subtypes with different gene expression profiles and functions in major keratinocytes. In SCCIS, we discovered the malignant subtypes of basal cells with differential proliferative and migration potential. Differentially expressed genes (DEGs) analysis screened out multiple key driver genes including transcription factors along AK to cSCC progression. Immunohistochemistry (IHC)/immunofluorescence (IF) experiments and single-cell ATAC sequencing (scATAC-seq) data verified the expression changes of these genes. The functional experiments confirmed the important roles of these genes in regulating cell proliferation, apoptosis, migration, and invasion in cSCC tumor. Furthermore, we comprehensively described the tumor microenvironment (TME) landscape and potential keratinocyte-TME crosstalk in cSCC providing theoretical basis for immunotherapy. Together, our findings provide a valuable resource for deciphering the progression from AK to cSCC and identifying potential targets for anticancer treatment of cSCC.
Keyphrases
- single cell
- rna seq
- squamous cell carcinoma
- genome wide
- genome wide identification
- high throughput
- cell cycle arrest
- transcription factor
- bioinformatics analysis
- end stage renal disease
- squamous cell
- induced apoptosis
- poor prognosis
- endoplasmic reticulum stress
- genome wide analysis
- oxidative stress
- gene expression
- chronic kidney disease
- cell death
- ejection fraction
- dna methylation
- cell cycle
- lymph node metastasis
- electronic health record
- deep learning
- prognostic factors
- data analysis
- radiation therapy
- risk assessment
- peritoneal dialysis
- artificial intelligence
- locally advanced
- long non coding rna
- smoking cessation
- big data
- replacement therapy
- dna binding