A prognostic model based on Scissor+ cancer associated fibroblasts identified from bulk and single cell RNA sequencing data in head and neck squamous cell carcinoma

• Published in: Cellular signalling Vol. 114; p. 110984
• Main Authors: Tian, Guoli, Zhang, Jiaqiang, Bao, Yong, Li, Qiuli, Hou, Jinsong
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.02.2024
• Subjects: Cancer-Associated Fibroblasts; Fibroblasts; Head and Neck Neoplasms - genetics; HNSCC; Humans; Prognosis; Scissor; scRNA-seq; Sequence Analysis, RNA; Squamous Cell Carcinoma of Head and Neck - genetics; Tumor Microenvironment - genetics; Fibroblasts; scRNA-seq; HNSCC; Scissor
• ISSN: 0898-6568; 1873-3913
• DOI: 10.1016/j.cellsig.2023.110984

Head and neck squamous cell carcinoma (HNSCC) is one of the most lethal diseases in the world, which often recur after multimodality treatment approaches, leading to a poor prognosis. Fibroblasts, a heterogeneous component of the tumor microenvironment, can modulate numerous aspects of tumor biology and have been increasingly acknowledged in dictating the clinical outcome of patients with HNSCC. However, the subpopulation of fibroblasts that are related to the prognosis of HNSCC has not yet been fully explored. To do so, we combined a single-cell RNA sequencing (scRNA-seq) dataset and bulk RNA-sequencing dataset with clinical information, identifying the fibroblast population that are related to poor prognosis of HNSCC. We found these specific population of fibroblasts are less differentiated. In addition, to identify the prognostic signatures of HNSCC, bioinformatics analysis included least absolute shrinkage and selection operator (LASSO) analyses and univariate cox and were performed. We selected 12 prognosis-related genes for constructing a risk model using The Cancer Genome Atlas (TCGA). The AUC values and calibration plots of this model indicated good prognostic prediction efficacy. This model also was validated in two Gene Expression Omnibus (GEO) datasets. In conclusion, we constructed an optimal model that was derived from single cell RNA-seq and bulk RNA-seq to predict the survival probability of HNSCC patients. Among this model, AKR1C3 higher expression in cancer associated fibroblasts (CAFs) of HNSCC has been confirmed by preliminary experiments. •We found a specific subpopulation CAFs correlated with poor prognosis of HNSCC.•Based on the signature of these CAFs, we selected 12 genes to construct a prognostic model of HNSCC.•AKR1C3 is a risk factor with higher expression in HNSCC, which has been verified by preliminary experiments.