Single-cell RNA sequencing and traditional RNA sequencing reveals the role of cancer-associated fibroblasts in head and neck squamous cell carcinomas cohort

Abstract

Background: Head and neck squamous cell carcinomas (HNSCs) are among the most common tumors worldwide. Despite the availability of various diagnostic and therapeutic strategies, the incidence and mortality rates of HNSC remain high. Cancer-associated fibroblasts (CAFs), as a major component of the tumor microenvironment, exhibit diverse biological characteristics in terms of origin, genetics, and phenotype, and have been increasingly recognized for their roles in tumor progression.

Methods: To investigate the potential role of CAFs in HNSC, we performed a comprehensive bioinformatics analysis based on the TCGA HNSC cohort. We applied single-sample gene set enrichment analysis (ssGSEA), single-cell RNA sequencing (scRNA-seq) analysis, differential expression analysis, Cox regression, LASSO regression, and pathway enrichment analysis to identify CAF-related genes and assess their prognostic value.

Results: We successfully identified a set of CAF-related genes and stratified the HNSC patients into high- and low-CAF groups. Based on the expression of these genes, we constructed a prognostic prediction model using LASSO and multivariate Cox regression analyses. A nomogram integrating the risk score and clinical characteristics was developed to improve individualized survival prediction. Enrichment analysis revealed that the type I interferon signaling pathway, cellular response to type I interferon, defense response to symbiont, and extracellular matrix organization were significantly associated with CAFs in HNSC.

Conclusion: Our study provides a novel CAF-based prognostic model and nomogram for predicting patient outcomes in HNSC. These findings highlight the importance of CAFs in the tumor microenvironment and their potential as therapeutic and prognostic biomarkers.

Keywords: Cancer-associated fibroblast; Head and neck squamous cell carcinoma; Immune cell infiltration; Single-cell RNA sequencing.

Fig. 1

A The results of cell clustering in single-cell RNA sequencing analysis. B The cell annotation of the single-cell RNA sequencing analysis. C, D The different distribution of the different cells in HNSC cohort; The expression level of ACTB (E), B2M (F), FTH1 (G), HNRNPH1 (H), SHISA9 (I), MTRNR2L1 (J), MTRNR2L2 (K), UGDH-AS1 (L), MTRNR2L8 (M), MTRNR2L10 (N) and EEF1A1 (O) in single-cell RNA sequencing

Fig. 2

A The analysis of ssGSEA algorithm based on the CAFs-related genes in HNSC cohort. B The different immune-related cohorts in HNSC cohort. C The heatmap demonstrated the expression of different immune-related cells, immune-related functions and fibroblasts

Fig. 3

A The correlation analysis between TMB and risk scores. B The correlation between HLA-related genes and risk scores. C, D The correlation analysis between immune checkpoint-related genes and risk scores

Fig. 4

A The different expressed analysis between low- and high-CAFs groups. B The univariate COX regression reveals the prognosis-related genes in different expressed genes. C, D The results of the lasso regression analysis. E The GO BP enrichment analysis. F The results of the GO CC enrichment analysis. G The results of GO MF enrichment analysis

Fig. 5

A The survival analysis demonstrated the OS between low- and high-risk groups. B The risk plots based on the risk models. C The univariate independent prognosis analysis. D The multivariate independent prognosis analysis. E The time-dependent ROC curve. F The ROC curve based on the clinical-related characteristics and risk score; The survival analysis between the low- and high-expression level of AGDRE1 (G), AQP5 (H), C5orf66-AS1 (I), GZMB (J), HOXC13 (K), IGHG2 (L), IGKV1-5 (M) and SPINK6 (N)

Fig. 6

A The nomogram based on the clinical-related characteristics and risk scores. B The calibration curve revealed the predictive value of nomogram; The correlation analysis between risk score and age (C), gender (D), grade (E), stage (F), T stage (G) and N stage (H)

Fig. 7

A The immune cell infiltration analysis. B The correlation analysis between HLA-related genes and risk score C, D The correlation analysis between immune checkpoint-related genes and risk score

Fig. 8

Knockdown of ADGRE1 and its effect on cell proliferation in HNSC cells. A Quantitative real-time PCR (qRT-PCR) analysis of ADGRE1 mRNA expression levels in HNSC cells transfected with shRNA targeting ADGRE1 (shADGRE1) or negative control (shNC). ADGRE1 expression was significantly reduced in the shADGRE1 group compared to the shNC group. B Cell proliferation was measured using the CCK-8 assay at day 0, day 2, and day 4 post-transfection. Cells with ADGRE1 knockdown (shADGRE1) showed enhanced proliferation compared to control cells (shNC) at day 4. Data are presented as mean ± SD from three independent experiments. P < 0.05, P < 0.01