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Forkhead box D1 (FOXD1) is a new member of FOX transcription factor family. FOXD1 has demonstrated multilevel roles during normal development, and several diseases’ pathogenesis. However, little is known about the role of FOXD1 in the progression of head and neck squamous cancer (HNSC). In the present study, we analyzed FOXD1 expression pattern using The Cancer Genome Atlas (TCGA) dataset, Gene Expression Omnibus (GEO) datasets, HNSC cell lines, and HNSC tissues. Then, we analyzed the correlation between FOXD1 expression and clinical characteristics, and evaluated the prognostic value of FOXD1 in HNSC. Moreover, we assessed the relationship between FOXD1 expression and tumor microenvironment (TME) and immune cell infiltration using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and Cell-type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithms. Finally, we predicted the FOXD1-related biological processes (BPs) and signal pathways. FOXD1 was up-regulated in HNSC tissues in TCGA datasets, validated by GEO datasets, HNSC cell lines and HNSC tissues. FOXD1 expression was significantly associated with tumor site and HPV infection. Univariate and multivariate Cox regression analyses showed that FOXD1 expression was an independent prognostic factor. Moreover, we found that the proportions of naïve B cells, plasma cells, and resting dendritic cells (DCs) were negatively correlated with FOXD1 expression, otherwise, the proportion of activated mast cells was positively correlated with FOXD1 expression using CIBERSORT algorithm. Gene Set Enrichment Analyses (GSEAs) revealed that FOXD1 was mainly involved in cancer-related signaling pathway and metabolism-related pathways. FOXD1 was a potential oncogene, and might represent an indicator for predicting overall survival (OS) of HNSC patients. Moreover, many cancer-related pathways and metabolism-related processes may be regulated by FOXD1.

Tumor immune infiltration cells (TIICs) are highly heterogeneous, not only in different cancer subtypes but also within different cancer regions. We conducted the Cell-type Identification using Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method. We assessed the relative proportions of 22 TIICs in HNSC using publicly available TCGA transcriptional datasets, analyzed the proportions of TIICs between HNSC tissues and normal tissues, along with accompanying clinicopathological data, and the impact of TIICs on clinical outcome. After the filter criteria, a total of 395 patients were included in the analysis. We found significant differences in naïve B cells, monocytes, resting mast cells, activated mast cells, CD8+ T cells, and M0 macrophages between HNSC tissues and adjacent non-cancer tissues. We also found that some TIIC subgroups were significantly associated with clinical parameters. Moreover, the patients with low Tregs fraction had worse OS and DFS than those with high Tregs fraction. However, low M0 macrophages fraction was associated with better OS and DFS in HNSC patients. Moreover, Tregs and M0 macrophages are likely to be important determinants of prognosis, which may serve as a potential immunotherapy target for HNSC. Then, we screened the immune-related differentially expressed genes (DEGs), performed the GO and KEGG enrichment analysis, constructed the protein–protein interaction network, and screened the prognosis-related hub genes in HNSC. However, further clinical investigation and basic experiments are needed to validate our results, and uncover the molecular mechanisms interlinking TIICs in HNSC and their roles in prognosis and therapy.