Identification of HOXA1 as a Novel Biomarker in Prognosis of Head and Neck Squamous Cell Carcinoma

Abstract

Hox genes, a highly conserved homolog in most animals, play vital functions in cell development and organ formation. In recent years, researchers have discovered that it can act as a tumor regulator, and its members can participate in tumorigenesis by regulating receptor signaling, cell differentiation, apoptosis, migration, EMT, and angiogenesis. Hox genes and which major members play a vital role in the progress of head and neck squamous cell carcinoma (HNSCC) is still unclear. After analyzing the expression differences and prognostic value of all Hox genes through the TCGA-HNSC database, we use histochemistry stains in 52 pairs of HNSCC slices to verify the expression level of the key member-HOXA1. In correlation analysis, we found that high HOXA1 expression is related to poor pathological grade (p = 0.0077), advanced T stage (p = 0.021) and perineural invasion (PNI) (p = 0.0019). Furthermore, we used Cox univariate and multivariate regression analysis to confirm the independent predictive power of HOXA1 expression. To explore the underlying mechanisms behind HOXA1, we ran GSVA and GSEA and found fourteen mutual signaling pathways, including neuroprotein secretion and transport, tumor-associated signaling pathways, cell adhere junction and metabolic reprogramming. Finally, we found that the high expression of HOXA1 is significantly related to the decrease of CD8+ T cell infiltration and the decline of DNA methylation level. Our findings demonstrated that HOXA1, as a notable member of the HOX family, maybe an independent prognostic indicator in HNSCC.

Integrated computational analysis reveals HOX genes cluster as oncogenic drivers in head and neck squamous cell carcinoma

“…Various genetic alterations have been found unique to HPV-positive oral squamous cell carcinoma (OSCC), including distinctive mutational signatures, overall mutational burden, frequent copy number changes, and candidate driver events 59 . The abnormal expression of HOX genes has been positively correlated with HPV infection in HPV-associated cancer types such as cervical squamous cell carcinoma (CESC) and HNSCC 48 , 60 , 61 . With regards to the HPV-associated HNSCC, HOXB13, HOXC5, HOXC6, HOXC9, and HOXD11 were differentially expressed between HPV-positive and HPV-negative HNSCC ( p < 0.05, Supplementary Table S5 ).…”

“…The aberrant expression of HOX cluster genes bears clinical significance for diagnosis, prognosis, and treatment in HNSCC 16 , 46 . However, the potential role of HOX genes as disease-specific markers of circulating tumor cells is gaining credence 48 , 82 . The present study investigated the oncogenic HOX genes cluster to elucidate their molecular mechanism and determine their clinical application in HNSCC.…”

“…Hence, determining the differential expression and functional enrichment of HOX genes in different clinical stages and HPV-associated HNSCC is critical. Researchers have reported that higher expression of HOXA1 in HPV-positive HNSCC leading to a reduction in the CD + T cell infiltration may be an independent prognostic factor in HNSCC 48 . Assessment of the expression of each DEHGs regarding HPV status revealed five DEHGs HOXB13, HOXC5, HOXC6, HOXC9, and HOXD11 which showed a significant difference in expression between HPV-positive and HPV-negative HNSCC tumors, implying their role in cancer-related molecular events in HPV-positive HNSCC.…”

Construction and Verification of a Combined Hypoxia and Immune Index for Clear Cell Renal Cell Carcinoma

“…To avoid gene abundance fitting, we used the “glmnet” package to perform LASSO regression to screen for genes with higher correlation ( Friedman et al, 2010 ). Finally, we used multivariate Cox regression analysis to determine the optimal prognostic indicators of the model ( Li et al, 2020 ). The prognostic risk score model was established as follows: ( is the coefficient of the gene in multivariate Cox analysis; represents the expression value of gene ; is the number of genes in the signature) ( Cai et al, 2020 ).…”

“…To avoid gene abundance fitting, we used the "glmnet" package to perform LASSO regression to screen for genes with higher correlation (Friedman et al, 2010). Finally, we used multivariate Cox regression analysis to determine the optimal prognostic indicators of the model (Li et al, 2020). The prognostic risk score model was established as follows:…”