A Robust Metabolic Enzyme-Based Prognostic Signature for Head and Neck Squamous Cell Carcinoma

Integrated analysis of high‑throughput sequencing reveals the regulatory potential of hsa_circ_0035431 in HNSCC

Circular RNAs (circRNAs) are molecular sponges that are involved in regulation of multiple types of cancer. The present study aimed to screen and explore the key circRNA/microRNA (miRNA or miR)/mRNA interactions in head and neck squamous cell carcinoma (HNSCC) using bioinformatics. A total of six pairs of cancerous and adjacent healthy tissue were obtained from patients with HNSCC and genome-wide transcriptional sequencing was performed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on differentially expressed genes (DEGs). Moreover, expression levels of DEGs were verified in HNSCC cells and tissues using reverse transcription-quantitative (RT-q)PCR. A molecular regulatory network consisting of three circRNAs, seven miRNAs and seven mRNAs was constructed, resulting in identification of two signaling axes, hsa_circ_0035431/hsa-miR-940/fucosyltransferase 6 (FUT6) and hsa_circ_0035431/hsa-miR-940/cingulin-like 1 (CGNL1). FUT6 and CGNL1 were downregulated in HNSCC compared with adjacent healthy tissue and the expression levels of these genes were associated with tumor stage. Low FUT6 and CGNL1 expression levels were associated with lower overall survival rate and progression-free intervals in HNSCC. RT-qPCR demonstrated that hsa_circ_0035431, FUT6 and CGNL1 were downregulated in HNSCC cells and tissue and hsa-miR-940 was upregulated. Notably, these results were consistent with those obtained using high-throughput sequencing. In conclusion, hsa_circ_0035431 may participate in regulation of FUT6 and CGNL1 expression by sponging hsa-miR-940, thus, impacting the occurrence, development and prognosis of HNSCC.

Nine-gene signature and nomogram for predicting survival in patients with head and neck squamous cell carcinoma

Background: Head and neck squamous cell carcinomas (HNSCCs) are derived from the mucosal linings of the upper aerodigestive tract, salivary glands, thyroid, oropharynx, larynx, and hypopharynx. The present study aimed to identify the novel genes and pathways underlying HNSCC. Despite the advances in HNSCC research, diagnosis, and treatment, its incidence continues to rise, and the mortality of advanced HNSCC is expected to increase by 50%. Therefore, there is an urgent need for effective biomarkers to predict HNSCC patients’ prognosis and provide guidance to the personalized treatment.

Methods: Both HNSCC clinical and gene expression data were abstracted from The Cancer Genome Atlas (TCGA) database. Intersecting analysis was adopted between the gene expression matrix of HNSCC patients from TCGA database to extract TME-related genes. Differential gene expression analysis between HNSCC tissue samples and normal tissue samples was performed by R software. Then, HNSCC patients were categorized into clusters 1 and 2 via NMF. Next, TME-related prognosis genes (p < 0.05) were analyzed by univariate Cox regression analysis, LASSO Cox regression analysis, and multivariate Cox regression analysis. Finally, nine genes were selected to construct a prognostic risk model and a prognostic gene signature. We also established a nomogram using relevant clinical parameters and a risk score. The Kaplan–Meier curve, survival analysis, time-dependent receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and the concordance index (C-index) were carried out to assess the accuracy of the prognostic risk model and nomogram. Potential molecular mechanisms were revealed by gene set enrichment analysis (GSEA). Additionally, gene correlation analysis and immune cell correlation analysis were conducted for further enriching our results.

Results: A novel HNSCC prognostic model was established based on the nine genes (GTSE1, LRRN4CL, CRYAB, SHOX2, ASNS, KRT23, ANGPT2, HOXA9, and CARD11). The value of area under the ROC curves (AUCs) (0.769, 0.841, and 0.816) in TCGA whole set showed that the model effectively predicted the 1-, 3-, and 5-year overall survival (OS). Results of the Cox regression assessment confirmed the nine-gene signature as a reliable independent prognostic factor in HNSCC patients. The prognostic nomogram developed using multivariate Cox regression analysis showed a superior C-index over other clinical signatures. Also, the calibration curve had a high level of concordance between estimated OS and the observed OS. This showed that its clinical net can precisely estimate the one-, three-, and five-year OS in HNSCC patients. The gene set enrichment analysis (GSEA) to some extent revealed the immune- and tumor-linked cascades.

Conclusion: In conclusion, the TME-related nine-gene signature and nomogram can effectively improve the estimation of prognosis in patients with HNSCC.