Autophagy-Related Signature for Head and Neck Squamous Cell Carcinoma

Abstract

Background. Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies in the world, with low survival and poor quality of life. Autophagy-associated genes (ATGs) have been reported to be involved in the initiation and progression of malignancies. Here, we aimed to investigate the association between autophagy-associated genes and the outcomes in HNSCC patients. Methods. We obtained ATGs with prognostic values by analyzing the datasets from The Cancer Genome Atlas (TCGA) and Human Autophagy Database (HADb). The enrichment functions of autophagy differential genes were analyzed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The Kaplan-Meier method was applied to the survival curve analysis. A prognostic autophagy-related gene signature was established, and its independence was verified. Results. We acquired a total of 529 samples and 232 ATGs; further, we identified 45 genes associated with prognosis and built a prognosis autophagy signature based on risk score of 15 genes. Patients were divided into two groups based on risk scores. The Kaplan-Meier curve illustrated that the survival rate of the high-risk group was significantly lower than that of the low-risk group in both the training group and validation group. The ROC curve revealed that the risk score had the highest AUC value in the 3rd and 5th years, reaching 0.703 and 0.724, which are higher than other risk factors such as gender, age, and TNM stage. The nomogram further confirmed its weight in the prognosis of HNSCC patients. Through KEGG and GO enrichment analyses, we observed that ATGs were involved in the tumorigenesis and invasion of tumor by various mediating pathways. We gained 3 hub genes (MAP1LC3B, FADD, and LAMP1) and further analyzed the survival curves, mutations, differential expressions, and their roles in tumors on the online websites. Conclusion. We identified a novel autophagy-related signature that may provide promising biomarker genes for the treatment and prognosis of HNSCC. We need to validate its prognostic value by applying it to the clinic.

Prognostic association of starvation-induced genes in head and neck cancer

“…After 24-hour starvation, mRNA sequencing of SAS cells detected 12 up-regulated ARGs (ATP6V0A2, ATP6V1B1, ATP6V1C2, DDIT3, ERN1, NHLRC1, NUPR1, PIM2, TMEM150A, TRIB3, WIPI1, and XBP1) and 13 down-regulated ARGs (BNIP3, BNIP3L, C10orf10, DAPK2, GAPDH, HMOX1, MEFV, PLK2, RRAGD, SESN3, SRPX, S100A8, and S100A9). These genes differed from the ARGs previously reported to predict the prognosis of HNSCC patients 29,33,34 . This starvation-induced approach may be bene cial in extrapolating ARGs that have not been previously identi ed as differentially expressed genes.…”

“…In a previous HNSCC bioinformatics study, Li et al 29 identi ed a novel autophagy-related signature consisting of three hub genes, MAP1LC3B, FADD, and LAMP1, that may provide promising biomarker genes for the treatment and prognosis of HNSCC. Similarly, Jin et al 33 determined 35 genes for HNSCC and identi ed ITGA3, CDKN2A, FADD, NKX2-3, BAK1, CXCR4, and HSPB8 as prognostic ARGs.…”

“…Autophagy is also considered a strong promoter of metabolic homeostasis, as it has been shown to play an important role in the regulation of several survival and death signaling pathways that determine the cell fate of cancer [26][27][28][29] . On the other hand, antineoplastic agents, such as survivin inhibitors and disul ram, may promote autophagic cell death in HNSCC cells, thus showing the opposite role of autophagy on cell survival 30,31 .…”

“…The complex multi-step process of autophagy is tightly controlled by a set of autophagy-related genes (ARGs). Some ARGs have been shown to be associated with the prognosis of HNSCC patients using bioinformatics 29,[32][33][34] . However, it has not been clari ed how these ARGs exhibit their expression kinetics in an environment where autophagy occurs.…”

Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

“…Furthermore, our model has a better ability of predicting prognosis for HNSCC than that of TNM staging, with increased AUC (Area Under Curve) values being obtained with ROC analysis. In addition, our model shows a better prognostic capacity based on AUC values as compared with previously published models based on TCGA HNSCC database analysis [41][42][43]. The enhanced ability of our model in evaluating the prognosis of HNSCC should substantially improve the clinical treatment of this condition.…”