Dataset Information

Prognostic Correlation of an Autophagy-Related Gene Signature in Patients with Head and Neck Squamous Cell Carcinoma.

ABSTRACT: Considerable evidence indicates that autophagy plays a vital role in the biological processes of various cancers. The aim of this study is to evaluate the prognostic value of autophagy-related genes in patients with head and neck squamous cell carcinoma (HNSCC). Transcriptome expression profiles and clinical data acquired from The Cancer Genome Atlas (TCGA) database were analyzed by Cox proportional hazards model and Kaplan-Meier survival analysis to screen autophagy-related prognostic genes that were significantly correlated with HNSCC patients' overall survival. Functional enrichment analyses were performed to explore biological functions of differentially expressed autophagy-related genes (ARGs) identified in HNSCC patients. Six ARGs (EGFR, HSPB8, PRKN, CDKN2A, FADD, and ITGA3) identified with significantly prognostic values for HNSCC were used to construct a risk signature that could stratify patients into the high-risk and low-risk groups. This signature demonstrated great value in predicting prognosis for HNSCC patients and was indicated as an independent prognostic factor in terms of clinicopathological characteristics (sex, age, clinical stage, histological grade, anatomic subdivision, alcohol history, smoking status, HPV status, and mutational status of the samples). The prognostic signature was also validated by data from the Gene Expression Omnibus (GEO) database and International Cancer Genome Consortium (ICGC). In conclusion, this study provides a novel autophagy-related gene signature for predicting prognosis of HNSCC patients and gives molecular insights of autophagy in HNSCC.

SUBMITTER: Yang C

PROVIDER: S-EPMC7785385 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prognostic Correlation of an Autophagy-Related Gene Signature in Patients with Head and Neck Squamous Cell Carcinoma.

Yang Cai C Mei Hongxiang H Peng Liang L Jiang Fulin F Xie Bingjie B Li Juan J

Computational and mathematical methods in medicine 20201228

Considerable evidence indicates that autophagy plays a vital role in the biological processes of various cancers. The aim of this study is to evaluate the prognostic value of autophagy-related genes in patients with head and neck squamous cell carcinoma (HNSCC). Transcriptome expression profiles and clinical data acquired from The Cancer Genome Atlas (TCGA) database were analyzed by Cox proportional hazards model and Kaplan-Meier survival analysis to screen autophagy-related prognostic genes tha ...[more]

PMID: 33456497

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Project description:Background: Head and neck squamous cell carcinoma (HNSCC) is a common malignancy of the mucosal epithelium of the oral cavity, pharynx, and larynx. Laryngeal squamous cell carcinoma (LSCC) and oral squamous cell carcinoma are common HNSCC subtypes. Patients with metastatic HNSCC have a poor prognosis. Therefore, identifying molecular markers for the development and progression of HNSCC is essential for improving early diagnosis and predicting patient outcomes. Methods: Gene expression RNA-Seq data and patient clinical traits were obtained from The Cancer Genome Atlas-Head and Neck Squamous Cell Carcinoma (TCGA-HNSC) and Gene Expression Omnibus databases. Differentially expressed gene (DEG) screening was performed using the TCGA-HNSC dataset. Intersection analysis between the DEGs and a list of core matrisome genes obtained from the Matrisome Project was used to identify differentially expressed matrisome genes. A prognostic model was established using univariate and multivariate Cox regression analyses, least absolute shrinkage, and selection operator (LASSO) regression analysis. Immune landscape analysis was performed based on the single-sample gene set enrichment analysis algorithm, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, prognostic value, receiver operating characteristic curve analysis, and gene mutation analyses. Immunohistochemical results regarding prognostic protein levels were obtained from the Human Protein Atlas. Single-gene RNA-sequencing data were obtained from GSE150321 and GSE172577 datasets. CCK-8 and Transwell assays were used to confirm cell proliferation and migration. Results: A total of 1,779 DEGs, including 939 upregulated and 840 downregulated genes, between tumor and normal samples were identified using the TCGA-HNSC microarray data. Intersection analysis revealed 52 differentially expressed matrisome-related genes. After performing univariate and multivariate Cox regression and LASSO analyses, a novel prognostic model based on six matrisome genes (FN1, LAMB4, LAMB3, DMP1, CHAD, and MMRN1) for HNSCC was established. This risk model can successfully predict HNSCC survival. The high-risk group had worse prognoses and higher enrichment of pathways related to cancer development than the low-risk group. Silencing LAMB4 in HNSCC cell lines promoted cell proliferation and migration. Conclusion: This study provides a novel prognostic model for HNSCC. Thus, FN1, LAMB4, LAMB3, DMP1, CHAD, and MMRN1 may be the promising biomarkers for clinical practice.

Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is a life-threatening disease with poor prognosis. Pyroptosis has been recently disclosed as a programmed cell death triggered by invasive infection, involved in cancer development. However, the prognosis role of pyroptosis-related genes in HNSCC has not been discussed.MethodsThe RNA sequence data of pyroptosis-related genes were obtained from The Cancer Genome Atlas (TCGA) database. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were performed to screen the HNSCC survival-related signature genes. We established a HNSCC risk model with the identified prognostic genes, then divided the HNSCC patients into low- and high-risk subgroups according to median risk score. Moreover, we utilized Gene Expression Omnibus (GEO) dataset to validate the risk model. Go and KEGG analyses were conducted to reveal the potential function of differential expression of genes that identified between low- and high-risk subgroups. ESTIMATE algorithm was performed to investigate the immune infiltration of tumors. Correlation between signature gene expression and drug-sensitivity was disclosed by Spearman's analysis.ResultsWe constructed a HNSCC risk model with identified seven pyroptosis-related genes (CASP1, GSDME, IL6, NLRP1, NLRP2, NLRP6, and NOD2) as prognostic signature genes. High-risk subgroup of HNSCC patients in TCGA cohort correlated with lower survival probability than patients from low-risk subgroup (p < .001), and the result is verified with GEO dataset. In addition, 161 genes were identified differentially expressed between the low- and high-risk subgroups in the TCGA cohort, mainly related to immune response. Higher PD-L1 expression level was found in the high-risk subgroup that indicated the possible employment of immune checkpoint inhibitors. IL6 was positively correlated with WZ3105 and MPS-1-IN-1 in the cancer therapeutics response portal database.ConclusionWe built and verified a risk model for HNSCC prognosis using seven pyroptosis-related signature genes, which could predict the overall survival of HNSCC patients and facilitate treatment.

Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is an extremely heterogeneous malignant cancer with poor prognosis. Pyroptosis is defined as a novel inflammation-dependent programmed cell death. However, the pyroptosis-associated gene expression in HNSCC and their relationship with prognosis are still indistinct.Material and methodsWe acquired the mRNA expression information of HNSCC patients from publicly available TCGA and GEO databases. We compared the tumor issues and adjacent normal tissues in terms of the gene expression for the purpose of identifying differentially expressed genes (DEGs). Based on these genes, we established a risk signature by the LASSO Cox regression in the TCGA cohort and validated the results in a GEO cohort. We also verified the levels of relevant mRNA expression in the model by RT-qPCR analysis. Eventually, functional enrichment approach was carried out to explore the potential mechanisms.ResultsOur team found a total of 18 differentially expressed genes (DEGs) between the HNSCC and healthy samples, and 4 DEGs displayed a remarkable association with the overall survival (OS) (P < 0.05). A 4-gene signature was constructed, presenting beneficial forecast power in both TCGA and GEO cohorts. Our team categorized patients into a group with high risk and another group with low risk as per the average risk value of the 4-gene feature. The individuals in the low risk group displayed a notably greater OS compared with the high risk one (P < 0.01). The Cox regression study demonstrated the independent forecast capability of the risk score. The receiver operating characteristic approach facilitated the verification of the forecast function of the gene signature. Posterior to verification, 4 genes were aberrantly expressed in the HNSCC and healthy samples. Functional study displayed that these groups presented diverse immunity conditions.ConclusionPyroptosis-associated genes are pivotal for the prognosis of HNSCC and can serve as potential therapeutic targets.

Project description:ObjectiveHead and neck squamous cell carcinoma (HNSCC) is a major threat to public health. Pyroptosis is a form of inflammatory programmed cell death that is still incompletely understood. The role of pyroptotic cell death in HNSCC remains to be fully defined. As such, the present study was developed to explore the potential prognostic utility of a pyroptosis-related gene (PRG) signature in HNSCC.MethodsPRG expression patterns and the associated mutational landscape in HNSCC were analyzed, after which a 6-gene prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) and Cox regression analyses using the TCGA dataset, followed by validation with two GEO datasets (GSE41643 and GSE65858). The relative expression of the genes in the prognostic model was assessed via RT-qPCR in tumor and paired adjacent normal tissue samples from a 32-patient cohort. Potential predictors of patient outcomes associated with this 6-gene model were identified through topological degree analyses of a protein-protein interaction network. Moreover, the prognostic value of NLRP3 as a predictor of HNSCC patient prognosis was established through immunohistochemical (IHC) analyses of samples from 176 HNSCC patients. Lastly, in vitro studies were performed to further demonstrate the relevance of NLRP3 in the context of HNSCC development.ResultsDifferentially expressed PRGs were able to readily differentiate between HNSCC tumors and normal tissues. Risk scores derived from the 6-gene PRG model were independent predictors of HNSCC patient prognosis, and genes that were differentially expressed between low- and high-risk groups were associated with tumor immunity. RT-qPCR assays also showed the potential protective role of NLRP3 in HNSCC patients. IHC analyses further supported the value of NLRP3 as a predictor of HNSCC patient outcomes. Invasion and migration assays demonstrated the potential role of NLRP3 in the inhibition of HNSCC development.ConclusionsOverall, these results highlight a novel prognostic gene signature that offers value in the context of HNSCC patient evaluation, although additional research will be essential to elucidate the mechanisms linking these PRGs to HNSCC outcomes.

Project description:Background: Ferroptosis is an iron-dependent programmed cell death (PCD) form that plays a crucial role in tumorigenesis and might affect the antitumor effect of radiotherapy and immunotherapy. This study aimed to investigate distinct ferroptosis-related genes, their prognostic value and their relationship with immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The differentially expressed ferroptosis-related genes in HNSCC were filtered based on multiple public databases. To avoid overfitting and improve clinical practicability, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were performed to construct a prognostic risk model. Moreover, a nomogram was constructed to forecast individual prognosis. The differences in tumor mutational burden (TMB), immune infiltration and immune checkpoint genes in HNSCC patients with different prognoses were investigated. The correlation between drug sensitivity and the model was firstly analyzed by the Pearson method. Results: Ten genes related to ferroptosis were screened to construct the prognostic risk model. Kaplan-Meier (K-M) analysis showed that the prognosis of HNSCC patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001), and the area under the curve (AUC) of the 1-, 3- and 5-year receiver operating characteristic (ROC) curve increased year by year (0.665, 0.743, and 0.755). The internal and external validation further verified the accuracy of the model. Then, a nomogram was build based on the reliable model. The C-index of the nomogram was superior to a previous study (0.752 vs. 0.640), and the AUC (0.729 vs. 0.597 at 1 year, 0.828 vs. 0.706 at 3 years and 0.853 vs. 0.645 at 5 years), calibration plot and decision curve analysis (DCA) also shown the satisfactory predictive capacity. Furthermore, the TMB was revealed to be positively correlated with the risk score in HNSCC patients (R = 0.14; P < 0.01). The differences in immune infiltration and immune checkpoint genes were significant (P < 0.05). Pearson analysis showed that the relationship between the model and the sensitivity to antitumor drugs was significant (P < 0.05). Conclusion: Our findings identified potential novel therapeutic targets, providing further potential improvement in the individualized treatment of patients with HNSCC.

Project description:Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis. Considerable evidence indicates that autophagy and non-coding RNA play essential roles in the biological processes involved in cancers, but associations between autophagy-related long non-coding RNAs (lncRNAs) and HNSCC remain unclear. In the present study, HNSCC RNA sequences and autophagy-related gene data were extracted from The Cancer Genome Atlas database and the Human Autophagy Database. A total of 1,153 autophagy-related lncRNAs were selected via calculating Pearson's correlation coefficient. Three prognosis-related autophagy lncRNAs were identified via univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis. We also constructed a prognostic model based on these autophagy-related lncRNAs and evaluated its ability to accurately and independently predict the prognosis of HNSCC patients. The area under the curve (AUC) was 0.864 (3-year) and 0.836 (5-year), and our model can independently predict the prognosis of HNSCC. The prognostic value of the three autophagy lncRNAs was confirmed via analysis of samples from five databases. To further identify the functions of the three lncRNAs, a co-expression network was constructed and pathway analysis was performed. In that analysis the lncRNAs were correlated with 189 related genes and 20 autophagy-related genes, and these lncRNAs mainly involved homologous recombination, the Fanconi anemia pathway, the autophagy-related pathway, and immune-related pathways. In addition, we validated the expression levels of three lncRNAs and autophagy markers (ATG12, BECN1, and MAP1LC3B) based on TIMER, Oncomine, and HPA database analysis. Our results indicated that TTTY15 was increased in HPV positive and HPV negative HNSCC patients, and three autophagy markers were up-regulated in all HNSCCC patients. Lastly, association between three lncRNAs and autophagy markers was performed, and our results showed that TTTY15 and MIF-AS1 were associated with autophagy markers. Collectively, these results suggested that three autophagy-related lncRNAs have prognostic value in HNSCC patients.

Dataset Information

Prognostic Correlation of an Autophagy-Related Gene Signature in Patients with Head and Neck Squamous Cell Carcinoma.

Publications

Prognostic Correlation of an Autophagy-Related Gene Signature in Patients with Head and Neck Squamous Cell Carcinoma.

Similar Datasets

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