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

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Cai Yang ◽  
Hongxiang Mei ◽  
Liang Peng ◽  
Fulin Jiang ◽  
Bingjie Xie ◽  
...  

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.

2021 ◽  
Author(s):  
Haimei Qin ◽  
Junli Wang ◽  
Biyun Liao ◽  
Zhonglin Liu ◽  
Rong Wang

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is most diagnosed at an advanced stage with poor prognosis. Single gene biomarkers cannot have enough predictive ability in HNSCC. Glycolysis participating in cancers was verified. Thus, this study aimed to identify glycolysis-related gene signature predict the outcome of HNSCC. Methods: The mRNA expression data of HNSCC downloaded The Cancer Genome Atlas (TCGA) project was analyzed by Gene Set Enrichment Analysis (GSEA). We use the Cox proportional regression model to construct a prognostic model. Kaplan–Meier and receiver operating characteristic (ROC) curves were employed to estimate the signature. We also analyzed the relationship of the signature and cancer subtypes. Results: We identified nine glycolysis-related genes including G6PD, EGFR, ALDH2, GPR87, STC2, PDK3, ELF3, STC1 and GNPDA1 as prognosis-related genes signature in HNSCC. HNSCC patients were divided into high and low risk group according to the signature. High risk group showed more poor prognosis and the risk score can precisely predict the prognosis of HNSCC. Additionally, the signature also can be used in cancer subtypes. Conclusion: This study established the 9-mRNA glycolysis signature which may serve as a prospective biomarker for prognosis and novel treatment target in HNSCC.


2018 ◽  
pp. 1-11
Author(s):  
Neeraja M. Krishnan ◽  
Mohanraj I ◽  
Janani Hariharan ◽  
Binay Panda

Purpose With large amounts of multidimensional molecular data on cancers generated and deposited into public repositories such as The Cancer Genome Atlas and International Cancer Genome Consortium, a cancer type agnostic and integrative platform will help to identify signatures with clinical relevance. We devised such a platform and showcase it by identifying a molecular signature for patients with metastatic and recurrent (MR) head and neck squamous cell carcinoma (HNSCC). Methods We devised a statistical framework accompanied by a graphical user interface–driven application, Clinical Association of Functionally Established MOlecular CHAnges ( CAFE MOCHA; https://github.com/binaypanda/CAFEMOCHA), to discover molecular signatures linked to a specific clinical attribute in a cancer type. The platform integrates mutations and indels, gene expression, DNA methylation, and copy number variations to discover a classifier first and then to predict an incoming tumor for the same by pulling defined class variables into a single framework that incorporates a coordinate geometry–based algorithm called complete specificity margin-based clustering, which ensures maximum specificity. CAFE MOCHA classifies an incoming tumor sample using either its matched normal or a built-in database of normal tissues. The application is packed and deployed using the install4j multiplatform installer. We tested CAFE MOCHA in HNSCC tumors (n = 513) followed by validation in tumors from an independent cohort (n = 18) for discovering a signature linked to distant MR. Results CAFE MOCHA identified an integrated signature, MR44, associated with distant MR HNSCC, with 80% sensitivity and 100% specificity in the discovery stage and 100% sensitivity and 100% specificity in the validation stage. Conclusion CAFE MOCHA is a cancer type and clinical attribute agnostic statistical framework to discover integrated molecular signatures.


2015 ◽  
Vol 8 (4) ◽  
pp. 287-295 ◽  
Author(s):  
Eleni M. Rettig ◽  
Christine H. Chung ◽  
Justin A. Bishop ◽  
Jason D. Howard ◽  
Rajni Sharma ◽  
...  

2013 ◽  
Vol 19 (19) ◽  
pp. 5444-5455 ◽  
Author(s):  
Roberto A. Lleras ◽  
Richard V. Smith ◽  
Leslie R. Adrien ◽  
Nicolas F. Schlecht ◽  
Robert D. Burk ◽  
...  

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