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Twenty?four signature genes predict the prognosis of oral squamous cell carcinoma with high accuracy and repeatability.

ABSTRACT: Oral squamous cell carcinoma (OSCC) is the sixth most common type cancer worldwide, with poor prognosis. The present study aimed to identify gene signatures that could classify OSCC and predict prognosis in different stages. A training data set (GSE41613) and two validation data sets (GSE42743 and GSE26549) were acquired from the online Gene Expression Omnibus database. In the training data set, patients were classified based on the tumor?node?metastasis staging system, and subsequently grouped into low stage (L) or high stage (H). Signature genes between L and H stages were selected by disparity index analysis, and classification was performed by the expression of these signature genes. The established classification was compared with the L and H classification, and fivefold cross validation was used to evaluate the stability. Enrichment analysis for the signature genes was implemented by the Database for Annotation, Visualization and Integration Discovery. Two validation data sets were used to determine the precise of classification. Survival analysis was conducted followed each classification using the package 'survival' in R software. A set of 24 signature genes was identified based on the classification model with the Fi value of 0.47, which was used to distinguish OSCC samples in two different stages. Overall survival of patients in the H stage was higher than those in the L stage. Signature genes were primarily enriched in 'ether lipid metabolism' pathway and biological processes such as 'positive regulation of adaptive immune response' and 'apoptotic cell clearance'. The results provided a novel 24?gene set that may be used as biomarkers to predict OSCC prognosis with high accuracy, which may be used to determine an appropriate treatment program for patients with OSCC in addition to the traditional evaluation index.

SUBMITTER: Gao J

PROVIDER: S-EPMC5783517 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Twenty‑four signature genes predict the prognosis of oral squamous cell carcinoma with high accuracy and repeatability.

Gao Jianyong J Tian Gang G Han Xu X Zhu Qiang Q

Molecular medicine reports 20171212 2

Oral squamous cell carcinoma (OSCC) is the sixth most common type cancer worldwide, with poor prognosis. The present study aimed to identify gene signatures that could classify OSCC and predict prognosis in different stages. A training data set (GSE41613) and two validation data sets (GSE42743 and GSE26549) were acquired from the online Gene Expression Omnibus database. In the training data set, patients were classified based on the tumor‑node‑metastasis staging system, and subsequently grouped ...[more]

PMID: 29257303

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Project description:BackgroundThe prognosis of oral squamous cell carcinoma (OSCC) patients is difficult to predict or describe due to its high-level heterogeneity and complex aetiologic factors. Ferroptosis is a novel form of iron-dependent cell death that is closely related to tumour growth and progression. This study aims to clarify the predictive value of ferroptosis-related genes (FRGs) on the overall survival(OS) of OSCC patients.MethodsThe mRNA expression profile of FRGs and clinical information of patients with OSCC were collected from the TCGA database. Candidate differentially expressed ferroptosis-related genes (DE-FRGs) were identified by analysing differences between OSCC and adjacent normal tissues. A gene signature of prognosis-related DE-FRGs was established by univariate Cox analysis and LASSO analysis in the training set. Patients were then divided into high- and low-risk groups according to the cut-off value of risk scores, A nomogram was constructed to quantify the contributions of gene signature and clinical parameters to OS. Then several bioinformatics analyses were used to verify the reliability and accuracy of the model in the validation set. Finally, single-sample gene set enrichment analysis (ssGSEA) was also performed to reveal the underlying differences in immune status between different risk groups.ResultsA prognostic model was constructed based on 10 ferroptosis-related genes. Patients in high-risk group had a significantly worse OS (p < 0.001). The gene signature was verified as an independent predictor for the OS of OSCC patients (HR > 1, p < 0.001). The receiver operating characteristic curve displayed the favour predictive performance of the risk model. The prediction nomogram successfully quantified each indicator's contribution to survival and the concordance index and calibration plots showed its superior predictive capacity. Finally, ssGSEA preliminarily indicated that the poor prognosis in the high-risk group might result from the dysregulation of immune status.ConclusionThis study established a 10-ferroptosis-releated gene signature and nomogram that can be used to predict the prognosis of OSCC patients, which provides new insight for future anticancer therapies based on potential FRG targets.

Project description:BackgroundCancers of the oral cavity are primarily oral squamous cell carcinomas (OSCCs). Many of the OSCCs present at late stages with an exceptionally poor prognosis. A probable limitation in management of patients with OSCC lies in the insufficient knowledge pertaining to the linkage between copy number alterations in OSCC and oral tumourigenesis thereby resulting in an inability to deliver targeted therapy.ObjectivesThe current study aimed to identify copy number alterations (CNAs) in OSCC using array comparative genomic hybridization (array CGH) and to correlate the CNAs with clinico-pathologic parameters and clinical outcomes.Materials and methodsUsing array CGH, genome-wide profiling was performed on 75 OSCCs. Selected genes that were harboured in the frequently amplified and deleted regions were validated using quantitative polymerase chain reaction (qPCR). Thereafter, pathway and network functional analysis were carried out using Ingenuity Pathway Analysis (IPA) software.ResultsMultiple chromosomal regions including 3q, 5p, 7p, 8q, 9p, 10p, 11q were frequently amplified, while 3p and 8p chromosomal regions were frequently deleted. These findings were in confirmation with our previous study using ultra-dense array CGH. In addition, amplification of 8q, 11q, 7p and 9p and deletion of 8p chromosomal regions showed a significant correlation with clinico-pathologic parameters such as the size of the tumour, metastatic lymph nodes and pathological staging. Co-amplification of 7p, 8q, 9p and 11q regions that harbored amplified genes namely CCND1, EGFR, TPM2 and LRP12 respectively, when combined, continues to be an independent prognostic factor in OSCC.ConclusionAmplification of 3q, 5p, 7p, 8q, 9p, 10p, 11q and deletion of 3p and 8p chromosomal regions were recurrent among OSCC patients. Co-alteration of 7p, 8q, 9p and 11q was found to be associated with clinico-pathologic parameters and poor survival. These regions contain genes that play critical roles in tumourigenesis pathways.

Project description:Objectives: This study was designed to identify a messenger RNA (mRNA) expression signature to predict survival in patients with oral squamous cell carcinoma (OSCC). Methods: mRNA expression profiles were integrated with clinical data from 280 samples, including 19 normal tissues and 261 OSCC tissues in The Cancer Genome Atlas. We identified differentially expressed mRNAs (DEmRNAs) between the OSCC and normal tissue samples and developed a novel mRNA-focused expression signature using a Cox regression analysis and other bioinformatic methods. The prognostic value of this signature was evaluated by Kaplan-Meier analysis, multivariable COX regression, and receiver operating characteristic (ROC) curve analysis. Protein-protein interaction (PPI) network, gene ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to predict the function of the DEmRNAs. Signature-related mRNAs were analyzed by gene set enrichment analyses (GSEA) and validated by quantitative real-time polymerase chain reaction (qRT-PCR) in 20 paired OSCC and adjacent healthy tissues. Results: We identified a novel 5-mRNA expression signature (HOXA1, CELSR3, HIST1H3J, ZFP42, and ASCL4) that could predict patient outcomes in OSCC. The risk score based on the signature was able to separate OSCC patients into high- and low-risk groups that showed significantly different overall survival (p < 0.001, log-rank test). The signature was further validated as an effective independent prognostic predictor of OSCC by multivariate Cox regression analysis (hazard ratio = 3.747, confidence interval: 2.279-5.677, p < 0.001) and ROC curve of the third year (area under the curve = 0.733). Functional analysis demonstrated that the key hub genes in the PPI network were mainly enriched in cell division, cell proliferation, and the p53 signaling pathway. GSEA results showed that the 5 mRNAs were significantly enriched in mismatch repair, DNA replication, and the NOTCH signaling pathway. Finally, qRT-PCR results showed that the 5 mRNAs were upregulated in OSCC tissue in agreement with the predictions from our bioinformatics analysis. Conclusions: We identified a novel 5-mRNA signature that could predict the survival of patients with OSCC and may be a promising biomarker for personalized cancer treatments.

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Twenty?four signature genes predict the prognosis of oral squamous cell carcinoma with high accuracy and repeatability.

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Twenty‑four signature genes predict the prognosis of oral squamous cell carcinoma with high accuracy and repeatability.

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