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Screening and identification of biomarkers associated with clinicopathological parameters and prognosis in oral squamous cell carcinoma.

ABSTRACT: Oral squamous cell carcinoma (OSCC) is a major type of malignant tumor of the oral cavity. Despite marked advances in the management and diagnosis of OSCC, the associated overall survival ratio has only exhibited a modest increase in recent years. The present study aimed to identify potential crucial genes associated with clinical features and prognosis for OSCC, and to provide a basis for further investigation. RNA-sequencing data and corresponding clinical information were downloaded from The Cancer Genome Atlas database and differentially expressed mRNAs (DEmRNAs) were identified using the edgeR package. Bioinformatics analysis was performed to identify differentially expressed clinical features-associated mRNAs (CFmRNAs) and enhance the current knowledge of the function of them. Functional enrichment analysis and protein-protein interplay (PPI) network analysis were then performed to better understand CFmRNAs. Survival-associated genes were analyzed with Kaplan-Meier survival curves and the log-rank test. A total of 2,013 DEmRNAs between OSCC samples and normal tissues were identified, 180 of which were associated with clinical features. A total of 17 GO terms and 4 KEGG pathways were significantly enriched in functional enrichment analysis. A total of 4 hub genes (albumin, statherin, neurotensin and mucin 7) were identified in the PPI network analysis. A total of 6 genes (DDB1 and CUL4 associated factor 4 like 2, opiorphin prepropeptide, R3H domain containing like, transmembrane phosphatase with tensin homology, actin like 8 and protocadherin ? 11) were observed to have an influence on survival. The DEmRNAs identified may have a crucial role in the genesis and development of OSCC and may be further developed for diagnostic, therapeutic and prognostic applications for OSCC in the future.

SUBMITTER: Wu Q

PROVIDER: S-EPMC6778814 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Screening and identification of biomarkers associated with clinicopathological parameters and prognosis in oral squamous cell carcinoma.

Wu Qiqi Q Cao Ruoyan R Chen Juan J Xie Xiaoli X

Experimental and therapeutic medicine 20190911 5

Oral squamous cell carcinoma (OSCC) is a major type of malignant tumor of the oral cavity. Despite marked advances in the management and diagnosis of OSCC, the associated overall survival ratio has only exhibited a modest increase in recent years. The present study aimed to identify potential crucial genes associated with clinical features and prognosis for OSCC, and to provide a basis for further investigation. RNA-sequencing data and corresponding clinical information were downloaded from The ...[more]

PMID: 31608128

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Project description:BackgroundWe aimed to develop novel diagnostic and prognostic signatures based on preoperative inflammatory, immunological, and nutritional parameters in blood (PIINPBs) by machine learning algorithms for patients with oral squamous cell carcinoma (OSCC).MethodsA total of 486 OSCC patients and 200 age and gender-matched non-OSCC patients who were diagnosed and treated at our institution for noninfectious, nontumor diseases were retrospectively enrolled and divided into training and validation cohorts. Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASSO) analyses were employed to construct prognostic signatures. A novel prognostic nomogram integrating a PIINPB-derived prognostic signature and selected clinicopathological parameters was further developed. Performances of these signatures were assessed by receiver operating characteristic (ROC) curves, calibrating curves, and decision tree.ResultsDiagnostic models developed by machine learning algorithms from 13 PIINPBs, which included counts of white blood cells (WBC), neutrophils (N), monocytes (M), lymphocytes (L), platelets (P), albumin (ALB), and hemoglobin (Hb), along with albumin-globulin ratio (A/G), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), displayed satisfactory discriminating capabilities in patients with or without OSCC, and among OSCC patients with diverse pathological grades and clinical stages. A prognostic signature based on 6 survival-associated PIINPBs (L, P, PNI, LMR, SII, A/G) served as an independent factor to predict patient survival. Moreover, a novel nomogram integrating prognostic signature and tumor size, pathological grade, cervical node metastasis, and clinical stage significantly enhanced prognostic power [3-year area under the curve (AUC) =0.825; 5-year AUC =0.845].ConclusionsOur results generated novel and robust diagnostic and prognostic signatures derived from PIINPBs by machine learning for OSCC. Performance of these signatures suggest the potential for PIINPBs to supplement current regimens and provide better patient stratification and prognostic prediction.

Dataset Information

Screening and identification of biomarkers associated with clinicopathological parameters and prognosis in oral squamous cell carcinoma.

Publications

Screening and identification of biomarkers associated with clinicopathological parameters and prognosis in oral squamous cell carcinoma.

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