Σφακιανάκης Αλέξανδρος
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5 Άγιος Νικόλαος
Κρήτη 72100
00302841026182
00306932607174
alsfakia@gmail.com

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Κυριακή 16 Δεκεμβρίου 2018

Obesity and genes related to lipid metabolism predict poor survival in oral squamous cell carcinoma

Publication date: February 2019

Source: Oral Oncology, Volume 89

Author(s): Qinchao Hu, Jianmin Peng, Xijuan Chen, Huan Li, Ming Song, Bin Cheng, Tong Wu

Abstract
Objectives

Obesity is an important risk factor for several malignancies, but its effect on oral squamous cell carcinoma (OSCC) prognosis is controversial. We aimed to disclose the association between obesity and the OSCC outcome, and explore the potential of some lipid metabolism-related genes as biomarkers for prognostic prediction.

Materials and methods

A total of 576 patients diagnosed as T1/2N0M0 OSCC without prediagnosis weight loss was included in this retrospective study. These patients were grouped according to body mass index (BMI). The univariate and multivariate analysis were used to compare the progression-free survival (PFS) and disease specific survival (DSS) between groups. Propensity score matching (PSM) was adopted to minimize confounders. Data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were employed to analyze the potential of some lipid metabolism-related genes for OSCC prognosis prediction.

Results

The PFS (P = 0.023) and DSS (P = 0.047) were poorer in obese patients than in normal weight ones. Obesity was an independent risk factor for PFS (Hazard Ratio = 2.016, 95% Confidence Interval 1.101–3.693, P = 0.023) and DSS (Hazard Ratio = 2.022, 95% Confidence Interval 1.040–3.932, P = 0.038). Furthermore, the PSM matched cohort analysis revealed that obesity was associated with poor prognosis of OSCC patients. Finally, 72 dysregulated lipid metabolism-related genes were identified in OSCC, and a combining signature of TGFB1, SPP1, and SERPINE1 was defined as a biomarker for prognostic prediction.

Conclusions

Obesity is an independent risk factor for T1/2N0M0 OSCC, and a combining signature of TGFB1, SPP1, and SERPINE1 may be applied to predict prognosis of OSCC patients.



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