08 June 2019 : Laboratory Research
Overexpression of Family with Sequence Similarity 83, Member A (FAM83A) Predicts Poor Clinical Outcomes in Lung Adenocarcinoma
Jing-Tao Zhang1ABEG, Ye-Chun Lin1ACFG, Bu-Fan Xiao2ABCD, Ben-Tong Yu1AG*DOI: 10.12659/MSM.910804
Med Sci Monit 2019; 25:4264-4272
Abstract
BACKGROUND: The aim of this study was to explore the expression levels of family with sequence similarity 83, member A (FAM83A) in lung adenocarcinoma (LUAD) and investigate its clinical prognostic value.
MATERIAL AND METHODS: Bioinformatics mining methods were used to predict the differential expression levels of FAM83A mRNA in LUAD and normal lung tissues based on the TCGA and Oncomine databases. Immunohistochemical staining was performed to demonstrate the FAM83A protein expression levels in 83 cases of LUAD combined with paired normal lung tissues. The correlation between clinicopathologic factors and FAM83A differential expression levels in LUAD was explored by the chi-square test. Kaplan-Meier univariate and Cox multivariate survival analyses were performed to investigate the clinical prognostic value of FAM83A expression in LUAD patients.
RESULTS: Results from TCGA and Oncomine databases revealed that FAM83A mRNA expression level was significantly higher in LUAD than that in normal lung tissues (both P<0.05). Immunohistochemical findings demonstrated that the high positive rate of FAM83A in LUAD was 73.49% (61/83), while that of matched normal lung tissues was only 22.89% (19/83). Moreover, LUAD patients with FAM83A mRNA or high protein levels had dramatically lower OS times than those with FAM83A mRNA or low protein levels (All P<0.05). Lastly, Cox multivariate survival analysis showed that FAM83A differential expression level (low vs. high) was the only independent factor predicting the prognosis of LUAD patients (P=0.001).
CONCLUSIONS: FAM83A was overexpressed in LUAD, and FAM83A overexpression could be used as an independent factor of poor prognosis in LUAD patients.
Keywords: Oncogene Protein v-cbl, Adenocarcinoma of Lung, Computational Biology, Databases, Genetic, Disease-Free Survival, Multivariate Analysis, Neoplasm Proteins, RNA, Messenger
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