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CUEDC2 down-regulation is associated with tumor growth and poor prognosis in lung adenocarcinoma.

ABSTRACT: CUE domain-containing 2 (CUEDC2) is a multi-functional protein, which regulates cell cycle, growth factor signaling and inflammation. We found that CUEDC2 was low in lung adenocarcinoma cell lines and lung adenocarcinoma tissues at both mRNA and protein levels. Low levels of CUEDC2 were correlated with a shorter survival time in patients with lung adenocarcinoma (p = 0.004). CUEDC2 expression was correlated with tumor T classification (P = 0.001) at clinical stage (P = 0.001) and tumor size (P = 0.033). Multivariate analysis suggested that CUEDC2 expression is an independent prognostic indicator for patients with lung adenocarcinoma. Ectopic expression of CUEDC2 decreased cell proliferation in vitro and inhibited tumor growth in nude mice in vivo. Knockdown of endogenous CUEDC2 by short hairpin RNAs (shRNAs) increased tumor growth. Inhibition of proliferation by CUEDC2 was associated with inactivation of the PI3K/Akt pathway, induction of p21 and down-regulation of cyclin D1. Our results suggest that decreased expression of CUEDC2 contributes to tumor growth in lung adenocarcinoma, leading to a poor clinical outcome.

SUBMITTER: Sun L

PROVIDER: S-EPMC4653035 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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CUEDC2 down-regulation is associated with tumor growth and poor prognosis in lung adenocarcinoma.

Sun Longhua L Bai Lihong L Lin Gengpeng G Wang Ran R Liu Yangli Y Cai Jinghuang J Guo Yubiao Y Zhu Zhiwen Z Xie Canmao C

Oncotarget 20150801 24

CUE domain-containing 2 (CUEDC2) is a multi-functional protein, which regulates cell cycle, growth factor signaling and inflammation. We found that CUEDC2 was low in lung adenocarcinoma cell lines and lung adenocarcinoma tissues at both mRNA and protein levels. Low levels of CUEDC2 were correlated with a shorter survival time in patients with lung adenocarcinoma (p = 0.004). CUEDC2 expression was correlated with tumor T classification (P = 0.001) at clinical stage (P = 0.001) and tumor size (P = ...[more]

PMID: 26023733

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Project description:Background:Lung adenocarcinoma (LUAD), as the main subtype of lung cancer, is one of the common causes of cancer-related deaths worldwide. The AHNAK family is correlated with cell structure and migration, cardiac calcium channel signaling, and tumor metastasis. Previous studies showed AHNAK2 could promote tumor progression and cell migration in melanoma and renal clear cell carcinoma. However, the role of AHNAK2 in LUAD remains unknown. Methods:We examined the levels of AHNAK2 in pathological specimens and the database of Clinical Proteomic Tumor Analysis Consortium-Lung adenocarcinoma (CPTAC-LUAD), The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD), Gene Expression Omnibus dataset (GSE72094, GSE26939), and The Genotype-Tissue Expression (GTEx) of lung tissue samples. Univariate Cox regression, multivariate Cox regression, and Kaplan-Meier survival analysis were performed to reveal the relationship between AHNAK2 and prognosis. A nomogram was constructed to predict 2- or 3-year overall survival and validated via calibration curves, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA). Furthermore, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to explore the functional role of AHNAK2 in lung adenocarcinoma. Finally, by transfecting siRNA, we examined the regulatory effect of AHNAK2 on cell migration. Results:The expression of AHNAK2 was upregulated in tumor samples and correlated with poor prognosis in LUAD patients. Nomogram with AHNAK2 and clinical parameters showed a good prediction in overall survival (OS), especially the 2-year OS. In addition, functional analyses and wound healing assay suggested that AHNAK2 might be involved in the regulation of migration in LUAD. Conclusion:In summary, our study showed that AHNAK2 might be a novel biomarker in LUAD and revealed the potential mechanism of AHNAK2 in LUAD progression which could provide new insights for target therapy.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

Dataset Information

CUEDC2 down-regulation is associated with tumor growth and poor prognosis in lung adenocarcinoma.

Publications

CUEDC2 down-regulation is associated with tumor growth and poor prognosis in lung adenocarcinoma.

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