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Prognostic and predictive values of CDK1 and MAD2L1 in lung adenocarcinoma.

ABSTRACT: Lung cancer remains as the leading cause of cancer-related death worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. This study aims to investigate biomarkers associated with cancer progression and prognosis of LUAD. We integrated expression profiles of 668 lung cancer patients in five datasets from the Gene Expression Omnibus (GEO) and identified a panel of differentially expressed genes (DEGs). Function enrichment analysis highlighted that these genes were closely associated with the carcinogenesis of LUAD, such as cell cycle, ECM-receptor interaction and p53 signaling pathway. Cyclin-dependent kinase 1 (CDK1) and MAD2 mitotic arrest deficient-like 1 (MAD2L1), two critical mitotic checkpoint genes, were selected for further study. Elevated expression of CDK1 and MAD2L1 was validated in an independent LUAD cohort. Kaplan-Meier analysis revealed that CDK1 and MAD2L1 expression was negatively correlated with both overall survival (OS) and relapse-free survival (RFS). In conclusion, CDK1 and MAD2L1 were adverse prognostic biomarkers for LUAD whose increased expression could render patients with LUAD a high risk of cancer recurrence and poor survival, suggesting that they might be applied as potential targets for LUAD treatment.

SUBMITTER: Shi YX

PROVIDER: S-EPMC5356732 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Prognostic and predictive values of CDK1 and MAD2L1 in lung adenocarcinoma.

Shi Yuan-Xiang YX Zhu Tao T Zou Ting T Zhuo Wei W Chen Yi-Xin YX Huang Ma-Sha MS Zheng Wei W Wang Chen-Jing CJ Li Xi X Mao Xiao-Yuan XY Zhang Wei W Zhou Hong-Hao HH Yin Ji-Ye JY Liu Zhao-Qian ZQ

Oncotarget 20161201 51

Lung cancer remains as the leading cause of cancer-related death worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. This study aims to investigate biomarkers associated with cancer progression and prognosis of LUAD. We integrated expression profiles of 668 lung cancer patients in five datasets from the Gene Expression Omnibus (GEO) and identified a panel of differentially expressed genes (DEGs). Function enrichment analysis highlighted that these genes were closel ...[more]

PMID: 27835911

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Project description:BackgroundThe predictive and prognostic impact of factors associated with visceral pleural invasion (VPI) on survival and recurrence in patients with resected lung adenocarcinomas is not clearly defined.Patients and methodsA total of 505 consecutive patients with stage Ia-IIIa lung adenocarcinomas treated with radical resection were included. The predominant growth pattern was classified according to the new classification system for lung adenocarcinoma proposed by the International Association for the Study of Lung Cancer, the American Thoracic Society, and the European Respiratory Society. The correlations of VPI with clinical and pathologic parameters were analyzed.ResultsThe incidence of VPI was significantly lower in lepidic predominant group (15.5% vs 4.5%, P<0.001) and higher in solid and micropapillary predominant group (28.6% vs 17.6%, P=0.004 and 14.7% vs 4.2%, P<0.001, respectively). VPI correlated with higher risk in regional postoperative recurrence (hazard ratio, 2.341; 95% confidence interval, 1.564-3.504) and distant recurrence (hazard ratio, 2.193; 95% confidence interval, 1.665-2.89) in surgically resected lung adenocarcinomas. However, when growth patterns of adenocarcinoma were lumped into multivariate analysis, VPI was not a significant independent predictive factor for survival (P=0.854 for overall survival [OS] and P=0.575 for disease-free survival [DFS]) and recurrence (P=0.38 for regional recurrence and P=0.089 for distant recurrence). Of the 95 patients with stage Ib, those who received adjuvant chemotherapy had longer DFS and OS than the patients who received no chemotherapy after surgery. However, these differences in DFS and OS did not reach statistical significance (P=0.063 for DFS, P=0.85 for OS).ConclusionVPI was associated with solid and micropapillary histology. In addition, stage Ib patients with solid histologic subtype tumor showed longer DFS and OS, highlighting a potential benefit in this subgroup of patients and necessitating the need for larger clinical trials.

Project description:BackgroundAlternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms.MethodRNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs).ResultsEach pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = - 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them.ConclusionIn summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

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Dataset Information

Prognostic and predictive values of CDK1 and MAD2L1 in lung adenocarcinoma.

Publications

Prognostic and predictive values of CDK1 and MAD2L1 in lung adenocarcinoma.

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