Dataset Information

The prognostic impact of RAP2A expression in patients with early and locoregionally advanced nasopharyngeal carcinoma in an endemic area.

ABSTRACT:

Background

By data mining from published transcriptomic databases, we identified RAP2A as a significantly upregulated gene in nasopharyngeal carcinoma (NPC) tissues. RAP2A, a member of the RAS oncogene family, is involved in the process of GTP binding and GTPase activity. The aim of this study was to evaluate the expression of RAP2A and its prognostic impact in patients with early and locoregionally advanced NPC.

Methods

RAP2A immunohistochemistry was performed for 124 NPC patients who were receiving standard treatment and had no initial distal metastasis. We also performed Western blotting to evaluate the endogenous protein expression of RAP2A in NPC cells and non-neoplastic mucosal cells. The result of RAP2A expression was further correlated with clinicopathological variables, disease-specific survival (DSS), distant metastasis-free survival (DMeFS), and local recurrence-free survival (LRFS).

Results

High expression of RAP2A was significantly associated with advanced primary tumor status (P = 0.024) and advanced TNM stage (P = 0.006). In univariate analysis, high expression of RAP2A served as a significant prognostic factor for inferior DSS (P < 0.0001), DMeFS (P < 0.0001), and LRFS (P < 0.0001). In multivariate analysis, RAP2A overexpression still independently predicted worse DSS (hazard ratio [HR] = 2.976, P < 0.001), DMeFS (HR = 4.233, P < 0.001), and LRFS (HR = 4.156, P < 0.001). Moreover, Both HONE1 and TW01 NPC cells, but not non-neoplastic DOK cells demonstrated significantly increased RAP2A expression.

Conclusion

Overexpression of RAP2A is associated with advanced disease status and may therefore be an important prognosticator for poor outcomes in NPC, as well as a potential therapeutic target to aid in developing effective treatment modalities.

SUBMITTER: Lee YE

PROVIDER: S-EPMC4494142 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

The prognostic impact of RAP2A expression in patients with early and locoregionally advanced nasopharyngeal carcinoma in an endemic area.

Lee Ying-En YE He Hong-Lin HL Chen Tzu-Ju TJ Lee Sung-Wei SW Chang I-Wei IW Hsing Chung-Hsi CH Li Chien-Feng CF

American journal of translational research 20150515 5

<h4>Background</h4>By data mining from published transcriptomic databases, we identified RAP2A as a significantly upregulated gene in nasopharyngeal carcinoma (NPC) tissues. RAP2A, a member of the RAS oncogene family, is involved in the process of GTP binding and GTPase activity. The aim of this study was to evaluate the expression of RAP2A and its prognostic impact in patients with early and locoregionally advanced NPC.<h4>Methods</h4>RAP2A immunohistochemistry was performed for 124 NPC patient ...[more]

PMID: 26175852

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Project description:PurposePrognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients.MethodsA total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan-Meier method and compared with the observed PFS probability.ResultsOur MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785-0.851), 0.752 (95% CI: 0.638-0.865), and 0.717 (95% CI: 0.641-0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822-0.895), 0.769 (95% CI: 0.642-0.896), and 0.730 (95% CI: 0.634-0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups.ConclusionOur study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning.

Project description:BackgroundInduction chemotherapy (IC) can alleviate locoregionally advanced nasopharyngeal carcinoma (LA-NPC), but effectiveness differs between patients, toxicity is problematic, and effective blood-based IC efficacy predictors are lacking. Here, we aimed to identify biomarkers for early identification of IC beneficiaries.MethodsSixty-four pairs of matched plasma samples collected before and after IC from LA-NPC patients including 34 responders and 30 non-responders, as well as 50 plasma samples of healthy individuals, were tested using data-independent acquisition mass spectrometry. The proteins associated with clinical traits or IC benefits were investigated by weighted gene co-expression network analysis (WGCNA) and soft cluster analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional annotations were performed to determine the potential function of the identified proteins. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of candidate biomarkers in predicting IC beneficiaries.ResultsCompared with healthy individuals, 1027 differentially expressed proteins (DEPs) were found in the plasma of LA-NPC patients. Based on feedback from IC outcomes, 463 DEPs were identified in the pre-IC plasma between responders and non-responders. A total of 1212 DEPs represented the proteomic changes before and after IC in responders, while 276 DEPs were identified in post-IC plasma between responders and non-responders. WGCNA identified nine protein co-expression modules correlated with clinical traits. Soft cluster analysis identified four IC benefits-related protein clusters. Functional enrichment analysis showed that these proteins may play a role in IC via immunity, complement, coagulation, glycosaminoglycan and serine. Four proteins differentially expressed in all group comparisons, paraoxonase/arylesterase 1 (PON1), insulin-like growth factor-binding protein 3 (IGFBP-3), rheumatoid factor D5 light chain (v-kappa-3) and RNA helicase (DDX55), were associated with clinical traits or IC benefits. A four-protein model accurately identified potential IC beneficiaries (AUC=0.95) while diagnosing LA-NPC (AUC=0.92), and the prediction performance was verified using the models to confirm the effective IC (AUC=0.97) and evaluate IC outcome (AUC=0.94).ConclusionThe plasma protein profiles among IC responders and non-responders were different. PON1, IGFBP3, v-kappa-3 and DDX55 could serve as potential biomarkers for early identification of IC beneficiaries for individualised treatment of LA-NPC.

Dataset Information

The prognostic impact of RAP2A expression in patients with early and locoregionally advanced nasopharyngeal carcinoma in an endemic area.

Background

Methods

Results

Conclusion

Publications

The prognostic impact of RAP2A expression in patients with early and locoregionally advanced nasopharyngeal carcinoma in an endemic area.

Similar Datasets

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