The Function and Significance of SELENBP1 Downregulation in Human Bronchial Epithelial Carcinogenic Process

Zeng, Gu-Qing; Yi, Hong; Zhang, Pengfei; Li, Xinhui; Hu, Rong; Li, Maoyu; Li, Cui; Qu, Jia-Quan; Deng, Xingming; Xiao, Zhi‐Qiang

doi:10.1371/journal.pone.0071865

Cited by 21 publications

(19 citation statements)

References 26 publications

(42 reference statements)

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“…SELENBP1, a member of selenoproteins family, binds and mediate intracellular transport of selenium. Many studies have shown that deficiency of selenium in daily diet is associated with an increased risk of epithelial cancers including lung cancer [28]. Downregulation of SELENBP1 plays a major role in regulation of cancer development and progression.…”

Section: Protein Diagnostic Biomarkers Detected In Nsclc Patient's Timentioning

confidence: 99%

“…Downregulation of SELENBP1 plays a major role in regulation of cancer development and progression. Zeng et al [28] have established that SELENBP1 is decreased in early LSCC carcinogenesis and its expression level is able to discriminate between normal bronchial epithelium, preneoplastic lesions, and invasive LSCC.…”

Section: Protein Diagnostic Biomarkers Detected In Nsclc Patient's Timentioning

confidence: 99%

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Proteomics biomarkers for non-small cell lung cancer

Kiśluk

Ciborowski

Niemira

et al. 2014

Journal of Pharmaceutical and Biomedical Analysis

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Section: Protein Diagnostic Biomarkers Detected In Nsclc Patient's Timentioning

confidence: 99%

Section: Protein Diagnostic Biomarkers Detected In Nsclc Patient's Timentioning

confidence: 99%

Proteomics biomarkers for non-small cell lung cancer

Kiśluk

Ciborowski

Niemira

et al. 2014

Journal of Pharmaceutical and Biomedical Analysis

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“…As shown in [50], the decreased SELENBP1 is an early event in LUSC, and it can act as a novel potential biomarker for early detection of LUSC. Moreover, SELENBP1 is downregulated in many cancer types, such as Lung cancer according to [51][52][53][54].…”

Section: Enrichment Analysismentioning

confidence: 94%

Building an Ensemble Feature Selection Approach for Cancer Microarray Datasets Using Different Classifiers

Sayed¹,

Nassef²,

Badr³

et al. 2019

IJIES

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The challenge of processing the Microarray datasets with its high dimensionality opened multiple research directions. Different feature selection techniques have been employed to reduce the dimensionality of such Microarray datasets before being attempted by classification algorithms. This study presents an ensemble feature selection approach based on t-test and Genetic Algorithm with five different classification algorithms as its fitness function: Support Vector Machine, Random Forest, Nearest Centroid, K Nearest Neighbour, and Maximum Likelihood with 5fold cross validation. The proposed approach has been applied on two different datasets for Lung cancer; Microarray Gene Expression and DNA methylation datasets aiming to find the Lung cancer biomarker genes. The experimental results showed that the three genes (DLX5, KRT5, and SELENBP1) resulted from processing both datasets have higher classification accuracy (92.31%) compared to separately processing the Gene Expression and the DNA methylation datasets with accuracies 90.38% and 86.54% respectively. Moreover, the classification accuracy achieved using the three aforementioned genes could not be achieved by other research studies unless by using more genes.

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“…Seleniumbinding proteins are known to play important roles in cancer prevention effects of selenium. Downregulation of SELENBP1 is associated with poor prognosis in NSCLC patients (Tan et al, 2016;Zeng et al, 2013). Overall, the proposed approach identifies more informative prognostic markers.…”

Section: Nsclc Datamentioning

confidence: 98%

“…They are TOP2A, ASPM, SELENBP1, MAD2L1, CDC20, PRC1, TYMS, and DLGAP5. Downregulation of SELENBP1 is associated with poor prognosis in NSCLC patients (Tan et al, 2016;Zeng et al, 2013). PRC1 (Protein regulator of Cytokinesis 1) has the highest correlation with PCLAF (r= 0.83).…”

Section: Nsclc Datamentioning

confidence: 99%

Robust network‐based regularization and variable selection for high‐dimensional genomic data in cancer prognosis

Ren

et al. 2019

Genetic Epidemiology

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In cancer genomic studies, an important objective is to identify prognostic markers associated with patients' survival. Network‐based regularization has achieved success in variable selections for high‐dimensional cancer genomic data, because of its ability to incorporate the correlations among genomic features. However, as survival time data usually follow skewed distributions, and are contaminated by outliers, network‐constrained regularization that does not take the robustness into account leads to false identifications of network structure and biased estimation of patients' survival. In this study, we develop a novel robust network‐based variable selection method under the accelerated failure time model. Extensive simulation studies show the advantage of the proposed method over the alternative methods. Two case studies of lung cancer datasets with high‐dimensional gene expression measurements demonstrate that the proposed approach has identified markers with important implications.

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The Function and Significance of SELENBP1 Downregulation in Human Bronchial Epithelial Carcinogenic Process

Cited by 21 publications

References 26 publications

Proteomics biomarkers for non-small cell lung cancer

Proteomics biomarkers for non-small cell lung cancer

Building an Ensemble Feature Selection Approach for Cancer Microarray Datasets Using Different Classifiers

Robust network‐based regularization and variable selection for high‐dimensional genomic data in cancer prognosis

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