Neuron-specific enolase, histopathological types, and age as risk factors for bone metastases in lung cancer

Zhou, Yang; Chen, Wenzhao; Peng, Ai-Fen; Tong, Weilai; Liu, Jiaming; Liu, Zhili

doi:10.1177/1010428317714194

Cited by 13 publications

(12 citation statements)

References 27 publications

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“…Genetic and proteomics-based biomarkers are two major types of biomarkers, which can be identified through tumor cells, urine, sputum, blood, or other body fluids [ 3 ]. Table 3 demonstrates numerous lung cancer markers [ 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 ].…”

Section: Biomarkers For Lung Cancer Detectionmentioning

confidence: 99%

“…Many types of proteomic biomarkers have been investigated for lung cancer detection, including Annexin II [ 122 ], APOA1 [ 123 ], carcinoembryonic antigen (CEA) [ 124 ], carbohydrate antigen 125 (CA125) [ 125 ], carbohydrate antigen 19-9 (CA19-9) [ 126 ], cytokeratin fragment 21-1 (CYFRA21-1) [ 127 ], CD59 glycoprotein [ 128 ], transthyretin (TTR) [ 129 ], GM2 activator protein (GM2AP) [ 130 ], haptoglobin-R2 [ 131 ], Ig-free light chain [ 132 ], neuron-specific enolase (NSE) [ 133 ], nitrated ceruloplasmin [ 134 ], plasma kallikrein B1 [ 135 ], ProGRP [ 136 ], retinol binding protein (RBP) [ 137 ], squamous cell carcinoma (SCC) [ 138 ], vascular endothelial growth factor (VEGF) [ 139 ], TPA [ 140 ], and tumor M2-pyruvate kinase [ 141 ].…”

Section: Biomarkers For Lung Cancer Detectionmentioning

confidence: 99%

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Screening and Biosensor-Based Approaches for Lung Cancer Detection

Wang

2017

Sensors

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Early diagnosis of lung cancer helps to reduce the cancer death rate significantly. Over the years, investigators worldwide have extensively investigated many screening modalities for lung cancer detection, including computerized tomography, chest X-ray, positron emission tomography, sputum cytology, magnetic resonance imaging and biopsy. However, these techniques are not suitable for patients with other pathologies. Developing a rapid and sensitive technique for early diagnosis of lung cancer is urgently needed. Biosensor-based techniques have been recently recommended as a rapid and cost-effective tool for early diagnosis of lung tumor markers. This paper reviews the recent development in screening and biosensor-based techniques for early lung cancer detection.

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Section: Biomarkers For Lung Cancer Detectionmentioning

confidence: 99%

Section: Biomarkers For Lung Cancer Detectionmentioning

confidence: 99%

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Wang

2017

Sensors

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“…Single nucleotide polymorphism of TIPARP in Prostate has been proved to be related with cancer 26 . In 27 ENO2 is reported to be a risk factor for bone metastases in cancer. The TPD52L2 in GSE32603 encodes a member of the tumor protein D52-like family, and contributes to proliferation of cancer cells 28 .…”

Section: Resultsmentioning

confidence: 99%

A novel logistic regression model combining semi-supervised learning and active learning for disease classification

Chai

Liang

Wang

et al. 2018

Sci Rep

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Traditional supervised learning classifier needs a lot of labeled samples to achieve good performance, however in many biological datasets there is only a small size of labeled samples and the remaining samples are unlabeled. Labeling these unlabeled samples manually is difficult or expensive. Technologies such as active learning and semi-supervised learning have been proposed to utilize the unlabeled samples for improving the model performance. However in active learning the model suffers from being short-sighted or biased and some manual workload is still needed. The semi-supervised learning methods are easy to be affected by the noisy samples. In this paper we propose a novel logistic regression model based on complementarity of active learning and semi-supervised learning, for utilizing the unlabeled samples with least cost to improve the disease classification accuracy. In addition to that, an update pseudo-labeled samples mechanism is designed to reduce the false pseudo-labeled samples. The experiment results show that this new model can achieve better performances compared the widely used semi-supervised learning and active learning methods in disease classification and gene selection.

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“…The serum level of NSE in 28 patients with brain metastasis was significantly higher than that of 98 patients without metastasis (34.18 ± 28.48 vs. 13.87 ± 4.49 ng/ml, P <0.05). Zhou et al [114] also found that age, pathological type and serum NSE concentration of lung cancer patients are independent risk factors for bone metastasis. The NSE serum concentration of patients with bone lesions is significantly higher than those without bone metastasis (39.18 ± 62.18 vs. 29.16 ± 40.21 ng/ml, P =0.018) [114].…”

Section: γ-Enolase In Lung Cancermentioning

confidence: 99%

“…Zhou et al [114] also found that age, pathological type and serum NSE concentration of lung cancer patients are independent risk factors for bone metastasis. The NSE serum concentration of patients with bone lesions is significantly higher than those without bone metastasis (39.18 ± 62.18 vs. 29.16 ± 40.21 ng/ml, P =0.018) [114]. However, other studies have proposed that serum NSE level in NSCLC patients has no prognostic significance [115,116].…”

Section: γ-Enolase In Lung Cancermentioning

confidence: 99%

Multifunctional neuron-specific enolase: its role in lung diseases

Luo

et al. 2019

Bioscience Reports

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Neuron-specific enolase (NSE), also known as gamma (γ) enolase or enolase-2 (Eno2), is a form of glycolytic enolase isozyme and is considered a multifunctional protein. NSE is mainly expressed in the cytoplasm of neurons and neuroendocrine cells, especially in those of the amine precursor uptake and decarboxylation (APUD) lineage such as pituitary, thyroid, pancreas, intestine and lung. In addition to its well-established glycolysis function in the cytoplasm, changes in cell localization and differential expression of NSE are also associated with several pathologies such as infection, inflammation, autoimmune diseases and cancer. This article mainly discusses the role and diagnostic potential of NSE in some lung diseases.

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Neuron-specific enolase, histopathological types, and age as risk factors for bone metastases in lung cancer

Cited by 13 publications

References 27 publications

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Screening and Biosensor-Based Approaches for Lung Cancer Detection

A novel logistic regression model combining semi-supervised learning and active learning for disease classification

Multifunctional neuron-specific enolase: its role in lung diseases

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