Serum protein profiles of patients with lung cancer of different histological types

Yang, Rui; Tian, Rui; Ren, Qiao Li; Chui, Hong Ying; Guo, Su; Zhang, Xu Dong; Song, Xuejiao

doi:10.1111/ajco.12441

Cited by 4 publications

(2 citation statements)

References 37 publications

(37 reference statements)

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“…Serum biomarker was known as effective for lung cancer diagnosis being minimally invasive. But no one type of biomarker was found to be highly sensitive and specific for detection of lung cancer at present [ 4 ]. So, combination detection multiple biomarker was general used for lung cancer diagnosis [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

The diagnostic value of serum CEA, NSE and MMP-9 for on-small cell lung cancer

2016

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AbstractNon-small cell lung cancer (NSCLC) is the leading cause of cancer related deaths worldwide. But no one type of serum biomarker was found to be highly sensitive and specific for detection of lung cancer at present. So, the aim of this study was to evaluate a diagnostic value of serum carcinoembryonic antigen (CEA), neuron specific enolase (NSE) and matrix metallo-proteinase (MMP-9) for non-small cell lung cancer. Thirty-six cases with pathology confirmed non-small cell lung cancer and thirty-two of subjects with benign lung disease were reviewed in our hospital and included in this retrospective study. The serum level of CEA, NSE and MMP-9 were tested and compared between the non-small cell lung cancer patients and benign lung disease. The diagnosis sensitivity, specificity and area under the receiver-operating characteristic (ROC) curve (AUC) for serum CEA, NSE and MMP-9 were calculated with STATA10.0 software. The serum CEA, NSE and MMP-9 were 32.0±16.7 ng/mL, 51.6±68.3 ng/mL, 30.6 ±15.7 μg/L for the NSCLC patients and 15.1±10.9 ng/mL, 4.9±3.1 ng/mL, 9.3±5.9 μg/L for the benign lung disease patients with statistical difference (Pall<0.05); The diagnosis sensitivity, specificity and AUC were 80.0%, 72.2%, 0.84 for the serum CEA; 71.0%, 83.3% and 0.80 for NSE and 87.1%, 80.56%, 0.89 for MMP-9, respectively. The serum CEA, NSE and MMP-9 were generally elevated in patients with non-small cell lung cancer and could be used as potential bio-markers for non-small cell lung cancer diagnosis.

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Section: Introductionmentioning

confidence: 99%

The diagnostic value of serum CEA, NSE and MMP-9 for on-small cell lung cancer

2016

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“…In patients with these tumors, serum levels of certain immunochemical markers are elevated. None, however, have been evaluated as sufficiently sensitive for individual use, nor have they been identified as specific for these types of tumors (5). Our study aimed to put forward a solution to this situation by determining the sensitivity and specificity of a panel of tumor markers.…”

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confidence: 99%

The Role of Serum Tumor Markers in Follow-up After Surgical Treatment of Malignant Lung Tumors

et al. 2021

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Aim: The aim of this study was to evaluate the utility of selected tumor markers for the detection of lung cancer recurrence during follow-up. Patients and Methods:The study group consisted of 109 patients and 109 healthy controls. The following biomarkers were selected: Carcinoembryonic antigen; cytokeratin fragment 19; neuronspecific enolase; tissue polypeptide-specific antigen; cytokeratin fragments 8, 18 and 19; insulin-like growth factor 1; pro-gastrin-releasing peptide; and 25hydroxyvitamin D. The biomarkers were assessed individually or using a multivariate analysis. Results: Carcinoembryonic antigen [area under the receiver operating characteristics curve (AUC)=0.6857, p<0.0001] and cytokeratin fragment 19 (AUC=0.6882, p<0.0001) proved best in detecting relapse. The multivariate model indicated insulin-like growth factor 1 (p=0.0006, AUC=0.6225) as the third most useful biomarker. The multivariate model using these three markers achieved the best AUC value of 0.7730 (p=0.0050). Conclusion: We demonstrated that carcinoembryonic antigen and cytokeratin fragment 19 play a key role in the detection of lung cancer recurrence. A multivariate approach can increase the effectiveness of detection. Patients and Methods Groups of patients.

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Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics

Wang

Long

et al. 2020

Sci Rep

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Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians' clinical decision-making. exhaled breath analysis provides a tremendous potential approach in non-invasive diagnosis of lung cancer but was rarely reported for lung cancer subtypes classification. In this paper, we firstly proposed a combined method, integrating K-nearest neighbor classifier (KNN), borderline2-synthetic minority over-sampling technique (borderlin2-SMOTE), and feature reduction methods, to investigate the ability of exhaled breath to distinguish Ac from Scc patients. The classification performance of the proposed method was compared with the results of four classification algorithms under different combinations of borderline2-SMOTE and feature reduction methods. The result indicated that the KNN classifier combining borderline2-SMOTE and feature reduction methods was the most promising method to discriminate Ac from Scc patients and obtained the highest mean area under the receiver operating characteristic curve (0.63) and mean geometric mean (58.50) when compared to others classifiers. The result revealed that the combined algorithm could improve the classification performance of lung cancer subtypes in breathomics and suggested that combining non-invasive exhaled breath analysis with multivariate analysis is a promising screening method for informing treatment options and facilitating individualized treatment of lung cancer subtypes patients. Lung cancer is one of the most malignant tumors threatening people's health and life, which is divided into small cell lung cancer (SCLC, ~15%) and non-small cell lung cancer (NSCLC, ~85%), NSCLC mainly includes adenocarcinoma (AC, ~38%) and squamous cell carcinoma (SCC, ~20%) 1,2. Numerous clinical trials have proved that the more exact the type of tumor histology we know, the more effective treatment would be 1,3. Therefore, how to quickly distinguish the exact subtypes of lung cancer, especially AC and SCC, has become a mandatory diagnostic requirement in the past decades years 4-6. In clinical practice, the histopathological analysis is a gold standard for diagnosing the lung cancer subtypes, but it can cause invasive injury and is complicated in operation. Another common method is imaging diagnosis (e. g., low-dose computed tomography (LD-CT)), which has certain limitations of radiation exposure and high false-positive rate. Thus, the non-invasive and safe alternative diagnosis methods based on omics analysis (e. g., proteomics and radiomics) are pursued by experts to distinguish lung cancer subtypes 6-12. Similar to other omics, breathomics as a non-invasive diagnostic method had shown its potential for early diagnosis of diseases, prognosis evaluation, and classification of disease subtypes 13,14. Relative studies also revealed the possibility of machine learning algorithms coupled with high-throughput platforms to the classification

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Serum protein profiles of patients with lung cancer of different histological types

Cited by 4 publications

References 37 publications

The diagnostic value of serum CEA, NSE and MMP-9 for on-small cell lung cancer

The diagnostic value of serum CEA, NSE and MMP-9 for on-small cell lung cancer

The Role of Serum Tumor Markers in Follow-up After Surgical Treatment of Malignant Lung Tumors

Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics

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