scholarly journals Combination of IL-6, IL-10, and MCP-1 with traditional serum tumor markers in lung cancer diagnosis and prognosis

2016 ◽  
Vol 15 (4) ◽  
Author(s):  
Y.W. Pan ◽  
Z.G. Zhou ◽  
M. Wang ◽  
J.Q. Dong ◽  
K.P. Du ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tumor Markers ◽  
Cancer Diagnosis ◽  
Serum Tumor Markers ◽  
Diagnosis And Prognosis ◽  
Lung Cancer Diagnosis ◽  
Cancer Diagnosis And Prognosis

scholarly journals High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shi-ang Qi ◽  
Qian Wu ◽  
Zhenpu Chen ◽  
Wei Zhang ◽  
Yongchun Zhou ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Resolution ◽  
Cancer Diagnosis ◽  
Human Cancer ◽  
Point Of Care ◽  
Area Under The Curve ◽  
Lung Cancer Screening ◽  
Diagnosis And Prognosis ◽  
Lung Cancer Diagnosis ◽  
Cancer Diagnosis And Prognosis

AbstractLung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.

scholarly journals Non-invasive lung cancer diagnosis and prognosis based on multi-analyte liquid biopsy

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Kezhong Chen ◽  
Jianlong Sun ◽  
Heng Zhao ◽  
Ruijingfang Jiang ◽  
Jianchao Zheng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Liquid Biopsy ◽  
Non Invasive ◽  
Diagnosis And Prognosis ◽  
Lung Cancer Diagnosis ◽  
Cancer Diagnosis And Prognosis

Abstract 427: Impact of glycosaminoglycans on lung cancer diagnosis and prognosis of patients

2011 ◽  
Author(s):  
Maristela P. Rangel ◽  
Vanessa K. de Sá ◽  
João Roberto Maciel Martins ◽  
Edwin Roger P. Cuentas ◽  
Aline Mendes ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Diagnosis And Prognosis ◽  
Lung Cancer Diagnosis ◽  
Cancer Diagnosis And Prognosis

scholarly journals Non-invasive lung cancer diagnosis and prognosis based on multi-analyte liquid biopsy

2020 ◽  
Author(s):  
Kezhong Chen ◽  
Jianlong Sun ◽  
Heng Zhao ◽  
Ruijingfang Jiang ◽  
Jianchao Zheng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Prognostic Markers ◽  
Lung Cancer Screening ◽  
Protein Marker ◽  
Non Invasive ◽  
Diagnosis And Prognosis ◽  
Lung Cancer Diagnosis ◽  
Mutational Status ◽  
Cancer Diagnosis And Prognosis

Chest LDCT provides an effective approach for lung cancer screening, yet has been found to generate a large number of false positives during practice due to excessive diagnosis of pulmonary lesions of indeterminate clinical significance. In this study, we performed comprehensive genetic and epigenetic profiling of cfDNA from lung cancer patients and individuals bearing benign lung lesions, using ultra-deep targeted sequencing and targeted bisulfite sequencing. We found that cfDNA mutation profile alone has relatively limited power in distinguishing malignant from benign plasma, while cfDNA methylation profiling showed a better performance for classification of the two groups and combination of genetic and epigenetic features of cfDNA along with serum protein marker further improved the classification accuracy. We also identified novel methylation-based prognostic markers and showed that an integrated model that combined cfDNA mutational status and methylation-based prognostic markers improved prediction for lung cancer survival. Our results highlight the potential of the multi-analyte assay for non-invasive lung cancer diagnosis and prognosis.

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