| Title |
Combined detection of dickkopf-1 subtype classification autoantibodies as biomarkers for the diagnosis and prognosis of non-small cell lung cancer
|
|---|---|
| Published in |
OncoTargets and therapy, July 2017
|
| DOI | 10.2147/ott.s134162 |
| Pubmed ID | |
| Authors |
Lei Shen, Xiaoguang Wu, Jinjing Tan, Meng Gu, Yu Teng, Zitong Wang, Wentao Yue |
| Abstract |
This study aims to identify the clinical significance of serum autoantibodies against dickkopf-1 (DKK1) and evaluate their feasibility in the immunodiagnosis and prognosis of non-small cell lung cancer (NSCLC). Epitope mapping by peptide microarray-based serum screening of NSCLC patients (n=72) and healthy controls (n=16) was performed. Indirect ELISA with peptides was used to measure the serum levels of autoantibodies in 206 NSCLC patients and 99 healthy controls. A 3-year follow-up was monitored to evaluate the correlation between serological levels of autoantibodies and overall survival (OS) and progression-free survival (PFS). Four highly reactive epitopes were identified, which included peptides 67-84 (Pep A), 37-54 (Pep B), 145-156 (Pep C) and 247-261 (Pep D). The autoantibodies levels were considerably higher in sera of NSCLC patients compared with controls (P<0.001), and a highly significant correlation with distant metastases was observed (Pep A: P=0.09, Pep B: P<0.01, Pep C: P<0.01 and Pep D: P<0.01). High levels of antibody subtype to Pep B were remarkably associated with better OS (P=0.004) and PFS (P=0.006). Subsequent Cox regression analysis disclosed that antibody to Pep B was an independent prognostic factor for NSCLC (OS: P=0.008, HR =0.435, 95% CI 0.236-0.802; PFS: P=0.032, HR =0.533, 95% CI 0.322-0.950). Identified linear epitopes of antigens by peptide microarray are easily available, and subtype classification of DKK1 autoantibodies as novel biomarkers for the diagnosis and prognosis of NSCLC. Our results also highlight the antibody subtype to Pep B as the most valuable biomarker for favorable prognosis of NSCLC. |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 25% |
| Student > Ph. D. Student | 2 | 17% |
| Professor | 2 | 17% |
| Student > Master | 2 | 17% |
| Student > Doctoral Student | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 1 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 50% |
| Medicine and Dentistry | 2 | 17% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 8% |
| Social Sciences | 1 | 8% |
| Agricultural and Biological Sciences | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 1 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 July 2017.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from OncoTargets and therapy
#2,078
of 3,016 outputs
Outputs of similar age
#286,158
of 326,871 outputs
Outputs of similar age from OncoTargets and therapy
#60
of 72 outputs
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