|Year : 2016 | Volume
| Issue : 8 | Page : 260-263
Serum pro-gastrin-releasing peptide in diagnosis of small cell lung cancer: A meta-analysis
Hongyuan Wang, Junfeng Qian
Department of Respiratory, The People's Hospital of Lishui, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, Zhejiang, China
|Date of Web Publication||22-Feb-2017|
Department of Respiratory, The People's Hospital of Lishui, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, Zhejiang
Source of Support: None, Conflict of Interest: None
Objective: The purpose of this study was to assess the diagnostic sensitivity and specificity for serum pro-gastrin-releasing peptide (Pro-GRP) in diagnosis of small cell lung cancer (SCLC) through pooling all the open published data.
Materials and Methods: Databases of PubMed, Cochrane, ISI Web of Knowledge, and CNKI were electronic, searched by two reviewers to find the diagnostic study serum Pro-GRP in diagnosis of SCLC. The diagnostic sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the receiver operating characteristic (ROC) were calculated by Med DiSc1.4 software.
Results: Finally, 21 studies were included in this meta-analysis. Because of statistical heterogeneity, the specificity, specificity, positive/negative likelihood ratio, and DOR were pooled by random effect model. The pooled sensitivity, specificity, PLR, NLR, DOR, and area under the ROC were 64% (95% confidence interval [CI]: 62%–66%), 94% (95% CI: 94%–95%), 11.87% (95% CI: 8.62–11.35), 0.32% (95% CI: 0.26%–0.39%), 40.98% (95% CI: 27.77%–60.64%), and 0.94 (95% CI: 0.91%–0.96%).
Conclusion: Serum Pro-GRP was promising biomarker for SCLC diagnosis.
Keywords: Diagnosis, meta-analysis, pro-gastrin-releasing peptide, small cell lung cancer
|How to cite this article:|
Wang H, Qian J. Serum pro-gastrin-releasing peptide in diagnosis of small cell lung cancer: A meta-analysis. J Can Res Ther 2016;12:260-3
| > Introduction|| |
Serum pro-gastrin-releasing peptide (Pro-GRP) and neuron-specific enolase (NSE) have been reported to be frequently elevated in patients with small cell lung cancer (SCLC). Many studies have discussed the diagnosis value of serum Pro-GRP for SCLC diagnosis. However, because of the small subjects included in each study, the conclusion was not consistent. In our present meta-analysis, we searched all the published diagnosis studies associated with serum Pro-GRP in diagnosis of SCLC and further pooled the sensitivity and specificity to evaluate its potential value for serum biomarker for SCLC diagnosis.
| > Materials and Methods|| |
Databases of PubMed, Cochrane, ISI Web of Knowledge, and CNKI were electronic searched by two reviewers to find the diagnostic study serum Pro-GRP in diagnosis of SCLC. The searching words were SCLC, lung cancer, Pro-GRP, Pro-GRP, and diagnosis.
The data of each included study were extracted by two reviewers independently. The extracted information and data included the first author of the study, the paper publication year, the country the experiment done, the cutoff value of Pro-GRP for SCLC diagnosis, the true positive (TP), false positive (FP), false negative (FN), and true negative (TN) of the each study.
The statistical analysis was done by Med DiSc1.4 software (http://www.biomedsearch.com/nih). The TP, TN, FP, and FN rates for each study were used for calculated the diagnostic sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the receiver operating characteristic (ROC) curve by random or fixed effect model. The statistical heterogeneity for the included study was evaluated by Chi-square test. P < 0.05 was statistically significant.
| > Results|| |
Literature search and study characteristics
A total of 288 publications were found by searching the databases initially. For the initial identified 288 studies, 201 articles were excluded after title and abstract reading and 66 papers excluded after reviewing the whole text paper. Finally, 21 studies were included in this meta-analysis. The included 21 publications were come from five countries (China, Japan, Germany, France, and Spain). Moreover, the characteristics of all the included 21 publications are given in [Table 1].
Synthesis of outcomes and assessments of heterogeneity
Statistical heterogeneity for the included studies
The statistical heterogeneity for the included studies was assessed by I2 test. Moreover, there was significant statistical heterogeneity in diagnostic sensitivity (I2 = 96.0%), diagnostic specificity (I2 = 82.7%), PLR (I2 = 74.8%), NLR (I2 = 86.1%), and DOR (I2 = 67.4%). Hence, the specificity, specificity, PLR/NLR, and DOR were pooled by random effect model.
Because of significant statistical heterogeneity among the studies, the sensitivity was calculated by random effect model. The pooled sensitivity was 64% (95% confidence interval [CI]: 62%–66%) [Figure 1].
The specificity ranged from 88% to 100% of the included 20 studies. The pooled diagnostic specificity was 94% (95% CI: 94%–95%) through random effect model [Figure 2].
Positive likelihood ratio
The pooled PLR was 11.87% (95% CI: 8.62%–11.35%) through random effect model [Figure 3].
Negative likelihood ratio
The NLR ranged from 0.17 to 1.06 of the included 21 studies. The pooled negative likelihood ratio was 0.32% (95% CI: 0.26%–0.39%) through random effect model [Figure 4].
Diagnostic odds ratio
The DOR was pooled by random effect model for significant statistical heterogeneity. The pooled DOR was 40.98% (95% CI: 27.77%–60.64%) [Figure 5].
Pooled summary receiver operating characteristic
The area under the ROC curve was 0.94% (95% CI: 0.91%–0.96%) [Figure 6].
The publication bias was evaluated by Deeks funnel plot [Figure 6] without significant publication bias (–2.05, P > 0.05) [Figure 7].
| > Discussion|| |
Lung cancer is most diagnosed malignant carcinoma clinically. And generally, lung cancer is divided into two types, one is non-SCLC, and the other is SCLC. SCLS is account for 15%–20% lung cancer with early remote metastasis and poor prognosis. The general treatment procedure is different for non-SCLC and SCLC. For non-SCLC, the treatment principle is surgery plus postoperation chemoradiation for non-metastasis disease. However, for SCLC, the most used treatment procedure was chemoradiation. The surgery is not suitable for SCLC because of early remote metastasis. Thus, the discrimination of SCLC and non-SCLC is important for treatment procedure section.
The most used biomarker of SCLC diagnosis is NSE. However, publications have demonstrated that Pro-GRP was elevated in serum of patients with SCLC. Wang et al. have discussed the clinical significance of serum Pro-GRP expression in patients with SCLC. In their study, they included 76 SCLC patients and 62 healthy controls. They found that the serum level of NSE and Pro-GRP was significant elevated in SCLC patients compared to healthy controls with the sensitivity of 56.8%, 67.6% and specificity of 77.5%, 96.4% for NSE and Pro-GRP, respectively. The sensitivity and specificity of Pro-GRP were significant higher than that of NSE.
In our present meta-analysis, we searched the databases and included all the open published studies related to serum Pro-GRP in diagnosis of SCLC. The pooled results showed the pooled sensitivity, specificity, PLR, NLR, DOR, and area under the ROC were 64% (95% CI: 62%–66%), 94% (95% CI: 94%–95%), 11.87% (95% CI: 8.62–11.35), 0.32% (95% CI: 0.26%–0.39%), 40.98% (95% CI: 27.77%–60.64%), and 0.94% (95% CI: 0.91%–0.96%). Therefore, serum Pro-GRP was promising biomarker for SCLC diagnosis. However, significant statistical heterogeneity was found in sensitivity, specificity, PLR, NLR, DOR, and area under the ROC. This heterogeneity was an important limitation for this meta-analysis which needs further validation by well-designed diagnostic study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]