EGFR plays an important role in tumor growth, participating in cell motility, adhesion, invasion, and angiogenesis.32 In recent years, EGFR has been a focus of numerous studies on tumor prognosis. Similar meta-analyses involving gastric cancer, non-small-cell lung cancer, nasopharyngeal carcinoma, pancreatic cancer, and esophageal adenocarcinoma have shown poor prognosis associated with EGFR expression.32–36 Comparatively speaking, biomarker studies in glioma have been relatively recent. There have been a large number of inconsistent and even contradictory results regarding the prognostic significance of EGFR in glioma. Among the included studies, 12 suggested that high expression of EGFR indicates poor prognosis, while five others were negative or uncertain on the question. In addition to the 17 studies included in the current meta-analysis, the studies that were excluded according to the inclusion criteria also showed a substantial degree of polarization. High expression of EGFR was not found to predict the poor prognosis of glioma in the studies of Reis-Filho et al,37 Bouvier-Labit et al,38 Smith et al,39 and Dorward et al,40 while other studies suggested that elevated EGFR predicted reduced OS.41–46
Similarly, as the direct source of EGER expression, EFEG amplification was also used as a prognostic marker of glioma. The literature also returned two levels of differentiation. Some studies showed that EGFR amplification was related to worse survival,47–50 while other studies returned the opposite result.51–53 Hobbs et al54 showed that low-to-moderate EGFR amplification was an independent adverse prognostic variable, while high-level EGFR amplification did not play a role in a similar model. In the study of EGFR gene polymorphisms, Costa et al55 found that EGFR variants “-191C/A” and “intron 1 (CA)n repeat” were prognostic markers in GBM patients. Li et al56 conducted a similar gene polymorphism study. In the molecular studies of the Tunisian population, both EGFR amplification and EGFR overexpression predicted significantly poor OS.57 The study on pediatric glioma indicated that no association was apparent between EGFR expression level and either PFS or OS.58,59 All the abovementioned studies have provided rigorous experimental design and accurate and reliable data analysis but contradictory results. A previous meta-analysis of EGFR amplification done by Chen et al60concluded that there is not enough evidence to suggest that EGFR amplification has prognostic value in GBM patients.
In addition to being a potential prognostic factor, EGFR is also a potential target for the treatment. Some reports claim that 97% of primary GBM show EGFR amplification.61 Because of the important role of EGFR signaling in the pathogenesis of malignant tumor, a growing number of studies have been devoted to developing therapeutic strategies targeting EGFR aberrant activity. As a transmembrane tyrosine kinase, small molecule tyrosine kinase inhibitors (TKIs) may theoretically inhibit EGFR expression and function in malignant tumor. The mechanism would involve displacement of ATP from the catalytic pocket of the enzyme to inhibit kinase activity. Monoclonal antibodies may play a role by combining with the extracellular domain of EGFR.9 Both the methods have been applied in clinical practice with remarkable results in the treatment of non-small-cell lung cancer and colon cancer. However, the use of TKIs and monoclonal antibodies in glioma is challenged. Preusser et al62 concluded in their clinical trial that only a small number of malignant glioma patients benefited from EGFR inhibitor monotherapy. Bevacizumab has a tendency to increase the risk of adverse effects in people older than 65 years, and there is not enough evidence to recommend the use of bevacizumab in pediatric glioma patients.8 Combined with other treatments, such as temozolomide and radiation therapy, stereotactic surgery may be helpful in controlling glioma. Newer and safer targeted therapies have yet to be developed to improve treatment efficacy.
There is a considerable degree of heterogeneity among various studies. Heterogeneity emerged from differences in experimental items, overall experimental design, and analyzed indicators. There was substantial heterogeneity (I2=79.8%, Ph=0.000) in pooled HR in the current meta-analysis. Similar results in pooled HR were observed in the GBM group (I2=83.8%, Ph=0.000). Although the final results suggested poor prognostic role of EGFR, high heterogeneity substantially reduces the reliability of the results. The operations from setting include criteria, subgroup analysis and sensitivity analysis were used to ensure the reliability of the final results, in other words, to provide a strong reference. All the included studies used HR with 95% CI as the index of survival analysis. With I2 and Ph as indicators of heterogeneity, subgroup analysis suggested high heterogeneity in tumor types, publication year, region, sample size, maximum follow-up time, source of HR, and quality score, as follows: GBM (I2=83.8%, Ph=0.000), publication year ≥2010 (I2=80.2%, Ph=0.000), Asia (I2=85.7%, Ph=0.000), sample size ≥100 (I2=85.5%, Ph=0.000), and other methods to divide cutoff value and cutoff value not available (I2=83.3%, Ph=0.000), maximum follow-up time <100 months (I2=81.8%, Ph=0.000), and quality score ≥7 (I2=84.8%, Ph=0.000). There was low heterogeneity in Europe (I2=53.3%, Ph=0.036) when HR was reported (I2=69.5%, Ph=0.003). The abovementioned items were the actual source of heterogeneity; other items showed low or no heterogeneity. Among 22 items in the nine subgroups, only three had HR with no statistical significance, including LGG (HR 1.67, 95% CI 0.37–7.62, P=0.508), America (HR 1.32, 95% CI 0.98–1.79, P=0.070), and HR that were reported (HR 1.28, 95% CI 0.98–1.66, P=0.072). This suggested a stable and reliable final result.
In the GBM subgroup analysis, there was substantial heterogeneity in items such as publication year ≥2010 (I2=82.2%, Ph=0.000), Europe (I2=73.7%, Ph=0.010), Asia (I2=88.1%, Ph=0.000), sample size ≥100 (I2=84.4%, Ph=0.000), other methods to divide cutoff value (I2=80.4%, Ph=0.000), unavailable cutoff value (I2=80.4%, Ph=0.000), maximum follow-up time <100 months (I2=82.0%, Ph=0.000), and quality score ≥7 (I2=86.2%, Ph=0.000). The condition was basically consistent with HR subgroup analysis of the whole set of glioma. Among 19 items in the eight subgroups, pooled HR of eight items showed no statistical significance, and the reliability and stability of the pooled results were lower than that of the entire group of glioma. The greater heterogeneity of the GBM group (83.8% vs 79.8%) is one of the reasons for these results.
In sensitivity analysis, individual studies were sequentially removed, and the bottom limits of 95% CI of pooled HR of other studies were all larger than 1, suggesting that individual studies had limited impact on the entire study. The pooled HR had statistical significance, and the entire study was stable.
Asymmetry of the funnel plot and Egger’s test demonstrated the existence of publication bias (the most common source of bias) affecting the authenticity and reliability of the data to some extent. First, positive results are easier to publish. Of the 17 studies, five were negative, and the inclusion of negative results would theoretically reduce publication bias. Second, repeatedly published theses will also lead to bias, if a single-center or multiple-center results are both published and included. Repeated calculation would lead to larger weight for some studies, affecting the reliability of the clinical research. The research institutions and localities were carefully checked, and overlapping of institutions or experimental data was not found. Next, the small sample size studies return low test efficiencies. In other words, positive results may be caused by opportunities, which may not provide a reliable basis for clinical practice. In the subgroup analysis, HR was merged with a sample size of 100 as the boundary. Our results suggested larger HR and lower P in the subgroup with a sample size of <100, without heterogeneity, while greater heterogeneity was observed in the subgroup with large sample size (≥100). This confirmed that bias was caused by small sample size from the side. There may be many other unknown confounding factors that play a role in publication bias.
As a meta-analysis of prognostic analysis, there are inevitable limitations in several aspects which should be further discussed. First, the areas and population involved in this study are irregular (Africa, the Middle East, South America, and other regions). In addition, China, with a large population, was not considered. Second, there are differences in diagnostic standard and inspection methods in studies on glioma at different times. The accuracy of IHC results is improved by the optimization of the equipment and test methods with the passage of time. The diverse definitions of cutoff values among the studies may also lead to bias. Among experimental designs, there were no randomized controlled trials, which might have improved the accuracy of the analysis. However, there were two prospective studies, which are superior to retrospective studies. Finally, patients in several studies underwent standard therapy with temozolomide plus radiotherapy after surgery15,22,29 and some patients did not undergo uniform treatments, while the remaining studies failed to report postoperative treatments. This too may result in heterogeneity of survival data.