We previously analyzed human prostate tissue containing stroma near to tumor and from cancer-negative tissues of volunteers. Over 100 candidate gene expression differences were identified and used to develop a classifier that could detect nearby tumor with an accuracy of 97% (sensitivity = 98% and specificity = 88%) based on 364 independent test cases from primarily European American cases. These stroma-based gene signatures have the potential to identify cancer patients among those with negative biopsies. In this study, we used prostate tissues from Chinese cases to validate six of these markers (CAV1, COL4A2, HSPB1, ITGB3, MAP1A and MCAM). In validation by real-time PCR, four genes (COL4A2, HSPB1, ITGB3, and MAP1A) demonstrated significantly lower expression in tumor-adjacent stroma compared to normal stroma (p value ≤ 0.05). Next, we tested whether these expression differences could be extended to the protein level. In IHC assays, all six selected proteins showed lower expression in tumor-adjacent stroma compared to the normal stroma, of which COL4A2, HSPB1 and ITGB3 showed significant differences (p value ≤ 0.05). These results suggest that biomarkers for diagnosing prostate cancer based on tumor microenvironment may be applicable across multiple racial groups.

EGFR is one of the key oncogenes subjected to targeted therapy for several cancers, as it is known to be amplified and/or mutated in up to 40% of malignant gliomas. EFEMP1, a fibulin-like extracellular protein, exerts both tumor suppressive and oncogenic effects in various cancers and glioma cell models. Although EFEMP1's anti-cancer activity has most commonly been attributed to its anti-angiogenic effects, we showed for gliomas that EFEMP1's binding to EGFR accounts for its suppression of the intracranial tumorigenicity of glioma cells expressing high levels of EGFR. In gliomas where EFEMP1 expression, and thus the anti-EGFR effect of EFEMP1, was suppressed, heightened levels of EGFR expression were associated with unfavorable patient outcomes in prognostic models. Results from the current study clearly demonstrate the impact that the anti-EGFR function of EFEMP1 has on the expression of EGFR and patient prognosis. A glioma prognostic model also suggests EFEMP1's context-dependent oncogenic function in gliomas expressing low levels of EGFR. Hence the level of EFEMP1 expression may have a predictive value for choosing patients for anti-EGFR therapy.