The tumor microenvironment (TME) is a considerably heterogeneous niche, which is created by tumor cells, the surrounding tumor stroma, blood vessels, infiltrating immune cells, and a variety of associated stromal cells. Intercellular communication within this niche is driven by soluble proteins synthesized by local tumor and stromal cells and include chemokines, growth factors, interferons, interleukins, and angiogenic factors. The interaction of tumor cells with their microenvironment is essential for tumorigenesis, tumor progression, growth, and metastasis, and resistance to drug therapy. Protein arrays enable the parallel detection of hundreds of proteins in a small amount of biological sample. Recent data have demonstrated that the application of protein arrays may yield valuable information regarding the structure and functional mechanisms of the TME. In this review, we will discuss protein array technologies and their applications in TME analysis to discern pathways involved in promoting the tumorigenic phenotype.

Background The aim of this study was to simultaneously and quantitatively assess the expression levels of 20 periodontal disease‐related proteins in gingival crevicular fluid (GCF) from normal controls (NOR) and severe periodontitis (SP) patients with an antibody array. Methods Antibodies against 20 periodontal disease‐related proteins were spotted onto a glass slide to create a periodontal disease antibody array (PDD). The array was then incubated with GCF samples collected from 25 NOR and 25 SP patients. Differentially expressed proteins between NOR and SP patients were then used to build receiver operator characteristic (ROC) curves and compare five classification models, including support vector machine, random forest, k nearest neighbor, linear discriminant analysis, and Classification and Regression Trees. Results Seven proteins (C‐reactive protein, interleukin [IL]‐1α, interleukin‐1β, interleukin‐8, matrix metalloproteinase‐13, osteoprotegerin, and osteoactivin) were significantly upregulated in SP patients compared with NOR, while receptor activator of nuclear factor‐kappa was significantly downregulated. The highest diagnostic accuracy using a ROC curve was observed for IL‐1β with an area under the curve of 0.984. Five of the proteins (IL‐1β, IL‐8, MMP‐13, osteoprotegerin, and osteoactivin) were identified as important features for classification. Linear discriminant analysis had the highest classification accuracy across the five classification models that were tested. Conclusion This study highlights the potential of antibody arrays to diagnose periodontal disease.

Background Chronic pelvic pain is often overlooked during primary examinations because of the numerous causes of such “vague” symptoms. However, this pain can often mask endometriosis, a smoldering disease that is not easily identified as a cause of the problem. As such, endometriosis has been shown to be a potentially long-term and often undiagnosed disease due to its vague symptoms and lack of any non-invasive testing technique. Only after more severe symptoms arise (severe pelvic pain, excessive vaginal bleeding, or infertility) is the disease finally uncovered by the attending physician. Due to the nature and complexity of endometriosis, high throughput approaches for investigating changes in protein levels may be useful for elucidating novel biomarkers of the disease and to provide clues to help understand its development and progression. Methods A large multiplex cytokine array which detects the expression levels of 260 proteins including cytokines, chemokines, growth factors, adhesion molecules, angiogenesis factors and other was used to probe biomarkers in plasma samples from endometriosis patients with the intent of detecting and/or understanding the cause of this disease. The protein levels were then analyzed using K-nearest neighbor and split-point score analysis. Results This technique identified a 14-marker cytokine profile with the area under the curve of 0.874 under a confidence interval of 0.81–0.94. Our training set further validated the panel for significance, specificity, and sensitivity to the disease samples. Conclusions These findings show the utility and reliability of multiplex arrays in deciphering new biomarker panels for disease detection and may offer clues for understanding this mysterious disease. Electronic supplementary material The online version of this article (10.1186/s12014-019-9248-y) contains supplementary material, which is available to authorized users.