Purpose
Currently, serum biomarkers that are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinomas are not known. In this study, ten serum markers were assessed using the Luminex system and enzyme-linked immunosorbent assay for the diagnosis of gastric cancer and analysis of the relation between prognosis and metastases.

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Serum biomarker panels for diagnosis of gastric cancer

Infection with Helicobacter pylori is established as the major risk factor for gastric cancer development. Damage of the mucosal barrier due to H. pylori -induced

Helicobacter pylori and gastric cancer.

Despite recent advances in individualised targeted therapy, gastric cancer remains one of the most challenging diseases in gastrointestinal oncology. Modern imaging techniques using endoscopic filter devices and in vivo molecular imaging are designed to enable early detection of the cancer and surveillance of patients at risk. Molecular characterisation of the tumour itself as well as of the surrounding inflammatory environment is more sophisticated in the view of tailored therapies and individual prognostic assessment. The broad application of high throughput techniques for the description of genome wide patterns of structural (copy number aberrations, single nucleotide polymorphisms, methylation pattern) and functional (gene expression profiling, proteomics, miRNA) alterations in the cancer tissue lead not only to a better understanding of the tumour biology but also to a description of gastric cancer subtypes independent from classical stratification systems. Biostatistical means are required for the interpretation of the massive amount of data generated by these approaches. In this review we give an overview on the current knowledge of diagnostic methods for detection, description and understanding of gastric cancer disease.

Molecular diagnostics in gastric cancer.

The malignant transformation of human prostatic epithelium is associated with the loss of androgen receptor (AR) in the surrounding stroma. However, the function and mechanisms of AR signaling in prostate cancer (PCa) stroma remain elusive. Here we report, by using proteomics pathway array analysis (PPAA), that androgen and its receptor inhibit the proliferation of prostate stromal cells through transcriptional suppression of cyclin B1, and we confirmed our findings at mRNA and protein levels using AR-negative or -positive primary prostate stromal cells. Furthermore, AR showed a negative correlation with cyclin B1 expression in stroma of human PCa samples in vivo. Mechanistically, we identify cyclin B1 as a bona fide AR target gene in prostate stromal cells. The negative regulation of cyclin B1 by AR is mediated through switching between E2F1 and E2F4 on the promoter of cyclin B1. E2F1 binds to the cyclin B1 promoter and maintains its expression and subsequent cell cycle progression in AR-negative stromal cells or AR-positive stromal cells when androgens are depleted. Upon stimulation with androgen in AR-positive stromal cells, E2F1 is displaced from the binding site by AR and replaced with E2F4, leading to the recruitment of the silencing mediator for retinoid and thyroid hormone receptor (SMRT)/histone deacetylase 3 (HDAC3) corepressor complex and repression of cyclin B1 at the chromatin level. The switch between E2F1 and E2F4 at the E2F binding site of the cyclin B1 promoter coincides with an androgen-dependent interaction between AR and E2F1 as well as the cytoplasmic-to-nuclear translocation of E2F4. Thus, we identified a novel mechanism for E2F factors in the regulation of cell cycle gene expression and cell cycle progression under the control of AR signaling.

Regulation of a Novel Androgen Receptor Target Gene, the Cyclin B1 Gene, through Androgen-Dependent E2F Family Member Switching

Lymph node status remains one of most crucial indicators of gastric cancer prognosis and treatment planning. Current imaging methods have limited accuracy in predicting lymph node metastasis. We sought to identify protein markers in primary gastric cancer and to define a risk model to predict lymph node metastasis. The Protein Pathway Array (PPA) (initial selection) and Western blot (confirmation) were used to assess the protein expression in a total of 190 freshly frozen gastric cancer samples. The protein expression levels were compared between samples with lymph node metastasis (n = 73) and those without lymph node metastasis (n = 57) using PPA. There were 27 proteins differentially expressed between lymph node positive samples and lymph node negative samples. Five proteins (Factor XIII B, TFIIH p89, ADAM8, COX-2 and CUL-1) were identified as independent predictors of lymph node metastasis. Together with vascular/lymphatic invasion status, a risk score model was established to determine the risk of lymph node metastasis for each individual gastric cancer patient. The ability of this model to predict lymph node metastasis was further confirmed in a second cohort of gastric cancer patients (33 with and 27 without lymph node metastasis) using Western blot. This study indicated that some proteins differentially expressed in gastric cancer can be selected as clinically useful biomarkers. The risk score model is useful for determining patients' risk of lymph node metastasis and prognosis.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ijc.27864

Protein predictive signatures for lymph node metastasis of gastric cancer.

Current methods have limited accuracy in predicting survival and stratifying patients with gastric cancer for appropriate treatment. We sought to identify protein signatures of gastric cancer for classification and prognostication. The Protein Pathway Array (initial study) and Western blot (confirmation) were used to assess the protein expression in a total of 199 fresh frozen gastric samples. There were 56 paired samples divided into a training set (n = 37) and a validation set (n = 19) for the identification of differentially expressed proteins between tumor and normal tissues. There were 56 tumor samples used to identify proteins correlating with tumor and nodal staging. All 93 tumor samples were used to identify candidate proteins for predicting survival. We confirmed the survival prediction of the candidate proteins by using an additional cohort of gastric cancer samples (n = 50). There were 22 proteins differentially expressed between normal and tumor tissues. Nine proteins were selected to build the predictor to classify normal and tumor samples. Ten proteins were differentially expressed among different T stages and four of these were associated with invasive behavior. An additional four proteins were associated with lymph node metastasis. Two proteins were identified as independent risk factors for overall survival. This study indicated that some dysregulated signaling proteins could be selected as useful biomarkers for tumor classification and predicting outcome in gastric cancer patients.

Protein Signatures for Classification and Prognosis of Gastric Cancer: A Signaling Pathway-Based Approach

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Protein Signatures for Classification and Prognosis of Gastric Cancer: A Signaling Pathway-Based Approach