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Background Ovarian cancer is the main cause of gynecological cancer-associated death. However, 5-year survival rates differ dramatically between the five main ovarian carcinoma histotypes. Therefore, we need to have a better understanding of the mechanisms that promote histotype-specific ovarian carcinogenesis and identify novel prognostic biomarkers. Methods Here, we evaluated the prognostic role of 29 genes for early-stage (I and II) ovarian carcinomas (n = 206) using immunohistochemistry (IHC). Results We provide evidence of aberrant protein expression patterns for Collagen type III alpha 1 chain (COL3A1), G protein-coupled receptor 158 (GPR158) and PITH domain containing 1 (PITHD1). Kaplan-Meier survival analysis revealed that COL3A1 expression was associated with shorter overall survival in the four major histotypes of epithelial ovarian carcinoma patients (P value = 0.026, HR = 2.99 (95% CI 1.089–8.19)). Furthermore, GPR158 and PITHD1 were shown to be histotype-specific prognostic biomarkers, with elevated GPR158 expression patterns in mucinous ovarian carcinoma patients with unfavorable overall survival (P value = 0.00043, HR = 6.13 (95% CI 1.98–18.98)), and an association with lower PITHD1 protein expression and unfavorable overall and disease-specific survival in clear-cell ovarian carcinoma patients (P value = 0.012, HR = 0.22 (95% CI 0.058–0.80); P value = 0.003, HR = 0.17 (95% CI 0.043–0.64)). Conclusions The novel biomarkers identified here may improve prognostication at the time of diagnosis and may assist in the development of future individualized therapeutic strategies for ovarian carcinoma patients.

Background: Squamous cell carcinoma of the oral cavity (OSCC) is a common cancer form with relatively low 5-year survival rates, due partially to late detection and lack of complementary molecular markers as targets for treatment. Molecular profiling of head and neck cancer has revealed biological similarities with basal-like breast and lung carcinoma. Recently, we showed that 16 genes were consistently altered in invasive breast tumors displaying varying degrees of aggressiveness. Methods: To extend our findings from breast cancer to another cancer type with similar characteristics, we performed an integrative analysis of transcriptomic and proteomic data to evaluate the prognostic significance of the 16 putative breast cancer-related biomarkers in OSCC using independent microarray datasets and immunohistochemistry. Predictive models for disease-specific (DSS) and/or overall survival (OS) were calculated for each marker using Cox proportional hazards models. Results: We found that CBX2, SCUBE2, and STK32B protein expression were associated with important clinicopathological features for OSCC (peritumoral inflammatory infiltration, metastatic spread to the cervical lymph nodes, and tumor size). Consequently, SCUBE2 and STK32B are involved in the hedgehog signaling pathway which plays a pivotal role in metastasis and angiogenesis in cancer. In addition, CNTNAP2 and S100A8 protein expression were correlated with DSS and OS, respectively. Conclusions: Taken together, these candidates and the hedgehog signaling pathway may be putative targets for drug development and clinical management of OSCC patients.

Background Breast cancer is a potentially fatal malignancy in females despite the improvement in therapeutic techniques. The identification of novel molecular signatures is needed for earlier detection, monitoring effects of treatment, and predicting prognosis. We have previously used microarray analysis to identify differentially expressed genes in aggressive breast tumors. The purpose of the present study was to investigate the prognostic value of the candidate biomarkers CCNB2, ASPM, CDCA7, KIAA0101, and SLC27A2 in breast cancer. Methods The expression levels and subcellular localization of the CCNB2, ASPM, CDCA7, KIAA0101, and SLC27A2 proteins were measured using immunohistochemistry (IHC) on a panel of 80 primary invasive breast tumors. Furthermore, the mRNA levels of CCNB2, KIAA0101, and SLC27A2 were subsequently examined by qRT-PCR to validate IHC results. Patient disease-specific survival (DSS) was evaluated in correlation to protein levels using the Kaplan-Meier method. Multivariate Cox regression analysis was used to determine the impact of aberrant protein expression of the candidate biomarkers on patient DSS and to estimate the hazard ratio at 8-year follow-up. Results Elevated cytoplasmic CCNB2 protein levels were strongly associated with short-term disease-specific survival of breast cancer patients (≤ 8 years; PP= 0.04). However, no association with other clinicopathological parameters was observed. Multivariate Cox regression analysis specified that CCNB2 protein expression is an independent prognostic marker of DSS in breast cancer. The predictive ability of several classical clinicopathological parameters was improved when used in conjunction with CCNB2 protein expression (C-index = 0.795) in comparison with a model without CCNB2 expression (C-index = 0.698). The protein levels of ASPM, CDCA7, KIAA0101, and SLC27A2 did not correlate with any clinicopathological parameter and had no influence on DSS. However, a significant correlation between the expression of the CCNB2 and ASPM proteins was detected (P = 0.03). Conclusion These findings suggest that cytoplasmic CCNB2 may function as an oncogene and could serve as a potential biomarker of unfavorable prognosis over short-term follow-up in breast cancer.

Background Previous studies have shown that the ADIPOR1, ADORA1, BTG2 and CD46 genes differ significantly between long-term survivors of breast cancer and deceased patients, both in levels of gene expression and DNA copy numbers. The aim of this study was to characterize the expression of the corresponding proteins in breast carcinoma and to determine their correlation with clinical outcome. Methods Protein expression was evaluated using immunohistochemistry in an independent breast cancer cohort of 144 samples represented on tissue microarrays. Fisher's exact test was used to analyze the differences in protein expression between dead and alive patients. We used Cox-regression multivariate analysis to assess whether the new markers predict the survival status of the patients better than the currently used markers. Results BTG2 expression was demonstrated in a significantly lower proportion of samples from dead patients compared to alive patients, both in overall expression (P = 0.026) and cell membrane specific expression (P = 0.013), whereas neither ADIPOR1, ADORA1 nor CD46 showed differential expression in the two survival groups. Furthermore, a multivariate analysis showed that a model containing BTG2 expression in combination with HER2 and Ki67 expression along with patient age performed better than a model containing the currently used prognostic markers (tumour size, nodal status, HER2 expression, hormone receptor status, histological grade, and patient age). Interestingly, BTG2 has previously been described as a tumour suppressor gene involved in cell cycle arrest and p53 signalling. Conclusions We conclude that high-level BTG2 protein expression correlates with prolonged survival in patients with breast carcinoma.

Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used for evaluation of the gene lists, a correlation-based classifier and a Voting Features Interval (VFI) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients. They had both developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients may have benefited from different adjuvant treatment than they recieved. Trial registration The research on these tumours was approved by the Medical Faculty Research Ethics Committee (Medicinska fakultetens forskningsetikkommitté, Göteborg, Sweden (S164-02)).