Hepatitis B Virus X Protein Drives Multiple Cross-Talk Cascade Loops Involving NF-κB, 5-LOX, OPN and Capn4 to Promote Cell Migration

Hepatitis B virus X protein (HBx) plays an important role in the development of hepatocellular carcinoma (HCC). However, the mechanism remains unclear. Recently, we have reported that HBx promotes hepatoma cell migration through the upregulation of calpain small subunit 1 (Capn4). In addition, several reports have revealed that osteopontin (OPN) plays important roles in tumor cell migration. In this study, we investigated the signaling pathways involving the promotion of cell migration mediated by HBx. We report that HBx stimulates several factors in a network manner to promote hepatoma cell migration. We showed that HBx was able to upregulate the expression of osteopontin (OPN) through 5-lipoxygenase (5-LOX) in HepG2-X/H7402-X (stable HBx-transfected cells) cells. Furthermore, we identified that HBx could increase the expression of 5-LOX through nuclear factor-κB (NF-κB). We also found that OPN could upregulate Capn4 through NF-κB. Interestingly, we showed that Capn4 was able to upregulate OPN through NF-κB in a positive feedback manner, suggesting that the OPN and Capn4 proteins involving cell migration affect each other in a network through NF-κB. Importantly, NF-κB plays a crucial role in the regulation of 5-LOX, OPN and Capn4. Thus, we conclude that HBx drives multiple cross-talk cascade loops involving NF-κB, 5-LOX, OPN and Capn4 to promote cell migration. This finding provides new insight into the mechanism involving the promotion of cell migration by HBx

Do serum biomarkers really measure breast cancer?

Background Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins. Methods This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis. Results The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 ± 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 ± 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 ± 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer. Conclusion Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.United States. Dept. of Defense. Breast Cancer Research Program (Grant No. W81XWH-05-1-0292)National Institutes of Health (U.S.) (R01 CA-112437-01)National Institutes of Health (U.S.) (NIH CA 84955