Immunohistochemical Expression and Prognostic Value of CD97 and Its Ligand CD55 in Primary Gallbladder Carcinoma

Background. CD97 as a member of the EGF-TM7 family with adhesive properties plays an important role in tumor aggressiveness by binding its cellular ligand CD55, which is a complement regulatory protein expressed by cells to protect them from bystander complement attack. Previous studies have shown that CD97 and CD55 both play important roles in tumor dedifferentiation, migration, invasiveness, and metastasis. The aim of this study was to investigate CD97 and CD55 expression in primary gallbladder carcinoma (GBC) and their prognostic significance. Methods. Immunohistochemistry was used to investigate the expression of CD97 and CD55 proteins in 138 patients with GBC. Results. CD97 and CD55 were absent or only weakly expressed in the normal epithelium of the gallbladder but in 69.6% (96/138) and 65.2% (90/138) of GBC, respectively, remarkably at the invasive front of the tumors. In addition, CD97 and CD55 expressions were both significantly associated with high histologic grade (both P = 0.009), advanced pathologic T stage (P = 0.01 and 0.009, resp.) and clinical stage (both P = 0.009), and positive venous/lymphatic invasion (both P = 0.009). Multivariate analyses showed that CD97 (hazard ratio, 3.236; P = 0.02) and CD55 (hazard ratio, 3.209; P = 0.02) expressions and clinical stage (hazard ratio, 3.918; P = 0.01) were independent risk factor for overall survival. Conclusion. Our results provide convincing evidence for the first time that the expressions of CD97 and CD55 are both upregulated in human GBC. The expression levels of CD97 and CD55 in GBC were associated with the severity of the tumor. Furthermore, CD97 and CD55 expressions were independent poor prognostic factors for overall survival in patients with GBC

A Systems Biology-Based Classifier for Hepatocellular Carcinoma Diagnosis

AIM: The diagnosis of hepatocellular carcinoma (HCC) in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71%) and area under ROC curve (approximating 1.0), and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier

Circulating LncRNAs Serve as Diagnostic Markers for Hepatocellular Carcinoma

Background/Aims: Circulating (serous or plasmic) long non-coding RNA (lncRNA) as biomarkers for predicting the diagnosis or prognosis of human disease have been well documented. Due to the sensibility or specificity limitation of Alpha Fetoprotein (AFP), a cluster lncRNAs were revealed as fingerprints for hepatocellular carcinoma (HCC). In this study, we enrolled all the reported circulating lncRNAs in HCC as candidate targets and examined in an independent cohort. Methods: The candidate lncRNAs were determined by qRT-PCR divided into training and validation sets. The risk score analysis was employed to evaluate the potential diagnosis ability of the lncRNAs independently or combining with AFP value. The receiver operating characteristic curve (ROC) was applied for presentation of sensibility or specificity. Results: Among the ten candidate circulating lncRNA, LINC00152, RP11-160H22.5, XLOC014172 and LOC149086 were screened with significant difference in training set. Further investigation in validation set indicated LINC00152, RP11-160H22.5 and XLOC014172 might be the fingerprints for HCC comparing with chronic hepatitis (CH) patients or healthy controls. The risk score analysis revealed the combination of three lncRNAs with AFP could distinguish the HCC from either CH or healthy control with the area under curve value (AUC) of 0.986 and 0.985, respectively. Conclusion: The three lncRNAs may act as novel biomarkers for acting as fingerprint in HCC combining with AFP