ABSTRACT
Context: Better management strategies are needed to improve the survival of patients with hilar cholangiocarcinoma (HCCA). Aims: This study was designed to examine the effects of different treatment methods on survival and prognostic factors in HCCA. Settings and Design: We retrospectively analyzed the clinical data of 354 patients with HCCA treated at our institution from 2003 to 2013. Materials and Methods: Patients were divided into three groups according to the treatment: the radical resection group, the nonradical resection group, and the biliary drainage-only group. Statistical Analysis Used: The Kaplan–Meier method was used to compare survival rates between the groups, and the independent prognostic factors were assessed using the Cox proportional hazards model. Results: There were 110 patients in the radical resection group, 93 patients in the nonradical resection group, and 151 patients in the biliary drainage-only group, and they showed differing survival rates: 1-year survival rates of 70.7%, 49.5%, and 31.3%; 2-year survival rates of 62.9%, 24.7%, and 9.0%; 3-year survival rates of 34.7%, 4.0%, and 0%; and median survival of 21.7 months, 13.6 months, and 8.7 months, respectively. The radical resection group had the longest overall survival (P< 0.001). Treatment method, albumin (ALB), total bilirubin (TBIL), postoperative pathological T-stage, and distant metastasis were identified as independent prognostic indicators of survival. Conclusions: Radical resection significantly increases survival in patients with HCCA, and an increase in ALB and a decrease in TBIL improve the prognosis of patients with HCCA
ABSTRACT
@#Objective: To screen the Hub genes associated with the occurrence and development of esophageal squamous cell carcinoma (ESCC) and to analyze their biological functions by using various bioinformatics analysis tools. Methods: ESCC chip profile GSE100942 from GEO database was used as study subject; GEO2R tool was used to analyze the data and to screen the differentially expressed genes (DEGs), and the bioinformatics tools (DAVID, String, Cytoscape) were further used to construct protein-protein interaction (PPI) network and identify the key Hub genes. GO and KEGG were used for the biological function enrichment analysis. In the meanwhile, MiRDB was applied to identify the miRNAs that might regulate Hub genes and to construct Hub gene–miRNA network. Importantly, the expression of DEGs and the patient survival were verified by the GEPIA analysis tool. Results: By analyzing GSE100942 database, a total of 1229 DEGs with difference of 2 times and 223 DEGs with difference of 4 times were screened out. In addition, 20 Hub genes, which were all up-regulated in ESCC tissues, were also identified. The functional enrichment analysis showed that these DEGs were mainly enriched in cancer related pathways and involved in cell division and mitotic nuclear division. Among those 20 Hub genes, DLGAP5, BUB1B, TPX2, TTK, CDC20, CCNB2, AURKA and DEPDC1 were identified as 8 key Hub genes that related with ESCC, and involved in many important biological processes, such as cell proliferation, cell cycle and signal pathway. Five Hub genes, CEP55, ECT2, NEK2, DEPDC1 and NUSAP1, were identified to be highly regulated by the miRNA regulatory network. Conclusion: Microarray combined with bioinformatics can effectively analyze the DEGs associated with the occurrence and development of ESCC. The identification of the 20 Hub genes and the 8 key Hub genes can provide theoretical guidance for further research on the molecular mechanism and molecular marker screening of ESCC. ·