Anti-MKI67 Monoclonal antibody, clone 30Sbk2 [eFluor® 615]

Gene co-expression network analysis is an effective method for predicting gene functions and disease biomarkers. However, few studies have systematically identified co-expressed genes involved in the molecular origin and development of various types of tumors. In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. We compared the results with networks found in multiple normal tissue types and discovered 18 tightly connected frequent networks in cancers, with highly enriched functions on cancer-related activities. Most networks identified also formed physically interacting networks. In contrast, only 6 networks were found in normal tissues, which were highly enriched for housekeeping functions. The largest cancer network contained many genes with genome stability maintenance functions. We tested 13 selected genes from this network for their involvement in genome maintenance using two cell-based assays. Among them, 10 were shown to be involved in either homology-directed DNA repair or centrosome duplication control including the well-known cancer marker MKI67. Our results suggest that the commonly recognized characteristics of cancers are supported by highly coordinated transcriptomic activities. This study also demonstrated that the co-expression network directed approach provides a powerful tool for understanding cancer physiology, predicting new gene functions, as well as providing new target candidates for cancer therapeutics.

Approximately 90 % of head and neck cancers are squamous cell carcinomas (HNSCC), and the overall 5-year survival rate is not higher than 50 %. There is much evidence that human papillomavirus (HPV) infection may influence the expression of commonly studied HNSCC markers. Our study was focused on the possible HPV-specificity of molecular markers that could be key players in important steps of cancerogenesis (MKI67, EGF, EGFR, BCL-2, BAX, FOS, JUN, TP53, MT1A, MT2A, VEGFA, FLT1, MMP2, MMP9, and POU5F). qRT-PCR analysis of these selected genes was performed on 74 biopsy samples of tumors from patients with histologically verified HNSCC (22 HPV-, 52 HPV+). Kaplan-Meier analysis was done to determine the relevance of these selected markers for HNSCC prognosis. In conclusion, our study confirms the impact of HPV infection on commonly studied HNSCC markers MT2A, MMP9, FLT1, VEGFA, and POU5F that were more highly expressed in HPV-negative HNSCC patients and also shows the relevance of studied markers in HPV-positive and HPV-negative HNSCC patients.