@article {EFFERTH17, author = {THOMAS EFFERTH and MANFRED VOLM}, title = {Protein Expression Profiles Indicative for Drug Resistance of Kidney Carcinoma}, volume = {1}, number = {1}, pages = {17--22}, year = {2004}, publisher = {International Institute of Anticancer Research}, abstract = {The purpose of this investigation was to evaluate whether different protein patterns exist between drug-sensitive and drug-resistant kidney carcinomas. As a first step, expressions of drug resistance proteins (P-glycoprotein (P-gp), glutathione S-transferase-σ (GST-σ), DNA topoisomerase IIα (Topo IIα), alkaline phosphatase (AP), catalase, thymidylate synthetase, metallothionein), signal transducers (protein kinase Cα/β (PKCα/β)), proliferation-associated proteins (Ki-67) and proteins of proto-oncogenes and tumor suppressors (ErbB1, ErbB2, Fos, Jun, Myc, Ras and p53) of primary cell cultures of human kidney carcinomas of 18 patients were determined. The expression levels of the proteins were compared with the response to doxorubicin, vincristine or mafosfamide measured by growth inhibition and nucleotide incorporation assays. As a second step, those proteins showing a relationship to doxorubicin resistance (P-gp, GST-σ, Topo IIα, PKCα/β, AP, ErbB1, ErbB2, Fos, K-Ras, p53, and Ki-67) were analyzed by hierarchical cluster analysis and clustered image mapping. The resulting clusters were correlated with the drug resistance data. The data shows that different protein expression profiles exist between drug-resistant and -sensitive kidney carcinoma cell cultures. Finally, the clustered image map (CIM) demonstrates a sensitive area that is characterized by a lower expression of proteins and a resistant area with a higher expression of proteins. These results may have important implications for the diagnosis and therapy of kidney cancer. The resistance proteins and the resistance-related factors found in the present analysis may be suitable parameters to predict treatment outcome of kidney cancer.}, issn = {1109-6535}, URL = {https://cgp.iiarjournals.org/content/1/1/17}, eprint = {https://cgp.iiarjournals.org/content/1/1/17.full.pdf}, journal = {Cancer Genomics \& Proteomics} }