Hermeking Lab

Experimental and Molecular Pathology

Univ.-Prof. Dr. rer. nat. Heiko Hermeking


Human tumors result from changes in specific genes. We are mainly interested in the function of two genes, c-MYC and p53, which are commonly altered in human cancer, and their downstream effectors. c-MYC represents a proto-oncogene, whereas p53 is a tumor suppressor gene. Both genes encode transcription factors, which bind to DNA in a sequence-specific manner. By activating a number of target genes, which encode proteins or microRNAs, c-MYC and p53 regulate cell cycle progression/proliferation, apoptosis and senescence in an antagonistic manner. Interestingly, the p53 pathway represents a fail-safe mechanism against c-MYC-driven proliferation by mediating apoptosis (Hermeking and Eick, 1994). Furthermore, c-MYC antagonizes the antiproliferative activity of p53 (Hermeking et al., 1995; Jung et al., 2008). We have characterized several genes and microRNAs directly regulated by c-MYC and p53 in detail (Hermeking et al. 1997, 2000; Menssen and Hermeking, 2002; Tarasov et al., 2007; Jung et al., 2008).

Interestingly, some of the genes induced by p53 are themselves inactivated in cancer. E.g. 14-3-3sigma and miR-34a are silenced by CpG methylation in a number of tumor types (Lodygin et al., 2003, 2004, 2005, 2008). Detection of these events in DNA released from tumor cells may have tumor diagnostic potential.


We are currently characterizing p53- and c-MYC-regulated pathways by multiple different approaches and technologies. Among these are SAGE, microarray analyses, parallel sequencing, quantitative PCR, ChIP, methylation-specific PCR, lasermicrodissection, proteomics (TAP-tagging/MudPIT), live cell imaging/laserscanning microscopy, FACS and the generation/analysis of mouse knock-out models.

p53 signalling

p53 signaling

We recently published a web tool, www.metamir34target.com, which facilitates the identification of targets of the microRNA-34 family. The tool is based on a meta-analysis of computational miRNA target prediction resources and experimental transcriptomic and proteomic datasets derived from cell lines, mouse models, and tumor tissues.

Published in:
Rokavec M, Huang Z and Hermeking H. (2003) Meta-Analysis of miR-34 target mRNAs using an integrative online application. Computational and Structural Biotechnology Journal 21, 267-274.


Dr. rer. nat. Markus Kaller
Dr. rer. nat. Matjaz Rokavec
Dr. med. vet. Nassim Bouznad, PhD
Dr. rer. nat. Stephanie Jaeckel
Uschi Götz, BTA
Janine König, M.Sc.
Jinjiang Chou, M.Sc.
Zekai Huang, M.Sc.
Chunfeng Liu, M.Sc.
Yuyun Du, M.Sc.
Xiaoyan Chen, M.Sc.