Meta-analysis of expression and targeting of cell adhesion associated genes in nine cancer types – a one research lab re-evaluation


Meta-analysis of expression and targeting of cell adhesion associated genes in nine cancer types – a one research lab re-evaluation

Borodins, O.; Broghammer, F.; Seifert, M.; Cordes, N.

Cancer presents as a highly heterogeneous disease with partly overlapping and partly distinct
(epi)genetic characteristics. These characteristics determine inherent and acquired resistance,
which need to be overcome for improving patient survival. In line with the global efforts in
identifying druggable resistance factors, extensive preclinical research of the Cordes lab and others
designated the cancer adhesome as a critical and general therapy resistance mechanism with
multiple druggable cancer targets. In our study, we addressed pancancer cell adhesion mechanisms
by connecting the preclinical datasets generated in the Cordes lab with publicly available
transcriptomic and patient survival data. We identified similarly changed differentially expressed
genes (scDEGs) in nine cancers and their corresponding cell models relative to normal tissues.
Those scDEGs interconnected with 212 molecular targets from Cordes lab datasets generated
during two decades of research on adhesome and radiobiology. Intriguingly, integrative analysis
of adhesion associated scDEGs, TCGA patient survival and protein-protein network
reconstruction revealed a set of overexpressed genes adversely affecting overall cancer patient
survival and specifically the survival in radiotherapy-treated cohorts. This pancancer gene set
includes key integrins (e.g. ITGA6, ITGB1, ITGB4) and their interconnectors (e.g. SPP1, TGFBI),
affirming their critical role in the cancer adhesion resistome. In summary, this meta-analysis
demonstrates the importance of the adhesome in general, and integrins together with their
interconnectors in particular, as potentially conserved determinants and therapeutic targets in
cancer.

Keywords: Integrins; Adhesion; gene expression; network analysis; pancancer

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