Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Cocktail & Poster Display session

102P - Co-expression analysis of genes encoding proteasome subunits and XPO1-related proteins in the Cancer Genome Atlas (TCGA) and the Gene Tissue Expression (GTEx) databases as a tool to devise new treatment strategies


06 Mar 2023


Cocktail & Poster Display session


Vito Spataro


Annals of Oncology (2023) 8 (1suppl_2): 100898-100898. 10.1016/esmoop/esmoop100898


V. Spataro1, A. Buetti-Dinh2

Author affiliations

  • 1 Medical Oncology, EOC - Ospedale Regionale Bellinzona e Valli - Istituto Oncologico della Svizzera Italiana (IOSI), 6500 - Bellinzona/CH
  • 2 Institute Of Microbiology, SUPSI, Scuola Universitaria Professionale della Svizzera Italiana, 6850 - Mendrisio/CH


This content is available to ESMO members and event participants.

Abstract 102P


The 26S proteasome is a multiprotein complex encoded by 32 genes. Proteasome inhibitors (PI) used in the clinic target subunit (SU) β5 encoded by the PSMB5 gene, but several additional SU are functionally relevant and potential drug targets. The nucleocytoplasmic export (NE) protein XPO1 is a novel established target for anticancer treatment and homologues of SU PSMD14 and of XPO1 are involved in AP-1 mediated drug resistance in the fission yeast model . An integrative study on the expression of proteasome genes and XPO1-related genes in tumors can be informative for the development of novel treatment strategies.


We extracted data on RNA expression of all 32 proteasome genes and all 37 genes encoding XPO1-interacting proteins according to OpenCell database in all tumours (T) of TCGA and in normal tissues (NT) of the GTEx Project. We performed gene co-expression analysis (GCA) for each gene pair and calculated Pearson correlation R coefficients. The matrices of GCA were compared across T and NT and the Euclidean distance was used to cluster the correlation matrices into a dendrogram. A machine learning algorithm was used to identify the genes with highest classification weights.


The R coefficients for co-expression of proteasome genes are very high in NT and significantly lower in the majority of T. Based on GCA, cluster analysis can clearly separate T and NT. Sixteen of the 20 genes contributing most to the separation are proteasome genes. This set includes PSMB5 (the target of approved PI), 8 SU of the proteasome core, 7 SU of the regulatory particle and XPO1 (the target of Selinexor). Several gene pairs encoding proteasome SU and NE proteins have highly correlated expression (R>0.7). PSMD14 and XPO1 are highly correlated (R>0.7) in some T types (breast, prostate, lung) and not in their NT counterparts.


We conclude that: 1) the expression of proteasome genes is severely altered in T and a subset of them is more frequently deregulated 2) there is a high positive correlation between the expression of some proteasome genes and several genes involved in NE 3) the gene pair PSMD14-XPO1 is frequently correlated in T and not in NT.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

V. Spataro.


Has not received any funding.


V. Spataro: Non-Financial Interests, Personal, Advisory Board: Roche Pharma, Takeda Pharma; Non-Financial Interests, Personal, Invited Speaker: Novartis Pharma. All other authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.