A multiomics analysis of S100 protein family in breast cancer

Abstract

// Patrizia Cancemi 1, 2, 3 , Miriam Buttacavoli 1 , Gianluca Di Cara 2 , Nadia Ninfa Albanese 2 , Serena Bivona 3 , Ida Pucci-Minafra 2 and Salvatore Feo 1, 3, 4 1 Department of Biological Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, Palermo, Italy 2 Center of Experimental Oncobiology (C.OB.S.), La Maddalena Hospital III Level Oncological Dept., Palermo, Italy 3 Advanced Technologies Network Center (ATeN), University of Palermo, Palermo, Italy 4 Institute of Biomedicine and Molecular Immunology, CNR, Palermo, Italy Correspondence to: Patrizia Cancemi, email: patrizia.cancemi@unipa.it Keywords: S100 proteins; breast cancer; expression analysis; proteomics; pathway analysis Received: February 05, 2018      Accepted: May 19, 2018      Published: June 26, 2018 ABSTRACT The S100 gene family is the largest subfamily of calcium binding proteins of EF-hand type, expressed in tissue and cell-specific manner, acting both as intracellular regulators and extracellular mediators. There is a growing interest in the S100 proteins and their relationships with different cancers because of their involvement in a variety of biological events closely related to tumorigenesis and cancer progression. However, the collective role and the possible coordination of this group of proteins, as well as the functional implications of their expression in breast cancer (BC) is still poorly known. We previously reported a large-scale proteomic investigation performed on BC patients for the screening of multiple forms of S100 proteins. Present study was aimed to assess the functional correlation between protein and gene expression patterns and the prognostic values of the S100 family members in BC. By using data mining, we showed that S100 members were collectively deregulated in BC, and their elevated expression levels were correlated with shorter survival and more aggressive phenotypes of BC (basal like, HER2 enriched, ER-negative and high grading). Moreover a multi-omics functional network analysis highlighted the regulatory effects of S100 members on several cellular pathways associated with cancer and cancer progression, expecially immune response and inflammation. Interestingly, for the first time, a pathway analysis was successfully applied on different omics data (transcriptomics and proteomics) revealing a good convergence between pathways affected by S100 in BC. Our data confirm S100 members as a promising panel of biomarkers for BC prognosis.