Integrative transcriptomic analysis of human monocyte revealed common signatures in cancer patients

Introduction: Our previous study demonstrated that monocytes from breast cancer patients display alterations in their transcriptomic profile that include the down regulation of genes involved in their differentiation and in their ability to stimulate the immune system, in addition to producing reduced levels of inflammatory cytokines when compared to healthy donors'monocytes. We aim here to reveal common gene signatures present in monocytes from patients with distinct hematologic and solid cancer. Methods: An integrative bioinformatics analysis was performed to investigate monocyte signatures in ten public RNA-seq datasets from Hematologic Tumor (HT) (Chronic Myeloid Leukemia, acute myeloid leukemia and Diffuse large B-cell lymphoma) and Solid Tumors (ST) (esophageal, lung, head and neck, brain, colon). We generated a list of Differentially Expressed Genes (DEGs) between monocytes from healthy donors versus cancer patients or between healthy and tumoral tissues using the DESeq2 package in R-studio and performed the functional enrichment analysis using Gene Ontology (GO) and protein-protein interactions considering the common up-regulated genes. Results: In blood monocytes from the HT datasets, we found 13 common genes, including MSR1, CAMP, ELANE and CCL3. These genes are mainly involved in the signaling pathways for Phagocytosis and Calcium Ion Homeostasis. For monocytes in ST tissues, we found 161 commonly expressed genes in all datasets, including genes belonging to the immunoglobulin family (IGHG3, IGHM, IGHG4), genes involved in the organization of the extracellular matrix (MMP3, MMP8, MMP9, MMP12) and genes from the CXCL chemokine family (CXCL10, CXCL9, CXCL13). Conclusion: Our data suggest that monocytes are involved in extracellular matrix organization, cell migration and antigen recognition in HT and TS. We will next evaluate the expression of those molecules in monocytes from multiple myeloma patients and correlate with clinical data.