ZNF643/ZFP69B exerts oncogenic properties and associates with cell adhesion and immune processes [RNA-seq]
The global cancer burden remains high; thus, it is crucial to entangle molecular mechanisms driving carcinogenesis to improve current prevention and treatment options. We previously detected the ZNF643/ZFP69B gene upregulated in multiple tumors, and we speculated it may play a role in tumor biology. To test this hypothesis, we employed TCGA-centered databases to correlate ZNF643 status with various clinicopathological parameters. We also performed RNA-seq analysis, in vitro studies assessing cancer cell phenotype, and searched for ZNF643-bound genomic loci. Our data indicated higher levels of ZNF643 in most analyzed tumors compared to normal samples, possibly due to copy number variations. ZNF643 mRNA correlated with diverse molecular and immune subtypes and clinicopathological features (tumor stage, grade, patient survival). RNA-seq analysis revealed that ZNF643 silencing triggers the deregulation of the genes implicated in various cancer-related processes, such as growth, adhesion, and the immune system. Moreover, we observed that ZNF643 positively influences cell proliferation, cell cycle, migration, and invasion in a cell type-dependent manner. Finally, our ChIP-seq analysis indicated that the genes associated with ZNF643 binding are linked to adhesion and immune signaling. In conclusion, our data confirm the oncogenic properties of ZNF643 and pinpoint its impact on cell adhesion and immune processes. Overall design: We analyzed 6 samples from two different lung cancer cell lines in biological triplicates (total 18 samples). For each cell line (H2073 and SKMES) we analyzed unmodified cells (WT), cells with knocked-down expression of ZNF643 (shZNF643) and cells carrying control shRNA sequence (shLUC). Pair-end RNA sequencing was performed for all samples on a NovaSeq 6000 Sequencing System (Illumina). DEG (Differentially Expressed Genes) analysis was performed on two different comparison pairs (sh643 vs. two control samples WT and shLUC separately) using edgeR.