bioRxivCancer BiologyPreprint
GJB3, a gap junction gene, supports cell growth by mediating cystine uptake and regulating cellular stress pathways in SLC7A11 low adenocarcinomas
Disha AcharyaiD, Annesha ChatterjeeiD, Nikita Bhandari, Payel Roy, Monishka Agrawal, Balkrishna ChaubeiD, Sudhanshu Kumar ShuklaiD
Abstract
Gap junctions are specialized intercellular connections that directly connect the cytoplasm of two cells via protein structure called connexins. Despite extensive research on many cell surface proteins, Gap junction proteins are understudied in cancer. In this study, we used TCGA data to identify genetic and epigenetic changes associated with Gap junction proteins. The analysis identified GJB3 as a key gene with differential methylation and expression patterns, with notable overexpression in COAD and LUAD, correlating significantly with patient survival outcomes. GJB3 knockdown studies revealed reduced cell proliferation and migration. Transcriptomic analysis revealed that GJB3 knockdown induced a cellular stress response, characterized by activation of starvation and autophagy pathways. Western blot analysis confirmed these findings, showing increased phosphorylation of eIF2 and activation of the GCN2-eIF2-ATF4 signaling axis subsequent autophagy induction. We also found that sustained autophagy induced apoptosis mediated cell death. Metabolic profiling revealed a significant decrease in cystine levels in GJB3-deficient cells. We demonstrated that GJB3 plays a crucial role in cystine uptake, especially in cells with low SLC7A11 expression. Furthermore, we showed that GJB3 can be targeted using specific antibodies, establishing it as a potential therapeutic strategy for GJB3-dependent cancers. These findings highlight the significance of GJB3 in cancer progression and its potential as a therapeutic target, offering new insights into its epigenetic regulation and functional role in cellular stress and survival mechanisms.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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