Purpose: Osteoarthritis (OA) is the most common chronic joint disease and is a leading cause of disability. It has a complex pathogeny and causes damage to the articular cartilage, increased joint-associated bone remodelling, and synovial inflammation. Currently, no cure exists and the molecular mechanisms maintaining cartilage homeostasis remain incompletely defined. Previously, an association between polymorphisms in the acidic leucine-rich nuclear phosphoprotein-32A (ANP32A) gene and OA was described. Then, our group showed that ANP32A plays a key protective role in OA by preventing oxidative stress via increasing the transcription of the ATM kinase. Also, we demonstrated that ANP32A is downregulated in OA cartilage as compared to non-OA cartilage. Therefore, maintaining the levels of ANP32A seems to be of crucial importance to preserve joint health and prevent the development and progression of OA. Yet the factors that regulate ANP32A in the joint remain unknown. In this study, we aim to investigate factors that regulate ANP32A expression since such knowledge may lead to the identification of specific novel targets for OA therapy. Methods: An in-house developed bioinformatic pipeline was used in which four bioinformatic tools (BindDB, CONSITE, PROMO and TFsitescan) were interrogated for regulatory factors that are predicted to interact with the ANP32A promotor. The sequence of the ANP32A promotor was obtained using the Eukaryotic Promotor Database (EPD). The Results from these four tools were assembled and then corrected for occurring synonyms. After this, we selected hits that were predicted by two or more databases. An in silico specificity analysis was performed to identify genes with predicted specificity for ANP32A regulation. In this analysis, we excluded hits that also seemed to modulate genes that characterize the chondrocyte identity, namely Aggrecan and Collagen2a1, as well as hits modulating the housekeeping gene Actin. Afterwards, network analysis using StringDB and Humanbase tools was performed, as well as pathway enrichment analysis using the Ingenuity Pathway Analysis (IPA) software. Results: We retrieved 209 hits that were predicted to interact with the ANP32A promoter in silico, of which 49 hits were simultaneously predicted by at least two different bioinformatic tools. After applying the specificity analysis, 16 hits were retained. Network analysis suggested several interesting pathways that may potentially modulate the expression of ANP32A. First, Wnt signaling appeared in the analysis using both the StringDB tool and the IPA software for the 49 hits. Of note, two Wnt signaling TCF/LEF transcription factors were present among the 16 final hits obtained after applying the specificity analysis. Wnt hyper-activation is known to contribute to OA and targeting this pathway is becoming increasingly interesting in the treatment of OA. Second, the network analysis indicated the hypoxia pathway as a potential regulator for ANP32A expression. This pathway is noteworthy since the oxygen gradients in OA cartilage are known to be disturbed. Lastly, network analysis further pointed out cellular senescence (StringDB tool and IPA analysis) and histone modifications (StringDB tool) among the enriched pathways. Both the senescence pathway and alterations in histone modifications have been linked to OA. Conclusions: The bioinformatic pipeline suggested 16 potential regulatory factors for ANP32A expression. Combined with the network analysis, we identified Wnt signaling, hypoxia, cellular senescence and histone modifications as potential regulatory networks for ANP32A transcription. In our future experiments, we will further investigate the significance of these in silico findings and their potential clinical implications in OA.