Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders including NRG1.

Major depressive disorder (MDD) is a common mental disorder and is amongst the most prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset due to its heterogeneous phenotype and complex etiology. Hence, early detection using diagnostic biomarkers is critical for rapid intervention. In this study, a mixture of AI and bioinformatics were used to mine transcriptomic data from publicly available datasets including 170 MDD patients and 121 healthy controls. Bioinformatics analysis using gene set enrichment analysis (GSEA) and machine learning (ML) algorithms were applied. The GSEA revealed that differentially expressed genes in MDD patients are mainly enriched in pathways related to immune response, inflammatory response, neurodegeneration pathways and cerebellar atrophy pathways. Feature selection methods and ML provided predicted models based on MDD-altered genes with ≥75% of accuracy. The integrative analysis between the bioinformatics and ML approaches identified ten key MDD-related biomarkers including NRG1, CEACAM8, CLEC12B, DEFA4, HP, LCN2, OLFM4, SERPING1, TCN1 and THBS1. Among them, NRG1, active in synaptic plasticity and neurotransmission, was the most robust and reliable to distinguish between MDD patients and healthy controls amongst independent external datasets consisting of a mixture of populations. Further evaluation using saliva samples from an independent cohort of MDD and healthy individuals confirmed the upregulation of NRG1 in patients with MDD compared to healthy controls. Functional mapping to the human brain regions showed NRG1 to have high expression in the main subcortical limbic brain regions implicated in depression. In conclusion, integrative bioinformatics and ML approaches identified putative non-invasive diagnostic MDD-related biomarkers panel for the onset of depression.

DKK3's protective role in prostate cancer is partly due to the modulation of immune-related pathways.

While it is considered one of the most common cancers and the leading cause of death in men worldwide, prognostic stratification and treatment modalities are still limited for patients with prostate cancer (PCa). Recently, the introduction of genomic profiling and the use of new techniques like next-generation sequencing (NGS) in many cancers provide novel tools for the discovery of new molecular targets that might improve our understanding of the genomic aberrations in PCa and the discovery of novel prognostic and therapeutic targets. In this study, we investigated the possible mechanisms through which Dickkopf-3 (DKK3) produces its possible protective role in PCa using NGS in both the DKK3 overexpression PCa cell line (PC3) model and our patient cohort consisting of nine PCa and five benign prostatic hyperplasia. Interestingly, our results have shown that DKK3 transfection-modulated genes are involved in the regulation of cell motility, senescence-associated secretory phenotype (SASP), and cytokine signaling in the immune system, as well as in the regulation of adaptive immune response. Further analysis of our NGS using our in vitro model revealed the presence of 36 differentially expressed genes (DEGs) between DKK3 transfected cells and PC3 empty vector. In addition, both CP and ACE2 genes were differentially expressed not only between the transfected and empty groups but also between the transfected and Mock cells. The top common DEGs between the DKK3 overexpression cell line and our patient cohort are the following: IL32, IRAK1, RIOK1, HIST1H2BB, SNORA31, AKR1B1, ACE2, and CP. The upregulated genes including IL32, HIST1H2BB, and SNORA31 showed tumor suppressor functions in various cancers including PCa. On the other hand, both IRAK1 and RIOK1 were downregulated and involved in tumor initiation, tumor progression, poor outcome, and radiotherapy resistance. Together, our results highlighted the possible role of the DKK3-related genes in protecting against PCa initiation and progression.