ArticleFrontiers in genetics2024
Depression-related innate immune genes and pan-cancer gene analysis and validation.
Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Article
- GRB10 as a pan-cancer biomarker linking oncogenic signaling, immunosuppression, and immunotherapy resistance.Discover oncology · 2025Article
- Identification of Potential Common Pathogenic Mechanisms Underlying Osteoarthritis and Major Depressive Disorder Using Bioinformatics Analysis.Immunity, inflammation and disease · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Depression, a prevalent chronic mental disorder, presents complexities and treatment challenges that drive researchers to seek new, precise therapeutic targets. Additionally, the potential connection between depression and cancer has garnered significant attention. Methods: This study analyzed depression-related gene expression data from the GEO database. Using data normalization, differential expression analysis, WGCNA, and machine learning, we identified core genes strongly associated with depression. These genes were validated in depression patients through q-PCR and examined for expression patterns and potential roles across various cancers. Results: We identified six core genes (GRB10, TDRD9, BCL7A, GPR18, KLRG1, and THEM4) significantly associated with depression and cancer. In depression, GRB10 and TDRD9, involved in cell growth and stress responses, exhibited elevated expression, while BCL7A, GPR18, KLRG1, and THEM4, linked to immune regulation and apoptosis, showed reduced expression, suggesting dysregulated cellular signaling and impaired immune function. In cancer, these genes displayed altered expression patterns across tumor types, influencing tumor progression, prognosis, and immune microenvironment modulation. Shared molecular pathways, such as immune dysregulation and apoptosis, highlight their potential as biomarkers and therapeutic targets for both depression and cancer. Conclusion: This study integrates bioinformatics and machine learning to uncover key molecular pathways and targets for depression, introducing innovative therapeutic prospects that may enhance precision treatment for depression. Furthermore, by revealing shared mechanisms between depression and cancer, we have identified six core genes with significant functional roles in immune regulation, apoptosis, and cellular signaling. These findings not only deepen our understanding of the molecular overlap between these conditions but also lay the groundwork for developing dual-targeted therapeutic strategies. This study uniquely contributes to bridging mental health and oncology research, offering new insights and hope for improving patient outcomes in both fields.
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