Dear Editor, We read with great interest the article by Yi et al1. This study innovatively applies a comprehensive multi-omics approach, integrating bioinformatics, transcriptomics, proteomics, and Mendelian Randomization (MR), to identify critical genes and immunometabolic networks in glioblastoma (GBM). This pioneering work not only provides robust evidence for the causal roles of LGALS9 and SELL in GBM but also elucidates their mechanistic pathways through immune cells and metabolic pathways. We believe this study represents a significant advancement in the field of GBM research and offers new directions for the development of more effective treatments. Here, we share some thoughts and suggestions to further enrich the discussion. We hereby declare that this manuscript was translated and linguistically checked using artificial intelligence to ensure compliance with journal requirements. The AI use report was submitted according to the requirements of the literature “Transparency In the reporting of Artificial Intelligence – The TITAN guideline”2. The identification of LGALS9 and SELL as critical genes in GBM highlights the importance of integrating multi-omics data to uncover novel therapeutic targets3. The use of MR analysis to establish causal relationships between these genes and GBM is particularly noteworthy. This approach not only identifies potential therapeutic targets but also provides insights into the underlying mechanisms of GBM pathogenesis. Future research should further explore the druggability of these genes and their potential as prognostic biomarkers. The identification of promising compounds such as meclofenamate targeting LGALS9 and SELL through drug–gene interaction analysis is a significant step forward in this direction. Further preclinical and clinical studies are needed to validate the therapeutic potential of these compounds. The two-step MR analysis elucidating the mediating effects of immune cells and cerebrospinal fluid metabolites on the association between LGALS9/SELL and GBM provides a novel perspective on the immunometabolic mechanisms underlying GBM. This integrative approach reveals the complexity of GBM pathogenesis and highlights the importance of considering both genetic and environmental factors in therapeutic target identification. Future research should focus on exploring the detailed mechanisms of these immunometabolic interactions and their potential as therapeutic targets. For example, the role of LGALS9 in promoting GBM via CD3 on CD39+ resting regulatory T cells and the mediating effect of cerebrospinal fluid metabolite X-22162 on SELL’s action on GBM warrant further investigation1. This study also underscores the potential of combining in vitro experiments with multi-omics data to validate the biological functions of critical genes in GBM. The functional validation of LGALS9 and SELL through in vitro experiments confirms their roles in promoting GBM cell proliferation, migration, and invasion. These findings not only strengthen the biological relevance of the multi-omics analysis but also provide a basis for future drug development. Further in vivo studies are needed to confirm these findings and explore the potential side effects and efficacy of targeting LGALS9 and SELL in GBM treatment. In conclusion, the study by Yi et al1 provides a comprehensive and innovative approach to identifying critical genes and immunometabolic networks in GBM. This work not only advances our understanding of GBM biology but also offers promising therapeutic targets and prognostic biomarkers. We extend our sincere gratitude and appreciation to the authors for their efforts in this research and look forward to their continued leadership in driving innovations in GBM treatment through future studies.
Xie et al. (Tue,) studied this question.