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Serotonin and serotonin metabolism has for decades been understood as playing a critical role in mood disorders and has more recently also been implicated in brain tumour biology. However, in part due to the lack of direct investigation of genetic and epigenetic variation affecting serotonin pathways within human brain tissue this understanding has recently been challenged. We analysed genetic and epigenetic variation in the Monoamine oxidase A (MAOA) and serotonin transporter (5HTT) genes using 232 biobanked glioma tissue samples from 216 adult patients. We further examined the association between use of antidepressants (targeting serotonergic pathways), serotonin levels and methylation. In male patients, genetic variation in the MAOA gene was significantly associated with tissue serotonin levels. Further analysis identified five single nucleotide variants (SNVs) that may contribute to this association. In contrast, 5HTT variants were not statistically associated with serotonin pathway metabolites, nor were MAOA variants in females. Increased methylation at several 5HTT CpG sites was positively correlated with serotonin levels and negatively correlated with 5-HIAA levels. In males, one CpG site in the MAOA gene was negatively associated with the 5-HIAA/serotonin ratio, suggesting reduced enzymatic degradation of serotonin due to lower MAOA activity. Patients using antidepressants had lower tissue serotonin levels. In males, genetic variation in the MAOA gene was significantly associated with tissue serotonin levels, although this association was not mediated by methylation. Our result supports the notion that the MAOA and 5HTT genes are related to serotonin metabolism and that such metabolism is related to antidepressant use.

Background: Glioblastoma is the most aggressive and malignant brain tumor, characterized by a high degree of heterogeneity, invasiveness, and resistance to treatment. Patients with glioblastoma have a very poor prognosis despite multimodal interventions. In this study, we investigated how 18F-fluorothymidine (18F-FLT) PET combined with contrast-enhanced MRI and blood metabolomics can contribute to evaluate prognosis and treatment response for patients with glioblastoma.

Methods: Patients, scheduled for surgery due to suspected high-grade glioma were included in this clinical study and underwent four 18F-FLT-PET/MRI examinations prior to surgery and during standard treatment. Blood samples were collected and analyzed by metabolomics. Patients were grouped according to survival as long-time survivors (>3 years) and short-time survivors (<500 days).

Results: Both 2 and 6 weeks into treatment, short-time survivors displayed a significantly larger tumor volume than long-time survivors. When comparing MRI findings during treatment, long-time survivors displayed a substantial tumor decrease, whereas the short-time survivors showed minor or no effect. Regarding 18F-FLT-PET the results were not as unambiguous. Furthermore, there was a clear and significant separation in the metabolomic pattern in blood between the survival groups and across treatment time points.

Conclusions: MRI measures of tumor volume and growth during treatment appear to be prognostic clinical factors that affect outcome. Metabolomic patterns in blood differ significantly between the defined survival groups and may serve as support for an early forecast of prognosis. We also observe a clear separation in metabolite levels between different time points during treatment, which likely reflects treatment effects.

Multiclass data sets and large-scale studies are increasingly common in omics sciences, drug discovery, and clinical research due to advancements in analytical platforms. Efficiently handling these data sets and discerning subtle differences across multiple classes remains a significant challenge. In metabolomics, two-class orthogonal projection to latent structures discriminant analysis (OPLS-DA) models are widely used due to their strong discrimination capabilities and ability to provide interpretable information on class differences. However, these models face challenges in multiclass settings. A common solution is to transform the multiclass comparison into multiple two-class comparisons, which, while more effective than a global multiclass OPLS-DA model, unfortunately results in a manual, time-consuming model-building process with complicated interpretation. Here, we introduce an extension of OPLS-DA for data-driven multiclass classification: orthogonal partial least squares-hierarchical discriminant analysis (OPLS-HDA). OPLS-HDA integrates hierarchical cluster analysis (HCA) with the OPLS-DA framework to create a decision tree, addressing multiclass classification challenges and providing intuitive visualization of interclass relationships. To avoid overfitting and ensure reliable predictions, we use cross-validation during model building. Benchmark results show that OPLS-HDA performs competitively across diverse data sets compared to eight established methods. This method represents a significant advancement, offering a powerful tool to dissect complex multiclass data sets. With its versatility, interpretability, and ease of use, OPLS-HDA is an efficient approach to multiclass data analysis applicable across various fields.

The aim of the present study was to investigate if use of antidepressants is related to the risk of developing lower (WHO grade 2-3) and higher grade (WHO grade 4) glioma. A registry based case-control study was performed using 1283 glioma cases and 6400 age-, sex- and geographically matched controls, diagnosed in Sweden 2009-2013. Conditional logistic regression was used to analyze whether Selective Serotonin Reuptake Inhibitors (SSRIs) or non-SSRIs were associated with the risk of developing lower- or higher-grade glioma in the study population. Our results show that use of antidepressant medication was not associated with the risk of developing glioma. We also performed a meta-analysis in which the dataset from the present study was combined with results from two previous epidemiological studies to answer the same questions. The meta-analysis showed a modest risk reduction of developing glioma in relation to antidepressant treatment (OR 0.90 [95% CI 0.83-0.97]), when all glioma subgroups and all forms of antidepressant medications were combined. In conclusion, it remains possible that antidepressants may have common monoaminergic mechanism(s) that reduce the risk of developing glioma.

Background: Depression and treatment with antidepressant medication is common in patients with malignant glioma. However, the extent to which antidepressants may affect the disease is not fully understood. Therefore, the purpose of the present study was to investigate possible associations between treatment with antidepressant medication and survival in glioma patients.

Methods: We performed a registry-based cohort study including 1231 patients with malignant glioma (WHO grade 2, 3 and 4) having undergone surgery, and 6400 matched controls without glioma. All data was extracted from the RISK North database, which contains information from multiple national population-based registries in Sweden.

Results: Treatment with antidepressants is more common in patients with malignant glioma (27%), compared to controls (16%), p<.001. Treatment with antidepressants after surgery for glioma was significantly associated with poorer survival. These effects were observed both for selective serotonin reuptake inhibitors (SSRIs) and non-SSRIs. In grade 4 glioma, SSRI treatment was associated with a HR of 3.32 (95% CI 2.69–4.10, p<.001), and non-SSRI treatment a HR of 3.54 (95% CI 2.52–4.99, p<.001), compared to glioma patients without antidepressants. In grade 2-3 glioma, the HR for SSRI treatment was 3.26 (95% CI 2.19–4.85, p<.001), and for non-SSRI treatment 7.71 (95% CI 4.22-14.12, p<.001).

Conclusions: Our results demonstrate a negative association between antidepressant medication and survival in glioma. Further research will be needed to clarify causation.

Gliomas are highly complex and metabolically active brain tumors associated with poor prognosis. Recent reports have found altered levels of blood metabolites during early tumor development, suggesting that tumor development could be detected several years before clinical manifestation. In this study, we performed metabolite analyses of blood samples collected from healthy controls and future glioma patients, up to eight years before glioma diagnosis, and on the day of glioma surgery. We discovered that metabolites related to early glioma development were associated with an increased energy turnover, as highlighted by elevated levels of TCA-related metabolites such as fumarate, malate, lactate and pyruvate in pre-diagnostic cases. We also found that metabolites related to glioma progression at surgery were primarily high levels of amino acids and metabolites of amino acid catabolism, with elevated levels of 11 amino acids and two branched-chain alpha-ketoacids, ketoleucine and ketoisoleucine. High amino acid turnover in glioma tumor tissue is currently utilized for PET imaging, diagnosis and delineation of tumor margins. By examining blood-based metabolic progression patterns towards disease onset, we demonstrate that this high amino acid turnover is also detectable in a simple blood sample. These findings provide additional insight of metabolic alterations during glioma development and progression.

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths worldwide, necessitating the identification of novel therapeutic targets. Lysosome Associated Protein Transmembrane 4B (LAPTM4B) is involved in biological processes critical to cancer progression, such as regulation of solute carrier transporter proteins and metabolic pathways, including mTORC1. However, the metabolic processes governed by LAPTM4B and its role in oncogenesis remain unknown. In this study, we conducted unbiased metabolomic screens to uncover the metabolic landscape regulated by LAPTM4B. We observed common metabolic changes in several knockout cell models suggesting of a role for LAPTM4B in suppressing ferroptosis. Through a series of cell-based assays and animal experiments, we demonstrate that LAPTM4B protects tumor cells from erastin-induced ferroptosis both in vitro and in vivo. Mechanistically, LAPTM4B suppresses ferroptosis by inhibiting NEDD4L/ZRANB1 mediated ubiquitination and subsequent proteasomal degradation of the cystine-glutamate antiporter SLC7A11. Furthermore, metabolomic profiling of cancer cells revealed that LAPTM4B knockout leads to a significant enrichment of ferroptosis and associated metabolic alterations. By integrating results from cellular assays, patient tissue samples, an animal model, and cancer databases, this study highlights the clinical relevance of the LAPTM4B-SLC7A11-ferroptosis signaling axis in NSCLC progression and identifies it as a potential target for the development of cancer therapeutics.

The serine/glycine (ser/gly) synthesis pathway branches from glycolysis and is hyperactivated in approximately 30% of cancers. In ~13% of glioblastoma cases, we observed frequent amplifications and rare mutations in the gene encoding the enzyme PSPH, which catalyzes the last step in the synthesis of serine. This urged us to unveil the relevance of PSPH genetic alterations and subsequent ser/gly metabolism deregulation in the pathogenesis of glioblastoma. Primary glioblastoma cells overexpressing PSPH and PSPHV116I showed an increased clonogenic capacity, cell proliferation, and migration, supported by elevated nucleotide synthesis and utilization of reductive NAD(P). We previously identified sertraline as an inhibitor of ser/gly synthesis and explored its efficacy at suboptimal dosages in combination with the clinically pretested chloroquine to target ser/glyhigh glioblastoma models. Interestingly, ser/glyhigh glioblastomas, including PSPHamp and PSPHV116I, displayed selective synergistic inhibition of proliferation in response to combination therapy. PSPH knockdown severely affected ser/glyhigh glioblastoma clonogenicity and proliferation, while simultaneously increasing its sensitivity to chloroquine treatment. Metabolite landscaping revealed that sertraline/chloroquine combination treatment blocks NADH and ATP generation and restricts nucleotide synthesis, thereby inhibiting glioblastoma proliferation. Our previous studies highlight ser/glyhigh cancer cell modulation of its microenvironment at the level of immune suppression. To this end, high PSPH expression predicts poor immune checkpoint therapy responses in glioblastoma patients. Interestingly, we show that PSPH amplifications in glioblastoma facilitate the expression of immune suppressor galectin-1, which can be inhibited by sertraline treatment. Collectively, we revealed that ser/glyhigh glioblastomas are characterized by enhanced clonogenicity, migration, and suppression of the immune system, which could be tackled using combined sertraline/chloroquine treatment, revealing novel therapeutic opportunities for this subgroup of GBM patients.

Genetic and metabolic changes in tissue and blood are reported to occur several years before glioma diagnosis. Since gliomas are currently detected late, a liquid biopsy for early detection could affect the quality of life and prognosis of patients. Here, we present a nested case-control study of 550 prediagnostic glioma cases and 550 healthy controls from the Northern Sweden Health and Disease study (NSHDS) and the European Prospective Investigation into Cancer and Nutrition (EPIC) study. We identified 93 significantly altered metabolites related to glioma development up to 8 years before diagnosis. Out of these metabolites, a panel of 20 selected metabolites showed strong disease correlation and a consistent progression pattern toward diagnosis in both the NSHDS and EPIC cohorts, and they separated future cases from controls independently of biological sex. The blood metabolite panel also successfully separated both lower-grade glioma and glioblastoma cases from controls, up to 8 years before diagnosis in patients within the NSHDS cohort and up to 2 years before diagnosis in EPIC. Pathway enrichment analysis detected metabolites related to the TCA cycle, Warburg effect, gluconeogenesis, and cysteine, pyruvate, and tyrosine metabolism as the most affected.

Genome-wide association studies (GWAS) have contributed to our understanding of glioma susceptibility. To date, 25 risk loci for development of any of the glioma subtypes are known. However, GWAS studies reveal little about the molecular processes that lead to increased risk, especially for non-coding single nucleotide polymorphisms (SNP). A particular SNP in intron 2 of LRIG1, rs11706832, has been shown to increase the susceptibility for IDH1 mutated low-grade gliomas (LGG). Leucine-rich repeats and immunoglobulin-like domains protein 1 (LRIG1) is important in cancer development as it negatively regulates the epidermal growth factor receptor (EGFR); however, the mechanism responsible for this particular risk SNP and its potential effect on LRIG1 are not known. Using CRISPR-CAS9, we edited rs11706832 in HEK293T cells. Four HEK293T clones with the risk allele were compared to four clones with the non-risk allele for LRIG1 and SLC25A26 gene expression using RT-qPCR, for global gene expression using RNA-seq, and for metabolites using gas chromatography-mass spectrometry (GC–MS). The experiment did not reveal any significant effect of the SNP on the expression levels or splicing patterns of LRIG1 or SLC25A26. The global gene expression analysis revealed that the risk allele C was associated with upregulation of several mitochondrial genes. Gene enrichment analysis of 74 differentially expressed genes in the genome revealed a significant enrichment of type I interferon response genes, where many genes were downregulated for the risk allele C. Gene expression data of IDH1 mutated LGGs from the cancer genome atlas (TCGA) revealed a similar under expression of type I interferon genes associated with the risk allele. This study found the expression levels and splicing patterns of LRIG1 and SLC25A26 were not affected by the SNP in HEK293T cells. However, the risk allele was associated with a downregulation of genes involved in the innate immune response both in the HEK293T cells and in the LGG data from TCGA.