Biological Pathways Research Articles

Mechanisms of Copper-Induced Autophagy and Links with Human Diseases

As a structural and catalytic cofactor, copper is involved in many biological pathways and is required for the biochemistry of all living organisms. However, excess intracellular copper can induce cell death due to its potential to catalyze the generation of reactive oxygen species, thus copper homeostasis is strictly regulated. And the deficiency or accumulation of intracellular copper is connected with various pathological conditions. Since the success of platinum-based compounds in the clinical treatment of various types of neoplasias, metal-based drugs have shown encouraging perspectives for drug development. Compared to platinum, copper is an essential intracellular trace element that may have better prospects for drug development than platinum. Recently, the potential therapeutic role of copper-induced autophagy in chronic diseases such as Parkinson’s, Wilson’s, and cardiovascular disease has already been demonstrated. In brief, copper ions, numerous copper complexes, and copper-based nano-preparations could induce autophagy, a lysosome-dependent process that plays an important role in various human diseases. In this review, we not only focus on the current advances in elucidating the mechanisms of copper or copper-based compounds/preparations on the regulation of autophagy but also outline the association between copper-induced autophagy and human diseases.

Systematic qualitative proteome-wide analysis of lysine malonylation profiling in Platycodon grandiflorus

In recent years, it was found that lysine malonylation modification can affect biological metabolism and play an important role in plant life activities. Platycodon grandiflorus, an economic crop and medicinal plant, had no reports on malonylation in the related literature. This study qualitatively introduces lysine malonylation in P. grandiflorus. A total of 888 lysine malonylation-modified proteins in P. grandiflorus were identified, with a total of 1755 modification sites. According to the functional annotation, malonylated proteins were closely related to catalysis, binding, and other reactions. Subcellular localization showed that related proteins were enriched in chloroplasts, cytoplasm, and nuclei, indicating that this modification could regulate various metabolic processes. Motif analysis showed the enrichment of Alanine (A), Cysteine (C), Glycine (G), and Valine (V) amino acids surrounding malonylated lysine residues. Metabolic pathway and protein-protein interaction network analyses suggested these modifications are mainly involved in plant photosynthesis. Moreover, malonylated proteins are also involved in stress and defense responses. This study shows that lysine malonylation can affect a variety of biological processes and metabolic pathways, and the contents are reported for the first time in P. grandiflorus, which can provide important information for further research on P. grandiflorus and lysine malonylation’s role in environment stress, photosynthesis, and secondary metabolites enrichment.

Classification and Mechanism of Action of Anti-Parasitic Drugs: A Review Article

The action of antiparasitic drugs is critical in the treatment of infections caused by parasites and that have devastating effects on millions of individuals worldwide. This paper consolidates different research findings on the classification and mechanisms of action of these drugs, highlighting new therapies and natural products. In particular, this special class of heterocyclic compounds, known as Benzimidazoles, has garnered a wide interest in medicinal chemistry due to their broad pharmacological activities. It recognizes the important contribution normally made by these drugs in the treatment of a wide range of different diseases, including cancer, bacterial infection, and parasitic disease. Drug-resistant parasites are now increasingly prevalent and have greatly impeded the management of parasitic infections; thus, there is an increasing demand for new therapeutic approaches. Anti-parasitic metallodrugs have emerged as another auspicious mode of intervention that carefully exploits the unique chemical features of metal ions and their complexes to affect the biological pathways important for the survival of the parasite. In this paper, we will draw up a range of knowledge gaps identified up-to-date and propose future research directions that would enhance the understanding and, what is more important, the efficiency of anti-parasitic therapies.

Navigating the landscape of plant proteomics.

In plants, proteins are fundamental to virtually all biological processes, such as photosynthesis, signal transduction, metabolic regulation, and stress responses. Studying protein distribution, function, modifications, and interactions at the cellular and tissue levels is critical for unraveling the complexities of these biological pathways. Protein abundance and localization are highly dynamic and vary widely across the proteome, presenting a challenge for global protein quantification and analysis. Mass spectrometry-based proteomics approaches have proven to be powerful tools for addressing this complex issue. In this review, we summarize recent advancements in proteomics research and their applications in plant biology, with an emphasis on the current state and challenges of studying post-translational modifications, single-cell proteomics, and protein-protein interactions. Additionally, we discuss future prospects for plant proteomics, highlighting potential opportunities that proteomics technologies offer in advancing plant biology research.

Developing predictive models for tocilizumab response in rheumatoid arthritis: a gene expression and machine learning approaches

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease treated with tocilizumab in patients unresponsive to methotrexate. This study aimed to identify gene expression profiles and predictive models for tocilizumab treatment response in RA patients. Methods: Using the GSE78068 dataset from 38 RA patients, we identified differentially expressed genes between remission and non-remission groups. Predictive models were created using CART, random forest, and SVM techniques, with model genes selected through LASSO regression. Gene set enrichment and immune cell landscape analyses were performed to understand biological pathways and immune cell composition. Results: Analysis revealed 40 differentially expressed genes, with LASSO regression identifying 8 model genes significantly associated with remission. The SVM-based model achieved the highest performance (AUC=1.0, Brier score=0.025). Conclusions: This study developed an effective 8-gene model for predicting tocilizumab treatment response, potentially supporting personalized therapy in RA patients.

Transcription Factor Fingerprint Provides Clues for Brain Tumor Cell of Origin.

Mouse models that faithfully represent the biology of human brain tumors are critical tools for unraveling the underlying tumor biology and screening for potential precision therapies. This is especially true of rare tumor types, many of which have correspondingly few xenograft or cell lines available. Although our understanding of the specific biological pathways driving cancer has improved significantly, identifying the appropriate progenitor populations to drive oncogenic processes represents a significant barrier to efficient mouse model production. In this issue of Cancer Research, Jessa and colleagues developed an innovative transcription factor fingerprinting method to map the cellular origin of central nervous system neuroblastoma, FOXR2-activated to medial ganglionic eminence-derived interneurons, which could then be efficiently targeted in the developing mouse brain using in utero electroporation. This approach serves as a blueprint for investigating other rare pediatric brain tumors, potentially accelerating progress toward the development of mouse models and identification of effective therapies. See related article by Jessa et al., p. 231.

Multi-omics architecture of childhood obesity and metabolic dysfunction uncovers biological pathways and prenatal determinants

Childhood obesity poses a significant public health challenge, yet the molecular intricacies underlying its pathobiology remain elusive. Leveraging extensive multi-omics profiling (methylome, miRNome, transcriptome, proteins and metabolites) and a rich phenotypic characterization across two parts of Europe within the population-based Human Early Life Exposome project, we unravel the molecular landscape of childhood obesity and associated metabolic dysfunction. Our integrative analysis uncovers three clusters of children defined by specific multi-omics profiles, one of which characterized not only by higher adiposity but also by a high degree of metabolic complications. This high-risk cluster exhibits a complex interplay across many biological pathways, predominantly underscored by inflammation-related cascades. Further, by incorporating comprehensive information from the environmental risk-scape of the critical pregnancy period, we identify pre-pregnancy body mass index and environmental pollutants like perfluorooctanoate and mercury as important determinants of the high-risk cluster. Overall, our work helps to identify potential risk factors for prevention and intervention strategies early in the life course aimed at mitigating obesity and its long-term health consequences.

Insulin and insulin-like growth factor and risk of postmenopausal estrogen receptor-positive breast cancer: a case-cohort analysis.

Higher concentration of insulin-like growth factor-1 (IGF-1) increases postmenopausal breast cancer risk, but evidence for insulin and c-peptide is limited. Further, not all studies have accounted for potential confounding by biomarkers from other biological pathways, and not all were restricted to estrogen receptor (ER)-positive breast cancer. This was a case-cohort study of 1,223 postmenopausal women (347 with ER-positive breast cancer) from the Melbourne Collaborative Cohort Study. We measured insulin, c-peptide, IGF-1, insulin-like growth factor binding protein-3 (IGFBP-3), and biomarkers of inflammatory and sex-steroid hormone pathways. Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (95% CIs) for ER-positive breast cancer per doubling plasma concentration and for quartiles, without and with adjustment for other, potentially confounding biomarkers. ER-positive breast cancer risk was not associated with doubling of insulin (RR = 0.97, 95% CI: 0.82, 1.14) or c-peptide (RR = 1.01, 95% CI: 0.80, 1.26). Risk appeared to decrease with doubling IGF-1 (RR = 0.80, 95% CI: 0.62, 1.03) and IGFBP-3 (RR = 0.62, 95% CI: 0.41, 0.90). RRs were not meaningfully different when exposures were modelled as quartiles. RRs were less than unity but imprecise after adjustment for inflammatory and sex-steroid hormone biomarkers. Circulating insulin, c-peptide, and IGF-1 were not positively associated with risk of ER-positive breast cancer in this case-cohort analysis of postmenopausal women. Associations between insulin and c-peptide and risk of ER-positive breast cancer in postmenopausal women are likely to be weak.

Omics-Based Interaction Analysis Reveals Interplay of Chemical Pollutant (Ozone) and Photoradiation (UVSSR) Stressors in Skin Damage

The skin acts as the first line of defense against various environmental stressors, such as solar ultraviolet radiation, visible light, pollution particles and ozone. Simultaneous exposure to different stressors is common in everyday life but has been less studied than exposure to single stressors. Herein, the combined effects of a chemical pollutant (ozone) and a UV radiation stressor (UVSSR) were investigated on a 3D pigmented living skin equivalent model. Our results demonstrate that skin lightness (L* value) was significantly decreased by exposure to either UVSSR or ozone alone and that co-exposure to UVSSR and ozone further exacerbated surface darkness, suggesting that these stressors had a significant joint effect. Conventional differential expression analysis showed that, among exposure groups, co-exposure dysregulated the most genes, followed by ozone and UVSSR alone. Omics-based interaction framework (OBIF) analysis showed that most interactive genes following ozone and UVSSR exposure displayed a cooperative effect and had functions related to the skin barrier; these genes with synergistic effects were enriched in biological pathways such as the chronic inflammatory response and the apoptotic signaling pathway. In summary, exposure to ozone in combination with UVSSR showed a joint effect on UVSSR-induced phenotypic changes in the skin; the underlying mechanism was determined by using transcriptome analysis, showing the additive impacts of ozone on UVSSR-induced skin damage, such as cellular stress and inflammatory responses. These findings shed light on how ozone exacerbates UVSSR damage and indicate that the synergistic response genes identified using OBIF analysis may drive the progression of skin damage induced by chemical/photoradiation stress co-exposure.

Venoms and Extracellular Vesicles: A New Frontier in Venom Biology

Extracellular vesicles (EVs) are nanoparticle-sized vesicles secreted by nearly all cell types under normal physiological conditions. In toxicological research, EVs have emerged as a crucial link between public health and multi-omics approaches, offering insights into cellular responses to disease-causing injury agents such as environmental and biological toxins, contaminants, and drugs. Notably, EVs present a unique opportunity to deepen our understanding of the pathophysiology of envenomation by natural toxins. Recent advancements in isolating and purifying EV cargo, mass spectrometry techniques, and bioinformatics have positioned EVs as potential biomarkers that could elucidate biological signaling pathways and provide valuable information on the relationship between venomous toxins, their mechanisms of action, and the effectiveness of antivenoms. Additionally, EVs hold promise as proxies for various aspects of envenomation, including the toxin dosage, biological characterization, injury progression, and prognosis during therapeutic interventions. These aspects can be explored through multi-omics technology applied to EV contents from the plasma, saliva, or urine samples of envenomated individuals, offering a comprehensive integrative approach to understanding and managing envenomation cases.

Integrative Analysis of Whole-Genome and Transcriptomic Data Reveals Novel Variants in Differentially Expressed Long Noncoding RNAs Associated with Asthenozoospermia

Background/Objectives: Asthenozoospermia, characterized by reduced sperm motility, is a common cause of male infertility. Emerging evidence suggests that noncoding RNAs, particularly long noncoding RNAs (lncRNAs), play a critical role in the regulation of spermatogenesis and sperm function. Coding regions have a well-characterized role and established predictive value in asthenozoospermia. However, this study was designed to complement previous findings and provide a more holistic understanding of asthenozoospermia, this time focusing on noncoding regions. This study aimed to identify and prioritize variants in differentially expressed (DE) lncRNAs found exclusively in asthenozoospermic men, focusing on their impact on lncRNA structure and lncRNA–miRNA–mRNA interactions. Methods: Whole-genome sequencing (WGS) was performed on samples from asthenozoospermic and normozoospermic men. Additionally, an RNA-seq dataset from normozoospermic and asthenozoospermic individuals was analyzed to identify DE lncRNAs. Bioinformatics analyses were conducted to map unique variants on DE lncRNAs, followed by prioritization based on predicted functional impact. The structural impact of the variants and their effects on lncRNA–miRNA interactions were assessed using computational tools. Gene ontology (GO) and KEGG pathway analyses were employed to investigate the affected biological processes and pathways. Results: We identified 4173 unique variants mapped to 258 DE lncRNAs. After prioritization, 5 unique variants in 5 lncRNAs were found to affect lncRNA structure, while 20 variants in 17 lncRNAs were predicted to disrupt miRNA–lncRNA interactions. Enriched pathways included Wnt signaling, phosphatase binding, and cell proliferation, all previously implicated in reproductive health. Conclusions: This study identifies specific variants in DE lncRNAs that may play a role in asthenozoospermia. Given the limited research utilizing WGS to explore the role of noncoding RNAs in male infertility, our findings provide valuable insights and a foundation for future studies.

Insights into the molecular underpinning of type 2 diabetes complications.

Type 2 diabetes (T2D) complications pose a significant global health challenge. Omics technologies have been employed to investigate these complications and identify the biological pathways involved. In this review, we focus on four major T2D complications: diabetic kidney disease, diabetic retinopathy, diabetic neuropathy, and cardiovascular complications. We discuss advancements in omics research, summarizing findings from genetic, epigenomic, transcriptomic, proteomic, and metabolomic studies across different ancestries and disease-relevant tissues. We stress the importance of integrating multi-omics techniques to elucidate the biological mechanisms underlying T2D complications and advocate for ancestrally diverse studies. Ultimately, these insights will improve risk prediction for T2D complications and inform translation strategies.

Investigating the Role of Osteopontin (OPN) in the Progression of Breast, Prostate, Renal and Skin Cancers

Background/Objectives: Cancer is caused by disruptions in the homeostatic state of normal cells, which results in dysregulation of the cell cycle, and uncontrolled growth and proliferation in affected cells to form tumors. Successful development of tumorous cells proceeds through the activation of pathways promoting cell development and functionality, as well as the suppression of immune signaling pathways; thereby providing these cells with proliferative advantages, which subsequently metastasize into surrounding tissues. These effects are primarily caused by the upregulation of oncogenes, of which SPP1 (secreted phosphoprotein 1), a non-collagenous bone matrix protein, is one of the most well-known. Methods: In this study, we conducted a further examination of the transcriptomic expression profile of SPP1 (Osteopontin) during the progression of cancer in four human tissues, breast, prostate, renal and skin, in order to understand the circumstances conducive to its activation and dysregulation, the biological pathways and other mechanisms involved as well as differences in its splicing patterns influencing its expression and functionality. Results: A significant overexpression of SPP1, as well as a set of other highly correlated genes, was seen in most of these tissues, indicating their extensive implication in cancer. Increased expression was observed with higher tumor stages, especially in renal and skin cancer, while applying therapeutic modalities targeting these genes dampened this effect in breast, prostate and skin cancer. Pathway analyses showed gene signatures related to cell growth and development enriched in tumorigenic conditions and earlier cancer stages, while later stages of cancer showed pathways associated with weakened immune response, in all cancers studied. Moreover, the utilization of therapeutic methods showed the activation of immunogenic pathways in breast, prostate and skin cancer, thereby confirming their viability. Further analyses of differential transcript expression levels in these oncogenes showed their exonic regions to be selectively overexpressed similarly in tumorigenic samples in all cancers studied, while also displaying significant differences in exon selectivity between constituent transcripts, providing a basis for their high degree of multifunctionality in cancer. Conclusions: Overall, this study corroborates the entrenched role of SPP1 in the progression of these four types of cancer, as confirmed by its overexpression and activation of related oncogenes, their co-involvement in key cellular pathways, and predisposition to exhibit differential splicing between their transcripts, while the above effects were found to be highly inhibitable through treatment methods, thereby highlighting its promising role in therapeutic development.

Gene Expression Profiles Reveal Distinct Mechanisms Driving Chronic Obstructive Pulmonary Disease Exacerbations

Chronic obstructive pulmonary disease (COPD) exacerbations are major contributors to morbidity and mortality, highlighting the need to better understand their molecular mechanisms to improve prevention, diagnosis, and treatment. This study investigated differential gene expression profiles and key biological processes in COPD exacerbations categorized based on sputum microbiome profiling. An observational study was performed on a cohort of 16 COPD patients, who provided blood and sputum samples during exacerbations, along with five stable-state samples as controls. Exacerbations were classified using 16S rRNA sequencing to analyze the sputum microbiota and multiplex PCR to detect respiratory viruses. Blood transcriptomic profiling was conducted using Oxford Nanopore technology, followed by differential gene expression and pathway enrichment analyses. A total of 768 regulated genes were identified across the exacerbation groups, with 35 shared genes associated with neutrophil activation. Bacterial exacerbations activated pathways related to phagocytosis and toll-like receptor signaling, while viral exacerbations were linked to pro-inflammatory responses and mitochondrial damage. Exacerbations of unknown origin showed activation of pathways involved in protozoan defense and neutrophilic asthma. Biomarkers such as IFITM3 and ISG15 for bacterial exacerbations, DEFA3 for viral, and CD47 for unknown-origin exacerbations were identified. These findings highlight distinct transcriptomic profiles and biological pathways in COPD exacerbations, emphasizing the central role of neutrophil-driven inflammation and identifying potential biomarkers for improved differential diagnosis and personalized management.

Identification of a global gene expression signature associated with the genetic risk of catastrophic fracture in iPSC-derived osteoblasts from Thoroughbred horses.

Bone fractures are a significant problem in Thoroughbred racehorses. The risk of fracture is influenced by both genetic and environmental factors. To determine the biological processes that are affected in genetically susceptible horses, we utilised polygenic risk scoring to establish induced pluripotent stem cells (iPSCs) from horses at high and low genetic risk. RNA-sequencing on iPSC-derived osteoblasts revealed 112 genes that were significantly differentially expressed. Forty-three of these genes have known roles in bone, 27 are not yet annotated in the equine genome and 42 currently have no described role in bone. However, many of the proteins encoded by the known and unknown genes have reported interactions. Functional enrichment analyses revealed that the differentially expressed genes were overrepresented in processes regulating the extracellular matrix and pathways known to be involved in bone remodelling and bone diseases. Gene set enrichment analysis also detected numerous biological processes and pathways involved in glycolysis with the associated genes having a higher expression in the iPSC-osteoblasts from horses with low polygenic risk scores for fracture. Therefore, the differentially expressed genes may be relevant for maintaining bone homeostasis and contribute to fracture risk. A deeper understanding of the consequences of mis-regulation of these genes and the identification of the DNA variants which underpin their differential expression may reveal more about the molecular mechanisms which are involved in equine bone health and fracture risk.

Exploring potential therapeutic targets for small cell lung cancer based on transcriptomics combined with Mendelian randomization analysis

ObjectiveThe main objective of this study was to explore and identify new genetic targets in small-cell lung cancer (SCLC) through transcriptomics analysis and Mendelian randomization (MR) analysis, which will help in the subsequent development of new therapeutic interventions.MethodsIn this study, we extracted the SCLC dataset from the Gene Expression Omnibus (GEO) database, processed the data, and screened out differentially expressed genes (DEGs) using R software. Based on expression quantitative trait loci data and the genome-wide association study data of SCLC, MR analysis was used to screen the genes closely related to SCLC disease, which intersect with DEGs to obtain co-expressed genes (CEGs), and the biological functions and pathways of CEGs were further explored by enrichment analysis. In addition, the CIBERSORT algorithm was applied to assess the level of immune cell infiltration in SCLC and to analyze the correlation between CEGs and immune cells. Meanwhile, we performed a survival analysis on these five CEGs using an independent cohort of SCLC patients. Finally, the results for the target genes were validated.ResultsIn this study, 857 DEGs were identified, including 443 up-regulated and 414 down-regulated genes, and 5 CEGs (PSAT1, PSRC1, COLEC12, PLLP, HP) that were significantly associated with SCLC were identified through further intersecting. The results of enrichment analyses indicated that CEGs play important roles in several key functions and pathways. Immune-cell-related analysis revealed the unique distribution of immune cell infiltration in SCLC and the mechanism of immune cell regulation by CEGs. Survival analysis results indicated that PSRC1 was significantly correlated with the overall survival of SCLC, and the survival rate of the high-expression group was markedly lower than that of the low-expression group. Finally, the consistency of the results between the validation group analyses and MR analysis confirmed that the results of this study is reliable.ConclusionThe CEGs and their associated functions and pathways screened in this study may be potential targets of therapeutic intervention in SCLC by targeting specific molecular pathways.

MiRNAs as major players in brain health and disease: current knowledge and future perspectives

Abstract MicroRNAs are regulators of gene expression and their dysregulation can lead to various diseases. MicroRNA-135 (MiR-135) exhibits brain-specific expression, and performs various functions such as neuronal morphology, neural induction, and synaptic function in the human brain. Dysfunction of miR-135 has been reported in brain tumors, and neurodegenerative and neurodevelopmental disorders. Several reports show downregulation of miR-135 in glioblastoma, indicating its tumor suppressor role in the pathogenesis of brain tumors. In this review, by performing in silico analysis of molecular targets of miR-135, we reveal the significant pathways and processes modulated by miR-135. We summarize the biological significance, roles, and signaling pathways of miRNAs in general, with a focus on miR-135 in different neurological diseases including brain tumors, and neurodegenerative and neurodevelopmental disorders. We also discuss methods, limitations, and potential of glioblastoma organoids in recapitulating disease initiation and progression. We highlight the promising therapeutic potential of miRNAs as antitumor agents for aggressive human brain tumors including glioblastoma.

The role of VANGL2 in glioma oncogenesis and progression: insights into expression profiles and prognostic relevance

IntroductionThe Wnt/planar cell polarity (PCP) signaling pathway is pivotal in regulating various biological processes such as early embryonic development, neural crest cell migration, and cancer invasion. Despite advances in understanding the role of Wnt/PCP pathway dysregulation in tumorigenesis, numerous unanswered questions remain. Our study focused on VANGL2, a core PCP gene, to elucidate its potential mechanistic involvement in cancer development.MethodsA systematic analysis was conducted to assess VANGL2 expression patterns at both transcriptional and proteomic levels. Functional enrichment analysis was performed to investigate the biological pathways associated with VANGL2 in glioma. In vitro experiments were conducted to assess the impact of VANGL2 on glioma cell behaviors. Furthermore, rigorous methodologies were employed in survival analysis to control for confounding factors.ResultsWe identified substantial upregulation of VANGL2 gene in both low-grade glioma (LGG) and glioblastoma (GBM). Functional enrichment analysis of genes positively associated with VANGL2 in glioma underscored their enrichment in Notch signaling and pathway regulating pluripotency of stem cells. In vitro experiments further confirmed that VANGL2 promotes glioma cell migration, invasion, proliferation, and tumor sphere formation. We identified a significant correlation between increased VANGL2 expression and IDH mutation in glioma patients. Elevated VANGL2 expression was identified as a predictor of poor prognosis in glioma.ConclusionOur analysis of the expression and prognostic features of the core PCP gene VANGL2 underscored its critical roles in glioma oncogenesis and progression.

Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor’s genetic complexity and heterogeneity.MethodsThis study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM. Five publicly available gene expression datasets were analyzed to identify differentially expressed genes (DEGs) associated with GBM. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify GBM-related gene modules. Further, gene set enrichment and variation analyses were conducted to explore the biological pathways involved. The machine learning models were evaluated using Receiver Operating Characteristic (ROC) curves and confusion matrices to assess their predictive accuracy, with the best-performing model validated across external datasets. MR analysis was performed to establish causal relationships between genetically predicted gene expression levels and GBM outcomes.ResultsThe study identified 286 DEGs between GBM and adjacent normal tissues across five datasets. WGCNA highlighted the yellow module as the most relevant to GBM, containing key genes such as KLHL3, FOXO4, and MAP1A. Of the 113 machine learning models tested, Ridge regression achieved the highest area under the curve (AUC) of 0.92, demonstrating robust predictive accuracy. Validation using external datasets confirmed the model's reliability, with a classification accuracy of 89.5% in the training set and 85.3% in the validation sets. MR analysis provided strong evidence of a causal relationship between the expression levels of the identified genes and GBM risk.ConclusionsThis study demonstrates the power of combining machine learning and Mendelian Randomization to uncover novel genetic markers for GBM. The identified genes offer promising potential as biomarkers for GBM diagnosis and therapy, providing new avenues for personalized treatment strategies.

Associations between biomarkers of inflammation and depressive symptoms—potential differences between diabetes types and symptom clusters of depression

Inflammation is a probable biological pathway underlying the relationship between diabetes and depression, but data on differences between diabetes types and symptom clusters of depression are scarce. Therefore, this cross-sectional study aimed to compare associations of a multimarker panel of biomarkers of inflammation with depressive symptoms and its symptom clusters between people with type 1 diabetes (T1D) and type 2 diabetes (T2D). This cross-sectional study combined data from five studies including 1260 participants (n = 706 T1D, n = 454 T2D). Depressive symptoms were assessed using the Center for Epidemiological Studies-Depression Scale (CES-D). Serum levels of 92 biomarkers of inflammation were quantified with proximity extension assay technology. After quality control, 76 biomarkers of inflammation remained for statistical analysis. Associations between biomarkers and depressive symptom scores and clusters (cognitive-affective, somatic, anhedonia) were estimated with multivariable linear regression models. Nine biomarkers were positively associated with depressive symptoms in the total sample (CCL11/eotaxin, CCL25, CDCP1, FGF-21, IL-8, IL-10RB, IL-18, MMP-10, TNFRSF9; all p < 0.05) without interaction by diabetes type. Associations differed for eight biomarkers (pinteraction < 0.05). TNFβ was inversely associated with depressive symptoms in T1D, whereas three biomarkers (GDNF, IL-18R1, LIF-R) were positively associated with depressive symptoms in T2D. For the remaining four biomarkers (CD6, CD244, FGF-5, IFNγ) associations were not significant in either subgroup. Biomarker associations were more pronounced with somatic and anhedonia than with cognitive-affective symptoms. These results indicate that different proinflammatory pathways may contribute to depression in T1D and T2D and that there may be a symptom specificity in the link between subclinical inflammation and depression.