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Glioblastoma stem cells (GSCs) are key drivers of relapse, metastasis, and therapy resistance in glioblastoma due to their adaptability and diversity, which make them challenging to target effectively. This study explores the O-glycosylation in differentiating two key GSC subtypes, CD133 and CD44. We utilized the TCGA dataset of GBM and presented the reproducible bioinformatics analysis for our results. Our profiling showed enriched O-glycosylation signatures in CD44-expressing GBM cells over CD133, with Cosmc, the chaperone for core mucin-type O-glycosylation, significantly upregulated in the CD44-positive group. Moreover, Cosmc was associated with shorter progression-free intervals, suggesting its potential as an indicator of aggressive disease. High Cosmc expression also enriched immune-related pathways, including inflammatory response and antigen presentation, and was associated with presence of myeloid cells, T cells, and NK cells. Additionally, elevated Cosmc correlated with extracellular matrix (ECM) pathways and stromal cell populations, such as perivascular fibroblasts. These findings position O-glycosylation, specially, Cosmc as a promising biomarker for distinguishing GSC subclones, with relevance to immune modulation, and ECM dynamics, identifying it as a potential target for novel GBM therapies.

Glioblastoma multiforme (GBM) represents the deadliest form of brain cancer, characterized by complex interactions within its microenvironment. Despite the understanding of GBM biology, GBM remains highly resistant to any therapy. Therefore, defining innovative biomarkers in GBM can provide insights into tumor biology and potential therapeutic targets. In this study, we explored the potential of GPRC5A to serve as a pertinent biomarker for GBM. We utilized the GBM-TCGA dataset and presented the reproducible bioinformatics analysis for our results. We identified that GPRC5A expression was significantly upregulated in GBM compared to normal tissues, with higher levels correlating with poor overall survival (OS) and progression-free interval (PFI). Moreover, it was associated with key genetic mutations, particularly NF1 and PTEN mutations, and strongly correlated with the mesenchymal stem-like phenotype. GPRC5A was also predominantly associated with aggressive GBM features, including hypoxia, high extracellular matrix (ECM) environments, and extensive stromal and immune infiltrations. Its strong correlation with mesenchymal markers and hypoxic regions underscores its potential as a biomarker and therapeutic target in GBM. These findings provide valuable insights into the role of GPRC5A in GBM pathology and its potential impact as a target for GBM stratifications and treatment strategies.

Given advancements in large-scale data and AI, integrating multimodal artificial intelligence into cancer research can enhance our understanding of tumor behavior by simultaneously processing diverse biomedical data types. In this review, we explore the potential of multimodal AI in comprehending B-cell non-Hodgkin lymphomas (B-NHLs). B-cell non-Hodgkin lymphomas (B-NHLs) represent a particular challenge in oncology due to tumor heterogeneity and the intricate ecosystem in which tumors develop. These complexities complicate diagnosis, prognosis, and therapy response, emphasizing the need to use sophisticated approaches to enhance personalized treatment strategies for better patient outcomes. Therefore, multimodal AI can be leveraged to synthesize critical information from available biomedical data such as clinical record, imaging, pathology and omics data, to picture the whole tumor. In this review, we first define various types of modalities, multimodal AI frameworks, and several applications in precision medicine. Then, we provide several examples of its usage in B-NHLs, for analyzing the complexity of the ecosystem, identifying immune biomarkers, optimizing therapy strategy, and its clinical applications. Lastly, we address the limitations and future directions of multimodal AI, highlighting the need to overcome these challenges for better clinical practice and application in healthcare.

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model’s components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.

Abstract We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective.

Graphical Abstract