Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma

Construction and validation of a TAMRGs prognostic signature for gliomas by integrated analysis of scRNA and bulk RNA sequencing data

BACKGROUND

This study aimed to construct and validate a prognostic model based on tumor associated macrophage-related genes (TAMRGs) by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data.

METHODS

The scRNA-seq data of three inhouse glioma tissues were used to identify the tumor-associated macrophages (TAMs) marker genes, the DEGs from the The Cancer Genome Atlas (TCGA) - Genotype-Tissue Expression (GTEx) dataset were used to further select TAMs marker genes. Subsequently, a TAMRG-score was constructed by Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis in the TCGA dataset and validated in the Chinese Glioma Genome Atlas (CGGA) dataset.

RESULTS

We identified 186 TAMs marker genes, and a total of 6 optimal prognostic genes including CKS2, LITAF, CTSB, TWISTNB, PPIF and G0S2 were selected to construct a TAMRG-score. The high TAMRG-score was significantly associated with worse prognosis (log-rank test, P<0.001). Moreover, the TAMRG-score outperformed the other three models with AUC of 0.808. Immune cell infiltration, TME scores, immune checkpoints, TMB and drug susceptibility were significantly different between TAMRG-score groups. In addition, a nomogram were constructed by combing the TAMRG-score and clinical information (Age, Grade, IDH mutation and 1p19q codeletion) to predict the survival of glioma patients with AUC of 0.909 for 1-year survival.

CONCLUSION

The high TAMRG-score group was associated with a poor prognosis. A nomogram by incorporating TMARG-score could precisely predict glioma survival, and provide evidence for personalized treatment of glioma.

The molecular landscape of neurological disorders: insights from single-cell RNA sequencing in neurology and neurosurgery

Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. In brain tumours, scRNA-seq has provided valuable insights into cancer heterogeneity, the tumour microenvironment, treatment resistance, and invasion patterns. It has also elucidated the brain tri-lineage cancer hierarchy and addressed limitations of current models. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have been molecularly subtyped, dysregulated pathways have been identified, and potential therapeutic targets have been revealed using scRNA-seq. In epilepsy, scRNA-seq has explored the cellular and molecular heterogeneity underlying the condition, uncovering unique glial subpopulations and dysregulation of the immune system. ScRNA-seq has characterised distinct cellular constituents and responses to spinal cord injury in spinal cord diseases, as well as provided molecular signatures of various cell types and identified interactions involved in vascular remodelling. Furthermore, scRNA-seq has shed light on the molecular complexities of cerebrovascular diseases, such as stroke, providing insights into specific genes, cell-specific expression patterns, and potential therapeutic interventions. This review highlights the potential of scRNA-seq in guiding precision medicine approaches, identifying clinical biomarkers, and facilitating therapeutic discovery. However, challenges related to data analysis, standardisation, sample acquisition, scalability, and cost-effectiveness need to be addressed. Despite these challenges, scRNA-seq has the potential to transform clinical practice in neurological and neurosurgical research by providing personalised insights and improving patient outcomes.

Comparative oncology: overcoming human cancer through companion animal studies

Comparative oncology is a field of study that has been recently adopted for studying cancer and developing cancer therapies. Companion animals such as dogs can be used to evaluate novel biomarkers or anticancer targets before clinical translation. Thus, the value of canine models is increasing, and numerous studies have been conducted to analyze similarities and differences between many types of spontaneously occurring cancers in canines and humans. A growing number of canine cancer models as well as research-grade reagents for these models are becoming available, leading to substantial growth in comparative oncology research spanning from basic science to clinical trials. In this review, we summarize comparative oncology studies that have been conducted on the molecular landscape of various canine cancers and highlight the importance of the integration of comparative biology into cancer research.

Folate enzyme MTHFD2 links one-carbon metabolism to unfolded protein response in glioblastoma

The mitochondrial folate enzyme methylenetetrahydrofolate dehydrogenase/cyclohydrolase (MTHFD2) has shown oncogenic roles in various cancers and may have non-metabolic functions. This study investigated the role of MTHFD2 in glioblastoma pathogenesis. We find that MTHFD2 expression is enriched in gliomas by analysing public databases and clinical specimens. RNA interference (RNAi) and inhibitor of MTHFD2 hamper the proliferation of glioblastoma and induce apoptosis in cell lines, glioma stem-like cells (GSCs) and patient-derived xenografts (PDX). Metabolomic analyses show that MTHFD2 depletion suppresses the central carbon metabolic pathways, including glycolysis, the pentose phosphate pathway (PPP), and the tricarboxylic acid (TCA) cycle. GSEA reveals a novel non-metabolic function of MTHFD2 in association with the unfolded protein response (UPR). MTHFD2 depletion activates the PERK/eIF2α axis which contributes to translation inhibition and apoptosis; these effects are attenuated by a PERK inhibitor. Mechanistically, MTHFD2 may be linked to UPR via the post-transcriptionally regulation of chaperone protein GRP78. In conclusion, MTHFD2 could be a promising therapeutic target for glioblastoma. Besides its canonical role, MTHFD2 may contribute to glioblastoma pathogenesis via UPR, highlighting a newly identified functional link between one-carbon metabolism and cell stress response.

BUB1 Is Identified as a Potential Therapeutic Target for Pancreatic Cancer Treatment

Pancreatic cancer is one of the most challenging cancer types in clinical treatment worldwide. This study aimed to understand the tumorigenesis mechanism and explore potential therapeutic targets for patients with pancreatic cancer. Single-cell data and expression profiles of pancreatic cancer samples and normal tissues from multiple databases were included. Comprehensive bioinformatics analyses were applied to clarify tumor microenvironment and identify key genes involved in cancer development. Immense difference of cell types was shown between tumor and normal samples. Four cell types (B cell_1, B cell_2, cancer cell_3, and CD1C+_B dendritic cell_3) were screened to be significantly associated with prognosis. Three ligand-receptor pairs, including CD74-MIF, CD74-COPA, and CD74-APP, greatly contributed to tumorigenesis. High expression of BUB1 (BUB1 Mitotic Checkpoint Serine/Threonine Kinase) was closely correlated with worse prognosis. CD1C+_B dendritic cell_3 played a key role in tumorigenesis and cancer progression possibly through CD74-MIF. BUB1 can serve as a prognostic biomarker and a therapeutic target for patients with pancreatic cancer. The study provided a novel insight into studying the molecular mechanism of pancreatic cancer development and proposed a potential strategy for exploiting new drugs.