Comprehensive analysis of mitochondrial dysfunction and necroptosis in intracranial aneurysms from the perspective of predictive, preventative, and personalized medicine.

Mitochondrial dysfunction and necroptosis are closely associated, and play vital roles in the medical strategy of multiple cardiovascular diseases. However, their implications in intracranial aneurysms (IAs) remain unclear. In this study, we aimed to explore whether mitochondrial dysfunction and necroptosis could be identified as valuable starting points for predictive, preventive, and personalized medicine for IAs. The transcriptional profiles of 75 IAs and 37 control samples were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis, and least absolute shrinkage and selection operator (LASSO) regression were used to screen key genes. The ssGSEA algorithm was performed to establish phenotype scores. The correlation between mitochondrial dysfunction and necroptosis was evaluated using functional enrichment crossover, phenotype score correlation, immune infiltration, and interaction network construction. The IA diagnostic values of key genes were identified using machine learning. Finally, we performed the single-cell sequencing (scRNA-seq) analysis to explore mitochondrial dysfunction and necroptosis at the cellular level. In total, 42 IA-mitochondrial DEGs and 15 IA-necroptosis DEGs were identified. Screening revealed seven  key genes invovled in mitochondrial dysfunction (KMO, HADH, BAX, AADAT, SDSL, PYCR1, and MAOA) and five genes involved in necroptosis (IL1B, CAMK2G, STAT1, NLRP3, and BAX). Machine learning confirmed the high diagnostic value of these key genes for IA. The IA samples showed  higher expression of mitochondrial dysfunction and necroptosis. Mitochondrial dysfunction and necroptosis exhibited a close association. Furthermore, scRNA-seq indicated that mitochondrial dysfunction and necroptosis were preferentially up-regulated in monocytes/macrophages and vascular smooth muscle cells (VSMCs) within IA lesions. In conclusion, mitochondria-induced necroptosis was involved in IA formation, and was mainly up-regulated in monocytes/macrophages and VSMCs within IA lesions. Mitochondria-induced necroptosis may be a novel potential target for diagnosis, prevention, and treatment of IA.

Machine learning-based identification of lower grade glioma stemness subtypes discriminates patient prognosis and drug response.

Glioma stem cells (GSCs) remodel their tumor microenvironment to sustain a supportive niche. Identification and stratification of stemness related characteristics in patients with glioma might aid in the diagnosis and treatment of the disease. In this study, we calculated the mRNA stemness index in bulk and single-cell RNA-sequencing datasets using machine learning methods and investigated the correlation between stemness and clinicopathological characteristics. A glioma stemness-associated score (GSScore) was constructed using multivariate Cox regression analysis. We also generated a GSC cell line derived from a patient diagnosed with glioma and used glioma cell lines to validate the performance of the GSScore in predicting chemotherapeutic responses. Differentially expressed genes (DEGs) between GSCs with high and low GSScores were used to cluster lower-grade glioma (LGG) samples into three stemness subtypes. Differences in clinicopathological characteristics, including survival, copy number variations, mutations, tumor microenvironment, and immune and chemotherapeutic responses, among the three LGG stemness-associated subtypes were identified. Using machine learning methods, we further identified genes as subtype predictors and validated their performance using the CGGA datasets. In the current study, we identified a GSScore that correlated with LGG chemotherapeutic response. Through the score, we also identified a novel classification of the LGG subtype and associated subtype predictors, which might facilitate the development of precision therapy.

LINC00978 regulates metabolic rewiring to promote the malignancy of glioblastoma through AKR1B1.

Glioma is a fatal primary brain tumor. Improved glioma treatment effectiveness depends on a better understanding of its underlying mechanisms. Glioblastoma (GBM), was classified as high-grade glioma with the most lethality and therapeutic resistance. Herein, we reported LINC00978 overexpressed in high-grade gliomas. Down-regulation of LINC00978 in glioblastoma cells inhibited cell proliferation, invasion, migration, and induced apoptosis. In vivo experiments confirmed that the CamK-A siRNA of LINC00978 could effectively inhibit the proliferation of glioblastoma cells. The main pathway and genes regulated by LINC00978 were detected using RNA sequencing to elucidate the molecular mechanism. The results suggest that LINC00978 regulates the expression of genes related to metabolic pathways, including aldo-keto reductase family 1 member B (AKR1B1), which mediates the cytotoxicity of 2-deoxyglucose. LINC00978 positively regulated AKR1B1 expression, and 2-deoxyglucose induced AKR1B1 expression via a LINC00978-dependent mechanism. This research has revealed that LINC00978 promotes the sensitivity of glioblastoma cells to 2DG. LINC00978 is highly expressed in most high-grade glioma patients. Thus, understanding the anticancer mechanism identified in this study may contribute to treating the majority of glioma patients. This study clarified the function and molecular mechanism of LINC00978 in glioblastoma and provided a study basis for LINC00978 to guide the clinical treatment of glioblastoma.

Comprehensive Analysis of Endoplasmic Reticulum Stress in Intracranial Aneurysm.

Background: Aberrant endoplasmic reticulum stress (ERS) plays an important role in multiple cardiovascular diseases. However, their implication in intracranial aneurysms (IAs) remains unclear. We designed this study to explore the general expression pattern and potential functions of ERS in IAs. Methods: Five Gene Expression Omnibus (GEO) microarray datasets were used as the training cohorts, and 3 GEO RNA sequencing (RNA-seq) datasets were used as the validating cohorts. Differentially expressed genes (DEGs), functional enrichment, Lasso regression, logistic regression, ROC analysis, immune cell profiling, vascular smooth muscle cell (VSMC) phenotyping, weighted gene coexpression network analysis (WGCNA), and protein-protein interaction (PPI) analysis were applied to investigate the role of ERS in IA. Finally, we predicted the upstream transcription factor (TF)/miRNA and potential drugs targeting ERS. Results: Significant DEGs were majorly associated with ERS, autophagy, and metabolism. Eight-gene ERS signature and IRE1 pathway were identified during the IA formation. WGCNA showed that ERS was highly associated with a VSMC synthesis phenotype. Next, ERS-VSMC-metabolism-autophagy PPI and ERS-TF-miRNA networks were constructed. Finally, we predicted 9 potential drugs targeting ERS in IAs. Conclusion: ERS is involved in IA formation. Upstream and downstream regulatory networks for ERS were identified in IAs. Novel potential drugs targeting ERS were also proposed, which may delay IA formation and progress.

The Role of m5C-Related lncRNAs in Predicting Overall Prognosis and Regulating the Lower Grade Glioma Microenvironment.

Glioma is the most lethal primary brain tumor with a poor prognosis and high recurrence rate. Enormous efforts have been made to find therapeutic targets for gliomas. In the current study, we identified m5C-related lncRNAs through Pearson correlation analysis by the criteria |R|>0.5 and p<0.001 in TCGA LGG and CGGA325 datasets. We then established an eight-lncRNA m5C-related prognostic signature (m5C LPS) through lasso cox regression analysis and multivariate analysis. The performance of the signature was confirmed in the CGGA325 dataset and evaluated in differential subgroups divided by relevant clinicopathological characteristics. Patients were then divided into high and low risk groups using risk scores calculated with the signature. Next, we performed GO, KEGG and gene set enrichment analysis (GSEA) and identified the m5C LPS to be related with glioma microenvironment, immune response, EMT, cell cycle, and hypoxia. Correlation of the risk groups with immune cell infiltration, somatic mutation, and CNVs was then explored. Responses to immuno- and chemotherapies in different risk groups were evaluated using submap and pRRophetic R packages respectively. The high-risk group was more sensitive to anti-CTLA4 therapy and to compounds including Temozolomide, Bleomycin, Cisplatin, Cyclopamine, A.443654 (Akt inhibitor), AZD6482 (PI3K inhibitor), GDC0941(PI3K inhibitor), and metformin. We present for the first time a m5C-related lncRNA signature for lower grade glioma patient prognosis and therapy response prediction with validated performance, providing a promising target for future research.

HMOX1 promotes lung adenocarcinoma metastasis by affecting macrophages and mitochondrion complexes.

Background: Metastasis is the leading cause of lung adenocarcinoma (LUAD) patient death. However, the mechanism of metastasis is unclear. We performed bioinformatic analyses for HMOX1 (Heme oxygenase-1), aiming to explore its role in LUAD metastasis. Methods: Pan-cancer analysis was first used to identify the metastasis-associated role of HMOX1 in LUAD. HMOX1-related genomic alterations were then investigated. Based on functional enrichment, we systematically correlated HMOX1 with immunological characteristics and mitochondrial activities. Furthermore, weighted gene co-expression network analysis (WGCNA) was applied to construct the HMOX1-mediated metastasis regulatory network, which was then validated at the proteomic level. Finally, we conducted the survival analysis and predicted the potential drugs to target the HMOX1 network. Results: HMOX1 expression was significantly associated with epithelial-mesenchymal transition (EMT) and lymph and distant metastasis in LUAD. High HMOX1 levels exhibited higher macrophage infiltration and lower mitochondrial complex expression. WGCNA showed a group of module genes co-regulating the traits mentioned above. Subsequently, we constructed an HMOX1-mediated macrophage-mitochondrion-EMT metastasis regulatory network in LUAD. The network had a high inner correlation at the proteomic level and efficiently predicted prognosis. Finally, we predicted 9 potential drugs targeting HMOX1-mediated metastasis in LUAD, like chloroxine and isoliquiritigenin. Conclusions: Our analysis elaborates on the role of HMOX1 in LUAD metastasis and identified a highly prognostic HMOX1-mediated metastasis regulatory network. Novel potential drugs targeting the HMOX1 network were also proposed, which should be tested for their activity against LUAD metastasis in future studies.

A Cuproptosis Activation Scoring model predicts neoplasm-immunity interactions and personalized treatments in glioma.

Gliomas are malignant tumors in the central nervous system. Cuproptosis is a newly discovered cell death mechanism targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that cuproptosis participates in tumor progression, but its role in gliomas is still elusive. Here, we systematically explored the bulk-tumor and single-cell transcriptome data to reveal its role in gliomas. The cuproptosis activity score (CuAS) was constructed based on cuproptosis-related genes, and machine learning techniques validated the score stability. High CuAS gliomas were more likely to have a poor prognosis and an aggressive mesenchymal (MES) subtype. Subsequently, the SCENIC algorithm predicted 20 CuAS-related transcription factors (TFs) in gliomas. Function enrichment and microenvironment analyses found that CuAS was associated with tumor immune infiltration. Accordingly, intercellular communications between neoplasm and immunity were explored by the R package "Cellchat". Five signaling pathways and 8 ligand-receptor pairs including ICAM1, ITGAX, ITGB2, ANXA1-FRR1, and the like, were identified to suggest how cuproptosis activity connected neoplastic and immune cells. Critically, 13 potential drugs targeting high CuAs gliomas were predicted according to the CTRP and PRISM databases, including oligomycin A, dihydroartemisinin, and others. Taken together, cuproptosis is involved in glioma aggressiveness, neoplasm-immune interactions, and may be used to assist in drug selection.

Outcome of Endoscopic Transsphenoidal Surgery for Recurrent or Residual Pituitary Adenomas and Comparison to Non-Recurrent or Residual Cohort by Propensity Score Analysis.

Objective: To evaluate the long-term outcomes and safety of endoscopic transsphenoidal surgery (ETS) in recurrent and residual pituitary adenomas (rrPAs), as well as the predictors of gross total resection (GTR) and intraoperative CSF leakage. Furthermore, to compare outcomes and complications with non-rrPAs cohort. Methods: Clinical and radiological characteristics of patients with rrPAs who underwent ETS were collected between 2017 and 2020. Data of patients with non-rrPAs were collected from 2019 to 2020. Logistic regression analyses were performed to investigate the factors influencing gross total resection (GTR) and intraoperative CSF leakage. Between-group comparisons of outcomes and complications were performed through propensity score analysis. Results: We enrolled 73 patients with rrPAs. GTR was achieved in 41 (56.1%) cases; further, GTR or near-total resection was achieved in 93.2% of patients. The mean tumor volumes for GTR and non-GTR cases were 6.2 ±7.2 cm3 and 11.1 ±9.1 cm3, respectively. Multivariate regression analysis of the GTR rate in patients with rrPAs revealed that Knosp grade was an independent factor (odds ratio [OR] = 0.324; p=0.005). Moreover, previous transcranial surgery and non-functional pituitary adenomas were risk factors for intraoperative CSF leakage in patients with rrPAs (OR=6.450, p=0.019 and OR=7.472, p=0.012, respectively). After propensity score matching, There was no significant difference in the GTR rate between patients with rrPAs and patients with non-rrPAs. Contrastingly, patients with rrPAs had a higher rate of intraoperative CSF leakage and longer postoperative hospital stay than patients with non-rrPAs. During the follow-up, vision improved in 9 (22.0%) and 24 (62.5%) patients with rrPAs and non-rrPAs, respectively. Although there was a trend that reoperation of rrPAs involved a lower hypopituitarism recovery rate and biochemical remission rate, as well as a higher hypopituitarism rate, there was no statistically significant between-group difference. Conclusions: Knosp grade was an independent factor for GTR in endoscopic transsphenoidal surgery in patients with rrPAs. Previous transcranial surgery and non-functional PAs were risk factors for intraoperative CSF leakage. Although associated with longer hospital stay, rrPAs did not associate with lower GTR rate or more frequent postoperative complications than non-rrPAs cohort.

CDH6 as a prognostic indicator and marker for chemotherapy in gliomas.

Glioma is the most malignant cancer of the central nervous system. There are various therapies for treating gliomas, but their outcomes are not satisfactory. Therefore, new targets for glioma treatment are needed. This study examined the cadherin-6 (CDH6) expression in gliomas using The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets. CDH6 expression positively correlated with the World Health Organization (WHO) tumor grade and negatively correlated with patient prognosis. A significant decrease in CDH6 promoter methylation was identified with an increase in the WHO grade severity. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that CDH6 might be involved in cell-cell interactions and immune processes in the glioma microenvironment. Weighted gene co-expression network analysis revealed a correlation between CDH6 and cell adhesion molecules, focal adhesions, phosphatidylinositol 3-kinase-protein kinase B signaling pathways, nuclear division, chromosome segregation, mitotic nuclear division, and immune-related pathways. CDH6 strongly correlated with immunosuppressive cells, including regulatory T cells, monocytes, macrophages, tumor-associated macrophages, and myeloid-derived suppressor cells. It also showed correlations with immune-active cells such as B cells, CD8+ T cells, and dendritic cells. Single-cell analysis showed that CDH6 was expressed mainly in astrocyte (AC)-like malignant cells. Differentially expressed genes of AC-like malignant cells were found to be associated with stress response, membranous processes, viral infections, and several types of cancers. Potential drugs associated with high CDH6 expression were also predicted, including AMG-22, rutin, CCT128930, deforolimus, bis(maltolato)oxovanadium, anagrelide, vemurafenib, CHIR-98014, and AZD5582. Thus, this study showed that CDH6 correlates with glioma immune infiltration, it is expressed mainly in AC-like malignant cells, and it may act as a new target for glioma therapy.

Glioma stem cell signature predicts the prognosis and the response to tumor treating fields treatment.

INTRODUCTION: Glioma stem cells (GSCs) play an important role in glioma recurrence and chemo-radiotherapy (CRT) resistance. Currently, there is a lack of efficient treatment approaches targeting GSCs. This study aimed to explore the potential personalized treatment of patients with GSC-enriched gliomas. METHODS: Single-cell RNA sequencing (scRNA-seq) was used to identify the GSC-related genes. Then, machine learning methods were applied for clustering and validation. The least absolute shrinkage and selection operator (LASSO) and COX regression were used to construct the risk scores. Survival analysis was performed. Additionally, the incidence of chemo-radiotherapy resistance, immunotherapy status, and tumor treating field (TTF) therapy response were evaluated in high- and low-risk scores groups. RESULTS: Two GSC clusters exhibited significantly different stemness indices, immune microenvironments, and genomic alterations. Based on GSC clusters, 11-gene GSC risk scores were constructed, which exhibited a high predictive value for prognosis. In terms of therapy, patients with high GSC risk scores had a higher risk of resistance to chemotherapy. TTF therapy can comprehensively inhibit the malignant biological characteristics of the high GSC-risk-score gliomas. CONCLUSION: Our study constructed a GSC signature consisting of 11 GSC-specific genes and identified its prognostic value in gliomas. TTF is a promising therapeutic approach for patients with GSC-enriched glioma.