Integration and Comparison of Transcriptomic and Proteomic Data for Meningioma

Dunn, Jemma; Lenis, Vasileios Panagiotis; Hilton, David A.; Warta, Rolf; Herold‐Mende, Christel; Hanemann, C. Oliver; Futschik, Matthias E.

doi:10.3390/cancers12113270

Cited by 12 publications

(17 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Different tumour entities, including brain and other solid tumours, have shown numerous metabolic alterations. A strong association between altered tumourmetabolism and chromosomal instability has been reported [8]. These metabolic alterations were detected independently of the WHO grade [9,10].…”

Section: Introductionmentioning

confidence: 90%

Metabolic alterations in meningioma reflect the clinical course

et al. 2021

View full text Add to dashboard Cite

Background Meningiomas are common brain tumours that are usually defined by benign clinical course. However, some meningiomas undergo a malignant transformation and recur within a short time period regardless of their World Health Organization (WHO) grade. The current study aimed to identify potential markers that can discriminate between benign and malignant meningioma courses. Methods We profiled the metabolites from 43 patients with low- and high-grade meningiomas. Tumour specimens were analyzed by nuclear magnetic resonance analysis; 270 metabolites were identified and clustered with the AutoPipe algorithm. Results We observed two distinct clusters marked by alterations in glycine/serine and choline/tryptophan metabolism. Glycine/serine cluster showed significantly lower WHO grades and proliferation rates. Also progression-free survival was significantly longer in the glycine/serine cluster. Conclusion Our findings suggest that alterations in glycine/serine metabolism are associated with lower proliferation and more recurrent tumours. Altered choline/tryptophan metabolism was associated with increases proliferation, and recurrence. Our results suggest that tumour malignancy can be reflected by metabolic alterations, which may support histological classifications to predict the clinical outcome of patients with meningiomas.

show abstract

Section: Introductionmentioning

confidence: 90%

Metabolic alterations in meningioma reflect the clinical course

et al. 2021

View full text Add to dashboard Cite

show abstract

“…We have also observed an up regulation of CST3 in meningioma tumour samples when compared to control tissues. CST3 is an inhibitor of cysteine proteases and has been reported to have a positive alteration in high-grade meningiomas (39).…”

Section: Discussionmentioning

confidence: 99%

Multiple Reaction Monitoring-Based Targeted Assays for the Validation of Protein Biomarkers in Brain Tumors

et al. 2021

View full text Add to dashboard Cite

The emergence of omics technologies over the last decade has helped in advancement of research and our understanding of complex diseases like brain cancers. However, barring genomics, no other omics technology has been able to find utility in clinical settings. The recent advancements in mass spectrometry instrumentation have resulted in proteomics technologies becoming more sensitive and reliable. Targeted proteomics, a relatively new branch of mass spectrometry-based proteomics has shown immense potential in addressing the shortcomings of the standard molecular biology-based techniques like Western blotting and Immunohistochemistry. In this study we demonstrate the utility of Multiple reaction monitoring (MRM), a targeted proteomics approach, in quantifying peptides from proteins like Apolipoprotein A1 (APOA1), Apolipoprotein E (APOE), Prostaglandin H2 D-Isomerase (PTGDS), Vitronectin (VTN) and Complement C3 (C3) in cerebrospinal fluid (CSF) collected from Glioma and Meningioma patients. Additionally, we also report transitions for peptides from proteins – Vimentin (VIM), Cystatin-C (CST3) and Clusterin (CLU) in surgically resected Meningioma tissues; Annexin A1 (ANXA1), Superoxide dismutase (SOD2) and VIM in surgically resected Glioma tissues; and Microtubule associated protein-2 (MAP-2), Splicing factor 3B subunit 2 (SF3B2) and VIM in surgically resected Medulloblastoma tissues. To our knowledge, this is the first study reporting the use of MRM to validate proteins from three types of brain malignancies and two different bio-specimens. Future studies involving a large cohort of samples aimed at accurately detecting and quantifying peptides of proteins with roles in brain malignancies could potentially result in a panel of proteins showing ability to classify and grade tumors. Successful application of these techniques could ultimately offer alternative strategies with increased accuracy, sensitivity and lower turnaround time making them translatable to the clinics.

show abstract

“…This study utilized genomic datasets from Gene Expression Omnibus. The data used in this study consisted of the series GSE74385 [46, 47] for meningioma tumor classification, GSE31095 [48] for glioma tumors, GSE4488 [49] for pituitary tumors, GSE63063 [50] for MCI classification, GSE6613 [51, 52] for PD classification, and GSE4226 [53, 54] for AD classification. Each gene expression study was used to find differentially expressed genes that would serve as blood biomarkers for diagnosing the specific neurological disorder.…”

Section: Methodsmentioning

confidence: 99%

NeuroXNet: Creating A Novel Deep Learning Architecture that Diagnoses Neurological Disorders, Finds New Blood Biomarkers, and Assesses Surgical, Drugs, and Radiation Treatment Plans Using Medical Imaging and Genomic Data

Mishra¹

2021

Preprint

View full text Add to dashboard Cite

Neurodegenerative diseases and cancerous brain tumors cause millions of patients worldwide to be fatally ill and face cognitive impairment each year. Current diagnosis and treatment of these neurological conditions take many days, are sometimes inaccurate, and use invasive approaches that could endanger the patient′ s life. Thus, this study′ s purpose is the creation of a novel deep learning model called NeuroXNet, which uses MRI images and genomic data to diagnose both neurodegenerative diseases like Alzheimer′ s disease, Parkinson′ s disease, and Mild Cognitive Impairment as well as cancerous brain tumors, including glioma, meningioma, and pituitary tumors. Moreover, the model helps find novel blood biomarkers of differentially expressed genes to aid in diagnosing the six neurological conditions. Furthermore, the model uses patient genomic data to give additional recommendations for treatment plans that include various treatment approaches, including surgical, radiation, and drugs for higher patient survival for each class of the disease. The NeuroXNet model achieves a training accuracy of 99.70%, a validation accuracy of 100%, and a test accuracy of 94.71% in multi-class classification of the six diseases and normal patients. Thus, NeuroXNet reduces the chances of misdiagnosis, helps give the best treatment options, and does so in a time/cost-efficient manner. Moreover, NeuroXNet efficiently diagnoses diseases and recommends treatment plans based on patient data using relatively few parameters causing it to be more cost and time-efficient in providing non-invasive approaches to diagnosis and treatment for neurological disorders than current procedures.

show abstract

Integration and Comparison of Transcriptomic and Proteomic Data for Meningioma

Cited by 12 publications

References 74 publications

Metabolic alterations in meningioma reflect the clinical course

Metabolic alterations in meningioma reflect the clinical course

Multiple Reaction Monitoring-Based Targeted Assays for the Validation of Protein Biomarkers in Brain Tumors

NeuroXNet: Creating A Novel Deep Learning Architecture that Diagnoses Neurological Disorders, Finds New Blood Biomarkers, and Assesses Surgical, Drugs, and Radiation Treatment Plans Using Medical Imaging and Genomic Data

Contact Info

Product

Resources

About