ArticleFrontiers in oncology2022
A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Multi-Omics Integration for Advancing Glioma Precision Medicine.Annals of clinical and translational neurology · 2026Review
- Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes.Biology · 2022Article
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14 authors.
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Abstract
Background: Lower-grade gliomas (LGGs) are characterized by remarkable genetic heterogeneity and different clinical outcomes. Classification of LGGs is improved by the development of molecular stratification markers including IDH mutation and 1p/19q chromosomal integrity, which are used as a hallmark of survival and therapy sensitivity of LGG patients. However, the reproducibility and sensitivity of the current classification remain ambiguous. This study aimed to construct more accurate risk-stratification approaches. Methods: According to bioinformatics, the sequencing profiles of methylation and transcription and imaging data derived from LGG patients were analyzed and developed predictable risk score and radiomics score. Moreover, the performance of predictable models was further validated. Results: In this study, we determined a cluster of 6 genes that were correlated with IDH mutation/1p19q co-deletion status. Risk score model was calculated based on 6 genes and showed gratifying sensitivity and specificity for survival prediction and therapy response of LGG patients. Furthermore, a radiomics risk score model was established to noninvasively assist judgment of risk score in pre-surgery. Taken together, a predictable nomogram that combined transcriptional signatures and clinical characteristics was established and validated to be preferable to the histopathological classification. Our novel multi-omics nomograms showed a satisfying performance. To establish a user-friendly application, the nomogram was further developed into a web-based platform: https://drw576223193.shinyapps.io/Nomo/, which could be used as a supporting method in addition to the current histopathological-based classification of gliomas. Conclusions: Our novel multi-omics nomograms showed the satisfying performance of LGG patients and assisted clinicians to draw up individualized clinical management.
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