A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients

Wei Wu; Yichang Wang; Jianyang Xiang; Xiaodong Li; Alafate Wahafu; Xiao Yu; Xiaobin Bai; Ge Yan; Chunbao Wang; Ning Wang; Changwang Du; Wanfu Xie; Maode Wang; Jia Wang

doi:10.3389/fonc.2022.729002

A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients

Front Oncol. 2022 May 12:12:729002. doi: 10.3389/fonc.2022.729002. eCollection 2022.

Authors

Wei Wu^{1

2}, Yichang Wang^{1

2}, Jianyang Xiang^{1

2}, Xiaodong Li^{1

2}, Alafate Wahafu^{1

2}, Xiao Yu¹, Xiaobin Bai¹, Ge Yan³, Chunbao Wang⁴, Ning Wang¹, Changwang Du¹, Wanfu Xie¹, Maode Wang^{1

2}, Jia Wang^{1

2}

Affiliations

¹ Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
² Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
³ Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
⁴ Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

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.

Keywords: LGG; multi-omics analysis; nomogram; prediction; radiomics.