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

Abstract

BackgroundLower-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.MethodsAccording 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.ResultsIn 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.ConclusionsOur novel multi-omics nomograms showed the satisfying performance of LGG patients and assisted clinicians to draw up individualized clinical management.

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

“…A deeper analysis of existing survival predictors reveals that among the 74 studies 54 utilized publicly accessible data from three key databases: the Cancer Genome Atlas Program (TCGA) 17 , NCI Genomic Data Commons (GDC) 18 , and the Gene Expression Omnibus (GEO) 31, 32, 72, 73, 80, 82, 87, 90, 91, 130, 131 . Apart from public databases, there also exist private databases that have been utilized in existing survival prediction studies 66,75,81,112,113,117,118 .…”

“…Table 8 provides information about 44 diseases and the corresponding survival prediction algorithms utilized in these diseases. A deeper analysis of Table 8 shows that Cox-PH and lasso Cox-PH models have been extensively utilized for disease specific survival prediction i.e., ASCVD 29,111 , bladder cancer 40,82 , colorectal cancer 74–77 , hepatocellular carcinoma 43,86,87 , ovarian cancer 88–90,103 , lung adenocarcinoma 101 , heart failure 118 , HER2-negative metastatic breast cancer 67 , pancreatic cancer 26,71 , trauma 120 , nasopharyngeal carcinoma 66 , triple-negative breast cancer 68 , lymphoma 85 , breast cancer 40,81,82 , ovarian cancer 88–90,103 , and lower-grade glioma 80 , cardiovascular disease 112,114–117 , invasive ductal carcinoma 70 , liver transplantation 119 , gastric cancer 42 , lung cancer 27 , esophageal squamous cell carcinoma 79 , glioma 69 , and liver cancer 41 . RSF has been employed in 13 studies for 6 diseases namely, ASCVD 29 , bladder cancer 82 , gastrointestinal cancer 30 , cervical cancer 73 , liver transplantation 119 , and heart failure 118 .…”

“…Univariate analyses, including ANOVA, chi-squared, and univariate Cox regression, have been prevalent in diseases such as pancreatic cancer and heart failure, underscoring the significance of statistical testing in identifying relevant features 71,118 . Furthermore, dimensionality reduction methods such as PCA, and NMF have been consistently used across various diseases namely, ovarian cancer 103 , lower grade glioma 80 , colon adenocarcinoma 39 , bladder and breast cancers 40,70 . In addition, the potential of AEs, and VAEs have also been explored in diseases like glioblastoma multiforme, breast cancer, pancancer, and Lung Adenocarcinoma for feature integration and dimensionality reduction 14,28,101 .…”

Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes

“…Numerous studies have demonstrated that using multi-omics and multi-modal data results in improved performance [ 33 , 34 , 35 ]. For example, a study proposed two models using multi-omics data: a risk score model that estimated the prognosis of LGG patients based on six specific genes and a radiomic risk score model that exploited magnetic resonance images to predict the pre-operation risk score [ 33 ]. Another study used a multivariate Cox-PH model with various data.…”

Editorial comment: “Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication”

“…Prior to the present, differentiation and prognostic analysis of high-grade glioma from low-grade glioma were the major topics of research. However, in light of the heterogeneity of gliomas, more attention is being paid to the diagnosis of lower grade gliomas [4,5].…”