Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma

Abstract

Background: Glioma is the most common type of primary central nervous system tumors. However, the relationship between gene mutations and transcriptome is unclear in diffuse glioma, and there are no systemic analyses with regard to the genotype-phenotype association currently. Methods: We performed the multi-omics analysis in large glioblastoma multiforme (GBM, n=126) and low-grade glioma (LGG, n=481) cohorts obtained from The Cancer Genome Atlas (TCGA) database. We used multivariate linear models to evaluate associations between driver gene mutations and global gene expression. We developed generalized linear models to evaluate associations between genetic/expression factors with clinicopathologic features. Multivariate Cox proportional hazards models were used to predict the overall survival. Results: The potential relationship between genotype and genetics, clinical as well as pathologic features, on diffused glioma was observed. At least one driver mutation correlated with expression changes of about 10% of genes in GBMs while about 80% of genes in LGGs. The strongest association between mutations and expression changes was observed for DRG2 and LRCC41 gene in GBMs and LGGs, respectively. Additionally, the association between genomics features and clinicopathologic features suggested the different underlying molecular mechanisms in molecular subtypes or histology subtypes. For predicting survival, among genetics, transcriptome and clinical variables, transcriptome features made the largest contribution. By combining all the available data, the accuracy in predicting the prognosis of diffuse glioma in patients was also improved. Conclusion: Our study results revealed the influences of driver gene mutations on global gene expression in diffuse glioma patients. A more accurate model in predicting the prognosis of patients was achieved when combining with all the available data than just transcriptomic data.

Molecular landscape of IDH-mutant astrocytoma and oligodendroglioma grade 2 indicate tumor purity as an underlying genomic factor

“…Patients could get timely and correct individualized treatments based on the diagnosis than the previous common treating process. The causality of the biomarkers in the A-O panel has been validated in other studies (Ushakov et al 2017;Vriend and Tate 2019;Yang et al 2020). Overall, our study benefits from multiomics data and provides new insights into the clinical, genomic, epigenetic and biological conditions of IDH-mt astrocytoma and grade 2 oligodendroglioma.…”

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

“…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 .…”

“…The specifics of different predictors, in terms of variations in the combinations of clinical and various omics data modalities, are outlined in Table 4. Among 54 survival prediction studies based on multiomics, 49 studies utilized different combinations of four distinct omics types: mRNA, methylation, miRNA, and CNV 14,26,27,42,43,69,72,73,77,82,84,89,96,97,100,101,106,108 . Only 7 studies utilized additional modalities such as whole exome sequencing (WES) 26,31 , long coding RNA (lncRNA) 31 , proteomics 22,23,108,113,115 , and mutation data 22,23,108,115 .…”

“…Out of 9 omics types, mRNA, CNV, miRNA, and methylation have been the most commonly utilized modalities for 33 cancer subtypes i.e., breast cancer 14,23,68,74,90,[98][99][100] , pancancer 24,91,[105][106][107][108]131 , colon can- cer 39,[74][75][76][77] , lung adenocarcinoma 27,101,102 , and ovarian cancer 72,84,[88][89][90]103 . In addition, mutation data has been utilized for 7 cancer subtypes namely, adult diffuse glioma 69 , breast cancer 23 , cervical cancer 73 , non-small cell lung cancer 95 , ovarian cancer 103 , and pancreatic cancer 32 . Among 10 data modalities, 3 modalities namely, proteomic, lncRNA and WES have been utilized the least having limited applicability to clear renal cell cancer 31 , pancreatic cancer 26 , breast cancer 23 , localized prostate cancer 22 , and pancancer 107 .…”

Characteristics of spinal dissemination in adult low-grade glioma: a retrospective cohort study at a single institute

“…Low-grade gliomas (LGGs) occur primarily in children and young to intermediate age adults and are classified as grade I and II tumors under the World Health Organization (WHO) classification system (1)(2)(3). LGGs in adults have highly variable clinical behaviors that are not easy to predict based on histological class (4,5). Most adult LGGs are indolent, and patients usually have an excellent probability of long-term survival under the current treatment approaches, but some adult LGGs progress rapidly to glioblastoma (GBM) (6,7).…”