Dataset Information

Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction.

ABSTRACT:

Objective

We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation.

Methods

We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: n = 85; validation cohort: n = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature. Gene ontology analysis of highly expressed genes in the high-risk score group was conducted to establish a radiogenomic map. A nomogram was constructed for individualized survival prediction.

Results

The six-feature radiomic signature stratified patients in the training cohort into low- or high-risk groups for overall survival (P = 0.0018). This result was successfully verified in the validation cohort (P = 0.0396). Radiogenomic analysis revealed that the prognostic radiomic signature was associated with hypoxia, angiogenesis, apoptosis, and cell proliferation. The nomogram resulted in high prognostic accuracy (C-index: 0.92, C-index: 0.70) and favorable calibration for individualized survival prediction in the training and validation cohorts.

Conclusions

Our results suggest a great potential for the use of radiomic signature as a biological surrogate in providing prognostic information for patients with LGGs.

SUBMITTER: Qian Z

PROVIDER: S-EPMC6224242 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction.

Qian Zenghui Z Li Yiming Y Sun Zhiyan Z Fan Xing X Xu Kaibin K Wang Kai K Li Shaowu S Zhang Zhong Z Jiang Tao T Liu Xing X Wang Yinyan Y

Aging 20181001 10

<h4>Objective</h4>We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation.<h4>Methods</h4>We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: <i>n</i> = 85; validation cohort: <i>n</i> = 148). Univariate Cox regression and linear risk score formula were applied to generate ...[more]

PMID: 30362964

Similar Datasets

Project description:BackgroundLower-grade gliomas (LGGs) are less aggressive with a long overall survival (OS) time span. Because of individualized genomic features, a prognostic system incorporating molecular signatures can more accurately predict OS.MethodsDifferential expression analysis between LGGs and normal tissues was performed using the Gene Expression Omnibus (GEO) datasets (GSE4290 and GSE12657). Immune-related differentially expressed genes (ImmPort-DEGs) were analyzed for functional enrichment. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an immune risk score signature (IRSS). We extracted information from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to establish and validate the model. The relationship of model gene sets with immune infiltration was analyzed based on gene set variation analysis (GSVA) scores. Patients were divided into low- and high-risk groups based on the median score. The time-dependent receiver-operating characteristic (ROC) curve and the Kaplan-Meier curve were used to evaluate the model. Then, a precise prognostic nomogram was established, and its efficacy was verified.ResultsA total of 18 related immune genes were identified, building a 6-gene IRSS (BMP2, F2R, FGF13, PCSK1, PRKCB, and PTGER3). DEGs were enriched in T cell and NK cell regulatory pathways. Immune infiltration analysis confirmed that the gene signature correlated with a decrease in innate immune cells. In terms of model evaluation, ROC curves at 1, 3, and 5 years showed moderate predictive ability of IRSS (AUC = 0.930, 0.797, and 0.728). The Cox regression analysis revealed that IRSS was an independent prognostic factor, and the nomogram model had good predictive ability (C - index = 0.828). Meanwhile, the predictive power of IRSS was also confirmed in the training cohort. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly worse in all cohorts.ConclusionIRSS may serve as a novel survival prediction tool in the classification of LGG patients.

Dataset Information

Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction.

Objective

Methods

Results

Conclusions

Publications

Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction.

Similar Datasets

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