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A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas.

ABSTRACT: Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.

SUBMITTER: Pan X

PROVIDER: S-EPMC8111095 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

Project description:BackgroundSupratotal resection is advocated in lower-grade gliomas (LGGs) based on theoretical advantages but with limited verification of functional risk and data on oncological outcomes. We assessed the association of supratotal resection in molecularly defined LGGs with oncological outcomes.MethodsIncluded were 460 presumptive LGGs; 404 resected; 347 were LGGs, 319 isocitrate dehydrogenase (IDH)-mutated, 28 wildtype. All patients had clinical, imaging, and molecular data. Resection aimed at supratotal resection without any patient or tumor a priori selection. The association of extent of resection (EOR), categorized on volumetric fluid attenuated inversion recovery images as residual tumor volume, along with postsurgical management with progression-free survival (PFS), malignant (M)PFS, and overall survival (OS) assessed by univariate, multivariate, and propensity score analysis. The study mainly focused on IDH-mutated LGGs, the "typical LGGs."ResultsMedian follow-up was 6.8 years (interquartile range, 5-8). Out of 319 IDH-mutated LGGs, 190 (59.6%) progressed, median PFS: 4.7 years (95% CI: 4-5.3). Total and supratotal resection obtained in 39% and 35% of patients with IDH1-mutated tumors. In IDH-mutated tumors, most patients in the partial/subtotal group progressed, 82.4% in total, only 6 (5.4%) in supratotal. Median PFS was 29 months (95% CI: 25-36) in subtotal, 46 months (95% CI: 38-48) in total, while at 92 months, PFS in supratotal was 94.0%. There was no association with molecular subtypes and grade. At random forest analysis, PFS strongly associated with EOR, radiotherapy, and previous treatment. In the propensity score analysis, EOR associated with PFS (hazard ratio, 0.03; 95% CI: 0.01-0.13). MPFS occurred in 32.1% of subtotal total groups; 1 event in supratotal. EOR, grade III, previous treatment correlated to MPFS. At random forest analysis, OS associated with EOR as well.ConclusionsSupratotal resection strongly associated with PFS, MPFS, and OS in LGGs, regardless of molecular subtypes and grade, right from the beginning of clinical presentation.

Project description:BackgroundVirtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions.MethodsBased on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review.FindingsAPOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10-69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness.InterpretationAPOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO).FundingNational Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).

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A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas.

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