Dataset Information

Development of a nomogram for prognostic prediction of lower-grade glioma based on alternative splicing signatures.

ABSTRACT:

Background

The prognosis of lower-grade glioma (LGG) differs from that of other grades gliomas. Although lots of studies on the prognostic biomarkers of LGG have been reported, few have significant clinical impact. Alternative splicing (AS) events can affect cell function by splicing precursor mRNA. Therefore, a prognostic model for LGG based on AS events are important to establish.

Methods

RNA sequencing, clinical, and AS event data of 510 LGG patients from the TCGA database were downloaded. Univariate Cox regression analysis was used to screen out prognostic-related AS events and LASSO regression and multivariate Cox regression were used to establish prognostic risk scores for patients in the training set (n = 340). After validation, a nomogram model was established based on the AS signature and clinical information, which was able to predict 1-, 3-, and 5-year survival rates. Finally, considering the regulatory effect of splicing factors (SFs) on AS events, an AS-SF regulatory network was analyzed.

Results

The most common AS event was exon skipping and the least was mutually exclusive exons. All the seven AS events were related to the prognosis of LGG patients, regardless of whether they were separated or considered as a whole event (integrated AS event), and the integrated AS event had the most significant correlation. After further inclusion of clinical indicators, eight factors were screened out: age, new event, KPS, WHO grade, treatment, integrated AS signature, IDH1 and TP53 mutation status, and a nomogram model was established. The study also constructed an AS-SF regulatory network.

Conclusion

The AS events and clinical factors that can predict the prognosis of LGG patients were screened, and a prognostic prediction model was established. The results of this study can play an important role in clinical work to better evaluate the prognosis of patients and impact treatment options.

SUBMITTER: Wang Y

PROVIDER: S-EPMC7774734 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of a nomogram for prognostic prediction of lower-grade glioma based on alternative splicing signatures.

Wang Yaning Y Wang Zihao Z Zhao Binghao B Chen Wenlin W Wang Yu Y Ma Wenbin W

Cancer medicine 20201013 24

<h4>Background</h4>The prognosis of lower-grade glioma (LGG) differs from that of other grades gliomas. Although lots of studies on the prognostic biomarkers of LGG have been reported, few have significant clinical impact. Alternative splicing (AS) events can affect cell function by splicing precursor mRNA. Therefore, a prognostic model for LGG based on AS events are important to establish.<h4>Methods</h4>RNA sequencing, clinical, and AS event data of 510 LGG patients from the TCGA database were ...[more]

PMID: 33047900

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Project description:Background: Low-grade glioma (LGG) is considered a fatal disease for young adults, with overall survival widely ranging from 1 to 15 years depending on histopathologic and molecular subtypes. As a novel type of programmed cell death, ferroptosis was reported to be involved in tumorigenesis and development, which has been intensively studied in recent years. Methods: For the discovery cohort, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to identify the differentially expressed and prognostic ferroptosis-related genes (FRGs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox were used to establish a prognostic signature with the above-selected FRGs. Then, the signature was developed and validated in TCGA and Chinese Glioma Genome Atlas (CGGA) databases. By combining clinicopathological features and the FRG signature, a nomogram was established to predict individuals' one-, three-, and five-year survival probability, and its predictive performance was evaluated by Harrell's concordance index (C-index) and calibration curves. Enrichment analysis was performed to explore the signaling pathways regulated by the signature. Results: A novel risk signature contains seven FRGs that were constructed and were used to divide patients into two groups. Kaplan-Meier (K-M) survival curve and receiver-operating characteristic (ROC) curve analyses confirmed the prognostic performance of the risk model, followed by external validation based on data from the CGGA. The nomogram based on the risk signature and clinical traits was validated to perform well for predicting the survival rate of LGG. Finally, functional analysis revealed that the immune statuses were different between the two risk groups, which might help explain the underlying mechanisms of ferroptosis in LGG. Conclusion: In conclusion, this study constructed a novel and robust seven-FRG signature and established a prognostic nomogram for LGG survival prediction.

Project description:Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear. Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N 6-methyladenosine (m6A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA). Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m6A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs. Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m6A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

Dataset Information

Development of a nomogram for prognostic prediction of lower-grade glioma based on alternative splicing signatures.

Background

Methods

Results

Conclusion

Publications

Development of a nomogram for prognostic prediction of lower-grade glioma based on alternative splicing signatures.

Similar Datasets

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