Dataset Information

A SEMA3 Signaling Pathway-Based Multi-Biomarker for Prediction of Glioma Patient Survival.

ABSTRACT: Glioma is a lethal central nervous system tumor with poor patient survival prognosis. Because of the molecular heterogeneity, it is a challenge to precisely determine the type of the tumor and to choose the most effective treatment. Therefore, novel biomarkers are essential to improve the diagnosis and prognosis of glioma tumors. Class 3 semaphorin proteins (SEMA3) play an important role in tumor biology. SEMA3 transduce their signals by using neuropilin and plexin receptors, which functionally interact with the vascular endothelial growth factor-mediated signaling pathways. Therefore, the aim of this study was to explore the potential of SEMA3 signaling molecules for prognosis of glioma patient survival. The quantitative real-time PCR method was used to evaluate mRNA expression of SEMA3(A-G), neuropilins (NRP1 and NRP2), plexins (PLXNA2 and PLXND1), cadherins (CDH1 and CDH2), integrins (ITGB1, ITGB3, ITGA5, and ITGAV), VEGFA and KDR genes in 59 II-IV grade glioma tissues. Seven genes significantly associated with patient overall survival were used for multi-biomarker construction, which showed 64%, 75%, and 68% of accuracy of predicting the survival of 1-, 2-, and 3-year glioma patients, respectively. The results suggest that the seven-gene signature could serve as a novel multi-biomarker for more accurate prognosis of a glioma patient's outcome.

SUBMITTER: Valiulyte I

PROVIDER: S-EPMC7582960 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A SEMA3 Signaling Pathway-Based Multi-Biomarker for Prediction of Glioma Patient Survival.

Valiulyte Indre I Steponaitis Giedrius G Kardonaite Deimante D Tamasauskas Arimantas A Kazlauskas Arunas A

International journal of molecular sciences 20201007 19

Glioma is a lethal central nervous system tumor with poor patient survival prognosis. Because of the molecular heterogeneity, it is a challenge to precisely determine the type of the tumor and to choose the most effective treatment. Therefore, novel biomarkers are essential to improve the diagnosis and prognosis of glioma tumors. Class 3 semaphorin proteins (SEMA3) play an important role in tumor biology. SEMA3 transduce their signals by using neuropilin and plexin receptors, which functionally ...[more]

PMID: 33036421

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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

A SEMA3 Signaling Pathway-Based Multi-Biomarker for Prediction of Glioma Patient Survival.

Publications

A SEMA3 Signaling Pathway-Based Multi-Biomarker for Prediction of Glioma Patient Survival.

Similar Datasets

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