Dataset Information

The Recurrent-Specific Regulation Network of Prognostic Stemness-Related Signatures in Low-Grade Glioma.

ABSTRACT: Gliomas including astrocytomas, oligodendrogliomas, mixed oligoastrocytic, and mixed glioneuronal tumors are an important group of brain tumors. Based on the 2016 WHO classification for tumors in the central nervous system, gliomas were classified into four grades, from I to IV, and brain lower grade glioma (LGG) consists of grade II and grade III. Patients with LGG may undergo recurrence, which makes clinical treatment tough. Stem cell-like features of cancer cells play a key role in tumor's biological behaviors, including tumorigenesis, development, and clinical prognosis. In this article, we quantified the stemness feature of cancer cells using the mRNA stemness index (mRNAsi) and identified stemness-related key genes based on correlation with mRNAsi. Besides, hallmark gene sets and translate factors (TFs) which were highly related to stemness-related key genes were identified. Therefore, a recurrency-specific network was constructed and a potential regulation pathway was identified. Several online databases, assay for transposase-accessible chromatin using sequencing (ATAC-seq), single-cell sequencing analysis, and immunohistochemistry were utilized to validate the scientific hypothesis. Finally, we proposed that aurora kinase A (AURKA), positively regulated by Non-SMC Condensin I Complex Subunit G (NCAPG), promoted E2F target pathway in LGG, which played an important role in LGG recurrence.

SUBMITTER: Li J

PROVIDER: S-EPMC9873439 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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The Recurrent-Specific Regulation Network of Prognostic Stemness-Related Signatures in Low-Grade Glioma.

Li Jin J Zhou Meng M Huang Dan D Lin Ruoyi R Cui Xiaomei X Chen Shaofeng S Yao Ying Y Xian Shuyuan S Wang Siqiao S Fu Qing Q Zhu Jiwen J Yue Xi X Huang Runzhi R Qi Enbo E Huang Zongqiang Z

Disease markers 20230117

Gliomas including astrocytomas, oligodendrogliomas, mixed oligoastrocytic, and mixed glioneuronal tumors are an important group of brain tumors. Based on the 2016 WHO classification for tumors in the central nervous system, gliomas were classified into four grades, from I to IV, and brain lower grade glioma (LGG) consists of grade II and grade III. Patients with LGG may undergo recurrence, which makes clinical treatment tough. Stem cell-like features of cancer cells play a key role in tumor's bi ...[more]

PMID: 36703644

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Project description:BackgroundBone is the most common metastatic site of Breast invasive carcinoma (BRCA). In this study, the bone metastasis-specific regulation network of BRCA was constructed based on prognostic stemness-related signatures (PSRSs), their upstream transcription factors (TFs) and downstream pathways.MethodsClinical information and RNA-seq data of 1,080 primary BRCA samples (1,048 samples without bone metastasis and 32 samples with bone metastasis) were downloaded from The Cancer Genome Atlas (TCGA). The edgeR method was performed to identify differential expressed genes (DEGs). Next, mRNA stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR). To analyze DEGs by classification, similar genes were integrated into the same module by weighted gene co-expression network analysis (WGCNA). Then, univariate and multivariate Cox proportional hazard regression were applied to find the PSRSs. Furthermore, PSRSs, 318 TFs obtained from Cistrome database and 50 hallmark pathways quantified by GSVA were integrated into co-expression analysis. Significant co-expression patterns were used to construct the bone metastasis-specific regulation network. Finally, spatial single-cell RNA-seq and chromatin immunoprecipitation sequence (ChIP-seq) data and multi-omics databases were applied to validate the key scientific hypothesis in the regulation network. Additionally, Connectivity Map (CMap) was utilized to select the potential inhibitors of bone metastasis-specific regulation network in BRCA.ResultsBased on edgeR and WGCNA method, 43 PSRSs were identified. In the bone metastasis-specific regulation network, MAF positively regulated CD248 (R = 0.435, P < 0.001), and hallmark apical junction was the potential pathway of CD248 (R = 0.353, P < 0.001). This regulatory pattern was supported by spatial single-cell RNA sequence, ChIP-seq data and multi-omics online databases. Additionally, alexidine was identified as the possible inhibitor for bone metastasis of BRCA by CMap analysis.ConclusionPSRSs played important roles in bone metastasis of BRCA, and the prognostic model based on PSRSs showed good performance. Especially, we proposed that CD248 was the most significant PSRS, which was positively regulated by MAF, influenced bone metastasis via apical junction pathway. And this axis might be inhibited by alexidine, which providing a potential treatment strategy for bone metastasis of BRCA.

Project description:ObjectiveAs a prevalent and infiltrative cancer type of the central nervous system, the prognosis of lower-grade glioma (LGG) in adults is highly heterogeneous. Recent evidence has demonstrated the prognostic value of the mRNA expression-based stemness index (mRNAsi) in LGG. Our aim was to develop a stemness index-based signature (SI-signature) for risk stratification and survival prediction.MethodsDifferentially expressed genes (DEGs) between LGG in the Cancer Genome Atlas (TCGA) and normal brain tissue samples from the Genotype-Tissue Expression (GTEx) project were screened out, and the weighted gene correlation network analysis (WGCNA) was employed to identify the mRNAsi-related gene sets. Meanwhile, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed for the functional annotation of the key genes. ESTIMATE was used to calculate tumor purity for acquiring the correct mRNAsi. Differences in overall survival (OS) between the high and low mRNAsi (corrected mRNAsi) groups were compared using the Kaplan Meier analysis. By combining the Lasso regression with univariate and multivariate Cox regression, the SI-signature was constructed and validated using the Chinese Glioma Genome Atlas (CGGA).ResultsThere was a significant difference in OS between the high and low mRNAsi groups, which was also observed in the two corrected mRNAsi groups. Based on threshold limits, 86 DEGs were most significantly associated with mRNAsi via WGCNA. Seven genes (ADAP2, ALOX5AP, APOBEC3C, FCGRT, GNG5, LRRC25, and SP100) were selected to establish a risk signature for primary LGG. The ROC curves showed a fair performance in survival prediction in both the TCGA and the CGGA validation cohorts. Univariate and multivariate Cox regression revealed that the risk group was an independent prognostic factor in primary LGG. The nomogram was developed based on clinical parameters integrated with the risk signature, and its accuracy for predicting 3- and 5-years survival was assessed by the concordance index, the area under the curve of the time-dependent receiver operating characteristics curve, and calibration curves.ConclusionThe SI-signature with seven genes could serve as an independent predictor, and suggests the importance of stemness features in risk stratification and survival prediction in primary LGG.

Project description:IntroductionMeningiomas are the most common primary central nervous system (CNS) tumors in adults, representing approximately one-third of all primary adult CNS tumors. Although several recent publications have proposed alternative grading systems of meningiomas that incorporate genomic and/or epigenomic data to better predict meningioma recurrence and progression-free survival, our understanding of driving forces of meningioma development is still limited.ObjectiveTo define gene expression signatures of the most common subtypes of meningiomas to better understand cellular processes and signaling pathways specific for each tumor genotype.MethodsWe used RNA sequencing (RNA-seq) to determine whole transcriptome profiles of twenty meningiomas with genomic alterations including NF2 inactivation, loss of chr1p, and missense mutations in TRAF7, AKT1 and KLF4.ResultsThe analysis revealed that meningiomas with NF2 gene inactivation expressed higher levels of BCL2 and GLI1 compared with tumors harboring TRAF7 missense mutations. Moreover, NF2 meningiomas were subdivided into two distinct groups based on additional loss of chr1p. NF2 tumors with intact chr1p were characterized by the high expression of tumor suppressor PTCH2 compared to NF2 tumors with chr1p loss. Taken together with the high expression of BCL2 and GLI1, these results suggest that activation of Sonic Hedgehog pathway may contribute to NF2 meningioma development. In contrast, NF2 tumors with chr1p loss expressed high levels of transcription factor FOXD3 and its antisense RNA FOXD3-AS1. Examination of TRAF7 tumors demonstrated that TRAF7 regulates a number of biomechanically responsive genes (KRT6a, KRT16, IL1RL1, and AQP3 among others). Interestingly, AKT1 and KLF4 meningiomas expressed genes specific for PI3K/AKT signaling pathway, suggesting overlapping gene signatures between the two subtypes. In addition, KLF4 meningiomas had high expression of carcinoembryonic antigen family members CEACAM6 and CEACAM5.ConclusionsEach group of meningiomas displayed a unique gene expression signature suggesting signaling pathways potentially implicated in tumorigenesis. These findings will improve our understanding of meningioma tumorigenesis and prognosis.

Dataset Information

The Recurrent-Specific Regulation Network of Prognostic Stemness-Related Signatures in Low-Grade Glioma.

Publications

The Recurrent-Specific Regulation Network of Prognostic Stemness-Related Signatures in Low-Grade Glioma.

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