A Signature of Nine lncRNA Methylated Genes Predicts Survival in Patients With Glioma.
Ontology highlight
ABSTRACT:
SUBMITTER: Cheng M
PROVIDER: S-EPMC8019920 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Background: Glioma, caused by carcinogenesis of brain and spinal glial cells, is the most common primary malignant brain tumor. To find the important indicator for glioma prognosis is still a challenge and the metabolic alteration of glioma has been frequently reported recently. Methods: In our current work, a risk score model based on the expression of twenty metabolic genes was developed using the metabolic gene expressions in The Cancer Genome Atlas (TCGA) dataset, the methods of which included the cox multivariate regression and the random forest variable hunting, a kind of machine learning algorithm, and the risk score generated from this model is used to make predictions in the survival of glioma patients in the training dataset. Subsequently, the result was further verified in other three verification sets (GSE4271, GSE4412 and GSE16011). Risk score related pathways collected in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were identified using Gene Set Enrichment Analysis (GSEA). Results: The risk score generated from our model makes good predictions in the survival of glioma patients in the training dataset and other three verification sets. By assessing the relationships between clinical indicators and the risk score, we found that the risk score was an independent and significant indicator for the prognosis of glioma patients. Simultaneously, we conducted a survival analysis of the patients who received chemotherapy and who did not, finding that the risk score was equally valid in both cases. And signaling pathways related to the genesis and development of multiple cancers were also identified. Conclusions: In summary, our risk score model is predictive for 967 glioma patients' survival from four independent datasets, and the risk score is a meaningful and independent parameter of the clinicopathological information.
| S-EPMC6930419 | biostudies-literature
Project description:A growing body of evidence confirms that long non‑coding RNAs (lncRNAs) have an important role in biological processes by regulating gene expression at multiple levels. Dysregulated lncRNAs may be potential prognostic biomarkers or targets for the development of cancer treatments. However, the prognostic role of an lncRNA signature in pancreatic cancer has not been investigated. Pancreatic cancer lncRNA expression profiles from The Cancer Genome Atlas (TCGA) were analyzed in the current study. The prognostic value of differentially expressed lncRNAs (DElncRNAs) was evaluated via the Kaplan‑Meier method. A risk score model was established based on the potential prognostic lncRNAs. The biological functions of lncRNAs were predicted by functional enrichment analysis. Then, an lncRNA‑mRNA co‑expression network was established and predicted the function of the lncRNAs. Seven DElncRNAs that were significantly associated with the prognosis of pancreatic cancer were identified. Patients were classified into high‑risk and low‑risk groups using a risk score based on a three‑lncRNA signature. There was a significant difference in overall survival (OS) between the groups (median OS 1.33 vs. 3.65 years; log‑rank test, P=0.0000). Cox regression analysis and ROC curves demonstrated that the three‑lncRNA signature may be an effective independent prognostic biomarker in patients with pancreatic. The functional enrichment analysis showed that lncRNA AL137789.1, one component of the three‑lncRNA signature, may be associated with tumor immune responses. In the present study, a novel three‑lncRNA signature that was established that may be useful in predicting survival among patients with pancreatic cancer. These lncRNAs may be involved in tumor immunity and thus affect the prognosis of patients.
| S-EPMC6196600 | biostudies-literature
Project description:Although radiation therapy (RT) plays a critical role in the treatment of low-grade glioma (LGG), many patients suffer from adverse effects without experiencing survival benefits. In various carcinomas, long non-coding RNAs (lncRNAs) contribute to pathogenic processes, including tumorigenesis, metastasis, chemoresistance, and radioresistance. Currently, the role of lncRNAs in the radiosensitivity of LGG is largely unknown. Here, we downloaded clinical data for 167 LGG patients from The Cancer Genome Atlas database and divided them between radiosensitive and radioresistant groups based on their clinical outcomes after receiving radiotherapy. We identified 37 lncRNAs that were differentially expressed (DElncRNAs) between the groups. Functional enrichment analysis revealed that their potential target mRNAs were mainly enriched in the PI3K-Akt and MAPK signaling pathways and in DNA damage response. Kaplan-Meier survival analysis revealed that increased expression of six lncRNAs was significantly associated with radiosensitivity. We then developed a risk signature based on three of the DElncRNAs that served as an independent biomarker for predicting LGG patient outcomes after radiotherapy. In vitro experiments further validated the biological function of these lncRNAs on low-grade glioma radiation response.
| S-EPMC7288909 | biostudies-literature
Project description:The N6-methyladenosine (m6A) RNA modification can modify long non-coding RNAs (lncRNAs), thereby affecting the tumorigenesis and progression of tumors. However, the underlying role of m6A-modified lncRNAs in colorectal cancer (CRC) remains largely unknown. Therefore, our aim was to assess the prognostic value of m6A-modified lncRNAs in CRC patients. The gene expression and clinicopathological data of CRC were extracted from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was used to investigate the m6A-modified lncRNAs. Consensus clustering was conducted to identify molecular subtypes of CRC, and the clinical significance of molecular subtypes was identified. The least absolute shrinkage and selection operator analysis (LASSO) was applied to establish a risk signature. Finally, a prognostic nomogram with risk score and clinicopathological variables was established. In total, 29 m6A-modified lncRNAs were identified as prognostic lncRNAs. Two molecular clusters were identified and significant differences were found with respect to clinicopathological features and prognosis. Cluster1 is associated with poor overall survival (OS), down-regulation of Programmed cell death ligand-1 (PD-L1) expression, lower immune score, and less immune cell infiltration. Then, an m6A-modified lncRNA signature for predicting OS was constructed in the TCGA training cohort. The signature demonstrated favorable prediction performance in both training and validation sets. Compared with low-risk patients, patients with high risk showed worse clinical outcomes, lower immune scores, and downregulated PD-L1 expression. Further analysis indicated that the signature was an independent prognostic indicator, and then a prognostic nomogram based on risk score, tumor location, and tumor stage was established. Our study identified a seven m6A-modified lncRNA signature and established a prognostic nomogram that reliably predicts OS in CRC. These findings may improve the understanding of m6A modifications in CRC and provide insights into the prognosis and treatment strategy of CRC.
| S-EPMC8472391 | biostudies-literature
Project description:BackgroundGlioma is the most common and fatal type of nerve neoplasm in the central nervous system. Several biomarkers have been considered for prognosis prediction, which is not accurate enough. We aimed to carry out a gene signature related to the expression of immune checkpoints which was enough for its performance in prediction.MethodsGene expression of immune checkpoints in TGGA database was filtrated. The 5 selected genes underwent verification by COX and Lasso-COX regression. Next, the selected genes were included to build a novel signature for further analysis.ResultsPatients were sub-grouped into high and low risk according to the novel signature. Immune response, clinicopathologic characters, and survival showed significant differences between those 2 groups. Terms including "naive," "effector," and "IL-4" were screened out by GSEA. The results showed strong relevance between the signature and immune response.ConclusionsWe constructed a gene signature with 5 immune checkpoints. The signature predicted survival effectively. The novel signature performed more functional than previous biomarkers.
| S-EPMC7856730 | biostudies-literature
Project description:Evidence suggests that altered DNA methylation plays a causative role in the pathogenesis of various cancers, including hepatocellular carcinoma (HCC). Thus, methylated differently expressed genes (MDEGs) could potentially serve as biomarkers and therapeutic targets in HCC. In the present study, screening four genomics profiling datasets (GSE62232, GSE84402, GSE73003 and GSE57956) enabled us to identify a total of 148 MDEGs. A signature was then established based on the top four MDEGs (BRCA1, CAD, CDC20 and RBM8A). Taking clinical variables into consideration, we constructed a risk score system consisting of the four-MDEG signature and the patients' clinical features, which was predictive of prognosis in HCC. The prognostic value of the HCC risk score system was confirmed using TCGA HCC samples. The scores were then used to construct a nomogram, performance of which was evaluated using Harrel's concordance index (C-index) and a calibration curve. The signature-based nomogram for prediction of overall survival in HCC patients exhibited good performance and was superior to traditional staging systems (C-index: 0.676 vs 0.629, P< 0.05). We have thus established a novel risk score system that is predictive of prognosis and is a potentially useful guide for personalized treatment of HCC patients.
| S-EPMC6339794 | biostudies-literature
Project description:This study aimed to explore immune-related lncRNAs for predicting the overall survival of patients with colon adenocarcinoma. RNA-sequencing data were downloaded from the TCGA data portal. The immune-related lncRNAs with differential expression were identified with Cox and LASSO regression analysis. With the stepwise regression analysis, a seven lncRNA signature was established for calculating the Risk Score with following formula: Risk Score = [Expression level of AC027307.2 * (0.156)] + [Expression level of AC074117.1 * (0.294)] + [Expression level of AC103702.2 * (-0.025)] + [Expression level of CYTOR * (0.205)] + [Expression level of LINC02381 * (0.251)] + [Expression level of MIR200CHG * (0.052)] + [Expression level of SNHG16 * (-0.101)]. The Risk Score was validated with survival analysis, achieving moderate area under the curve (AUC) of receiver operating characteristic (ROC) curve over 0.7. GSEA and immune-cell abundance analysis further supported the involved lncRNAs were immune-relevant. Finally, the prognosis prediction efficacy was verified with clinical samples with an AUC of 0.674 in ROC curve. Both the Risk Score and involved immune-related lncRNAs presented promising clinical significance.
| S-EPMC7724340 | biostudies-literature
Project description:BackgroundIncreasing evidence shows that dysregulated long non-coding RNAs (lncRNAs) can serve as potential biomarkers for cancer prognosis. However, lncRNA signatures, as potential prognostic biomarkers for esophageal squamous cell carcinoma (ESCC), have been seldom reported.MethodsBased on our previous transcriptome RNA sequencing analysis from 15 paired ESCC tissues and adjacent normal tissues, we selected 10 lncRNAs with high score rank and characterized the expression of those lncRNAs, by qRT-PCR, in 138 ESCC and paired adjacent normal samples. These 138 patients were divided randomly into training (n = 77) and test (n = 59) groups. A prognostic signature of lncRNAs was identified in the training group and validated in the test group and in an independent cohort (n = 119). Multivariable Cox regression analysis evaluated the independence of the signature in overall survival (OS) and disease-free survival (DFS) prediction. GO and KEGG pathway analysis, combined with cell transwell and proliferation assays, are applied to explore the function of the three lncRNAs.ResultsA novel three-lncRNA signature, comprised of RP11-366H4.1.1 (ENSG00000248370), LINC00460 (ENSG00000233532) and AC093850.2 (ENSG00000230838), was identified. The signature classified patients into high-risk and low-risk groups with different overall survival (OS) and disease-free survival (DFS). For the training group, median OS: 23.1 months vs. 39.1 months, P < 0.001; median DFS: 15.2 months vs. 33.3 months, P < 0.001. For the test group, median OS: 23 months vs. 59 months, P < 0.001; median DFS: 16.4 months vs. 50.8 months, P < 0.001. For the independent cohort, median OS: 22.4 months vs. 60.4 months, P < 0.001). The signature indicates that patients in the high-risk group show poor OS and DFS, whereas patients with a low-risk group show significantly better outcome. The independence of the signature was validated by multivariable Cox regression analysis. GO and KEGG pathway analysis for 588 protein-coding genes-associated with the three lncRNAs indicated that the three lncRNAs were involved in tumorigenesis. In vitro assays further demonstrated that the three lncRNAs promoted the migration and proliferation of ESCC cells.ConclusionsThe three-lncRNA signature is a novel and potential predictor of OS and DFS for patients with ESCC.
| S-EPMC5801805 | biostudies-literature
Project description:Background: Recent studies have highlighted the important roles of long non-coding RNAs (lncRNAs) in pancreatic adenocarcinoma (PCa) prognosis. However, most studies explored a limited number of lncRNAs based on small sample size. Methods: Systematic and comprehensive analysis of the data from The Cancer Genome Atlas (TCGA) was performed to identify a panel of lncRNA signature for predicting prognosis in PCa. Results: A total of 160 PCa patients with complete clinical data were included in our study. Twelve lncRNAs were identified to be significantly associated with overall survival (OS) in PCa patients using Cox regression analysis. A risk score formula was constructed to assess the prognostic value of the lncRNA signature in PCa. Patients with high risk score had worse OS than those with low risk score. The multivariate Cox regression analyses revealed that the lncRNA signature was an independent prognostic factor. Additionally, the signature might act as an indicator to predict treatment outcome. Functional enrichment analyses showed that the lncRNAs might involve in several molecular pathways closely related with PCa such as DNA replication, pancreatic cancer and regulation of tor signaling. Conclusions: Our study demonstrated a lncRNA signature including 12 lncRNAs with the potential to be served as an independent prognostic biomarker of PCa.
| S-EPMC6485218 | biostudies-literature
Project description:Glioma is the most common primary brain tumor with various genetic alterations; among which, IDH mutation is the most common mutation and plays an important role in glioma early development, especially in lower grade glioma (LGG, WHO II-III). Previous studies have found that IDH mutation is tightly associated with extensive methylation across whole genome in glioma. To further investigate the role of IDH, we obtained methylation data of 777 samples from CGGA (Chinese Glioma Genome Atlas) and TCGA (The Cancer Genome Atlas) with IDH mutation status available. A package compiled under R language called Tspair was used as the main analytic tool to find potential probes that were significantly affected by IDH mutation. As a result, we found one pair of probes, cg06940792 and cg26025891, which was capable of predicting IDH mutation status precisely. The hypermethylated probe was cg06940792, designed in the promoter region of MEGF10, while the hypomethylated probe was cg26025891, designed in the promoter region of PSTPIP1. Survival analysis proved that hypermethylation or low expression of MEGF10 indicated a favorable prognosis in 983 glioma samples. Moreover, gene ontology analysis demonstrated that MEGF10 was associated with cell migration, cell proliferation, and regulation of apoptosis in glioma. All findings above can be validated in three other independent cohorts. In a word, our results suggested that methylation level and mRNA expression of MEGF10 in glioma were not only correlated with IDH mutation but also associated with clinical outcome of patients, providing potential guide for future dissection of IDH role in glioma.
| S-EPMC5985127 | biostudies-literature