Identification of a Four-lncRNA Signature Predicting Prognosis of Patients with NSCLC
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ABSTRACT:
PROVIDER: PRJNA599191 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:LncRNA expression profiles were sucessfully contructed in 370 patients with NSCLC and 198 corresponding adjacent noncancerous tissues using a custom microarray containing probes for 2412 lncRNAs and an outcome prediction of lncRNA signature was successfully identified for predicting prognosis in NSCLC.
2020-08-31 | GSE143018 | GEO
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2017-08-22 | GSE53882 | GEO
Project description:Eighty-four completely resected stage I/II non-small cell lung cancer (NSCLC) without adjuvant therapy were profiled using whole genome expression microarrys in order to identify novel classifications associated with RFS. Association with relapse-free survival (RFS) and clinicopathological parameters was assessed. An external cohort of 162 tumors was used to validate results.
2014-07-29 | E-MTAB-1790 | biostudies-arrayexpress
Project description:A model for predicting response to PD-1 inhibitors in NSCLC
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Project description:A model for predicting response to PD-1 inhibitors in NSCLC
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Project description:BackgroundGastric adenocarcinoma (GAD) is one of the most common tumors in the world and the prognosis is still very poor.ObjectiveWe sought to identify reliable prognostic biomarkers for the progression of GAD and the sensitivity to drug therapy.MethodThe RNA sequencing data of GAD was downloaded from the Cancer Genome Atlas (TCGA) database and used for analysis. Differentially expressed, immune-related lncRNA (DEIRlncRNA) was characterized by differential analysis and correlation analysis. Univariate Cox regression analysis was used to identify DEIRlncRNA associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed us to determine a signature composed of eight IRlncRNAs. Based on this signature, we further performed gene set enrichment analysis (GSEA) and somatic mutation analysis to evaluate the ability of this signature to predict prognosis.ResultsIn total, 72 immune-related lncRNAs (DEIRlncRNAs) with prognostic value were identified. These lncRNAs were used to construct a model containing eight immune-related lncRNAs (8-IRlncRNAs). Based on this risk model, we divided GAD patients into high-risk and low-risk groups. The analysis showed that the prognosis of the two groups was different and that the high-risk group had worse overall survival (OS). Immune cell infiltration analysis showed that the proportion of memory B cells increased in the high-risk group while the proportion of macrophages M1, T cells, CD4 memory-activated cells, and T cell follicular helpers decreased. GSEA results showed that 8-IRlncRNA was significantly enriched in tumorigenesis pathways such as myc. The results of somatic mutation analysis showed that the CDH1 gene was significantly mutated in the high-risk group.ConclusionA prognostic signature of 8-IRlncRNAs in GAD was established and this signature was able to predict the prognosis of GAD patients.
| S-EPMC9061059 | biostudies-literature
Project description:Osteosarcoma (OS) is one of the most prevalent bone tumors in adolescents, and the correlation between aging and OS remains unclear. Currently, few accurate and reliable biomarkers have been determined for OS prognosis. To address this issue, we carried out a detailed bioinformatics analysis based on OS with data from the Cancer Genome Atlas data portal and Human Aging Genomic Resources database, as well as in vitro experiments. A total of 88 OS samples with gene expression profiles and corresponding clinical characteristics were obtained. Through univariate Cox regression analysis and survival analysis, 10 aging-associated survival lncRNAs (AASRs) were identified to be associated with the overall survival of OS patients. Based on the expression levels of the 10 AASRs, the OS patients were classified into two clusters (Cluster A and Cluster B). Cluster A had a worse prognosis, while Cluster B had a better prognosis. Then, 5 AASRs were ultimately included in the signature through least absolute shrinkage and selection operator-Cox regression analysis. Kaplan‒Meier survival analysis verified that the high-risk group exhibited a worse prognosis than the low-risk group. Furthermore, univariate and multivariate Cox regression analyses confirmed that the riskScore was an independent prognostic factor for OS patients. Subsequently, we discovered that the risk signature was correlated with the properties of the tumor microenvironment and immune cell infiltration. Specifically, there was a positive association between the risk model and naïve B cells, resting dendritic cells and gamma delta T cells, while it was negatively related to CD8+ T cells. Finally, in vitro experiments, we found that UNC5B-AS1 inhibited OS cells from undergoing cellular senescence and apoptosis, thereby promoting OS cells proliferation. In conclusion, we constructed and verified a 5 AASR-based signature, that exhibited excellent performance in evaluating the overall survival of OS patients. In addition, we found that UNC5B-AS1 might inhibit the senescence process, thus leading to the development and progression of OS. Our findings may provide novel insights into the treatment of OS patients.
| S-EPMC10791644 | biostudies-literature
Project description:Anaplastic glioma is Grade III and the median overall survival is about 37.6 months. However, there are still other factors that affect the prognosis for anaplastic glioma patients due to variable overall survival. So we screened four-lncRNA signature (AGAP2-AS1, TPT1-AS1, LINC01198 and MIR155HG) from the lncRNA expression profile from the GSE16011, CGGA and REMBRANDT datasets. The patients in low risk group had longer overall survival than high risk group (median OS 2208.25 vs. 591.30 days; P < 0.0001). Moreover, patients in the low risk group showed similar overall survival to Grade II patients (P = 0.1669), while the high risk group showed significant different to Grade IV (P = 0.0005) with similar trend. So based on the four-lncRNA, the anaplastic gliomas could be divided into grade II-like and grade IV-like groups. On the multivariate analysis, it showed the signature was an independent prognostic factor (P = 0.000). The expression of four lncRNAs in different grades showed that AGAP2-AS1, LINC01198 and MIR155HG were increased with tumor grade, while TPT1-AS1 was decreased. Knockdown of AGAP2-AS1 can inhibit the cell proliferation, migration and invasion, while increase the apoptosis cell rates in vitro. In conclusion, our results showed that the four-lncRNA signature has prognostic value for anaplastic glioma. Moreover, clinicians should conduct corresponding therapies to achieve best treatment with less side effects for two groups patients.
| S-EPMC5363582 | biostudies-literature
Project description:Analysis of Immune and Prognostic-Related lncRNA PRKCQ-AS1 for Predicting Prognosis and Regulating Effect in Sepsis
| PRJNA900185 | ENA
Project description:BACKGROUND:The prognosis of the glioblastoma (GBM) is dismal. This study aims to select an optimal RNA signature for prognostic prediction of GBM patients. METHODS:For the training set, the long non-coding RNA (lncRNA) and mRNA expression profiles of 151 patients were downloaded from the TCGA. Differentially expressed mRNAs (DEGs) and lncRNAs (DE-lncRNAs) were identified between good prognosis and bad prognosis patients. Optimal prognostic mRNAs and lncRNAs were selected respectively, by using univariate Cox proportional-hazards (PH) regression model and LASSO Cox-PH model. Subsequently, four prognostic scoring models were built based on expression levels or expression status of the selected prognostic lncRNAs or mRNAs, separately. Each prognostic model was applied to the training set and an independent validation set. Function analysis was used to uncover the biological roles of these prognostic DEGs between different risk groups classified by the mRNA-based signature. RESULTS:We obtained 261 DEGs and 33 DE-lncRNAs between good prognosis and bad prognosis patients. A panel of eight mRNAs and a combination of ten lncRNAs were determined as predictive RNAs by LASSO Cox-PH model. Among the four prognostic scoring models using the eight-mRNA signature or the ten-lncRNA signature, the one based on the expression levels of the eight mRNAs showed the greatest predictive power. The DEGs between different risk groups using the eight prognostic mRNAs were functionally involved in calcium signaling pathway, neuroactive ligand-receptor interaction pathway, and Wnt signaling pathway. CONCLUSION:The eight-mRNA signature has greater prognostic value than the ten-lncRNA-based signature for GBM patients based on bioinformatics analysis.
| S-EPMC7081624 | biostudies-literature