Dataset Information

A ten-long non-coding RNA signature for predicting prognosis of patients with cervical cancer.

ABSTRACT: Purpose:The aim of the present study was to construct a novel long non-coding RNA (lncRNA) signature to predict the prognosis of patients with cervical cancer (CC). Materials and methods:We downloaded lncRNA expression profiles and clinical characteristics from The Cancer Genome Atlas database and randomly divided them into a training dataset (n=200) and a testing dataset (n=87). Using a Cox-based iterative sure independence screening procedure combined with a resampling technique, a lncRNA signature was calculated from prognostic lncRNAs in the training dataset and was independently verified in the testing and the entire datasets. In addition, multivariate Cox regression and further stratified analyses were performed, taking into consideration the lncRNA signature as well as other clinical characteristics. Finally, we predicted the underlying functional effects of the prognostic lncRNAs by using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Results:We constructed a promising ten-lncRNA signature that was significantly associated with the prognosis of CC on the basis of a risk score formula. The risk score was used to classify patients into high-risk and low-risk groups with different overall survival in the training dataset, and was confirmed in the testing and entire datasets. Compared with the clinical factors, the ten-lncRNA signature was found to be an independent prognostic indicator and displayed robust prognostic performance. A functional analysis indicated that these ten lncRNAs were enriched in immune response, cell adhesion molecules and nuclear factor kappa B signaling. Conclusion:Our results demonstrated that this ten-lncRNA signature may serve as a prognostic biomarker for patients with CC.

SUBMITTER: Shen L

PROVIDER: S-EPMC6167985 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A ten-long non-coding RNA signature for predicting prognosis of patients with cervical cancer.

Shen Liang L Yu Haochen H Liu Ming M Wei Deying D Liu Wei W Li Changzhong C Chang Qin Q

OncoTargets and therapy 20180928

<h4>Purpose</h4>The aim of the present study was to construct a novel long non-coding RNA (lncRNA) signature to predict the prognosis of patients with cervical cancer (CC).<h4>Materials and methods</h4>We downloaded lncRNA expression profiles and clinical characteristics from The Cancer Genome Atlas database and randomly divided them into a training dataset (n=200) and a testing dataset (n=87). Using a Cox-based iterative sure independence screening procedure combined with a resampling technique ...[more]

PMID: 30319271

Similar Datasets

Project description:BackgroundBladder cancer (BLCA) typically has a poor prognosis due to high relapse and metastasis rates. A growing body of evidence indicates that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play crucial roles in the progression of BLCA and the treatment response of patients with BLCA. Therefore, we conducted a comprehensive RNA-seq analysis of BLCA using data from The Cancer Genome Atlas (TCGA) to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for BLCA.MethodsConsensus clustering analysis was used to investigate clusters of BLCA patients with varying prognoses. The least absolute shrinkage and selection operator Cox regression were used to develop the m6A-RLPS. The ESTIMATE and CIBERSORT algorithms were used to evaluate the immune composition.ResultsA total of 745 m6A-related lncRNAs were identified using Pearson correlation analysis (|R| > 0.4, p < 0.001). Fifty-one prognostic m6A-related lncRNAs were screened using univariate Cox regression analysis. Through consensus clustering analysis, patients were divided into two clusters (clusters 1 and 2) with different overall survival rates and tumor stages based on the differential expression of the lncRNAs. Enrichment analysis demonstrated that terms related to tumor biological processes and immune-related activities were increased in patient cluster 2, which was more likely to exhibit low survival rates. Nine m6A-related prognostic lncRNAs were finally determined and subsequently used to construct the m6A-RLPS, which was verified to be an independent predictor of prognosis using univariate and multivariate Cox regression analyses. Further, a nomogram based on age, tumor stage, and the m6A-RLPS was generated and showed high accuracy and reliability with respect to predicting the survival outcomes of BLCA patients. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression.ConclusionsWe established a novel m6A-RLPS with a favorable prognostic value for patients with BLCA. We believe that this prognostic signature can provide new insights into the tumorigenesis of BLCA and predict the treatment response in patients with BLCA.

Project description:Background: Ferroptosis is a form of regulated cell death that follows cell membrane damage and mostly depends on iron-mediated oxidative. Long non-coding RNAs (LncRNAs) are associated with the development of a variety of tumors. Till date, LncRNAs have been reported to intervene in ferroptosis. Therefore, we intended to provide a prognostic ferroptosis-related-lncRNA signature in stomach adenocarcinoma (STAD). Methods: We downloaded ferroptosis-related genes from the FerrDb database and RNA sequencing data and clinicopathological characteristics from The Cancer Genome Atlas. Gene differential expression analysis was performed using the "limma" package. We used Cox regression analysis to determine the ferroptosis-related lncRNAs signature with the lowest AIC value. The Kaplan-Meier curve, ROC curve, and nomogram were used to evaluate the prognostic value of the risk score. Gene set enrichment analysis (GSEA) was used to explore the biologic functions of the three ferroptosis-related lncRNAs. LINC01615 expression in gastric cancer cell lines and tissues was measured by real-time PCR. A nuclear-cytoplasmic fractionation assay was used to analyze the subcellular localization for LINC01615. Furthermore, we used bioinformatics to predict potential target microRNAs (miRNAs) of LINC01615 and their target ferroptosis-related mRNAs. Results: Three ferroptosis-related-lncRNA signatures (AP000695.2, AL365181.3, and LINC01615) were identified, and then Kaplan-Meier, Cox regression analyses, and ROC curve confirmed that the ferroptosis-related-lncRNA model could predict the prognosis of STAD. The GSEA indicated that the three ferroptosis-related lncRNAs might be related to the extracellular matrix and cellular activities. LINC01615 is highly expressed in gastric cancer cell lines and tissues. A nuclear-cytoplasmic fractionation assay confirmed that in gastric cancer cell lines, most LINC01615 was enriched in the cytoplasm. Bioinformatics further predicts four potential target miRNAs of LINC01615 and then figured out 26 target ferroptosis-related mRNAs. Conclusion: We established a three-ferroptosis-related-lncRNA model (AP000695.2, AL365181.3, and LINC01615) that can predict the prognosis of STAD patients. We also expected to provide a promising target for LINC01615 for research in the future, which was highly expressed in gastric cancer and cell lines and acted as a ceRNA to get involved in ferroptosis.

Dataset Information

A ten-long non-coding RNA signature for predicting prognosis of patients with cervical cancer.

Publications

A ten-long non-coding RNA signature for predicting prognosis of patients with cervical cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets