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A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer.

ABSTRACT: Accumulated evidence suggests that dysregulated expression of long non-coding RNAs (lncRNAs) may play a critical role in tumorigenesis and prognosis of cancer, indicating the potential utility of lncRNAs as cancer prognostic or diagnostic markers. However, the power of lncRNA signatures in predicting the survival of patients with non-small cell lung cancer (NSCLC) has not yet been investigated.We performed an array-based transcriptional analysis of lncRNAs in large patient cohorts with NSCLC by repurposing microarray probes from the gene expression omnibus database. A risk score model was constructed based on the expression data of these eight lncRNAs in the training dataset of NSCLC patients and was subsequently validated in other two independent NSCLC datasets. The biological implications of prognostic lncRNAs were also analyzed using the functional enrichment analysis.An expression pattern of eight lncRNAs was found to be significantly associated with overall survival (OS) of NSCLC patients in the training dataset. With the eight-lncRNA signature, patients of the training dataset could be classified into high- and low-risk groups with significantly different OS (median survival 1.67 vs. 6.06 years, log-rank test p = 4.33E-09). The prognostic power of eight-lncRNA signature was further validated in other two non-overlapping independent NSCLC cohorts, demonstrating good reproducibility and robustness of this eight-lncRNA signature in predicting OS of NSCLC patients. Multivariate regression and stratified analysis suggested that the prognostic power of the eight-lncRNA signature was independent of clinical and pathological factors. Functional enrichment analyses revealed potential functional roles of the eight prognostic lncRNAs in tumorigenesis.These findings indicate that the eight-lncRNA signature may be an effective independent prognostic molecular biomarker in the prediction of NSCLC patient survival.

SUBMITTER: Zhou M

PROVIDER: S-EPMC4504221 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer.

Zhou Meng M Guo Maoni M He Dongfeng D Wang Xiaojun X Cui Yinqiu Y Yang Haixiu H Hao Dapeng D Sun Jie J

Journal of translational medicine 20150717

<h4>Background</h4>Accumulated evidence suggests that dysregulated expression of long non-coding RNAs (lncRNAs) may play a critical role in tumorigenesis and prognosis of cancer, indicating the potential utility of lncRNAs as cancer prognostic or diagnostic markers. However, the power of lncRNA signatures in predicting the survival of patients with non-small cell lung cancer (NSCLC) has not yet been investigated.<h4>Methods</h4>We performed an array-based transcriptional analysis of lncRNAs in l ...[more]

PMID: 26183581

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Project description:Background: With a high incidence and dismal survival rate, hepatocellular carcinoma (HCC) tops the list of the world's most frequent malignant tumors. Immunotherapy is a new approach to cancer treatment, and its effect on prolonging overall survival (OS) varies from patient to patient. For a more effective prognosis and treatment of HCC, we are committed to identifying immune infiltration-related long non-coding RNAs (IIRLs) with prognostic value in hepatocellular carcinoma. Methods: In our study, we calculated immune scores of 369 hepatocellular carcinoma samples from the Cancer Genome Atlas (TCGA) database by using an estimation algorithm, and obtained long non-coding RNAs (lncRNAs) associated with immune infiltration by using Weighted Gene Co-expression Network analysis (WGCNA). For training cohort, univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis were used to determine prognostic IIRLs, we established a prognostic IIRLs signature. By testing cohort and entire cohort, we confirmed that the signature is practical. The prognosis of people with different clinicopathological stages and risk scores were predicted by the nomogram we constructed. In addition, Immune cell infiltration analysis and prediction of therapeutic drugs were performed. Results: 93 IIRLs were obtained by WGCNA. Furthermore, the prognostic value of these IIRLs were evaluated by using univariate Cox, Lasso and multivariate Cox analysis. Four IIRLs were used to create a signature with a prognosis. Time-related receiver operating characteristic (ROC) curve revealed that this model had an acceptable prognostic value for HCC patients. By using univariate and multivariate Cox regression analysis, this risk score has been shown to be an independent prognostic factor for HCC. The nomogram we made showed good predictions. Except for that, the treatment with immune checkpoint inhibitors (ICI) was likely to be more effective for low-risk patients. Conclusion: Based on four IIRLs, a prognostic signature was created in this research showed good accuracy in predicting OS. This study also provided valuable references for Immunotherapy of hepatocellular carcinoma.

Project description:Background Findings from numerous studies have revealed that ferroptosis is closely related to tumorigenesis and immune cell infiltration. Long non-coding RNAs (lncRNAs) are reportedly involved in the progression of various cancers, including prostate cancer (PCa). This study was designed to establish a ferroptosis-related lncRNA (frlncRNA) signature to predict PCa prognosis. Methods The frlncRNAs were identified by studying their expression by Pearson’s correlation analysis. Differentially expressed prognosis related frlncRNAs were identified by the Wilcoxon test and univariate Cox regression analysis. The LASSO Cox regression model was used to build a model to predict biochemical recurrence (BCR) based on frlncRNAs. The GSEA software (version 4.1.0) was used to explore the enriched pathways in high- and low- risk groups. Patients with PCa were clustered into different subgroups by unsupervised clustering based on the frlncRNAs considered in the prognostic model. Real-time PCR and CCK8 assays were performed to verify the expression and function of frlncRNAs. Results We identified 35 differentially expressed prognosis related frlncRNAs based on data on PCa from TCGA. A risk signature based on five frlncRNAs (AP006284.1, AC132938.1, BCRP3, AL360181.4 and AL135999.1), was confirmed to perform well in predicting BCR. The high-risk group had higher disease grades and a greater number of infiltrating immune cells. Besides this, we found that the five frlncRNAs were connected with typical immune checkpoints. With respect to molecular mechanisms, several metabolic pathways were found to enriched in the low-risk group. Furthermore, patients could be classified into different subtypes with different PSA-free times using the five frlncRNAs. Notably, AP006284.1, AC132938.1, BCRP3 and AL135999.1 were upregulated in PCa cells and tissues, whereas AL360181.4 exhibited the opposite trend. The downregulation of BCRP3 and AP006284.1 impaired the proliferation of 22RV1 cells. Conclusion We generated a prognostic model based on five frlncRNAs, with clinical usefulness, and thus provided a novel strategy for predicting the BCR of patients with PCa. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09876-8.

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A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer.

Publications

A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer.

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