Dataset Information

Novel lncRNA UPLA1 mediates tumorigenesis and prognosis in lung adenocarcinoma.

ABSTRACT: With the development of molecular biotechnology and sequencing techniques, long non-coding RNAs (lncRNAs) have been shown to play a vital role in a variety of cancers including lung cancer. In our previous study, we used RNA sequencing and high-content screening proliferation screening data to identify lncRNAs that were significantly associated with tumour biological functions such as LINC01426. Herein, based on previous work, we report a novel lncRNA UPLA1 (upregulation promoting LUAD-associated transcript-1), which has not been explored or reported in any previous studies. Our results showed that UPLA1 is highly expressed and regulates important biological functions in lung adenocarcinoma. In vitro experiments revealed that UPLA1 promoted the migration, invasion, and proliferation abilities, and is related to cell cycle arrest, in lung adenocarcinoma cells. Moreover, the upregulation of UPLA1 significantly improved the growth of tumours in vivo. We identified that UPLA1 was mainly located in the nucleus using fluorescence in situ hybridisation, and that it promoted Wnt/?-catenin signalling by binding to desmoplakin using RNA pulldown assay and mass spectrometry. Additionally, luciferase reporter assay revealed that YY1 is the transcription factor of UPLA1 and suppressed the expression of UPLA1 as a transcriptional inhibitor. This finding provides important evidence regarding the two roles of YY1 in cancer. Furthermore, in situ hybridisation assay results showed that UPLA1 was closely related to the prognosis and tumour, node, metastasis (TNM) stage of lung adenocarcinoma. In summary, our results suggest that the novel lncRNA UPLA1 promotes the progression of lung adenocarcinoma and may be used as a prognostic marker, and thus, has considerable clinical significance.

SUBMITTER: Han X

PROVIDER: S-EPMC7680460 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Novel lncRNA UPLA1 mediates tumorigenesis and prognosis in lung adenocarcinoma.

Han Xiaoyang X Jiang Hua H Qi Jianni J Li Jiamei J Yang Jinghan J Tian Yingying Y Li Wei W Jing Qi Q Wang Chuanxi C

Cell death & disease 20201121 11

With the development of molecular biotechnology and sequencing techniques, long non-coding RNAs (lncRNAs) have been shown to play a vital role in a variety of cancers including lung cancer. In our previous study, we used RNA sequencing and high-content screening proliferation screening data to identify lncRNAs that were significantly associated with tumour biological functions such as LINC01426. Herein, based on previous work, we report a novel lncRNA UPLA1 (upregulation promoting LUAD-associate ...[more]

PMID: 33221813

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Project description:BackgroundCuproptosis, a recently discovered type of programmed cell death (PCD), paves a new avenue for cancer treatment. It has been revealed that PCD-related lncRNAs play a critical role in various biological processes of lung adenocarcinoma (LUAD). However, the role of cuproptosis-related lncRNA (CuRLs) remains unclear. This study aimed to identify and validate a CuRLs-based signature for the prognostic prediction of patients with LUAD.MethodsRNA sequencing data and clinical information of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Pearson correlation analysis was used to identify CuRLs. Univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, and stepwise multivariate Cox analysis were applied to construct a novel prognostic CuRLs signature. A nomogram was developed for the prediction of patient survival outcomes. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to explore potential functions underlying the CuRLs signature. Patients were divided into low- and high-risk groups. Several algorithms, such as tumor immune estimation resource (TIMER), cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and QuanTIseq, were combined to comprehensively investigate the differences in immune landscape between different risk groups. Sensitivity to common anticancer drugs was analyzed using the pRRophetic algorithm.ResultsWe constructed a novel prognostic signature based on 10 CuRLs, including CARD8-AS1, RUNDC3A-AS1, TMPO-AS1, MIR31HG, SEPSECS-AS1, DLGAP1-AS1, LINC01137, ZSCAN16-AS1, APTR, and ELOA-AS1. This 10-CuRLs risk signature showed great diagnostic accuracy combined with traditional clinical risk factors, and a nomogram was constructed for potential clinical translation. The tumor immune microenvironment was significantly different between different risk groups. Among drugs commonly used in the treatment of lung cancer, the sensitivity of cisplatin, docetaxel, gemcitabine, gefitinib, and paclitaxel was higher in low-risk patients, and patients in the low-risk group may benefit more from imatinib.ConclusionsThese results revealed the outstanding contribution of the CuRLs signature to the evaluation of prognosis and treatment modalities for patients with LUAD. The differences in characteristics between different risk groups provide an opportunity for better patient stratification and to explore novel drugs in different risk groups.

Project description:Background: Cellular senescence has recently been considered a new cancer hallmark. However, the factors regulating cellular senescence have not been well characterized. The aim of this study is to identify long non-coding RNAs (lncRNAs) associated with senescence and prognosis in patients with lung adenocarcinoma (LUAD). Methods: Using RNA sequence data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a subset of senescence-related lncRNAs was first identified. Then, using univariate and multivariate Cox regression analyses, a senescence lncRNA signature (LUADSenLncSig) associated with LUAD prognosis was developed. Based on the median LUADSenLncSig risk score, LUAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) in the high- and low-risk score subgroups. Differences in Gene Set Enrichment Analysis (GSEA), immune infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) module score, chemotherapy, and targeted therapy selection were also compared between the high-risk and low-risk groups. Results: A prognostic risk model was obtained consisting of the following nine senescence-related lncRNAs: LINC01116, AC005838.2, SH3PXD2A-AS1, VIMS-AS1, SH3BP5-AS1, AC092279.1, AC026355.1, AC027020.2, and LINC00996. The LUADSenLncSig high-risk group was associated with poor OS (hazard ratio = 1.17, 95% confidence interval = 1.102-1.242; p < 0.001). The accuracy of the model was further supported based on receiver operating characteristic (ROC), principal component analysis (PCA), and internal validation cohorts. In addition, a nomogram was developed consisting of LUADSenLncSig for LUAD prognosis, which is consistent with the actual probability of OS. Furthermore, immune infiltration analysis showed the low-risk group had a stronger anti-tumor immune response in the tumor microenvironment. Notably, the levels of immune checkpoint genes such as CTLA-4, PDCD-1, and CD274, and the TIDE scores were significantly higher in the low-risk subgroups than in high-risk subgroups (p < 0.001). This finding indicates the LUADSenLncSig can potentially predict immunotherapy efficacy. Conclusion: In this study, a lncRNA signature, LUADSenLncSig, that has dual functions of senescence phenotype identification and prognostic prediction as well as the potential to predict the LUAD response to immunotherapy was developed.

Dataset Information

Novel lncRNA UPLA1 mediates tumorigenesis and prognosis in lung adenocarcinoma.

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Novel lncRNA UPLA1 mediates tumorigenesis and prognosis in lung adenocarcinoma.

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