Dataset Information

Systematic identification of long non-coding RNAs with cancer-testis expression patterns in 14 cancer types.

ABSTRACT: Cancer-testis (CT) genes are a group of genes that are potential targets of immunotherapy and candidate epi-drivers participating in the development of cancers. Previous studies mainly focused on protein-coding genes, neglecting long non-coding RNAs with the same expression patterns. In this study, we performed a systematic investigation of cancer-testis long non-coding RNAs (CT-lncRNAs) with multiple independent open-access databases.We identified 1,325 extremely highly expressed CT-lncRNAs (EECT-lncRNAs) in 14 cancer types. Functional annotation revealed that CT-lncRNAs reactivated in cancers could promote genome instability and the malignant potential of cancers. We observed a mutually exclusive pattern of EECT-lncRNA activation and mutation in known oncogenes, suggesting their potential role as drivers of cancer that complement known mut-driver genes. Additionally, we provided evidence that testis-specific regulatory elements and promoter hypo-methylation may be EECT-lncRNA activation mechanisms, and EECT-lncRNAs may regulate CT gene reactivation. Taken together, our study puts forth a new hypothesis in the research field of CT genes, whereby CT-lncRNAs/EECT-lncRNAs play important roles in the progression and maintenance of tumorigenesis, expanding candidate CT epi-driver genes from coding genes to non-coding RNAs.

SUBMITTER: Qin N

PROVIDER: S-EPMC5706911 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Systematic identification of long non-coding RNAs with cancer-testis expression patterns in 14 cancer types.

Qin Na N Wang Cheng C Lu Qun Q Ma Zijian Z Dai Juncheng J Ma Hongxia H Jin Guangfu G Shen Hongbing H Hu Zhibin Z

Oncotarget 20171019 55

Cancer-testis (CT) genes are a group of genes that are potential targets of immunotherapy and candidate epi-drivers participating in the development of cancers. Previous studies mainly focused on protein-coding genes, neglecting long non-coding RNAs with the same expression patterns. In this study, we performed a systematic investigation of cancer-testis long non-coding RNAs (CT-lncRNAs) with multiple independent open-access databases.We identified 1,325 extremely highly expressed CT-lncRNAs (EE ...[more]

PMID: 29212265

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Project description:In the past decade, hundreds of long noncoding RNAs (lncRNAs) have been identified as significant players in diverse types of cancer; however, the functions and mechanisms of most lncRNAs in cancer remain unclear. Several computational methods have been developed to detect associations between cancer and lncRNAs, yet those approaches have limitations in both sensitivity and specificity. With the goal of improving the prediction accuracy for associations of lncRNA with cancer, we upgraded our previously developed cancer-related lncRNA classifier, CRlncRC, to generate CRlncRC2. CRlncRC2 is an eXtreme Gradient Boosting (XGBoost) machine learning framework, including Synthetic Minority Over-sampling Technique (SMOTE)-based over-sampling, along with Laplacian Score-based feature selection. Ten-fold cross-validation showed that the AUC value of CRlncRC2 for identification of cancer-related lncRNAs is much higher than previously reported by CRlncRC and others. Compared with CRlncRC, the number of features used by CRlncRC2 dropped from 85 to 51. Finally, we identified 439 cancer-related lncRNA candidates using CRlncRC2. To evaluate the accuracy of the predictions, we first consulted the cancer-related long non-coding RNA database Lnc2Cancer v2.0 and relevant literature for supporting information, then conducted statistical analysis of somatic mutations, distance from cancer genes, and differential expression in tumor tissues, using various data sets. The results showed that our approach was highly reliable for identifying cancer-related lncRNA candidates. Notably, the highest ranked candidate, lncRNA AC074117.1, has not been reported previously; however, integrated multi-omics analyses demonstrate that it is the target of multiple cancer-related miRNAs and interacts with adjacent protein-coding genes, suggesting that it may act as a cancer-related competing endogenous RNA, which warrants further investigation. In conclusion, CRlncRC2 is an effective and accurate method for identification of cancer-related lncRNAs, and has potential to contribute to the functional annotation of lncRNAs and guide cancer therapy.

Project description:Abnormal regulation of long non-coding RNAs (lncRNAs) appears to be a primary feature of numerous types of human cancer. However, the association between the dysregulation of lncRNAs and functional alterations in gastric cancer (GC) remains unclear. In previous studies, we applied microarray and bioinformatics analyses to screen for key lncRNAs from the tumor tissues and matched adjacent non-tumor tissues of 10 patients with GC. There were seven key lncRNAs demonstrated to be significantly different between carcinoma tissues and adjacent non-tumor tissues. In the present study, the expression of these seven selected lncRNAs were validated in 82 patients with GC to further investigate the association between lncRNAs and GC clinical characterization. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) results demonstrated that RP5-919F19, MCPH1 antisense RNA 1 (CTD-2541M15) and urothelial carcinoma-associated 1 (UCA1) exhibited consistent upregulation in cancer compared with adjacent non-tumor tissues, whereas AP000459, LOC101928316, tumor suppressor candidate 8 (LINC01071) and maternally expressed 3 (MEG3) showed consistent downregulation. The results from the microarray and RT-qPCR experiments achieved 100% agreement. A correlation analysis indicated that RP5-919F19, LOC101928316 and MEG3 were significantly associated with tumor differentiation degree, RP5-919F19, UCA1 and MEG3 were significantly associated with lymph node metastasis, and RP5-919F19, CTD-2541M15 and UCA1 were significantly associated with tumor-node-metastasis stage (P<0.05). In addition, it was identified that the differential expression of LINC01071 and LOC101928316 significantly correlated with the age and gender of the GC patients, respectively (P<0.05). The results suggest that the lncRNAs RP5-919F19, LOC101928316, CTD-2541M15, UCA1 and MEG3 are closely associated with the invasion and metastasis of GC, which reveals these indicators as potential specificity biomarkers for the diagnosis, prognosis and classification of GC. Thus, these lncRNAs merit further study as novel candidate biomarkers for the clinical diagnosis of GC and as potential targets for therapy.

Dataset Information

Systematic identification of long non-coding RNAs with cancer-testis expression patterns in 14 cancer types.

Publications

Systematic identification of long non-coding RNAs with cancer-testis expression patterns in 14 cancer types.

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