Dataset Information

Identification of novel long non-coding RNA biomarkers for prognosis prediction of papillary thyroid cancer.

ABSTRACT: Papillary thyroid carcinoma (PTC) is the most frequent type of malignant thyroid tumor. Several lncRNA signatures have been established for prognosis prediction in some cancers. However, the prognostic value of lncRNAs has not been investigated in PTC yet. In this study, we performed genome-wide analysis of lncRNA expression profiles in a large cohort of PTC patients from The Cancer Genome Atlas and identified 111 differentially expressed lncRNAs between tumor and non-tumor samples and between recurrent and recurrence-free samples. From the 111 differentially expressed lncRNAs, four independent lncRNA biomarkers associated with prognosis were identified and were integrated into a four-lncRNA signature which classified the patients of training dataset into the high-risk group and low-risk group with significantly different overall survival (p=0.016, log-rank test). The prognostic value of the four-lncRNA signature was validated in the independent testing dataset. Multivariate analysis indicated that the four-lncRNA signature was an independent prognostic predictor. Moreover, identified four lncRNA biomarkers demonstrates good performance in predicting disease recurrence with AUC of 0.833 using leave one out cross-validation. Our study not only highlighted the potential role for lncRNAs to improve clinical prognosis prediction in patients with PTC and but also provided alternative biomarkers and therapeutic targets for PTC patients.

SUBMITTER: Li Q

PROVIDER: S-EPMC5542255 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Identification of novel long non-coding RNA biomarkers for prognosis prediction of papillary thyroid cancer.

Li Qiuying Q Li Haihong H Zhang Lu L Zhang Chunming C Yan Wentao W Wang Chao C

Oncotarget 20170701 28

Papillary thyroid carcinoma (PTC) is the most frequent type of malignant thyroid tumor. Several lncRNA signatures have been established for prognosis prediction in some cancers. However, the prognostic value of lncRNAs has not been investigated in PTC yet. In this study, we performed genome-wide analysis of lncRNA expression profiles in a large cohort of PTC patients from The Cancer Genome Atlas and identified 111 differentially expressed lncRNAs between tumor and non-tumor samples and between r ...[more]

PMID: 28545026

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Project description:Introduction: Thyroid cancer (THCA) has become a serious malignant tumor worldwide. Identification of non-coding RNA related regulators is very necessary to improve the knowledge of THCA treatment. The aim of this study was to identify novel therapeutic targets and prognosis biomarkers for predicting pathological characteristics and subsequently treating THCA. Methods: We investigated the alterations of miRNAs, mRNAs and lncRNAs in THCA. Functional enrichment and clustering analysis were conducted for these aberrantly expressed RNAs. Multiple interaction networks among miRNAs, mRNAs and lncRNAs were constructed and the functional modules associated with THCA patients' prognosis were identified. Furthermore, we evaluated the prognostic roles of the important miRNAs, mRNAs and lncRNAs in THCA and investigated the regulatory potential of non-coding RNAs on immune cell infiltration. Results: We firstly identified that miR-4709-3p and miR-146b-3p could significantly classify patients into high/low risk groups, which may be potential prognosis biomarkers of THCA. Secondly, we constructed a THCA-related miRNA-mRNA network, which displayed small world network topological characters. Two THCA-related functional modules were identified from the miRNA-mRNA network by MCODE. Results showed that two modules could implicate in known cancer pathways, such as apoptosis and focal adhesion. Thirdly, a THCA-related miRNA-lncRNA network was constructed. A subnetwork of miRNA-lncRNA network showed strong prognosis effect in THCA. Fourthly, we constructed a THCA-related mRNA-lncRNA network and detected several typical lncRNA-miRNA-mRNA crosstalk, such as AC068138, BCL2, miR-21 and miR-146b, which had good prognosis effect in THCA. Immune infiltration results showed that lncRNAs LA16c-329F2, RP11-395N3, RP11-423H2, RP11-399B17 and RP11-1036E20 were high related to neutrophil and dendritic cell infiltration. Discussion: Non-coding RNA-mediated gene regulatory network has the strong regulatory potential in pathological processes of THCA. All these results could help us uncover the non-coding RNA-mediated regulatory mechanism in THCA.

Project description:It has been revealed from microarray data analysis that long intergenic non?coding RNA 02454 (LINC02454) is highly expressed in papillary thyroid cancer (PTC). The aim of the present study was to explore the potential role of LINC02454 in the tumorigenesis of PTC. The mRNA expression levels of LINC02454 were assessed using data from The Cancer Genome Atlas (TCGA) and the GSE66783 cohort in thyroid cancer, and were validated using reverse transcription?quantitative PCR in 104 patients with PTC recruited in the present study. The association between the LINC02454 mRNA expression levels and the clinicopathological features of the 104 patients with PTC were also analyzed. Functional enrichment analyses were conducted on the differentially expressed genes in the high and low LINC02454 expression groups that were identified from the TCGA cohort. RNA interference, using short interfering (si)RNA against LINC02454, was used to investigate the role of LINC02454 in the biological functions of PTC cells in vitro. The expression level of LINC02454 was significantly increased in PTC tissues (P=0.0011) and was significantly associated with a larger tumor size, T stage, an advanced TNM stage and an increased lymph node metastasis (P<0.05), which was consistent with that in the TCGA and GSE66783 cohort. High expression levels of LINC02454 were observed in patients with PTC that also had BRAF mutations (P<0.001), and were significantly associated with a poorer disease?free survival in the TCGA cohort (P<0.05). Functional enrichment analysis indicated that LINC02454?related genes were significantly enriched in Gene Ontology terms, such as 'positive regulation of cell proliferation', 'positive regulation of cell division' and 'cell adhesion', and the following Kyoto Encyclopedia of Genes and Genomes pathways: 'Pathways in cancer' 'proteoglycans in cancer' and 'ECM?receptor interaction'. In vitro, the knockdown of LINC02454 markedly arrested the cells in the G0/G1 phase of the cell cycle, and also led to an overall increase in apoptosis, as well as to an unexpected decrease in cell proliferation. LINC02454 may thus potentially function as an oncogene, which inhibits the apoptosis and enhances proliferation of PTC cells. Thus, as suggested by the findings of the present study, LINC02454 may be used as a diagnostic and prognostic biomarker for PTC in the future.

Project description:PURPOSE:Esophageal squamous cell carcinoma (ESCC) remains a common aggressive malignancy in the world. Several long non-coding RNAs (lncRNAs) are reported to predict the prognosis of ESCC. Therefore, an in-depth research is urgently needed to further investigate the prognostic value of lncRNAs in ESCC. RESULTS:From the training set, we identified a eight-lncRNA signature (including AP000487, AC011997, LINC01592, LINC01497, LINC01711, FENDRR, AC087045, AC137770) which separated the patients into two groups with significantly different overall survival (hazard ratio, HR = 3.79, 95% confidence interval, 95% CI [2.56-5.62]; P < 0.001). The signature was applied to the validation set (HR = 2.73, 95%CI [1.65-4.53]; P < 0.001) and showed similar prognostic values. Stratified, univariate and multivariate Cox regression analysis indicated that the signature was an independent prognostic factor for patients with ESCC. A nomogram based on the lncRNAs signature, age, grade and stage was developed and showed good accuracy for predicting 1-, 3- and 5-year survival probability of ESCC patients. We found a strong correlation between the gene significance for the survival time and T stage. Eight modules were constructed, among which the key module most closely associated with clinical information was identified. CONCLUSIONS:Our eight-lincRNA signature and nomogram could be practical and reliable prognostic tools for esophageal squamous cell carcinoma. METHODS:We downloaded the lncRNA expression profiles of ESCC patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets and separated to training and validation cohort. The univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identify a lncRNA-based signature. The predictive value of the signature was assessed using the Kaplan-Meier method, receiver operating characteristic (ROC) curves and area under curve (AUC). Weighted gene co-expression network analysis (WGCNA) was applied to predict the intrinsic relationship between gene expressions. In addition, we further explored the combination of clinical information and module construction.

Dataset Information

Identification of novel long non-coding RNA biomarkers for prognosis prediction of papillary thyroid cancer.

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Identification of novel long non-coding RNA biomarkers for prognosis prediction of papillary thyroid cancer.

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