Siyi Xu

Systematic analyses of a novel lncRNA-associated signature as the prognostic biomarker for Hepatocellular Carcinoma

Accumulating evidence implies that long noncoding RNAs (lncRNAs) play a crucial role in predicting survival for Hepatocellular carcinoma (HCC) patients. This study aims to capture the current research hotspots of HCC, based on the analysis of publications related to HCC research from 2013 to 2017, and to identify a novel lncRNA signature for HCC prognosis through the data mining in The Cancer Genome Atlas (TCGA). “Prognosis” and “biomarker” were located in the core of the HCC research hotspot. Moreover, long noncoding RNA was the top one research frontier in HCC research. The associations between survival outcome and the expression of lncRNAs were evaluated by the univariate and multivariate Cox proportional hazards regression analyses. Four lncRNAs (LINC00261, TRELM3P, GBP1P1, and CDKN2B‐AS1) were identified as significantly correlated with overall survival (OS). These four lncRNAs were gathered as a single prognostic signature. There was a significant positive correlation between HCC patients with low‐risk scores and overall survival (HR = 1.802, 95%CI [1.224‐2.652], P = .003). Further analysis suggested that the prognostic value of this four‐lncRNA signature was independent in clinical features. The enrichment analysis of prognostic lncRNA‐related gene was performed to find out the related pathways. Our study indicates that this novel lncRNA expression signature may be a useful biomarker of the prognosis for HCC patients, based on bioinformatics analysis.

Trends of long noncoding RNA research from 2007 to 2016: a bibliometric analysis

Purpose This study aims to analyze the scientific output of long noncoding RNA (lncRNA) research and construct a model to evaluate publications from the past decade qualitatively and quantitatively. Methods Publications from 2007 to 2016 were retrieved from the Web of Science Core Collection database. Microsoft Excel 2016 and CiteSpace IV software were used to analyze publication outputs, journals, countries, institutions, authors, citation counts, ESI top papers, H-index, and research frontiers. Results A total of 3,008 papers on lncRNA research were identified published by June 17, 2017. The journal, Oncotarget (IF2016, 5.168) ranked first in the number of publications. China had the largest number of publications (1,843), but the United States showed its dominant position in both citation frequency (45,120) and H-index (97). Zhang Y (72 publications) published the most papers, and Guttman M (1,556 citations) had the greatest co-citation counts. The keyword “database” ranked first in research frontiers. Conclusion The annual number of publications rapidly increased in the past decade. China showed its significant progress in lncRNA research, but the United States was the actual leading country in this field. Many Chinese institutions engaged in lncRNA research but significant collaborations among them were not noted. Guttman M, Mercer TR, Rinn JL, and Gupta RA were identified as good candidates for research collaboration. “Database,” “Xist RNA,” and “Genome-wide association study” should be closely observed in this field.

Comprehensive analysis of a novel four-lncRNA signature as a prognostic biomarker for human gastric cancer

Emerging evidence indicates that long non-coding RNAs (lncRNAs) play a crucial role in predicting survival for gastric cancer (GC) patients. This study aims to identify a lncRNA-related signature for evaluating the overall survival of 379 GC patients from The Cancer Genome Atlas (TCGA) database. The associations between survival outcome and the expression of lncRNAs were evaluated by the univariate and multivariate Cox proportional hazards regression analyses. Four lncRNAs (LINC01018, LOC553137, MIR4435-2HG, and TTTY14) were identified as significantly correlated with overall survival. These four lncRNAs were gathered as a single prognostic signature. There was a significant positive correlation between GC patients with low-risk scores and overall survival (P = 0.001). Further analysis suggested that the prognostic value of this four-lncRNA signature was independent in clinical features. Gene set enrichment analysis found that these four lncRNAs were correlated with several molecular pathways of the tumor. Our study indicates that this novel lncRNA expression signature may be a useful biomarker of the prognosis for GC patients, based on bioinformatics analysis.

Molecular characterization of lung adenocarcinoma: A potential four-long noncoding RNA prognostic signature: SUI et al.

Lung adenocarcinoma (LUAD), mainly originated in lung glandular cells, is the most frequent pathological type of lung cancer and the 5‐year survival rate of LUAD patients is still very low. Therefore, we aim to identify a long noncoding RNA (lncRNA)–related signature as the sensitive and novel prognostic biomarkers.

Molecular characterization of papillary thyroid carcinoma: a potential three-lncRNA prognostic signature

Purpose Papillary thyroid carcinoma (PTC), the most frequent type of malignant thyroid tumor, lacks novel and reliable biomarkers of patients’ prognosis. In the current study, we mined The Cancer Genome Atlas (TCGA) to develop lncRNA signature of PTC. Patients and methods The intersection of PTC lncRNAs was obtained from the TCGA database using integrative computational method. By the univariate and multivariate Cox analysis, key lncRNAs were identified to construct the prognostic model. Then, all patients were divided into the high-risk group and low-risk group to perform the Kaplan–Meier (K–M) survival curves and time-dependent receiver operating characteristic (ROC) curve, estimating the prognostic power of the prognostic model. Functional enrichment analysis was also performed. Finally, we verified the results of the TCGA analysis by the Gene Expression Omnibus (GEO) databases and quantitative real-time PCR (qRT-PCR). Results After the comprehensive analysis, a three-lncRNA signature (PRSS3P2, KRTAP5-AS1 and PWAR5) was obtained. Interestingly, patients with low-risk scores tended to gain obviously longer survival time, and the area under the time-dependent ROC curve was 0.739. Furthermore, gene ontology (GO) and pathway analysis revealed the tumorigenic and prognostic function of the three lncRNAs. We also found three potential transcription factors to help understand the mechanisms of the PTC-specific lncRNAs. Finally, the GEO databases and qRT-PCR validation were consistent with our TCGA bioinformatics results. Conclusion We built a three-lncRNA signature by mining the TCGA database, which could effectively predict the prognosis of PTC.

Integrative analysis of competing endogenous RNA network focusing on long noncoding RNA associated with progression of cutaneous melanoma

Cutaneous melanoma (CM) is the most malignant tumor of skin cancers because of its rapid development and high mortality rate. Long noncoding RNAs (lncRNAs), which play essential roles in the tumorigenesis and metastasis of CM and interplay with microRNAs (miRNAs) and mRNAs, are hopefully considered to be efficient biomarkers to detect deterioration during the progression of CM to improve the prognosis. Bioinformatics analysis was fully applied to predict the vital lncRNAs and the associated miRNAs and mRNAs, which eventually constructed the competing endogenous RNA (ceRNA) network to explain the RNA expression patterns in the progression of CM. Further statistical analysis emphasized the importance of these key genes, which were statistically significantly related to one or few clinical features from the ceRNA network. The results showed the lncRNAs MGC12926 and LINC00937 were verified to be strongly connected with the prognosis of CM patients.

Comprehensive analysis of aberrantly expressed microRNA profiles reveals potential biomarkers of human lung adenocarcinoma progression

Lung adenocarcinoma (LUAD) is a complex disease that poses challenges for diagnosis and treatment. The aim of the present study is to investigate LUAD-specific key microRNAs (miRNAs) from large-scale samples in The Cancer Genome Atlas (TCGA) database. We used an integrative computational method to identify LUAD-specific key miRNAs related to TNM stage and lymphatic metastasis from the TCGA database. Twenty-five LUAD-specific key miRNAs (fold change >2, p<0.05) from the TCGA database were investigated, and 15 were found to be aberrantly expressed with respect to clinical features. Three miRNAs were correlated with overall survival (log-rank p<0.05). Then, 5 miRNAs were randomly selected for verification of expression in 53 LUAD patient tissues using qRT-PCR. Diagnostic value of these above 5 miRNAs was determined by areas under receiver operating characteristic curves (ROC). Finally, the LUAD-related miRNA miR-30a-3p was selected for verification of biologic function in A549 cells. The results of tests for cell proliferation, apoptosis, and target genes suggested that miR-30a-3p decreases cell proliferation and promotes apoptosis through targeting AKT3. Therefore, miR-30a-3p may be a promising biomarker for the early screening of high-risk populations and early diagnosis of LUAD. Our studies provide insights into identifying novel potential biomarkers for diagnosis and prognosis of LUAD.