Sheng Yang

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.

Comprehensive analysis of a novel lncRNA profile reveals potential prognostic biomarkers in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the main subtype of malignant kidney cancer. Long non‑coding RNA (lncRNA) serves a key role in predicting survival in patients with cancer. The present study aimed to develop an lncRNA‑related signature of prognostic values for patients with ccRCC. RNA sequencing data of 454xa0patients were analyzed from The Cancer Genome Atlas (TCGA). To identify the differentially expressed lncRNAs, the patients from four groups classified by tumor stages were compared. The association between survival outcome and lncRNA expression profile was assessed by the univariate and multivariate Cox proportional hazards model. Survival was analyzed using the log‑rank test, and functions of target lncRNAs were investigated through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Finally, 19xa0lncRNAs were identified as significantly associated with overall survival (OS) time. These lncRNAs were gathered as a signal prognostic signature, which may be a potential biomarker for the prognosis of ccRCC. The risk score was built to evaluate the predictive value of the lncRNA signature. There was a significant positive correlation between ccRCC patients with the low‑risk score and OS time (P<0.001). Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) was used to verify the result in 17xa0pairs of ccRCC and adjacent non‑tumor tissues. Functional enrichment analysis revealed that these lncRNAs were associated with several molecular pathways of the tumor. The RT‑qPCR validation was consistent with the TCGA bioinformatics results. In conclusion, a tumor‑specific lncRNA signature of 19xa0lncRNAs was identified and the joint prognostic power was evaluated in the present study, and this signature was determined to be a potential biomarker for the prognosis of ccRCC.

Integrated analysis of long non‑coding RNA competing interactions revealed potential biomarkers in cervical cancer: Based on a public database

Cervical cancer (CC) is a common gynecological malignancy in women worldwide. Using an RNA sequencing profile from The Cancer Genome Atlas (TCGA) and the CC patient information, the aim of the present study was to identify potential long non‑coding RNA (lncRNA) biomarkers of CC using bioinformatics analysis and building a competing endogenous RNA (ceRNA) co‑expression network. Results indicated several CC‑specific lncRNAs, which were associated with CC clinical information and selected some of them for validation and evaluated their diagnostic values. Bioinformatics analysis identified 51xa0CC‑specific lncRNAs (fold‑change >2 and P<0.05), and 42 of these were included in ceRNA network consisting of lncRNA‑miRNA‑mRNA interactions. Further analyses revealed that differential expression levels of 19xa0lncRNAs were significantly associated with different clinical features (P<0.05). A total of 11xa0key lncRNAs in the ceRNA network for reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) analysis to detect their expression levels in 31xa0pairs of CC clinical samples. The results indicated that 7xa0lncRNAs were upregulated and 4xa0lncRNAs were downregulated in CC patients. The fold‑changes between the RT‑qPCR experiments and the TCGA bioinformatics analyses were the same. Furthermore, the area under the receiver operating characteristic (ROC) curve of four lncRNAs (EMX20S, MEG3, SYS1‑DBNDD2 and MIR9‑3HG) indicated that their combined use may have a significant diagnostic value in CC (P<0.05). To the best of our knowledge, the present study is the first to have identified CC‑specific lncRNAs to construct a ceRNA network and has also provided new insights for further investigation of a lncRNA‑associated ceRNA network in CC. In additon, the verification results suggested that the method of bioinformatics analysis and screening of lncRNAs was accurate and reliable. To conclude, the use of multiple lncRNAs may thus improve diagnostic efficacy in CC. In addition, these specific lncRNAs may serve as new candidate biomarkers for clinical diagnosis, classification and prognosis of CC.

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.

Trends on PM2.5 research, 1997–2016: a bibliometric study

Particulate matter with the aerodynamic equivalent diameter ≤ 2.5 μm is considered as fine particulate matter (PM2.5) (Callen et al. 2012). Because of its small size, light weight, long-time retention and drift in the atmosphere, it has become the primary pollutant in large cities and has attracted wide attention. PM2.5 has attracted scholars’ wide attention because the massive evidence indicated that PM2.5 had significant impact in many aspects, including air quality (Buczynska et al. 2014; Collins et al. 2014), human health (respiratory system, cardiovascular health) (Weichenthal et al. 2014; Xing et al. 2016), and climate (cloud and rain) (Huo et al. 2009; Lin et al. 2015). The study interest on PM2.5 has been increasing dramatically recently, and many academic journals have published papers about it. Bibliometric provides a good choice to assess the trend in research activity over time, analyzes the contributions of countries, and institutes journals and scholars (Wang et al. 2016). Presently, there is no study on PM2.5 bibliometric. The present study attempted to get a comprehensive understanding of the current state of PM2.5 research. By analyzing publication, we captured the collaboration pattern between countries/territories, institutions, and authors, better understood the global trend, and discovered the research frontiers in this field.

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.

Diagnosis value of aberrantly expressed microRNA profiles in lung squamous cell carcinoma: a study based on the Cancer Genome Atlas

Background Lung cancer is considered as one of the most frequent and deadly cancers with high mortality all around the world. It is critical to find new biomarkers for early diagnosis of lung cancer, especially lung squamous cell carcinoma (LUSC). The Cancer Genome Atlas (TCGA) is a database which provides both cancer and clinical information. This study is a comprehensive analysis of a novel diagnostic biomarker for LUSC, based on TCGA. Methods and Results The present study investigated LUSC-specific key microRNAs (miRNAs) from large-scale samples in TCGA. According to exclusion criteria and inclusion criteria, the expression profiles of miRNAs with related clinical information of 332 LUSC patients were obtained. Most aberrantly expressed miRNAs were identified between tumor and normal samples. Forty-two LUSC-specific intersection miRNAs (fold change >2, p < 0.05) were obtained by an integrative computational method, among them six miRNAs were found to be aberrantly expressed concerning characteristics of patients (gender, lymphatic metastasis, patient outcome assessment) through Student t-test. Five miRNAs correlated with overall survival (log-rank p < 0.05) were obtained through the univariate Cox proportional hazards regression model and Mantel–Haenszel test. Then, five miRNAs were randomly selected to validate the expression in 47 LUSC patient tissues using quantitative real-time polymerase chain reaction. The results showed that the test findings were consistent with the TCGA findings. Also, the diagnostic value of the specific key miRNAs was determined by areas under receiver operating characteristic curves. Finally, 577 interaction mRNAs as the targets of 42 LUSC-specific intersection miRNAs were selected for further bioinformatics analysis. Conclusion This study indicates that this novel microRNA expression signature may be a useful biomarker of the diagnosis for LUSC patients, based on bioinformatics analysis.

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.