Dong-yue Wen

Overexpression of LncRNA HOTAIR is Associated with Poor Prognosis in Thyroid Carcinoma: A Study Based on TCGA and GEO Data

The role of long non-coding RNA (lncRNA) HOX transcript antisense RNA (HOTAIR) in thyroid carcinoma (TC) remains unclear. The current study was aimed to assess the clinical value of HOTAIR expression levels in TC based on publically available data and to evaluate its potential signaling pathways. The expression data of HOTAIR and clinical information concerning TC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. Furthermore, 3 online biological databases, Starbase, Cbioportal, and Multi Experiment Matrix, were used to identify HOTAIR-related genes in TC. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Panther pathway analyses were then undertaken to study the most enriched signaling pathways in TC (EASE score<0.1, Bonferroni<0.05). The TCGA results demonstrated that the expression level of HOTAIR in TC tissues was significantly increased compared with non-cancerous tissues (p<0.001). HOTAIR over-expression was significantly associated with poor survival in TC patients (p=0.03). Meta-analyses of GEO datasets revealed a trend consistent with the above results on HOTAIR expression levels in TC (SMD=0.23; 95%CI, 0.00-0.45; p=0.047). Finally, the results of functional analysis for HOTAIR-related genes indicated that HOTAIR might participate in tumorigenesis via the Wnt signaling pathway. In conclusion, our study demonstrates that HOTAIR may be involved in thyroid carcinogenesis, and the over-expression of HOTAIR could act as a biomarker associated with a poor outcome in TC patients. Moreover, the Wnt signaling pathway may be the key pathway regulated by HOTAIR in TC.

Diagnostic and prognostic roles of IRAK1 in hepatocellular carcinoma tissues: an analysis of immunohistochemistry and RNA-sequencing data from the cancer genome atlas

Background IRAK1 has been repoted to play an essential role in the development of multiple cancers. However, the clinical significance of IRAK1 in hepatocellular carcinoma (HCC) and the underlying molecular mechanism remain unclear. Therefore, we aimed to investigate the role of IRAK1 in the pathogenesis of HCC in this study. Materials and methods HCC tissues and para-carcinoma tissues were collected for immunohistochemistry (IHC) analysis to evaluate IRAK1 expression. Data of IRAK1 expression were downloaded from the cancer genome atlas (TCGA) for analyzing the clinical significance of IRAK1. Receiver operating characteristic (ROC) curve and survival analyses were carried out to assess the diagnostic and prognostic significance of IRAK1 in IHC and TCGA data. Additionally, we investigated the alteration of IRAK1 gene in HCC from cBioPortal to generate a network of the interaction between IRAK1 and the neighboring genes. The influence of IRAK1 gene alteration on the prognosis of HCC patients was evaluated by survival analysis. Results Analysis of both IHC and TCGA data revealed a significant upregulation of IRAK1 in HCC tissues. The IHC analysis revealed there was an increasing trend in IRAK1 expression among normal liver tissues, liver cirrhosis tissues, para-carcinoma tissues and HCC tissues. The ROC curves for IHC and TCGA data demonstrated that IRAK1 exhibited a significant diagnostic value for HCC. Moreover, IRAK1 expression was observed to be associated with tumor size, metastasis and T-stage. The survival analysis indicated that the upregulation of IRAK1 predicted a worse overall survival of HCC. Additionally, data from cBioPortal confirmed that 29% of HCC tissues possessed an alteration of the IRAK1 gene. Conclusion IRAK1 may act as an oncogene in the development of HCC with its overexpression in HCC. Moreover, IRAK1 might serve as a promising diagnostic and therapeutic target for HCC.

RNA-sequencing investigation identifies an effective risk score generated by three novel lncRNAs for the survival of papillary thyroid cancer patients

Scholars are striving to apply molecular biology involving long non-coding RNA (lncRNA) in the prognostication of papillary thyroid cancer (PTC). However, the clinical role of lncRNAs in the prognostic setting of PTC is still unclear. Herein, a comprehensive inquiry was performed to screen lncRNA expression profiling with 507 PTC patients from The Cancer Genome Atlas RNA-sequencing datasets. A total of 734 lncRNAs were detected to be aberrantly expressed, among which three novel lncRNAs including AC079630.2, CRNDE and CTD-2171N6.1 were markedly related to the progression and survival of PTC. Furthermore, the aberrant expression of these lncRNAs could be verified by other cohorts from gene expression omnibus (GEO) as detected by microarrays. Next, we established a three-lncRNA signature and divided the PTC patients into two subgroups of high- and low-risk. Interestingly, patients with high-risk tended to gain obviously poorer outcome. Most importantly, this three-lncRNA signature was an independent biomarker to predict the patient survival of PTC. The accurate molecular roles of these three lncRNAs remains unclarified and warrants further characterization, but our current data propose that they might play pivotal roles in PTC tumorigenesis and more importantly, these novel lncRNAs are closely related to patients’ survival. These discoveries will have far-reaching consequences with respect to molecular prediction of PTC.

The diagnostic and prognostic values of Ki-67/MIB-1 expression in thyroid cancer: a meta-analysis with 6,051 cases

Background Growing evidence has demonstrated that Ki-67/MIB-1 has an effect on the clinical progression and prognosis in cancers. However, the diagnostic and prognostic values of Ki-67/MIB-1 in thyroid cancer remain unclear. Materials and methods The meta-analysis was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies were retrieved from PubMed, EBSCO, EMBASE, ISI Web of Science, China National Knowledge Infrastructure, WanFang and Chinese VIP databases. MetaDiSc and STATA12.0 were used to analyze the meta-analysis. Fixed-effect analysis and random-effect analysis were applied to pool the relative ratio based on heterogeneity in this meta-analysis. Results In the meta-analysis, 51 eligible studies were included. The pooled sensitivity of Ki-67/MIB-1 was 0.61 (95% confidence interval [CI]: 0.59–0.63) and specificity was 0.75 (95% CI: 0.74–0.77) in thyroid cancer. The pooled positive likelihood ratio was 3.19 (95% CI: 2.30–4.42) and negative likelihood ratio was 0.43 (95% CI: 0.35–0.54). In the diagnosis of thyroid cancer, the pooled diagnostic odds ratio of Ki-67/MIB-1 was 8.54 (95% CI: 5.03–14.49). The area under the symmetric receiver operating characteristic curve was 0.804 (standard error =0.031). Our results showed that there were statistical associations between Ki-67/MIB-1 and age (odds ratio [OR] =1.71, 95% CI: 1.14–2.57, P=0.010), tumor size (OR =1.86, 95% CI: 1.17–2.96, P=0.008), lymph node metastasis (OR =2.49, 95% CI: 1.42–4.39, P=0.002), metastasis status (OR =6.96, 95% CI: 2.46–19.69, P<0.001), tumor node metastasis stage (OR =6.56, 95% CI: 3.80–11.34, P<0.001) and extrathyroid extension (OR =1.91, 95% CI: 1.27–2.87, P=0.002). Furthermore, thyroid cancer patients with a high level of Ki-67/MIB-1 had a worse disease-free survival as compared to patients with a low level of Ki-67/MIB-1 (hazard ratio =5.19, 95% CI: 3.18–8.46, P<0.001). Also, Ki-67/MIB-1 was found to be associated with increased risk of mortality (hazard ratio =3.56, 95% CI: 1.17–10.83, P=0.025). Conclusion Our results demonstrated that Ki-67/MIB-1 might act as a potential factor in diagnosing thyroid cancer in Chinese. Also, the meta-analysis indicated that Ki-67/MIB-1 might have an effect on prognosis in non-Chinese thyroid cancer patients.

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

Background Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in. Methods Big data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values. Results A list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, ‘pathways in cancer,’ was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982–0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences. Discussion The study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, ‘pathways in cancer.’ The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

The anticipating value of PLK1 for diagnosis, progress and prognosis and its prospective mechanism in gastric cancer: a comprehensive investigation based on high-throughput data and immunohistochemical validation

Polo-like kinase 1 (PLK1) is a multi-functional protein and its aberrant expression is a driver of cancerous transformation and progression. To increase our understanding of the clinical value and potential molecular mechanism of PLK1 in gastric cancer (GC), we performed this comprehensive investigation. A total of 25 datasets and 12 publications were finally incorporated. Additional immunohistochemistry was conducted to validate the expression pattern of PLK1 in GC. The pooled standard mean deviation (SMD) indicated that PLK1 mRNA was up-regulated in GC (SMD=1.21, 95% CI: 0.65-1.77, P< 0.001). Similarly, the pooled odds ratio (OR) revealed that PLK1 protein was overexpressed in GC compared with normal gastric tissue (OR=12.12, 95% CI: 5.41-27.16, P<0.001). The area under the curve (AUC) of the summary receiver operating characteristic (SROC) curve was 0.86. Furthermore, our results demonstrated that GC patients with PLK1 overexpression were significantly associated with unfavorable overall survival (HR =1.54, 95% CI: 1.30–1.83, P<0.001), lymph node metastasis (OR = 1.78, 95% CI: 1.13–2.80, P=0.013) and advanced TNM stage (OR=1.48, 95% CI: 1.02-2.15, P=0.038). Altogether, 100 similar genes were identified by Gene Expression Profiling Interactive Analysis (GEPIA) and further with gene-set enrichment analysis. These genes were related to gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways relevant to the cell cycle. Gene set enrichment analysis (GSEA) indicated that PLK1 is associated with various cancer-related pathways. Collectively, this study suggests that PLK1 overexpression could play vital roles in the carcinogenesis and deterioration of GC via regulating tumor-related pathways.

An autophagy-related gene expression signature for survival prediction in multiple cohorts of hepatocellular carcinoma patients

Prognostic signatures have been proposed as clinical tools to estimate prognosis in hepatocellular carcinoma (HCC), which is the second most common contributor to cancer-related death at present globally. Autophagy-related genes play a dynamic and fundamental role in HCC, but knowledge of their utility as prognostic markers is limited. Here, we facilitated univariate and multivariate Cox proportional hazards regression analyses to reveal that 3 autophagy-related genes (BIRC5, FOXO1 and SQSTM1) were closely related to the survival of HCC. Then, we generated a prognosis index (PI) for predicting overall survival (OS) based on the three genes, which was an independent prognostic indicator for the OS of HCC (HR = 1.930, 95% CI: 1.200–3.104, P = 0.007). The PI showed moderate performance for predicting the survival of HCC patients and its efficacy was validated by data from three microarrays (GSE10143, GSE10186 and GSE17856). Furthermore, we deeply mined the integrated large-scale datasets from public microarrays and immunohistochemistry to validate the overexpression of BIRC5 and SQSTM1 while down-regulated FOXO1 expression in HCC. Bioinformatic analysis offered the hypothesis that proliferative signals in high-risk HCC patients were disturbing and thereby facilitated inferior clinical outcomes. Collectively, the prognostic signature we proposed is a promising biomarker for monitoring outcome of HCC. Nevertheless, prospective experimental studies are needed to validate the clinical utility.

Expression of the Long Intergenic Non-Protein Coding RNA 665 (LINC00665) Gene and the Cell Cycle in Hepatocellular Carcinoma Using The Cancer Genome Atlas, the Gene Expression Omnibus, and Quantitative Real-Time Polymerase Chain Reaction

Background Long non-coding RNAs (lncRNAs) have a role in physiological and pathological processes, including cancer. The aim of this study was to investigate the expression of the long intergenic non-protein coding RNA 665 (LINC00665) gene and the cell cycle in hepatocellular carcinoma (HCC) using database analysis including The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and quantitative real-time polymerase chain reaction (qPCR). Material/Methods Expression levels of LINC00665 were compared between human tissue samples of HCC and adjacent normal liver, clinicopathological correlations were made using TCGA and the GEO, and qPCR was performed to validate the findings. Other public databases were searched for other genes associated with LINC00665 expression, including The Atlas of Noncoding RNAs in Cancer (TANRIC), the Multi Experiment Matrix (MEM), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) networks. Results Overexpression of LINC00665 in patients with HCC was significantly associated with gender, tumor grade, stage, and tumor cell type. Overexpression of LINC00665 in patients with HCC was significantly associated with overall survival (OS) (HR=1.47795%; CI: 1.046–2.086). Bioinformatics analysis identified 469 related genes and further analysis supported a hypothesis that LINC00665 regulates pathways in the cell cycle to facilitate the development and progression of HCC through ten identified core genes: CDK1, BUB1B, BUB1, PLK1, CCNB2, CCNB1, CDC20, ESPL1, MAD2L1, and CCNA2. Conclusions Overexpression of the lncRNA, LINC00665 may be involved in the regulation of cell cycle pathways in HCC through ten identified hub genes.

A circRNA–miRNA–mRNA network identification for exploring underlying pathogenesis and therapy strategy of hepatocellular carcinoma

BackgroundCircular RNAs (circRNAs) have received increasing attention in human tumor research. However, there are still a large number of unknown circRNAs that need to be deciphered. The aim of this study is to unearth novel circRNAs as well as their action mechanisms in hepatocellular carcinoma (HCC).MethodsA combinative strategy of big data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology was employed to dig HCC-related circRNAs and to explore their potential action mechanisms. A connectivity map (CMap) analysis was conducted to identify potential therapeutic agents for HCC.ResultsSix differently expressed circRNAs were obtained from three Gene Expression Omnibus microarray datasets (GSE78520, GSE94508 and GSE97332) using the RobustRankAggreg method. Following the RT-qPCR corroboration, three circRNAs (hsa_circRNA_102166, hsa_circRNA_100291 and hsa_circRNA_104515) were selected for further analysis. miRNA response elements of the three circRNAs were predicted. Five circRNA–miRNA interactions including two circRNAs (hsa_circRNA_104515 and hsa_circRNA_100291) and five miRNAs (hsa-miR-1303, hsa-miR-142-5p, hsa-miR-877-5p, hsa-miR-583 and hsa-miR-1276) were identified. Then, 1424 target genes of the above five miRNAs and 3278 differently expressed genes (DEGs) on HCC were collected. By intersecting the miRNA target genes and the DEGs, we acquired 172 overlapped genes. A protein–protein interaction network based on the 172 genes was established, with seven hubgenes (JUN, MYCN, AR, ESR1, FOXO1, IGF1 and CD34) determined from the network. The Gene Oncology, Kyoto Encyclopedia of Genes and Genomes and Reactome enrichment analyses revealed that the seven hubgenes were linked with some cancer-related biological functions and pathways. Additionally, three bioactive chemicals (decitabine, BW-B70C and gefitinib) based on the seven hubgenes were identified as therapeutic options for HCC by the CMap analysis.ConclusionsOur study provides a novel insight into the pathogenesis and therapy of HCC from the circRNA–miRNA–mRNA network view.

Comprehensive analysis of the clinical significance and prospective molecular mechanisms of differentially expressed autophagy-related genes in thyroid cancer

Thyroid cancer (TC) is the most common endocrine malignancy, accounting for approximately 90% of all malignancies of the endocrine system. Despite the fact that patients with TC tend to have good prognoses, the high incidence rate and lymph node metastases remain unresolved issues. Autophagy is an indispensable process that maintains intracellular homeostasis; however, the role of autophagy in several steps of the initiation and progression of TC has not yet been elucidated. In this study, we first identified several autophagy-related genes (ARGs) that were provoked in the onset of TC. Subsequently, a bioinformatics analysis hinted that these genes were markedly disturbed in several proliferative signaling pathways. Moreover, we demonstrated that the differentially expressed ARGs were closely related to several aggressive clinical manifestations, including an advanced tumor stage and lymph node metastasis. Our study further selected prognostic ARGs and developed a prognostic signature based on three key genes (ATG9B, BID and B1DNAJB1), which displayed a moderate ability to predict the prognosis of TC. On the whole, the findings of this study demonstrate that ARGs disrupt proliferation-related pathways and consequently lead to aggressive clinical manifestations. These findings provide insight into the potential molecular mechanisms of action of ARGs and their clinical significance, and also provide classification information of potential therapeutic significance.