Bioinformatic analyses of microRNA-targeted genes and microarray-identified genes correlated with Barrett's esophagus

Lv, Jing; Liu, Jijun; Guo, Lei; Zhang, Jun; Cheng, Yan; Chu, Chengcai; Zhao, Heping; Wang, Jihan

doi:10.1080/15384101.2018.1431597

Cited by 9 publications

(8 citation statements)

References 51 publications

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“…Numerous studies related to EAC and BE have been conducted over the last decades, and more attention should be paid to the pathogenesis and the molecular mechanisms involved in the progression of BE and EAC, which could help in prevention and improve prognosis[19]. Currently, histological examinations are the golden standard for the diagnosis of EAC and BE; however, the inter- and intra-observer variabilities are not ideal[20].…”

Section: Discussionmentioning

confidence: 99%

Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett’s esophagus

Guo

Wang

et al. 2019

WJG

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BACKGROUNDEsophageal adenocarcinoma (EAC) is an aggressive disease with high mortality and an overall 5-year survival rate of less than 20%. Barrett’s esophagus (BE) is the only known precursor of EAC, and patients with BE have a persistent and excessive risk of EAC over time. Individuals with BE are up to 30-125 times more likely to develop EAC than the general population. Thus, early detection of EAC and BE could significantly improve the 5-year survival rate of EAC. Due to the limitations of endoscopic surveillance and the lack of clinical risk stratification strategies, molecular biomarkers should be considered and thoroughly investigated.AIMTo explore the transcriptome changes in the progression from normal esophagus (NE) to BE and EAC.METHODSTwo datasets from the Gene Expression Omnibus (GEO) in NCBI Database (https://www.ncbi.nlm.nih.gov/geo/) were retrieved and used as a training and a test dataset separately, since NE, BE, and EAC samples were included and the sample sizes were adequate. This study identified differentially expressed genes (DEGs) using the R/Bioconductor project and constructed trans-regulatory networks based on the Transcriptional Regulatory Element Database and Cytoscape software. Enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) terms was identified using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resources. The diagnostic potential of certain DEGs was assessed in both datasets.RESULTSIn the GSE1420 dataset, the number of up-regulated DEGs was larger than that of down-regulated DEGs when comparing EAC vs NE and BE vs NE. Among these DEGs, five differentially expressed transcription factors (DETFs) displayed the same trend in expression across all the comparison groups. Of these five DETFs, E2F3, FOXA2, and HOXB7 were up-regulated, while PAX9 and TFAP2C were down-regulated. Additionally, the majority of the DEGs in trans-regulatory networks were up-regulated. The intersection of these potential DEGs displayed the same direction of changes in expression when comparing the DEGs in the GSE26886 dataset to the DEGs in trans-regulatory networks above. The receiver operating characteristic curve analysis was performed for both datasets and found that TIMP1 and COL1A1 could discriminate EAC from NE tissue, while REG1A, MMP1, and CA2 could distinguish BE from NE tissue. DAVID annotation indicated that COL1A1 and MMP1 could be potent biomarkers for EAC and BE, respectively, since they participate in the majority of the enriched KEGG and GO terms that are important for inflammation and cancer.CONCLUSIONAfter the construction and analyses of the trans-regulatory networks in EAC and BE, the results indicate that COL1A1 and MMP1 could be potential biomarkers for EAC and BE, respectively.

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Section: Discussionmentioning

confidence: 99%

Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett’s esophagus

Guo

Wang

et al. 2019

WJG

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“…The online software JASPAR (http://jaspar.genereg.net/) 27 and meme-FIMO (http://meme-suite.org/) 28 were utilized to predicate SMAD3 binding sites on the HCP5 promoter. Additionally, we used miRDB database (http://www.mirdb.org/) 29 to analyze whether HCP5 is contained complementary region of the miRNA 'seed' sequence.…”

Section: Methodsmentioning

confidence: 99%

HCP5 is a SMAD3-responsive long non-coding RNA that promotes lung adenocarcinoma metastasis via miR-203/SNAI axis

Jiang

Wang

et al. 2019

Theranostics

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Introduction : Transforming growth factor-beta (TGFβ) signaling plays a vital role in lung adenocarcinoma (LUAD) progression. However, the involvement of TGFβ-regulated long non-coding RNAs (lncRNAs) in metastasis of LUAD remains poorly understood. Methods : We performed bioinformatic analyses to identify putative lncRNAs regulated by TGF-β/SMAD3 and validated the results by quantitative PCR in LUAD cells. We performed luciferase reporter and chromatin immunoprecipitation assays to demonstrate the transcriptional regulation of the lncRNA histocompatibility leukocyte antigen complex P5 (HCP5) we decided to focus on. Stable HCP5 knockdown and HCP5-overexpressing A549 cell variants were generated respectively, to study HCP5 function and understand its mechanism of action. We also confirmed our findings in mouse xenografts and metastasis models. We analyzed the correlation between the level of lncRNA expression with EGFR, KRAS mutations, smoke state and prognostic of LUAD patients. Results : We found that the lncRNA HCP5 is induced by TGFβ and transcriptionally regulated by SMAD3 , which promotes LUAD tumor growth and metastasis. Moreover, HCP5 is overexpressed in tumor tissues of patients with LUAD, specifically in patients with EGFR and KRAS mutations and current smoker. HCP5 high expression level is positively correlated with poor prognosis of patients with LUAD. Finally, we demonstrated that upregulation of HCP5 increases the expression of Snail and Slug by sponging the microRNA-203 ( miR-203 ) and promoting epithelial-mesenchymal transition (EMT) in LUAD cells. Conclusions : Our work demonstrates that the lncRNA HCP5 is transcriptionally regulated by SMAD3 and acts as a new regulator in the TGFβ/SMAD signaling pathway. Therefore, HCP5 can serve as a potential therapeutic target in LUAD.

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“…MELK is an oncogenic kinase essential for early recurrence of hepatocellular carcinoma (Xia et al, 2016). Studies have shown that MELK is a novel biomarker and a potential therapeutic target in cervical cancer (Lv et al, 2018), triple-negative breast cancer (Moreno, 2016) and gastric cancer (Zhang et al, 2016a). NDC80 (Nuclear division cycle 80) is also known as highly expressed in cancer 1 (HEC1).…”

Section: Discussionmentioning

confidence: 99%

Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

Zhang

Zou

Yin

et al. 2019

PeerJ

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Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset was downloaded from the Gene Expression Omnibus database. The dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

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Bioinformatic analyses of microRNA-targeted genes and microarray-identified genes correlated with Barrett's esophagus

Cited by 9 publications

References 51 publications

Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett’s esophagus

Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett’s esophagus

HCP5 is a SMAD3-responsive long non-coding RNA that promotes lung adenocarcinoma metastasis via miR-203/SNAI axis

Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

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