Identification of the circRNA-miRNA-mRNA Regulatory Network in Bladder Cancer by Bioinformatics Analysis

In recent years, increasing evidence shows that circular RNA (circRNA) disorder is closely related to tumorigenesis and cancer progression. However, the regulatory functions of most circRNAs in bladder cancer (BCa) remain unclear. This study was aimed at exploring the molecular regulatory mechanism of circRNAs in BCa. We obtained four datasets of circRNA, microRNA (miRNA), and messenger (mRNA) expression profiles from the Gene Expression Omnibus and The Cancer Genome Atlas microarray databases and identified 434, 367, and 4799/4841 differentially expressed circRNAs, miRNAs, and mRNAs, respectively. With these differentially expressed RNAs, we established a circRNA-miRNA-mRNA targeted interaction network. A total of 18, 24, and 51 central circRNAs, miRNAs, and mRNAs were identified, respectively. Among them, the top 10 mRNAs that had high connectivity with other circRNAs and miRNAs were regarded as hub genes. We detected the expression levels of these 10 mRNAs in 16 pairs of BCa tissues and adjacent normal tissues through quantitative real-time polymerase chain reaction. The differentially expressed mRNAs and central mRNAs were enriched in the processes and pathways that are associated with the growth, differentiation, proliferation, and apoptosis of tumor cells. The outstanding genes (CDCA4, GATA6, LATS2, RHOB, ZBTB4, and ZFPM2) also interacted with numerous drugs, indicating their potency as biomarkers and drug targets. The findings of this study provide a deep understanding of the circRNA-related competitive endogenous RNA regulatory mechanism in BCa pathogenesis.

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Genome-Wide Identification of differently expressed lncRNAs, mRNAs, and circRNAs in patients with osteoarthritis

Current Bioinformatics ◽

10.2174/1574893615999200706002907 ◽

2020 ◽

Vol 15 ◽

Author(s):

Yeqing Sun ◽

Lei Chen ◽

Yingqi Zhang ◽

Jincheng Zhang ◽

Shashi Ranjan Tiwari

Keyword(s):

Regulatory Networks ◽

Expression Profiles ◽

Interleukin 1 ◽

Interaction Network ◽

Circular Rna ◽

Negative Regulation ◽

Differentially Expressed ◽

Positive Regulation ◽

Proteinprotein Interaction ◽

Differently Expressed Genes

Background: Osteoarthritis (OA), one of the most important causes leading to joint disability, was considered as an untreatable disease. A series of genes were reported to regulate the pathogenesis of OA, including microRNAs, Long non-coding RNAs and Circular RNA. So far, the expression profiles and functions of lncRNAs, mRNAs, and circRNAs in OA are not fully understood. Objective: The present study aimed to identify differently expressed genes in OA. Methods: The present study conducted RNA-seq to identify differently expressed genes in OA. Ontology (GO) analysis was used to analysis the Molecular Function and Biological Process. KEGG pathway analysis was used to perform the differentially expressed lncRNAs in biological pathways. Results: Hierarchical clustering revealed a total of 943 mRNAs, 518 lncRNAs, and 300 circRNAs were dysregulated in OA compared to normal samples. Furthermore, we constructed differentially expressed mRNAs mediated proteinprotein interaction network, differentially expressed lncRNAs mediated trans regulatory networks, and competitive endogenous RNA (ceRNA) to reveal the interaction among these genes in OA. Bioinformatics analysis revealed these dysregulated genes were involved in regulating multiple biological processes, such as wound healing, negative regulation of ossification, sister chromatid cohesion, positive regulation of interleukin-1 alpha production, sodium ion transmembrane transport, positive regulation of cell migration, and negative regulation of inflammatory response. To the best of our knowledge, this study for the first time revealed the expression pattern of mRNAs, lncRNAs and circRNAs in OA. Conclusion: This study provided novel information to validate these differentially expressed RNAs may be as possible biomarkers and targets in OA.

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Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

Frontiers in Genetics ◽

10.3389/fgene.2020.595361 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xin Qiu ◽

Qin-Han Hou ◽

Qiu-Yue Shi ◽

Hai-Xing Jiang ◽

Shan-Yu Qin

Keyword(s):

Oxidative Stress ◽

Gene Expression ◽

Pancreatic Cancer ◽

Cancer Progression ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Cox Regression Analysis

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

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Comprehensive Expression Profiling Analysis of Pituitary Indicates that circRNA Participates in the Regulation of Sheep Estrus

Genes ◽

10.3390/genes10020090 ◽

2019 ◽

Vol 10 (2) ◽

pp. 90 ◽

Cited By ~ 3

Author(s):

Xiaoyue Li ◽

Cunyuan Li ◽

Junchang Wei ◽

Wei Ni ◽

Yueren Xu ◽

...

Keyword(s):

Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Circular Rna ◽

Differentially Expressed ◽

Rna Seq ◽

Endocrine Organ ◽

Reverse Transcription Quantitative Pcr ◽

Significant Difference ◽

Profiling Analysis

The pituitary gland is the most important endocrine organ that mainly regulates animal estrus by controlling the hormones synthesis. There is a significant difference between the estrus state and anestrus state of sheep pituitary system. Here, we studied the circular RNA (circRNA) expression profiles of the anterior pituitary of estrus and anestrus sheep using RNA-seq technology. Through this study, we identified a total of 12,468 circRNAs and 9,231 differentially expressed circRNAs in the estrus and anestrus pituitary system of sheep. We analyzed some differentially expressed circRNAs by reverse transcription quantitative-PCR (RT-qPCR), and some circRNAs were demonstrated using RNase-R+ resistance experiments. CircRNAs involving the regulation of estrus-related terms and pathways are enriched by using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. In addition, we also predicted partial microRNA-circRNA interaction network for circRNAs that regulate sheep estrus. Overall, this study explored a potential substantial role played by circRNAs involved in pituitary regulation on sheep estrus and proposed new questions for further study.

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Identification of Key Differentially Expressed MicroRNAs in Cancer Patients Through Pan-cancer Analysis

10.1101/388892 ◽

2018 ◽

Author(s):

Yu Hu ◽

Hayley Dingerdissen ◽

Samir Gupta ◽

Robel Kahsay ◽

Vijay Shanker ◽

...

Keyword(s):

Differential Expression ◽

Cancer Progression ◽

Expression Profiles ◽

Expression Patterns ◽

Differential Expression Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Literature Mining ◽

Wide Range ◽

Cancer Types

AbstractA number of microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common expression patterns of abnormally expressed miRNAs and their potential roles in multiple cancer types have not yet been evaluated. To minimize the difference of patients, we collected miRNA sequencing data of 575 patients with tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA), and performed differential expression analysis using DESeq2 and edgeR. The results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) unique to one of the 14 cancers may affect patient survival rate, and 21 key SDEmiRNAs (nine overexpressed and 12 under-expressed) associated with at least eight cancers and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effect of these 21 SDEmiRNAs on cellular functions was evaluated through enrichment and pathway analysis of 7,186 protein-coding gene targets from literature mining with known differential expression profiles in cancers. It enables identification of their functional similarity in cell proliferation control across a wide range of cancers and to build common regulatory networks over cancer-related pathways. This is validated by construction of a regulatory network in PI3K pathway. This study provides evidence of the value of further analysis on SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

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Development of A Prognostic Index and Screening of Prognosis Related Genes Based on an Immunogenomic Landscape Analysis of Bladder Cancer

10.21203/rs.3.rs-135671/v1 ◽

2021 ◽

Author(s):

GenYi Qu ◽

Guang Yang ◽

Yong Xu ◽

Maolin Xiang ◽

Cheng Tang

Keyword(s):

Bladder Cancer ◽

Cox Regression ◽

Malignant Tumors ◽

Expression Profiles ◽

Prognostic Index ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Cox Analysis

Abstract Background: Bladder cancer (BLCA) is one of the most common urinary tract malignant tumors. It is associated with poor outcomes, and its etiology and pathogenesis are not fully understood. There is great hope for immunotherapy in treating many malignant tumors; therefore, it is worthwhile to explore the use of immunotherapy for BLCA.Methods: Gene expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal. Differentially-expressed and survival-associated IRGs in patients with BLCA were identified using computational algorithms and Cox regression analysis. We also performed functional enrichment analysis. Based on IRGs, we employed multivariate Cox analysis to develop a new prognostic index.Results: We identified 261 IRGs that were differentially expressed between BLCA tissue and adjacent tissue, 30 of which were significantly associated with the overall survival (all P<0.01). According to multivariate Cox analysis, nine survival-related IRGs (MMP9, PDGFRA, AHNAK, OAS1, OLR1, RAC3, IGF1, PGF, and SH3BP2) were high-risk genes. We developed a prognostic index based on these IRGs and found it accurately predicted BLCA outcomes associated with the TNM stage. Intriguingly, the IRG-based prognostic index reflected infiltration of macrophages.Conclusions: An independent IRG-based prognostic index provides a practical approach for assessing patients' immune status and prognosis with BLCA. This index independently predicted outcomes of BLCA.

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The circular RNA circSlc7a11 promotes bone cancer pain pathogenesis in rats by modulating LLC-WRC 256 cell proliferation and apoptosis

Molecular and Cellular Biochemistry ◽

10.1007/s11010-020-04020-1 ◽

2021 ◽

Author(s):

Han-Wen Chen ◽

Xiao-Xia Zhang ◽

Zhu-Ding Peng ◽

Zu-Min Xing ◽

Yi-Wen Zhang ◽

...

Keyword(s):

Cell Proliferation ◽

Cancer Pain ◽

Expression Profiles ◽

Bone Cancer ◽

Circular Rna ◽

Differentially Expressed ◽

Bone Cancer Pain ◽

Circular Rnas ◽

Altered Expression ◽

Proliferation And Apoptosis

AbstractTreatment of bone cancer pain (BCP) caused by bone metastasis in advanced cancers remains a challenge in clinical oncology, and the underlying mechanisms of BCP are poorly understood. This study aimed to investigate the pathogenic roles of circular RNAs (circRNAs) in regulating cancer cell proliferation and BCP development. Eight differentially expressed circRNAs in the rat spinal cord were validated by agarose gel electrophoresis and Sanger sequencing. Expression of circRNAs and mRNAs was detected by quantitative RT-PCR. MTS assay and flow cytometry were performed to analyze cell proliferation and apoptosis, respectively. Differentially expressed mRNA profiles were characterized by deep RNA sequencing, hierarchical clustering, and functional categorization. The interactions among circRNAs, microRNAs (miRNAs), and mRNAs were predicted using TargetScan. Additionally, western blot was performed to determine the protein levels of Pax8, Isg15, and Cxcl10. Multiple circRNAs were differentially expressed in the spinal cords of BCP model rats; of these, circSlc7a11 showed the greatest increase in expression. The overexpression of circSlc7a11 significantly promoted cell proliferation and repressed apoptosis of LLC-WRC 256 and UMR-106 cells, whereas circSlc7a11 silencing produced the opposite effects. Altered expression of circSlc7a11 also induced substantial changes in the mRNA expression profiles of LLC-WRC 256 cells; these changes were linked to multiple apoptotic processes and signaling pathways, such as the chemokine signaling pathway, and formed a complex circRNA/miRNA/mRNA network. Additionally, Pax8, Isg15, and Cxc110 protein level in LLC-WRC 256 cells was consistent with the mRNA results. The circRNA circSlc7a11 regulates rat BCP development by modulating LLC-WRC 256 cell proliferation and apoptosis through multiple-signaling mechanisms.

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Iron-Bound Lipocalin-2 from Tumor-Associated Macrophages Drives Breast Cancer Progression Independent of Ferroportin

Metabolites ◽

10.3390/metabo11030180 ◽

2021 ◽

Vol 11 (3) ◽

pp. 180

Author(s):

Christina Mertens ◽

Matthias Schnetz ◽

Claudia Rehwald ◽

Stephan Grein ◽

Eiman Elwakeel ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Progression ◽

Tumor Cells ◽

Cancer Progression ◽

Lung Metastases ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Lipocalin 2 ◽

Tumor Associated Macrophages

Macrophages supply iron to the breast tumor microenvironment by enforced secretion of lipocalin-2 (Lcn-2)-bound iron as well as the increased expression of the iron exporter ferroportin (FPN). We aimed at identifying the contribution of each pathway in supplying iron for the growing tumor, thereby fostering tumor progression. Analyzing the expression profiles of Lcn-2 and FPN using the spontaneous polyoma-middle-T oncogene (PyMT) breast cancer model as well as mining publicly available TCGA (The Cancer Genome Atlas) and GEO Series(GSE) datasets from the Gene Expression Omnibus database (GEO), we found no association between tumor parameters and Lcn-2 or FPN. However, stromal/macrophage-expression of Lcn-2 correlated with tumor onset, lung metastases, and recurrence, whereas FPN did not. While the total iron amount in wildtype and Lcn-2−/− PyMT tumors showed no difference, we observed that tumor-associated macrophages from Lcn-2−/− compared to wildtype tumors stored more iron. In contrast, Lcn-2−/− tumor cells accumulated less iron than their wildtype counterparts, translating into a low migratory and proliferative capacity of Lcn-2−/− tumor cells in a 3D tumor spheroid model in vitro. Our data suggest a pivotal role of Lcn-2 in tumor iron-management, affecting tumor growth. This study underscores the role of iron for tumor progression and the need for a better understanding of iron-targeted therapy approaches.

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Circular RNA MYLK as a competing endogenous RNA promotes bladder cancer progression through modulating VEGFA/VEGFR2 signaling pathway

Cancer Letters ◽

10.1016/j.canlet.2017.06.027 ◽

2017 ◽

Vol 403 ◽

pp. 305-317 ◽

Cited By ~ 211

Author(s):

Zhenyu Zhong ◽

Mengge Huang ◽

Mengxin Lv ◽

Yunfeng He ◽

Changzhu Duan ◽

...

Keyword(s):

Bladder Cancer ◽

Signaling Pathway ◽

Cancer Progression ◽

Circular Rna ◽

Competing Endogenous Rna ◽

Vegfr2 Signaling

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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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Identification of a prognostic long non-coding RNA signature in breast cancer

10.21203/rs.3.rs-27286/v1 ◽

2020 ◽

Author(s):

Gaochen Lan ◽

Xiaoling Yu ◽

Yanna Zhao ◽

Jinjian Lan ◽

Wan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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