A circular RNAs dataset landscape reveals potential signatures for the detection and prognosis of early-stage lung adenocarcinoma

Abstract Background This study aimed to identify potential circular ribonucleic acid (circRNA) signatures involved in the pathogenesis of early-stage lung adenocarcinoma (LAC). Methods The circRNA sequencing dataset of early-stage LAC was downloaded from the Gene Expression Omnibus database. First, the differentially expressed circRNAs (DEcircRNAs) between tumour and non-tumour tissues were screened. Then, the corresponding miRNAs and their target genes were predicted. In addition, prognosis-related genes were identified using survival analysis and further used to build a network of competitive endogenous RNAs (ceRNAs; DEcircRNA–miRNA–mRNA). Finally, the functional analysis and drug–gene interaction analysis of mRNAs in the ceRNA network was performed. Results A total of 35 DEcircRNAs (30 up-regulated and 5 down-regulated circRNAs) were identified. Moreover, 135 DEcircRNA–miRNA and 674 miRNA–mRNA pairs were predicted. The survival analysis of these target mRNAs revealed that 60 genes were significantly associated with survival outcomes in early-stage LAC. Of these, high levels of PSMA 5 and low levels of NAMPT, CPT 2 and TNFSF11 exhibited favourable prognoses. In addition, the DEcircRNA–miRNA–mRNA network was constructed, containing 5 miRNA–circRNA (hsa_circ_0092283/hsa-miR-762/hsa-miR-4685-5p; hsa_circ_0070610/hsa-let-7a-2-3p/hsa-miR-3622a-3p; hsa_circ_0062682/hsa-miR-4268) and 60 miRNA–mRNA pairs. Functional analysis of the genes in the ceRNA network showed that they were primarily enriched in the Wnt signalling pathway. Moreover, PSMA 5, NAMPT, CPT 2 and TNFSF11 had strong correlations with different drugs. Conclusion Three circRNAs (hsa_circ_0062682, hsa_circ_0092283 and hsa_circ_0070610) might be potential novel targets for the diagnosis of early-stage LAC.

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Identification of key miRNA and hub genes in lung adenocarcinoma by bioinformatics and functional analysis

10.21203/rs.3.rs-26896/v1 ◽

2020 ◽

Author(s):

Jiayao Zhu ◽

Yan Zhang ◽

Jingjing Lu ◽

Le Wang ◽

Xiaoren Zhu ◽

...

Keyword(s):

Survival Analysis ◽

Lung Adenocarcinoma ◽

Target Gene ◽

Target Genes ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes ◽

Protein Protein Interaction ◽

Kaplan Meier ◽

Differentially Expressed Mirnas

Abstract Background: lung adenocarcinoma is the main subtype of lung cancer and the most fatal malignant disease in the world. However, the pathogenesis of lung adenocarcinoma has not been fully elucidated.Methods: Three LUAD-associated datesets (GSE118370, GSE43767 and GSE74190) were downloaded from the Gene Expression Omnibus (GEO) datebase and the differentially expressed miRNAs (DEMs) and genes (DEGs) were screened by GEO2R. The prediction of target gene of differentially expressed miRNA were used miRWALK. Metascape was used to enrich the overlapped genes of DEGs and target genes. Then, the protein-protein interaction(PPI) and DEMs-DEGs regulatory network were created via String datebase and Cytoscape. Finally, overall survival analysis was established via the Kaplan–Meier curve and look for the possible prognostic biomarkers.Result: In this study, 433 differential genes were identified. There were 267 genes overlapped with the target gene of Dems, and eight hub genes (CDH1, CDH5, CAV1, MMP9, PECAM1, CD24, ENG, MME) were screened out. There were 85 different miRNAs in total, among which 16 miRNA target genes intersect with DEGs, 12 miRNAs with the highest interaction were screened out, and survival analysis of miRNA and hub genes was carried out.Conclusion: we found that miRNA-940, miRNA-125a-3p, miRNA-140-3p, miRNA-542-5p, CDH1, CDH5, CAV1, MMP9, PECAM1 may be related to the development of LUAD.

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Construction and Analysis of a circRNA-Mediated ceRNA Network in Lung Adenocarcinoma

10.21203/rs.3.rs-32720/v1 ◽

2020 ◽

Author(s):

Zhaojun Wang ◽

Haifeng Li ◽

Li Wei ◽

Junhang Zhang

Keyword(s):

Lung Adenocarcinoma ◽

Patient Survival ◽

Gene Prediction ◽

Differential Expression Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Circular Rnas ◽

Cerna Network ◽

Intersection Analysis ◽

Microarray Datasets

Abstract Background: Circular RNAs (circRNAs), a new class of regulatory noncoding RNAs, are involved in gene regulation and may play a role in cancer development. This study aimed to identify circRNAs involved in lung adenocarcinoma (LUAD) using bioinformatics analysis.Methods: CircRNA (GSE101684, GSE101586), miRNA (GSE135918), and mRNA (GSE130779) microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNAs (DECs), miRNAs (DEMs), and mRNAs (DEGs) in LUAD. Circinteractome and StarBase were used to predict miRNAs and mRNAs, respectively. A circRNA-miRNA-mRNA-ceRNA network was constructed. Patient survival was analyzed using UALCAN, and a sub-network was established.Results: Hsa_circ_0072088 was identified as a differentially expressed (upregulated) circRNA in the two datasets. Intersection analysis identified hsa-miR-532-3p and hsa-miR-942 as the two miRNAs with the highest potential for binding to hsa_circ_0072088. Differential expression analysis and target gene prediction were performed to build a ceRNA network of hsa_circ_0072088 using Circinteractome/StarBase 3.0. Intersection analysis showed that TMEM52, IL24, POF1B, KIF1A, NHS, LBH, HIST2H2BE, ABCC3, PYCR1, CD79A, IGF2BP3, ANKRD17, GTSE1, MKI67, CLSPN, PLAU, LUC7L, MAGIX, GPATCH4, and ABAT were potential downstream mRNAs. The association between the expression level of 20 DEGs and LUAD patient survival was analyzed using UALCAN, which showed that IGF2BP3, MKI67, CD79A, and ABAT were related to patient survival.Conclusion: The circRNA hsa_circ_0072088, the miRNAs hsa-miR-532-3p and hsa-miR-942-5p, and the genes IGF2BP3, MKI67, CD79A, and ABAT may serve as prognostic markers in LUAD.

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Identification of Differentially Expressed Circular RNAs as miRNA Sponges in Lung Adenocarcinoma

Journal of Oncology ◽

10.1155/2021/5193913 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Yuechao Liu ◽

Xin Wang ◽

Lulu Bi ◽

Hongbo Huo ◽

Shi Yan ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Enrichment Analysis ◽

Stem Cell Differentiation ◽

Diagnostic Value ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Normal Lung ◽

Circular Rnas ◽

Cerna Network ◽

Kaplan Meier

Background. Circular RNAs (circRNAs) may function as the decoys for microRNAs (miRNAs) or proteins, the templates for translation, and the sources of pseudogene generation. The purpose of this study is to determine the diagnostic circRNAs, which are related to lung adenocarcinoma (LUAD), that adsorb miRNAs on the basis of the competing endogenous RNA (ceRNA) hypothesis. Methods. The differentially expressed circRNAs (DEcircRNAs) in LUAD were revealed by the microarray data (GSE101586 and GSE101684) that were obtained from the Gene Expression Omnibus (GEO) database. The miRNAs that were targeted by the DEcircRNAs were predicted with the CircInteractome, and the target mRNAs of the miRNAs were found by the miRDB and the TargetScan. The ceRNA network was built by the Cytoscape. The potential biological roles and the regulatory mechanisms of the circRNAs were investigated by the Gene Ontology (GO) enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The expression of the host genes of circRNAs was examined by the Ualcan. The survival analysis was performed by the Kaplan–Meier plotter. Results. In comparison with normal lung tissues, LUAD tissues contained 7 overlapping cancer-specific DEcircRNAs with 294 miRNA response elements (MREs). Among the 7 DEcircRNAs, 3 circRNAs (hsa_circ_0072088, hsa_circ_0003528, and hsa_circ_0008274) were upregulated and 4 circRNAs (hsa_circ_0003162, hsa_circ_0029426, hsa_circ_0049271, and hsa_circ_0043256) were downregulated. A circRNA-miRNA-mRNA regulatory network, which included 33 differentially expressed miRNAs (DEmiRNAs) and 2007 differentially expressed mRNAs (DEmRNAs), was constructed. These mRNAs were enriched in the biological function of cell-cell adhesion, response to hypoxia, and stem cell differentiation and were involved in the PI3K-Akt signaling, HIF-1 signaling, and cAMP signaling pathways. Conclusion. Our results indicated that 7 DEcircRNAs could have diagnostic value for LUAD. Additionally, the circRNAs-mediated ceRNA network might provide a novel perspective into unraveling the pathogenesis and progression of LUAD.

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Co-Expression of Coxsackievirus/Adenovirus Receptors and Desmoglein 2 in Lung Adenocarcinoma: A Comprehensive Analysis of Bioinformatics and Tissue Microarrays

Journal of Clinical Medicine ◽

10.3390/jcm9113693 ◽

2020 ◽

Vol 9 (11) ◽

pp. 3693

Author(s):

Ching-Fu Weng ◽

Chi-Jung Huang ◽

Mei-Hsuan Wu ◽

Henry Hsin-Chung Lee ◽

Thai-Yen Ling

Keyword(s):

Overall Survival ◽

Survival Analysis ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Early Stage ◽

The Cancer Genome Atlas ◽

Cancer Tissue ◽

Mesenchymal Transition ◽

Kaplan Meier

Introduction: Coxsackievirus/adenovirus receptors (CARs) and desmoglein-2 (DSG2) are similar molecules to adenovirus-based vectors in the cell membrane. They have been found to be associated with lung epithelial cell tumorigenesis and can be useful markers in predicting survival outcome in lung adenocarcinoma (LUAD). Methods: A gene ontology enrichment analysis disclosed that DSG2 was highly correlated with CAR. Survival analysis was then performed on 262 samples from the Cancer Genome Atlas, forming “Stage 1A” or “Stage 1B”. We therefore analyzed a tissue microarray (TMA) comprised of 108 lung samples and an immunohistochemical assay. Computer counting software was used to calculate the H-score of the immune intensity. Cox regression and Kaplan–Meier analyses were used to determine the prognostic value. Results: CAR and DSG2 genes are highly co-expressed in early stage LUAD and associated with significantly poorer survival (p = 0.0046). TMA also showed that CAR/DSG2 expressions were altered in lung cancer tissue. CAR in the TMA was correlated with proliferation, apoptosis, and epithelial–mesenchymal transition (EMT), while DSG2 was associated with proliferation only. The Kaplan–Meier survival analysis revealed that CAR, DSG2, or a co-expression of CAR/DSG2 was associated with poorer overall survival. Conclusions: The co-expression of CAR/DSG2 predicted a worse overall survival in LUAD. CAR combined with DSG2 expression can predict prognosis.

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LIMD2 is a Prognostic and Predictive Marker in Patients With Esophageal Cancer Based on a ceRNA Network Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.774432 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yuanmei Chen ◽

Xinyi Huang ◽

Kunshou Zhu ◽

Changkun Li ◽

Haiyan Peng ◽

...

Keyword(s):

Esophageal Cancer ◽

Cox Regression ◽

Predictive Marker ◽

Gene Expression Omnibus ◽

Akt Signaling ◽

The Cancer Genome Atlas ◽

Circular Rnas ◽

Messenger Rnas ◽

Cerna Network ◽

Functional Pathway

Globally, esophageal cancer (ECA) is the seventh most common cancer and sixth most common cause of cancer-associated mortality. However, there are no reliable prognostic and predictive molecular markers for ECA; in addition, the pathogenesis of ECA is not fully elucidated. The expressions of circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) of ECA and control groups were obtained from the RNA-sequencing (RNA-seq) data of our hospital, the Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) datasets. Analyses of differentially expressed genes, the circRNA–miRNA–mRNA–competing endogenous RNA (ceRNA) network, and functional/pathway enrichment were conducted. The key targets in the ceRNA network that showed significant results in survival Cox regression analyses were selected. Furthermore, analyses of immune infiltration and autophagy genes related to the key targets were performed. Seven circRNAs, 22 miRNAs, and 34 mRNAs were identified as vital genes in ECA; the nuclear factor-κ-gene binding (NF-κB) and phosphatidylinositol-3 kinase/protein kinase B (PI3K-Akt) signaling were identified as the most enriched pathways. In addition, the LIM domain containing 2 (LIMD2) was an independent predictor of prognosis in ECA patients and closely associated with immunity and autophagy. Moreover, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) revealed significant upregulation of LIMD2 expression in ECA tissues. ECA may be closely correlated with NF-κB and PI3K/Akt signaling. In addition, LIMD2 could be a potential prognostic and predictive marker of ECA.

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Identification of hub driving genes and regulators of lung adenocarcinoma based on the gene Co-expression network

Bioscience Reports ◽

10.1042/bsr20200295 ◽

2020 ◽

Vol 40 (4) ◽

Author(s):

Zihao Xu ◽

Zilong Wu ◽

Jiatang Xu ◽

Jingtao Zhang ◽

Bentong Yu

Keyword(s):

Lung Adenocarcinoma ◽

Target Genes ◽

Characteristic Curve ◽

Interaction Network ◽

Mapk Signaling ◽

The Cancer Genome Atlas ◽

Circular Rnas ◽

Node Degree ◽

Protein Protein Interaction ◽

Cancer Tissues

Abstract Lung adenocarcinoma (LUAD) remains the leading cause of cancer-related deaths worldwide. Increasing evidence suggests that circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) can regulate target gene expression and participate in tumor genesis and progression. However, hub driving genes and regulators playing a potential role in LUAD progression have not been fully elucidated yet. Based on data from The Cancer Genome Atlas database, 2837 differentially expressed genes, 741 DE-regulators were screened by comparing cancer tissues with paracancerous tissues. Then, 651 hub driving genes were selected by the topological relation of the protein–protein interaction network. Also, the target genes of DE-regulators were identified. Moreover, a key gene set containing 65 genes was obtained from the hub driving genes and target genes intersection. Subsequently, 183 hub regulators were selected based on the analysis of node degree in the ceRNA network. Next, a comprehensive analysis of the subgroups and Wnt, mTOR, and MAPK signaling pathways was conducted to understand enrichment of the subgroups. Survival analysis and a receiver operating characteristic curve analysis were further used to screen for the key genes and regulators. Furthermore, we verified key molecules based on external database, LRRK2, PECAM1, EPAS1, LDB2, and HOXA11-AS showed good results. LRRK2 was further identified as promising biomarker associated with CNV alteration and various immune cells’ infiltration levels in LUAD. Overall, the present study provided a novel perspective and insight into hub driving genes and regulators in LUAD, suggesting that the identified signature could serve as an independent prognostic biomarker.

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Identification of Hub Genes, Modules and Metabolic Pathways Associated With Lung Adenocarcinoma: A System Biology Approach

10.21203/rs.3.rs-111252/v1 ◽

2020 ◽

Author(s):

Raheleh Roudi ◽

Behnaz Beikzadeh ◽

Giandomenico Roviello ◽

Alberto D'angelo ◽

Morteza Hadizadeh

Keyword(s):

Lung Adenocarcinoma ◽

Metabolic Pathways ◽

Molecular Mechanisms ◽

Early Stage ◽

Meta Analysis ◽

Malignant Tumour ◽

Gene Expression Omnibus ◽

Normal Lung ◽

Hub Genes ◽

Unique Genes

Abstract Lung cancer is the most common and fatal malignant tumour worldwide with a five‐year overall survival rate of only 15%. Lung adenocarcinoma (LUAD) is a heterogeneous disease. The use of microarray datasets along with bioinformatics knowledge might help to clarify the expression profile of cancer, molecular markers for the initial screening of tumour and the underlying biological mechanisms. The present study is designed to identify differential expression genes and molecular mechanisms of LUAD compared to normal lung tissues using systems biology approaches.Methods Four GSE datasets (GSE75037, GSE63459, GSE32863 and GSE10072) were selected from the Gene Expression Omnibus (GEO) database. Data processing and meta-analysis were performed using the R statistical programming language, The differentially expressed genes (DEGs) associated with each stage were obtained. The common and unique DEGs between stages of LUAD and adjacent normal lung tissues were initiated by Venny tool. Common genes, including upregulated and downregulated genes, were then analyzed to a STRING database to obtain protein-protein interaction (PPI). STRING output was analyzed by MCODE and CytoHubba applications of Cytoscape to identify modules of co-expression and hub genes, respectively.Results The shared upregulated and downregulated DEGs among LUAD stages were 22 and 140 genes, respectively, when compared to normal lung tissues. Unique genes for each stage were also identified. The hub genes were PECAM1, TEK, CDH5, VWF and ANGPT1. Most of the top cluster genes were enriched for Gα(s) signalling events, GPCR ligand binding, class B/2 (Secretin family receptors), platelet activation, signalling and aggregation in the main three co-expression clusters. Most of the shared genes (16 genes) were enriched in the metabolic pathway of hemostasis. Meaningful signaling pathways for unique genes were found at each stage.Conclusions In the present study main three co-expression clusters, metabolic pathways and biological processes were identified to understand mechanisms underlying LUAD pathogenesis, development and progression at different stages. Unique upregulated and downregulated DEGs at each stage were identified with FERMT1 and SIX1 as specific early-stage diagnostic biomarkers for stage IB and IIB. 5 hub genes were observed, including PECAM1, TEK, CDH5, vWF and ANGPT1 which might be crucial for the onset and progression of LUAD.

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Integrated analysis of lncRNA-miRNA-mRNA ceRNA network and the potential prognosis indicators in sarcomas

10.21203/rs.3.rs-21667/v3 ◽

2021 ◽

Author(s):

Lu Gao ◽

Yu Zhao ◽

Xuelei Ma ◽

Ling Zhang

Keyword(s):

Survival Analysis ◽

Kegg Pathway ◽

Gene Prediction ◽

Expression Profiles ◽

Gene Expression Omnibus ◽

Integrated Analysis ◽

Ppi Network ◽

Tissue Samples ◽

Pathway Enrichment ◽

Cerna Network

Abstract Background: Competitive endogenous RNA (ceRNA) has revealed a new mechanism of interaction between RNAs and been demonstrated to play crucial roles in multiple biological processes and in the development of neoplasms that potentially serve as diagnostic and prognosis markers as well as therapeutic targets.Methods：In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcoma by comparing the genes expression profiles between sarcoma samples and normal tissue samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed in Cytoscape. In addition, the protein-protein interaction (PPI) network was constructed and survival analysis was performed.Results: A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG pathway enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA-miRNA-mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients.Conclusions: Overall, we constructed a ceRNA network in sarcomas, which likely provides insights for further research on the molecular mechanism and potential prognosis biomarkers.

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Discovery of Biomarkers in Hepatocellular Carcinoma Metastasis Using Bioinformatic Analysis

Current Bioinformatics ◽

10.2174/1574893615999200713163643 ◽

2020 ◽

Vol 15 ◽

Author(s):

Jinrui Wei ◽

Haroon ur Rashid ◽

Lichuan Wu

Keyword(s):

Data Mining ◽

Liver Cancer ◽

Mirna Target ◽

Cancer Metastasis ◽

Target Genes ◽

Interaction Analysis ◽

Gene Interaction ◽

Bioinformatic Analysis ◽

Mirna Microarray ◽

Carcinoma Metastasis

Background: Liver cancer is one of the most deadly malignancies worldwide. Tumor metastasis is the main cause of liver cancer related death. So far the mechanism of liver cancer metastasis are far away from fully elucidated. In this study, we aim to discover key regulators involved in liver cancer metastasis by data mining. Methods: Two different types of data including mRNA microarray (GSE6222 and GSE6764) and miRNA microarray (GSE67138) were analyzed. A total of 83 intersectant differently expressed genes (DEGs) with same expression pattern in GSE6222 and GSE6764 were identified. One hundred and thirty one differently expressed miRNAs (DEMs) were identified in GSE 67138. Further, a total of 26 pairs of miRNAtarget including 18 DEMs and 13 DEGs were identified as critical miRNA-target axis via miRNA-target gene interaction analysis. Result and Conclusion: Among the 18 DEMs and 13 DEGs, 10 miRNAs and 10 target genes are significantly correlated with patients’ survival (p < 0.05). Our results and methods might be interesting for data mining and helpful for further experimental functional validation.

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Identification of Three lncRNAs as Potential Predictive Biomarkers of Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2020/7573689 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Donghui Jin ◽

Yuxuan Song ◽

Yuan Chen ◽

Peng Zhang

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Predictive Biomarkers ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Protein Kinase Activity ◽

Ppi Network ◽

Survival Analyses ◽

Normal Tissues ◽

Cerna Network

Background. Lung cancer is the most common cancer and the most common cause of cancer-related death worldwide. However, the molecular mechanism of its development is unclear. It is imperative to identify more novel biomarkers. Methods. Two datasets (GSE70880 and GSE113852) were downloaded from the Gene Expression Omnibus (GEO) database and used to identify the differentially expressed genes (DEGs) between lung cancer tissues and normal tissues. Then, we constructed a competing endogenous RNA (ceRNA) network and a protein-protein interaction (PPI) network and performed gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival analyses to identify potential biomarkers that are related to the diagnosis and prognosis of lung cancer. Results. A total of 41 lncRNAs and 805 mRNAs were differentially expressed in lung cancer. The ceRNA network contained four lncRNAs (CLDN10-AS1, SFTA1P, SRGAP3-AS2, and ADAMTS9-AS2), 21 miRNAs, and 48 mRNAs. Functional analyses revealed that the genes in the ceRNA network were mainly enriched in cell migration, transmembrane receptor, and protein kinase activity. mRNAs DLGAP5, E2F7, MCM7, RACGAP1, and RRM2 had the highest connectivity in the PPI network. Immunohistochemistry (IHC) demonstrated that mRNAs DLGAP5, MCM7, RACGAP1, and RRM2 were upregulated in lung adenocarcinoma (LUAD). Survival analyses showed that lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2 were associated with the prognosis of LUAD. Conclusion. lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2 might be the biomarkers of LUAD. For the first time, we confirmed the important role of lncRNA CLDN10-AS1 in LUAD.

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