scholarly journals Comparative Bioinformatics Characteristic of Bladder Cancer Stage 2 from Stage 4 Expression Profile: A Network-Based Study

2018 ◽  
Vol 7 ◽  
pp. e1279
Author(s):  
Mona Zamanian Azodi ◽  
Mostafa Rezaei-Tavirani ◽  
Mohammad Rostami-Nejad ◽  
Majid Rezaei-Tavirani
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Omnibus ◽  
Expression Data ◽  
Molecular Feature ◽  
Online Tool ◽  
Therapeutic Protocol ◽  
Stage 4 ◽  
Differential Network ◽  
Geo Database

Background: Bladder cancer (BC) has remained as one of the most challenging issues in medicine. The aim of this study was to investigate the differential network analysis of stages 2 and 4 of BC to better understand the molecular pathology of these states. Materials and Methods: We chose gene expression data of GSE52519 from Gene Expression Omnibus (GEO) database analyzed by the GEO2R online tool. Cytoscape version 3.6.1 and its algorithms are the methods applied for the network construction and investigation of differentially expressed genes (DEG) in these states. Result: Our result revealed that the analysis DEGs provides useful information about a common molecular feature of stages 2 and 4 of BC. Conclusion: Consequently, the network finding revealed that more investigation about stage 2 is required to achieve an effective therapeutic protocol to block the transition from stage 2 to stage 4.[GMJ.2018;7:e1279] 

scholarly journals COnTORT: COmprehensive Transcriptomic ORganizational Tool for Simultaneously Retrieving and Organizing Numerous Gene Expression Data Sets from the NCBI Gene Expression Omnibus Database

2020 ◽  
Vol 9 (25) ◽  
Author(s):  
Kevin S. Myers ◽  
Michael Place ◽  
Daniel R. Noguera ◽  
Timothy J. Donohue
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Expression Data ◽  
Gene Expression Omnibus Database ◽  
Ncbi Gene Expression Omnibus ◽  
Geo Database

ABSTRACT We introduce COnTORT (COmprehensive Transcriptomic ORganizational Tool), a publicly available program that retrieves all available gene expression data and associated metadata for an organism from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. The data are compiled into text files that can be used for downstream bioinformatic applications.

scholarly journals SMC4, CCNB1 and CKS1B as potential targets and new critical biomarkers for the prognosis of human bladder cancer

2021 ◽  
Author(s):  
Hongpeng Fang ◽  
Zhansen Huang ◽  
Xianzi Zeng ◽  
Jiaming Wan ◽  
Jieying Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Transcriptional Level ◽  
Hub Genes ◽  
Kaplan Meier ◽  
High Form

Abstract Background As a common malignant cancer of the urinary system, the precise molecular mechanisms of bladder cancer remain to be illuminated. The purpose of this study was to identify core genes with prognostic value as potential oncogenes for the diagnosis, prognosis or novel therapeutic targets of bladder cancer. Methods The gene expression profiles GSE3167 and GSE7476 were available from the Gene Expression Omnibus (GEO) database. Next, PPI network was built to filter the hub gene through the STRING database and Cytoscape software and GEPIA and Kaplan-Meier plotter were implemented. Frequency and type of hub genes and sub groups analysis were performed in cBioportal and ULCAN database. Finally,We used RT-qPCR to confirm our results. Results Totally, 251 DEGs were excavated from two datasets in our study. We only founded high expression of SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 was associated with worse outcomes in bladder cancer patients and no matter from the type of mutation or at the transcriptional level of hub genes, the tumor showed a high form of expression. However, only the expression of SMC4,CCNB1and CKS1B remained changed between the cancer and the normal samples in our results of RT-qPCR. Conclusion In conclusion,These findings indicate that the SMC4,CCNB1 and CKS1B may serve as critical biomarkers in the development and poor prognosis.

scholarly journals mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 257 ◽  
Author(s):  
Yitong Zhang ◽  
Joseph Ta-Chien Tseng ◽  
I-Chia Lien ◽  
Fenglan Li ◽  
Wei Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Lung Adenocarcinoma ◽  
Tumor Necrosis Factor Receptor ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Clinical Stages ◽  
Key Genes ◽  
Geo Database

Cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, lead to therapeutic resistance in lung cancer. In this study, we aimed to investigate the expressions of stem cell-related genes in lung adenocarcinoma (LUAD). The stemness index based on mRNA expression (mRNAsi) was utilized to analyze LUAD cases in the Cancer Genome Atlas (TCGA). First, mRNAsi was analyzed with differential expressions, survival analysis, clinical stages, and gender in LUADs. Then, the weighted gene co-expression network analysis was performed to discover modules of stemness and key genes. The interplay among the key genes was explored at the transcription and protein levels. The enrichment analysis was performed to annotate the function and pathways of the key genes. The expression levels of key genes were validated in a pan-cancer scale. The pathological stage associated gene expression level and survival probability were also validated. The Gene Expression Omnibus (GEO) database was additionally used for validation. The mRNAsi was significantly upregulated in cancer cases. In general, the mRNAsi score increases according to clinical stages and differs in gender significantly. Lower mRNAsi groups had a better overall survival in major LUADs, within five years. The distinguished modules and key genes were selected according to the correlations to the mRNAsi. Thirteen key genes (CCNB1, BUB1, BUB1B, CDC20, PLK1, TTK, CDC45, ESPL1, CCNA2, MCM6, ORC1, MCM2, and CHEK1) were enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Eight of the thirteen genes have been reported to be associated with the CSC characteristics. However, all of them have been previously ignored in LUADs. Their expression increased according to the pathological stages of LUAD, and these genes were clearly upregulated in pan-cancers. In the GEO database, only the tumor necrosis factor receptor associated factor-interacting protein (TRAIP) from the blue module was matched with the stemness microarray data. These key genes were found to have strong correlations as a whole, and could be used as therapeutic targets in the treatment of LUAD, by inhibiting the stemness features.

scholarly journals Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

2019 ◽  
Author(s):  
ChenChen Yang ◽  
Aifeng Gong
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Mrna Levels ◽  
Pcr Assay ◽  
Microarray Datasets ◽  
Geo Database

Abstract Background Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1and MMP1 in GC tissues and cell lines, respectively.Results We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1.Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

scholarly journals Integrated analysis of cancer stem cells-associated lncRNA-miRNA-mRNA network for ovarian cancer via microarray and Gene Expression Omnibus database

2020 ◽  
Author(s):  
Zheng Li ◽  
Zhijiao Wang ◽  
Yingying Zhou
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Stem Cells ◽  
Stem Cell ◽  
Cancer Stem Cells ◽  
Signaling Pathway ◽  
Gene Expression Omnibus ◽  
Integrated Analysis ◽  
Cerna Network ◽  
Geo Database

Abstract Background: Cancer stem cells (CSCs) are associated with the recurrence, metastasis and chemoresistance of epithelial ovarian cancer. Competing endogenous RNAs (CeRNAs) play an important role in maintenance of ovarian cancer stem cell-like cells (OCSCs) characteristics. To construct a ceRNA regulatory network for OCSCs, microarray technology and Gene Expression Omnibus (GEO) database had been used. Human serous epithelial ovarian carcinoma cell line COC1 cells were treated with cisplatin and paclitaxel then maintained in stem cell conditions for 6 days to obtain CD117+/CD133+ cells (OCSCs). We identified the differentially expressed miRNAs (DEMs), lncRNA (DELs) and mRNA (DEGs) between OCSCs and COC1 by microarray and combined them with representative microarray profiles in GEO Database. Results: According to the combination, 28 DEMs were identified at first, and 452 DEGs were obtained combining with the predicted targets of these miRNAs and our mRNA microarray results. Up-regulated DEGs of them were significantly enriched in ‘p53 signaling pathway’, ‘FoxO signaling pathway’ and ‘MicroRNAs in cancer’, whereas down-regulated DEGs were significantly enriched in ‘Adherens junction’ and ‘Hepatitis C’ pathway. 29 transcripts of 17 lncRNAs should be the ceRNAs of 10 of these miRNAs according to bioinformatics predicted results and lncRNA microarray. Finally, we obtained ceRNA network with 10 DEMs, 21 DEGs, and 25 transcripts of 13 DELs which should play an important role in maintenance of OCSCs characteristics. LINC00665-miR-146a-5p-NRP2 should be one of ceRNA pathways of the network. The qPCR results indicated that the expression of miR-146a-5p in OCSCs was lower than that in COC1, and LINC00665 shows the opposite trend. These results were consistent with the results of microarray partially. When LINC00665 expression was up-regulated in COC1, the cell proliferation ability enhanced, apoptosis rate reduced, and the percentage of G2/M phase cells increased. Conclusions: The ceRNA network we constructed may be involved in the stem cell characteristics maintenance of OCSCs and provide directions for further OCSCs research in the future, so as to assist the development and treatment of ovarian cancer.

scholarly journals The Analysis of Gene Expression Data Incorporating Tumor Purity Information

2021 ◽  
Vol 12 ◽  
Author(s):  
Seungjun Ahn ◽  
Tyler Grimes ◽  
Somnath Datta
Keyword(s):  
Gene Expression ◽  
Tumor Cells ◽  
Gene Expression Data ◽  
The Cancer Genome Atlas ◽  
Data Sets ◽  
Expression Data ◽  
Tumor Purity ◽  
Robust Model ◽  
Differential Network ◽  
Cancer Genome Atlas

The tumor microenvironment is composed of tumor cells, stroma cells, immune cells, blood vessels, and other associated non-cancerous cells. Gene expression measurements on tumor samples are an average over cells in the microenvironment. However, research questions often seek answers about tumor cells rather than the surrounding non-tumor tissue. Previous studies have suggested that the tumor purity (TP)—the proportion of tumor cells in a solid tumor sample—has a confounding effect on differential expression (DE) analysis of high vs. low survival groups. We investigate three ways incorporating the TP information in the two statistical methods used for analyzing gene expression data, namely, differential network (DN) analysis and DE analysis. Analysis 1 ignores the TP information completely, Analysis 2 uses a truncated sample by removing the low TP samples, and Analysis 3 uses TP as a covariate in the underlying statistical models. We use three gene expression data sets related to three different cancers from the Cancer Genome Atlas (TCGA) for our investigation. The networks from Analysis 2 have greater amount of differential connectivity in the two networks than that from Analysis 1 in all three cancer datasets. Similarly, Analysis 1 identified more differentially expressed genes than Analysis 2. Results of DN and DE analyses using Analysis 3 were mostly consistent with those of Analysis 1 across three cancers. However, Analysis 3 identified additional cancer-related genes in both DN and DE analyses. Our findings suggest that using TP as a covariate in a linear model is appropriate for DE analysis, but a more robust model is needed for DN analysis. However, because true DN or DE patterns are not known for the empirical datasets, simulated datasets can be used to study the statistical properties of these methods in future studies.

scholarly journals PIPE-T: a new Galaxy tool for the analysis of RT-qPCR expression data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Nicolò Zanardi ◽  
Martina Morini ◽  
Marco Antonio Tangaro ◽  
Federico Zambelli ◽  
Maria Carla Bosco ◽  
...  
Keyword(s):  
Gene Expression ◽  
Main Tool ◽  
Gene Expression Omnibus ◽  
Fast Method ◽  
Expression Data ◽  
Web Based ◽  
Qpcr Data ◽  
The Galaxy ◽  
R Packages ◽  
Polymerase Chain

AbstractReverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is an accurate and fast method to measure gene expression. Reproducibility of the analyses is the main limitation of RT-qPCR experiments. Galaxy is an open, web-based, genomic workbench for a reproducible, transparent, and accessible science. Our aim was developing a new Galaxy tool for the analysis of RT-qPCR expression data. Our tool was developed using Galaxy workbench version 19.01 and functions implemented in several R packages. We developed PIPE-T, a new Galaxy tool implementing a workflow, which offers several options for parsing, filtering, normalizing, imputing, and analyzing RT-qPCR data. PIPE-T requires two input files and returns seven output files. We tested the ability of PIPE-T to analyze RT-qPCR data on two example datasets available in the gene expression omnibus repository. In both cases, our tool successfully completed execution returning expected results. PIPE-T can be easily installed from the Galaxy main tool shed or from Docker. Source code, step-by-step instructions, and example files are available on GitHub to assist new users to install, execute, and test PIPE-T. PIPE-T is a new tool suitable for the reproducible, transparent, and accessible analysis of RT-qPCR expression data.

Expression Signature of E2F1 and Its Associated Genes Predict Superficial to Invasive Progression of Bladder Tumors

2010 ◽  
Vol 28 (16) ◽  
pp. 2660-2667 ◽  
Author(s):  
Ju-Seog Lee ◽  
Sun-Hee Leem ◽  
Sang-Yeop Lee ◽  
Sang-Cheol Kim ◽  
Eun-Sung Park ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Cancer Progression ◽  
Gene Expression Data ◽  
Gene Expression Signature ◽  
Molecular Signature ◽  
Expression Data ◽  
Bladder Tumors ◽  
Expression Signature ◽  
Leave One Out

Purpose In approximately 20% of patients with superficial bladder tumors, the tumors progress to invasive tumors after treatment. Current methods of predicting the clinical behavior of these tumors prospectively are unreliable. We aim to identify a molecular signature that can reliably identify patients with high-risk superficial tumors that are likely to progress to invasive tumors. Patients and Methods Gene expression data were collected from tumor specimens from 165 patients with bladder cancer. Various statistical methods, including leave-one-out cross-validation methods, were applied to identify a gene expression signature that could predict the likelihood of progression to invasive tumors and to test the robustness of the expression signature in an independent cohort. The robustness of the gene expression signature was validated in an independent (n = 353) cohort. Results Supervised analysis of gene expression data revealed a gene expression signature that is strongly associated with invasive bladder tumors. A molecular classifier based on this gene expression signature correctly predicted the likelihood of progression of superficial tumor to invasive tumor. Conclusion We present a molecular signature that can predict, at diagnosis, the likelihood of bladder cancer progression and, possibly, lead to improvements in patient therapy.

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