scholarly journals CellExpress: a comprehensive microarray-based cancer cell line and clinical sample gene expression analysis online system

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Yi-Fang Lee ◽  
Chien-Yueh Lee ◽  
Liang-Chuan Lai ◽  
Mong-Hsun Tsai ◽  
Tzu-Pin Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Analysis ◽  
Next Generation Sequencing Data ◽  
Clinical Samples ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract With the advancement of high-throughput technologies, gene expression profiles in cell lines and clinical samples are widely available in the public domain for research. However, a challenge arises when trying to perform a systematic and comprehensive analysis across independent datasets. To address this issue, we developed a web-based system, CellExpress, for analyzing the gene expression levels in more than 4000 cancer cell lines and clinical samples obtained from public datasets and user-submitted data. First, a normalization algorithm can be utilized to reduce the systematic biases across independent datasets. Next, a similarity assessment of gene expression profiles can be achieved through a dynamic dot plot, along with a distance matrix obtained from principal component analysis. Subsequently, differentially expressed genes can be visualized using hierarchical clustering. Several statistical tests and analytical algorithms are implemented in the system for dissecting gene expression changes based on the groupings defined by users. Lastly, users are able to upload their own microarray and/or next-generation sequencing data to perform a comparison of their gene expression patterns, which can help classify user data, such as stem cells, into different tissue types. In conclusion, CellExpress is a user-friendly tool that provides a comprehensive analysis of gene expression levels in both cell lines and clinical samples. The website is freely available at http://cellexpress.cgm.ntu.edu.tw/. Source code is available at https://github.com/LeeYiFang/Carkinos under the MIT License. Database URL: http://cellexpress.cgm.ntu.edu.tw/

scholarly journals Comprehensive analysis of the gene expression profiles in human gastric cancer cell lines

Oncogene ◽  
2002 ◽  
Vol 21 (42) ◽  
pp. 6549-6556 ◽  
Author(s):  
Jiafu Ji ◽  
Xin Chen ◽  
Suet Yi Leung ◽  
Jen-Tsan A Chi ◽  
Kent Man Chu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Analysis ◽  
Human Gastric Cancer ◽  
Gastric Cancer Cell Lines

Development of myeloid-derived suppressor cells (MDSC) targeted therapy for the treatment of cisplatin-resistant bladder cancer.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 415-415
Author(s):  
Yuji Takeyama ◽  
Minoru Kato ◽  
Yasuomi Shimizu ◽  
Kosuke Hamada ◽  
Taro Iguchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Suppressor Cells ◽  
Bladder Cancer Cell ◽  
Myeloid Derived Suppressor Cells ◽  
Expression Levels ◽  
Tumor Bearing ◽  
Gene Expression Levels

415 Background: The emergence of immune checkpoint inhibitors (ICI) has brought hope for cure and survival for those suffering from various cancers, including bladder cancer. However, the response rate of ICI monotherapy is modest, and recent reports indicate that myeloid-derived suppressor cells (MDSC) might play a role in the resistant mechanism of ICI. In this study, we assess the effect of chemokine signal on the proliferation of bladder cancer and investigate whether MDSC could be a new target for the treatment of cisplatin-resistant bladder cancer. Methods: We established a cisplatin resistant strain (MB49R) of mice bladder cancer cell line MB49, and examined the alteration of the expression levels of inflammatory chemokines by chemokine array. Next, we isolated MDSCs from spleen and tumor in tumor-bearing mice to examine gene expression levels of chemokine receptors (CXCR2 and CCR2) and immunosuppression genes (Arg-1 and iNOS). Furthermore, we assessed the efficacy of CDDP, α-PD-L1 and chemokine antagonists against the proliferation of tumors in MB49 and MB49R xenograft models. Results: Expression levels of CCL2 and CXCL1/2, which are involved in the migration of MDSC, were significantly increased in the culture supernatant of MB49R compared to those in MB49 cell lines. This result was confirmed by real-time RT-qPCR of tumor extract, and this increase was also observed in human bladder cancer cell lines (T24 and T24R). CXCR2 and CCR2 were highly expressed in PMN-MDSC and M-MDSC, respectively, which were isolated from spleen or tumors in tumor-bearing mice, and gene expression levels of Arg-1 and iNOS were dramatically increased in M-MDSCs from the tumor tissues compared to those from spleen. Also, analysis by flow cytometry revealed that PMN-MDSC dramatically decreased in MB49R compared to parental MB49 tumors, while the proportion of M-MDSC was not changed in MB49R, which indicates that M-MDSC could be a target for the treatment of CDDP resistant bladder cancer. Conclusions: The results in the present study might indicate that the combination treatment with ICI and MDSC-targeting therapy could be an option for the treatment of cisplatin-resistant bladder cancer.

scholarly journals Revisiting inconsistency in large pharmacogenomic studies

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2333 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
Mark Freeman ◽  
Nehme El-Hachem ◽  
Adrian She ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Targeted Drugs ◽  
Software Analysis ◽  
Sensitivity Data

In 2013, we published a comparative analysis mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis — that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines, and that the software analysis tools we provided should have been easier to run, particularly as the GDSC and CCLE released additional data.             Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs.             Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

scholarly journals Evaluation of retinoblastoma (Rb) and protein-53 (p53) gene expression levels in breast cancer cell lines (MCF-7) induced with some selected cytotoxic plants

2013 ◽  
Vol 5 (7) ◽  
pp. 120-126 ◽  
Author(s):  
Titilola Aderonke Samuel ◽  
Ayorinde Babatunde James ◽  
Temitope Adesola Oshodi ◽  
Uchennaya Okereke Odii ◽  
Innocent Chidume ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
P53 Gene ◽  
Breast Cancer Cell Lines ◽  
Expression Levels ◽  
Gene Expression Levels ◽  
Mcf 7

scholarly journals Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

2020 ◽  
Vol 21 (S9) ◽  
Author(s):  
Mona Maharjan ◽  
Raihanul Bari Tanvir ◽  
Kamal Chowdhury ◽  
Wenrui Duan ◽  
Ananda Mohan Mondal
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Lines ◽  
High Expression ◽  
Different Types ◽  
Low Expression

Abstract Background Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

scholarly journals Altered Gene Expression Profiles Define Pathways in Colorectal Cancer Cell Lines Affected by Celecoxib

2008 ◽  
Vol 17 (11) ◽  
pp. 3051-3061 ◽  
Author(s):  
Naheed Fatima ◽  
Ming Yi ◽  
Sadia Ajaz ◽  
Robert M. Stephens ◽  
Stacey Stauffer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Colorectal Cancer Cell ◽  
Altered Gene Expression ◽  
Colorectal Cancer Cell Lines ◽  
Altered Gene
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