Gene expression profiles of liver cancer cell lines reveal two hepatocyte-like and fibroblast-like clusters

Cancer cell lines are widely used in basic research to study cancer development, growth, invasion, or metastasis. They are also used for the development and screening of anticancer drugs. However, there are no clear criteria for choosing the most suitable cell lines among the wide variety of cancer cell lines commercially available for research, and the choice is often based on previously published reports. Here, we investigated the characteristics of liver cancer cell lines by analyzing the gene expression data available in the Cancer Cell Line Encyclopedia. Unsupervised clustering analysis of 28 liver cancer cell lines yielded two main clusters. One cluster showed a gene expression pattern similar to that of hepatocytes, and the other showed a pattern similar to that of fibroblasts. Analysis of hepatocellular carcinoma gene expression profiles available in The Cancer Genome Atlas showed that the gene expression patterns in most hepatoma tissues were similar to those in the hepatocyte-like cluster. With respect to liver cancer research, our findings may be useful for selecting an appropriate cell line for a specific study objective. Furthermore, our approach of utilizing a public database for comparing the properties of cell lines could be an attractive cell line selection strategy that can be applied to other fields of research.

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Metabolic evaluation of sporadic papillary kidney cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.5_suppl.377 ◽

2012 ◽

Vol 30 (5_suppl) ◽

pp. 377-377

Author(s):

Brian Shuch ◽

Christopher Ricketts ◽

Carole Sourbier ◽

Shinji Tsutsumi ◽

Xiu-ying Zhang ◽

...

Keyword(s):

Gene Expression ◽

Membrane Potential ◽

Cell Line ◽

Cell Lines ◽

Cancer Cell ◽

Kidney Cancer ◽

Expression Profiles ◽

Fumarate Hydratase ◽

Gene Expression Profiles ◽

Cancer Cell Lines

377 Background: Papillary kidney cancer, which occurs in 15% of patients with kidney cancer, can be aggressive and there is currently no effective form of therapy for this disease. To evaluate the metabolic characteristics of sporadic papillary kidney cancer, we have evaluated metabolic parameters of several papillary kidney cancer cell lines and available gene expression profiles. Methods: Established cell lines derived from patients with sporadic papillary kidney cancer (LABAZ, MDACC-55, HRC-86T2) and from a hereditary form of fumarate hydratase-deficient kidney cancer (UOK262) were evaluated. All sporadic lines were initially sequenced for fumarate hydratase (FH). All cell lines were metabolically profiled using the Seahorse Extracellular Flux Analyzer and further evaluated for reactive oxygen species (ROS), mitochondrial membrane potential, and glucose dependence. Finally gene expression profiles of publically available datasets of papillary and HLRCC tumors were downloaded, normalized, and analyzed. Results: Sporadic lines had no alterations in FH and metabolic analysis demonstrated normal oxygen consumption and minimal lactate production, in contrast to highly glycolytic UOK262. Also unlike UOK262, the sporadic papillary kidney cancer lines were not sensitive to glucose withdrawal, had low levels of ROS, and had normal mitochondria membrane potential. Principal component analysis (PCA) demonstrated that HLRCC tumor specimens are very different from sporadic papillary tumors at the molecular level. Conclusions: Our study of established sporadic papillary RCC and fumarate hydratase-deficient HLRCC cell line together with analysis of available gene expression profiles demonstrates that these sporadic papillary kidney cancer cell lines appear to have a distinct metabolic profile from those in the fumarate hydratase deficient kidney cancer cell line. Understanding the metabolic basis of papillary kidney cancer could provide the foundation for the development of targeted approaches to therapy for patients with this disease.

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Underutilized Mexican Plants: Screening of Antioxidant and Antiproliferative Properties of Mexican Cactus Fruit Juices

Plants ◽

10.3390/plants10020368 ◽

2021 ◽

Vol 10 (2) ◽

pp. 368

Author(s):

Elda M. Melchor Martínez ◽

Luisaldo Sandate-Flores ◽

José Rodríguez-Rodríguez ◽

Magdalena Rostro-Alanis ◽

Lizeth Parra-Arroyo ◽

...

Keyword(s):

Cell Proliferation ◽

Liver Cancer ◽

Cell Line ◽

Cell Lines ◽

Cancer Cell ◽

Cancer Cell Lines ◽

In Vitro Assays ◽

Total Phenolic ◽

Liver Cancer Cell ◽

Antiproliferative Activities

Cacti fruits are known to possess antioxidant and antiproliferative activities among other health benefits. The following paper evaluated the antioxidant capacity and bioactivity of five clarified juices from different cacti fruits (Stenocereus spp., Opuntia spp. and M. geomettizans) on four cancer cell lines as well as one normal cell line. Their antioxidant compositions were measured by three different protocols. Their phenolic compositions were quantified through high performance liquid chromatography and the percentages of cell proliferation of fibroblasts as well as breast, prostate, colorectal, and liver cancer cell lines were evaluated though in vitro assays. The results were further processed by principal component analysis. The clarified juice from M. geomettizans fruit showed the highest concentration of total phenolic compounds and induced cell death in liver and colorectal cancer cells lines as well as fibroblasts. The clarified juice extracted from yellow Opuntia ficus-indica fruit displayed antioxidant activity as well as a selective cytotoxic effect on a liver cancer cell line with no toxic effect on fibroblasts. In conclusion, the work supplies evidence on the antioxidant and antiproliferative activities that cacti juices possess, presenting potential as cancer cell proliferation preventing agents.

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Identification and Validation of Three Core Genes in p53 Signaling Pathway in Hepatitis B Virus-Related Hepatocellular Carcinoma

10.21203/rs.3.rs-136505/v1 ◽

2020 ◽

Author(s):

Mingxue Yu ◽

Wenli Xu ◽

Yusheng Jie ◽

Jiahui Pang ◽

Siqi Huang ◽

...

Keyword(s):

Gene Expression ◽

Liver Cancer ◽

Signaling Pathway ◽

Cell Lines ◽

Cancer Cell ◽

Cancer Cell Lines ◽

Liver Cancer Cell ◽

P53 Signaling ◽

P53 Signaling Pathway ◽

Core Genes

Abstract Background：Hepatocellular carcinoma (HCC) is a common cancer and the leading cause is persistent hepatitis B virus infection. We aimed to identify some core genes and pathways for HBV-related HCC. Methods: Gene expression profiles of GSE62232, GSE121248, and GSE94660 were available from Gene Expression Omnibus. The GEO2R online tool and Venn diagram software were applied to analyze commonly differentially expressed genes. Then functional enrichment analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Gene and Genome (KEGG) as well as the protein-protein interaction (PPI) network was conducted. The overall survival rates and the expression levels were detected by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA). Next, gene set enrichment analysis (GSEA) was performed to verify the KEGG pathway analysis. Furthermore, quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was performed to validate the levels of gene expression in tumor tissues from HBV related HCC patients, HBV-related liver cell lines, and transfection si-p53 and knock-out p53 liver cancer cell lines. Finally, the prediction of the ceRNA network was constructed with R software. Results: Fifteen highly expressed genes associated with significantly worse prognoses were selected and CCNB1, CDK1, and RRM2 in the p53 signaling pathway were identified as core genes. GSEA results showed highly-expressed samples of three core genes were all enriched in the p53 signaling pathway in a validation dataset(P<0.0001). Expression of these three core genes were consistently higher in tumor tissue samples (P<0.0001) and liver cancer cell lines (P<0.05). However, transfection si-p53 and knock-out p53 liver cancer cell lines had lower expression (P<0.05). LncRNAs, including NEAT1, MALAT1, XIST, AC021078.1, and SNHG16, were identified by close interactions with core genes. Conclusions: CCNB1, CDK1, and RRM2 were enriched in the p53 signaling pathway and could be potential biomarkers and therapeutic targets for HBV-related HCC.

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Correction to: Liver cancer cell lines distinctly mimic the metabolic gene expression pattern of the corresponding human tumours

Journal of Experimental & Clinical Cancer Research ◽

10.1186/s13046-018-0939-4 ◽

2018 ◽

Vol 37 (1) ◽

Author(s):

Zeribe C. Nwosu ◽

Nadia Battello ◽

Melanie Rothley ◽

Weronika Piorońska ◽

Barbara Sitek ◽

...

Keyword(s):

Gene Expression ◽

Liver Cancer ◽

Expression Pattern ◽

Cell Lines ◽

Cancer Cell ◽

Gene Expression Pattern ◽

Cancer Cell Lines ◽

Liver Cancer Cell ◽

Metabolic Gene ◽

Metabolic Gene Expression

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Transcriptional gene expression profiles indicate response of breast cancer cell lines to aminoflavone prodrug.

10.1158/0008-5472.sabcs-2153 ◽

2009 ◽

Cited By ~ 1

Author(s):

PM Shelton ◽

B Nguyen ◽

A Chen ◽

AM Burger

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cell Lines ◽

Cancer Cell ◽

Breast Cancer Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Cell Lines ◽

Breast Cancer Cell Lines

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

BMC Bioinformatics ◽

10.1186/s12859-020-3524-8 ◽

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.

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