scholarly journals The Jekyll and Hyde of Cellular Senescence in Cancer

Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 208
Author(s):  
Dilara Demirci ◽  
Bengisu Dayanc ◽  
Fatma Aybuke Mazi ◽  
Serif Senturk
Keyword(s):  
Cancer Cell ◽  
Cellular Senescence ◽  
Expression Profiles ◽  
Treatment Strategies ◽  
Gene Expression Profiles ◽  
Therapy Resistance ◽  
Disease Recurrence ◽  
Adverse Outcomes ◽  
Cell Senescence ◽  
Great Promise

Cellular senescence is a state of stable cell cycle arrest that can be triggered in response to various insults and is characterized by distinct morphological hallmarks, gene expression profiles, and the senescence-associated secretory phenotype (SASP). Importantly, cellular senescence is a key component of normal physiology with tumor suppressive functions. In the last few decades, novel cancer treatment strategies exploiting pro-senescence therapies have attracted considerable interest. Recent insight, however, suggests that therapy-induced senescence (TIS) elicits cell-autonomous and non-cell-autonomous implications that potentially entail detrimental consequences, reflecting the Jekyll and Hyde nature of cancer cell senescence. In essence, the undesirable manifestations that generally culminate in inflammation, cancer stemness, senescence reversal, therapy resistance, and disease recurrence are dictated by the persistent accumulation of senescent cells and the SASP. Thus, mitigating these pro-tumorigenic effects by eliminating these cells or inhibiting their SASP production holds great promise for developing innovative therapeutic strategies. In this review, we describe the fundamental aspects and dynamics of cancer cell senescence and summarize the comprehensive research on the adverse outcomes of TIS. Furthermore, we underline the rationale and motivation of emerging senotherapeutic modalities surrounding the removal of senescent cells and the SASP to help maximize the overall efficacy of cancer therapies.

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

scholarly journals Circulating tumor cell characterization of lung cancer brain metastasis in the cerebrospinal fluid through single-cell transcriptome analysis

2020 ◽  
Author(s):  
Haoyu Ruan ◽  
Yihang Zhou ◽  
Jie Shen ◽  
Yue Zhai ◽  
Ying Xu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cerebrospinal Fluid ◽  
Single Cell ◽  
Tumor Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Unknown Primary ◽  
Great Promise ◽  
Cell Cycle Gene

AbstractMetastatic lung cancer accounts for about half of the brain metastases (BM). Development of leptomeningeal metastases (LM) are becoming increasingly common, and its prognosis is still poor despite the advances in systemic and local approaches. Cytology analysis in the cerebrospinal fluid (CSF) remains the diagnostic gold standard. Although several previous studies performed in CSF have offered great promise for the diagnostics and therapeutics of LM, a comprehensive characterization of circulating tumor cells (CTCs) in CSF is still lacking. To fill this critical gap of lung adenocarcinoma LM (LUAD-LM), we analyzed the transcriptomes of 1,375 cells from 5 LUAD-LM patient and 3 control samples using single-cell RNA sequencing technology. We defined CSF-CTCs based on abundant expression of epithelial markers and genes with lung origin, as well as the enrichment of metabolic pathway and cell adhesion molecules, which are crucial for the survival and metastases of tumor cells. Elevated expression of CEACAM6 and SCGB3A2 was discovered in CSF-CTCs, which could serve as candidate biomarkers of LUAD-LM. We identified substantial heterogeneity in CSF-CTCs among LUAD-LM patients and within patient among individual cells. Cell-cycle gene expression profiles and the proportion of CTCs displaying mesenchymal and cancer stem cell properties also vary among patients. In addition, CSF-CTC transcriptome profiling identified one LM case as cancer of unknown primary site (CUP). Our results will shed light on the mechanism of LUAD-LM and provide a new direction of diagnostic test of LUAD-LM and CUP cases from CSF samples.

scholarly journals Stromal-derived interleukin 6 drives epithelial-to-mesenchymal transition and therapy resistance in esophageal adenocarcinoma

2019 ◽  
Vol 116 (6) ◽  
pp. 2237-2242 ◽  
Author(s):  
Eva A. Ebbing ◽  
Amber P. van der Zalm ◽  
Anne Steins ◽  
Aafke Creemers ◽  
Simone Hermsen ◽  
...  
Keyword(s):  
Esophageal Adenocarcinoma ◽  
Epithelial To Mesenchymal Transition ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Treatment Resistance ◽  
Serum Levels ◽  
Therapy Resistance ◽  
Mesenchymal Transition ◽  
Dismal Prognosis ◽  
Organoid Cultures

Esophageal adenocarcinoma (EAC) has a dismal prognosis, and survival benefits of recent multimodality treatments remain small. Cancer-associated fibroblasts (CAFs) are known to contribute to poor outcome by conferring therapy resistance to various cancer types, but this has not been explored in EAC. Importantly, a targeted strategy to circumvent CAF-induced resistance has yet to be identified. By using EAC patient-derived CAFs, organoid cultures, and xenograft models we identified IL-6 as the stromal driver of therapy resistance in EAC. IL-6 activated epithelial-to-mesenchymal transition in cancer cells, which was accompanied by enhanced treatment resistance, migratory capacity, and clonogenicity. Inhibition of IL-6 restored drug sensitivity in patient-derived organoid cultures and cell lines. Analysis of patient gene expression profiles identified ADAM12 as a noninflammation-related serum-borne marker for IL-6–producing CAFs, and serum levels of this marker predicted unfavorable responses to neoadjuvant chemoradiation in EAC patients. These results demonstrate a stromal contribution to therapy resistance in EAC. This signaling can be targeted to resensitize EAC to therapy, and its activity can be measured using serum-borne markers.

scholarly journals Deciphering the Correlation between Breast Tumor Samples and Cell Lines by Integrating Copy Number Changes and Gene Expression Profiles

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yi Sun ◽  
Qi Liu
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Breast Tumors ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Tumor Group

Breast cancer is one of the most common cancers with high incident rate and high mortality rate worldwide. Although different breast cancer cell lines were widely used in laboratory investigations, accumulated evidences have indicated that genomic differences exist between cancer cell lines and tissue samples in the past decades. The abundant molecular profiles of cancer cell lines and tumor samples deposited in the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas now allow a systematical comparison of the breast cancer cell lines with breast tumors. We depicted the genomic characteristics of breast primary tumors based on the copy number variation and gene expression profiles and the breast cancer cell lines were compared to different subgroups of breast tumors. We identified that some of the breast cancer cell lines show high correlation with the tumor group that agrees with previous knowledge, while a big part of them do not, including the most used MCF7, MDA-MB-231, and T-47D. We presented a computational framework to identify cell lines that mostly resemble a certain tumor group for the breast tumor study. Our investigation presents a useful guide to bridge the gap between cell lines and tumors and helps to select the most suitable cell line models for personalized cancer studies.

scholarly journals CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles

2019 ◽  
Author(s):  
Qiong Zhang ◽  
Mei Luo ◽  
Chun-Jie Liu ◽  
An-Yuan Guo
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Cancer Cell ◽  
Expression Profiles ◽  
Web Server ◽  
Gene Expression Profiles ◽  
Cancer Cell Line ◽  
Model Systems ◽  
Validation Data ◽  
Cell Line Authentication

AbstractCancer cell lines (CCLs) as important model systems play critical roles in cancer researches. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphisms, whereas no method is available for CCL authentication using gene expression profiles. Here, we developed a novel method and homonymic web server (CCLA, Cancer Cell Line Authentication, http://bioinfo.life.hust.edu.cn/web/CCLA/) to authenticate 1,291 human CCLs of 28 tissues using gene expression profiles. CCLA curated CCL-specific gene signatures and employed machine learning methods to measure overall similarities and distances between the query sample and each reference CCL. CCLA showed an excellent speed advantage and high accuracy with a top 1 accuracy of 96.58% or 92.15% (top 3 accuracy of 100% or 95.11%) for microarray or RNA-Seq validation data (719 samples, 461 CCLs), respectively. To the best of our knowledge, CCLA is the first approach to authenticate CCLs based on gene expression. Users can freely and conveniently authenticate CCLs using gene expression profiles or NCBI GEO accession on CCLA website.

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 ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets

2020 ◽  
Vol 48 (W1) ◽  
pp. W455-W462 ◽  
Author(s):  
Sisira Kadambat Nair ◽  
Christopher Eeles ◽  
Chantal Ho ◽  
Gangesh Beri ◽  
Esther Yoo ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Rat Hepatocytes ◽  
Adverse Outcomes ◽  
Easy Access ◽  
Chemical Safety ◽  
Safety Testing

Abstract In the past few decades, major initiatives have been launched around the world to address chemical safety testing. These efforts aim to innovate and improve the efficacy of existing methods with the long-term goal of developing new risk assessment paradigms. The transcriptomic and toxicological profiling of mammalian cells has resulted in the creation of multiple toxicogenomic datasets and corresponding tools for analysis. To enable easy access and analysis of these valuable toxicogenomic data, we have developed ToxicoDB (toxicodb.ca), a free and open cloud-based platform integrating data from large in vitro toxicogenomic studies, including gene expression profiles of primary human and rat hepatocytes treated with 231 potential toxicants. To efficiently mine these complex toxicogenomic data, ToxicoDB provides users with harmonized chemical annotations, time- and dose-dependent plots of compounds across datasets, as well as the toxicity-related pathway analysis. The data in ToxicoDB have been generated using our open-source R package, ToxicoGx (github.com/bhklab/ToxicoGx). Altogether, ToxicoDB provides a streamlined process for mining highly organized, curated, and accessible toxicogenomic data that can be ultimately applied to preclinical toxicity studies and further our understanding of adverse outcomes.

scholarly journals Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression

Blood ◽  
2012 ◽  
Vol 120 (13) ◽  
pp. 2639-2649 ◽  
Author(s):  
Han-Yu Chuang ◽  
Laura Rassenti ◽  
Michelle Salcedo ◽  
Kate Licon ◽  
Alexander Kohlmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chronic Lymphocytic Leukemia ◽  
Disease Progression ◽  
Expression Profiles ◽  
Treatment Strategies ◽  
Gene Expression Profiles ◽  
Lymphocytic Leukemia ◽  
Cancer Evolution ◽  
Sample Collection ◽  
Over Time

Abstract The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for CLL progression. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection, more accurately than established markers. The prognostic power of these subnetworks then was validated on 2 other cohorts of patients. We noted reduced divergence in gene expression between leukemia cells of CLL patients classified at diagnosis with aggressive versus indolent disease over time. The predictive subnetworks vary in levels of expression over time but exhibit increased similarity at later time points before therapy, suggesting that degenerate pathways apparently converge into common pathways that are associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.

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.

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