Prediction of Genes Involved in Lung Cancer with a Systems Biology Approach Based on Comprehensive Gene Information

2021 ◽  
Author(s):  
Shahram Parvin ◽  
Hamid Sedighian ◽  
Ehsan Sohrabi ◽  
Mahdieh Mahboobi ◽  
Milad Rezaei ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systems Biology ◽  
Gene Information

Systems Biology and translational oncology: a case study of predictive modeling in molecular biology of the lung cancer

2010 ◽  
Vol 150 ◽  
pp. 542-543
Author(s):  
Fortunato Bianconi ◽  
Mauro Boccadoro ◽  
Vienna Ludovini ◽  
Paolo Valigi
Keyword(s):  
Lung Cancer ◽  
Systems Biology ◽  
Molecular Biology ◽  
Predictive Modeling ◽  
Translational Oncology

scholarly journals An Integrative Systems Biology and Experimental Approach Identifies Convergence of Epithelial Plasticity, Metabolism, and Autophagy to Promote Chemoresistance

2019 ◽  
Vol 8 (2) ◽  
pp. 205 ◽  
Author(s):  
Shengnan Xu ◽  
Kathryn Ware ◽  
Yuantong Ding ◽  
So Kim ◽  
Maya Sheth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Transforming Growth Factor ◽  
Epithelial Mesenchymal Transition ◽  
Phylogenetic Analyses ◽  
Host Immune System ◽  
Expression Data ◽  
Mesenchymal Transition

The evolution of therapeutic resistance is a major cause of death for cancer patients. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure of the host immune system. These selective forces often lead to evolutionary convergence on pathways or hallmarks that drive progression. Thus, a deeper understanding of the evolutionary convergences that occur could reveal vulnerabilities to treat therapy-resistant cancer. To this end, we combined phylogenetic clustering, systems biology analyses, and molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during transforming growth factor-β (TGF-β)-mediated epithelial–mesenchymal transition in lung cancer. Phylogenetic analyses of gene expression data from TGF-β-treated cells revealed convergence of cells toward amine metabolic pathways and autophagy during TGF-β treatment. Knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. In addition, high ATG16L expression was found to be a poor prognostic marker in multiple cancer types. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities for cancer.

scholarly journals Systems biology analysis of the lung cancer‑related secretome

2018 ◽  
Author(s):  
Lin Feng ◽  
Yikun Yang ◽  
Min Li ◽  
Jie Song ◽  
Yanning Gao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systems Biology

Systems biology in personalized medicine in oncology: A case study in non-small cell lung cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e21096-e21096
Author(s):  
Fortunato Bianconi ◽  
Katia Perruccio ◽  
Vienna Ludovini ◽  
Elisa Baldelli ◽  
Guido Bellezza ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Mathematical Model ◽  
Cell Proliferation ◽  
Survival Analysis ◽  
Systems Biology ◽  
Small Cell Lung Cancer ◽  
Gene Amplification ◽  
Small Cell ◽  
Small Cell Lung ◽  
Kras Mutations

e21096 Background: Systems biology together with translational oncology is a new approach to discover sensitive pathways in specific cancers. In this study, we propose a computational modeling technique to investigate the possible effects various alterations, such as protein overexpression, gene amplification or mutations, may have on signaling, through of the EGFR and IGF1R pathways, in non-small cell lung cancer (NSCLC). Methods: EGFR and IGF1R pathways and the downstream MAPK and PIK3 networks have been reproduced through a mathematical model. One hundred-twenty five tumors from surgical NSCLC patients were evaluated for EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by fluorescence in situ hybridization (FISH). KRAS mutations (exons 2 and 3) were evaluated by direct sequencing Results: To correlate EGFR and IGF1R expression levels, and KRAS mutations to tumor cell proliferation, we focused on the ERK signaling pathway, which plays a central role in several steps of cancer development including proliferation and cancer cell migration. The mathematical model predicts a relationship between a simultaneous high expression level of both receptors and a modification on ERK time behavior, implying a stronger attitude for cell proliferation. Furthermore KRAS activating mutations predict high level of active ERK and high probability to have cell proliferation. Cell growth can be closely related to disease progression and act, in survival analysis, as DFS estimator. Patients with concomitant IGF1R/EGFR FISH/IHC positivity had a worse DFS ( p=0.005). KRAS mutations have a statistically significant shorter DFS (p<0.001) as well Conclusions: We propose a Systems Biology approach, combined with Translational Oncology methodologies, to understand the interaction between EGFR, IGF1R and KRAS pathways in NSCLC. Computational model predictions confirm clinical evidences of survival analysis. Future work will validate our model with experiments on various NSCLC cell cultures and further investigate the response to drug administration. We thank AIRC and Fondazione Cassa di Risparmio for supporting the study.

Computational model of EGFR and IGF1R pathways in lung cancer: A Systems Biology approach for Translational Oncology

2012 ◽  
Vol 30 (1) ◽  
pp. 142-153 ◽  
Author(s):  
Fortunato Bianconi ◽  
Elisa Baldelli ◽  
Vienna Ludovini ◽  
Lucio Crinò ◽  
Antonella Flacco ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systems Biology ◽  
Computational Model ◽  
Translational Oncology

scholarly journals Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

2020 ◽  
Author(s):  
Xanthoula Atsalaki ◽  
Lefteris Koumakis ◽  
George Potamias ◽  
Manolis Tsiknakis
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Integrative Approach ◽  
Expression Data ◽  
Gene Regulatory

AbstractHigh-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.

scholarly journals Systems biology analysis of publicly available transcriptomic data reveals a critical link between AKR1B10 gene expression, smoking and occurrence of lung cancer

PLoS ONE ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. e0222552 ◽  
Author(s):  
Juan M. Cubillos-Angulo ◽  
Eduardo R. Fukutani ◽  
Luís A. B. Cruz ◽  
María B. Arriaga ◽  
João Victor Lima ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Transcriptomic Data ◽  
Critical Link

scholarly journals An integrative systems biology and experimental approach identifies convergence of epithelial plasticity, metabolism, and autophagy to promote chemoresistance

2018 ◽  
Author(s):  
Shengnan Xu ◽  
Kathryn E. Ware ◽  
Yuantong Ding ◽  
So Young Kim ◽  
Maya Sheth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Epithelial Mesenchymal Transition ◽  
Host Immune System ◽  
Mechanism Analysis ◽  
Expression Data ◽  
Mesenchymal Transition ◽  
Evolutionary Convergence

AbstractThe evolution of therapeutic resistance is a major cause of death for patients with solid tumors. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure induced by the host immune system. These ecological and selective forces often lead to evolutionary convergence on one or more pathways or hallmarks that drive progression. These hallmarks are, in turn, intimately linked to each other through gene expression networks. Thus, a deeper understanding of the evolutionary convergences that occur at the gene expression level could reveal vulnerabilities that could be targeted to treat therapy-resistant cancer. To this end, we used a combination of phylogenetic clustering, systems biology analyses, and wet-bench molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during TGF-β-mediated epithelial-mesenchymal transition in a lung cancer model system. Phylogenetics analyses of gene expression data from TGF-β treated cells revealed evolutionary convergence of cells toward amine-metabolic pathways and autophagy during TGF-β treatment. Using high-throughput drug screens, we found that knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. Analysis of publicly-available clinical data sets validated the adverse prognostic importance of ATG16L expression in multiple cancer types including kidney, lung, and colon cancer patients. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities.

scholarly journals Systems biology analysis reveals new insights into invasive lung cancer

2018 ◽  
Vol 12 (S7) ◽  
Author(s):  
Dan Li ◽  
William Yang ◽  
Carolyn Arthur ◽  
Jun S. Liu ◽  
Carolina Cruz-Niera ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systems Biology
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