PKG II effectively reversed EGF‐induced protein expression alterations in human gastric cancer cell lines

Qian, Hai; Yan, Tao; Jiang, Lu; Wang, Ying; Lan, Ting; Wu, Min; Pang, Ji; Appiah-Kubi, Kwaku; Chen, Yongchang; Wu, Yan

doi:10.1002/cbin.10912

Cited by 3 publications

(2 citation statements)

References 38 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HMG 20 (Potri.005G246700, Potri.011G168800) was reported to have a potential role for β-dystrobrevin in neuronal differentiation, and histone deacetylase activity regulation [ 33 , 34 ]. PKGII (Potri.018G084900) inhibited the proliferation of various cancer cell lines in humans [ 35 , 36 , 37 ]. MORPHEUS (Potri.013G006000) MORPHEUS (Potri.013G006000) was a plant-specific epigenetic regulator of transcriptional gene silencing and affected cytosine methylation status [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

Function and Evolution of C1-2i Subclass of C2H2-Type Zinc Finger Transcription Factors in POPLAR

Sun

et al. 2022

Genes

View full text Add to dashboard Cite

C2H2 zinc finger (C2H2-ZF) transcription factors participate in various aspects of normal plant growth regulation and stress responses. C1-2i C2H2-ZFs are a special subclass of conserved proteins that contain two ZnF-C2H2 domains. Some C1-2i C2H2-ZFs in Arabidopsis (ZAT) are involved in stress resistance and other functions. However, there is limited information on C1-2i C2H2-ZFs in Populus trichocarpa (PtriZATs). To analyze the function and evolution of C1-2i C2H2-ZFs, eleven PtriZATs were identified in P. trichocarpa, which can be classified into two subgroups. The protein structure, conserved ZnF-C2H2 domains and QALGGH motifs, showed high conservation during the evolution of PtriZATs in P. trichocarpa. The spacing between two ZnF-C2H2 domains, chromosomal locations and cis-elements implied the original proteins and function of PtriZATs. Furthermore, the gene expression of different tissues and stress treatment showed the functional differentiation of PtriZATs subgroups and their stress response function. The analysis of C1-2i C2H2-ZFs in different Populus species and plants implied their evolution and differentiation, especially in terms of stress resistance. Cis-elements and expression pattern analysis of interaction proteins implied the function of PtriZATs through binding with stress-related genes, which are involved in gene regulation by via epigenetic modification through histone regulation, DNA methylation, ubiquitination, etc. Our results for the origin and evolution of PtriZATs will contribute to understanding the functional differentiation of C1-2i C2H2-ZFs in P. trichocarpa. The interaction and expression results will lay a foundation for the further functional investigation of their roles and biological processes in Populus.

show abstract

Section: Resultsmentioning

confidence: 99%

Function and Evolution of C1-2i Subclass of C2H2-Type Zinc Finger Transcription Factors in POPLAR

Sun

et al. 2022

Genes

View full text Add to dashboard Cite

show abstract

“…Among the other top-performing drugs, the sensitivity of Gefitinib, an EGFR inhibitor has been attributed to the expression of ARHGAP8, a gene implicated in EGFR-mediated ERK1/2 phosphorylation and oncogenesis [51][52][53] . The expression of other bimodal genes associated with lapatinib sensitivity such as MARVELD3 and EPN3 has been reported to promote migration and invasion of cancer cells [54][55][56] .…”

Section: Validation Of Predictorsmentioning

confidence: 99%

Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity

Ba-Alawi

Nair

et al. 2020

Preprint

View full text Add to dashboard Cite

Identifying biomarkers predictive of cancer cells’ response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have boosted the research for finding predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions and which features were the most associated with response, hindering the clinical translation of these models. To alleviate this issue, we develop a new machine learning pipeline based on the recent LOBICO approach that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. Using our method, we used a compendium of three of the largest pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets.

show abstract

Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity

Ba-Alawi

Nair

et al. 2022

Cancer Research

View full text Add to dashboard Cite

Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation, support a better translation of gene expression biomarkers between model systems using bimodal gene expression.

show abstract

PKG II effectively reversed EGF‐induced protein expression alterations in human gastric cancer cell lines

Cited by 3 publications

References 38 publications

Function and Evolution of C1-2i Subclass of C2H2-Type Zinc Finger Transcription Factors in POPLAR

Function and Evolution of C1-2i Subclass of C2H2-Type Zinc Finger Transcription Factors in POPLAR

Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity

Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity

Contact Info

Product

Resources

About