Frequent p53 mutation in brain (fetal)-type glycogen phosphorylase positive foci adjacent to human ‘de novo’ colorectal carcinomas

Shimada, Shinya; Shiomori, Kenji; Tashima, Satoshi; Tsuruta, Junji; Ogawa, Michio

doi:10.1054/bjoc.2001.1824

Cited by 10 publications

(9 citation statements)

References 43 publications

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“…In our study, AS-raf-ODN treatment has led to a maximum inhibition of glycogen phosphorylase, brain isoform (PYGB) expression. PYGB is a key enzyme in glycogen degradation for inducing an emergency glucose supply during stress and ischemia (40). Overexpression of PYGB has been reported in early stages of gastric and colorectal cancers (41).…”

Section: Discussionmentioning

confidence: 99%

Gene expression profile by inhibiting Raf-1 protein kinase in breast cancer cells

Mewani¹,

Shen²,

Li³

et al. 2006

Int J Mol Med

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Abstract. Raf-1 protein serine-threonine kinase plays an important role in cell growth, proliferation, and cell survival. Previously, we and others have demonstrated that antisense raf oligonucleotide-mediated inhibition of Raf-1 expression leads to tumor growth arrest, radiosensitization and chemosensitization in vivo. Raf-1 inhibition is also associated with apoptotic cell death. In this study, we inhibited Raf-1 using an antisense raf oligonucleotide (AS-raf-ODN) to identify downstream targets of Raf-1 using microarray gene expression analysis. Treatment of MDA-MB-231 breast cancer cells with 250 nM AS-raf-ODN led to significant inhibition of Raf-1 protein (75.2±9.6%) and c-raf-1 mRNA levels (86.2±3.3%) as compared to untreated control cells. The lipofectin control or mismatch oligonucleotide had no effect on Raf-1 expression. To determine the changes in gene expression profiles that were due to inhibition of Raf-1, we simultaneously compared the gene expression patterns in AS-raf-ODN treated cells with untreated control cells and cells treated with lipofectin alone or MM-ODN. A total of 17 genes (4 upregulated and 13 down-regulated) including c-raf-1 were identified that were altered after AS-raf-ODN treatment. Functional clustering analysis revealed genes involved in apoptosis (Bcl-X L ), cell adhesion (paxillin, plectin, Rho GDI·, CCL5), metabolism (GM2A, SLC16A3, PYGB), signal transduction (protein kinase C nu), and transcriptional regulation (HMGA1), and membrane-associated genes (GNAS, SLC16A3). Real-time PCR, Northern analysis and Western analysis confirmed the microarray findings. Our study provides insight into Raf-1 related signaling pathways and a model system to identify potential target genes.

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Section: Discussionmentioning

confidence: 99%

Gene expression profile by inhibiting Raf-1 protein kinase in breast cancer cells

Mewani¹,

Shen²,

Li³

et al. 2006

Int J Mol Med

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“…Normal-appearing tissue near a tumor could, for example, be genotypically altered or exhibit an altered gene expression profile. [10][11][12][13] Moreover, factors such as the degree of disease-associated inflammation may have a significant impact on gene expression profiles. Other bystander effects, epiphenomena, or secondary disease processes could all play important roles in determining expression profiles within these adjacent, so-called normal tissues.…”

Section: Sample Variation: Proximity To Disease Anatomic Location Amentioning

confidence: 99%

Gene Expression Profile Analysis by DNA Microarrays

King¹,

Sinha²

2001

JAMA

175

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DNA microarrays represent a technological intersection between biology and computers that enables gene expression analysis in human tissues on a genome-wide scale. This application can be expected to prove extremely valuable for the study of the genetic basis of complex diseases. Despite the enormous promise of this revolutionary technology, there are several issues and possible pitfalls that may undermine the authority of the microarray platform. We discuss some of the conceptual, practical, statistical, and logistical issues surrounding the use of microarrays for gene expression profiling. These issues include the imprecise definition of normal in expression comparisons; the cellular and subcellular heterogeneity of the tissues being studied; the difficulty in establishing the statistically valid comparability of arrays; the logistical logjam in analysis, presentation, and archiving of the vast quantities of data generated; and the need for confirmational studies that address the functional relevance of findings. Although several complicated issues must be resolved, the potential payoff remains large.

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“…As an oncogene, 64,65 the overexpression of DDIT4 correlates with tumor progression and worse outcomes in several human cancers, including OV 18,66–68 . Brain‐type glycogen phosphorylase ( PYGB ) could regulate multiple biological characteristics of cancer cells, such as proliferation, invasion, and apoptosis, and metastatic phenotypes of several cancers 69–74 . PYGB regulates the Wnt/β‐catenin signaling pathway to achieve cancer‐promoting effects in OV, 75 non‐small cell lung cancer, 76 and gastric cancer 77 .…”

Section: Discussionmentioning

confidence: 99%

Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

Zhang

Yang

et al. 2021

Cancer Medicine

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Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets. Results A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed. Conclusion Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

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Frequent p53 mutation in brain (fetal)-type glycogen phosphorylase positive foci adjacent to human ‘de novo’ colorectal carcinomas

Cited by 10 publications

References 43 publications

Gene expression profile by inhibiting Raf-1 protein kinase in breast cancer cells

Gene expression profile by inhibiting Raf-1 protein kinase in breast cancer cells

Gene Expression Profile Analysis by DNA Microarrays

Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

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