Ribonucleotide reductase M2 (RRM2): Regulation, function and targeting strategy in human cancer

Zuo, Zan-Wen; Zhou, Zerong; Chang, Yuzhou; Liu, Yan; Yang, Huan; Li, Qizhang; Zhang, Lei

doi:10.1016/j.gendis.2022.11.022

Cited by 37 publications

(20 citation statements)

References 207 publications

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“…Table 5 shows drugs and drug targets within IDG Drug Targets 2022. Hydroxycarbamide and gemcitabine are ribonucleotide reductase enzyme (RNR ) inhibitors that have been used for breast cancer treatment (See Figure 5) [70, 71]. The two drugs are associated with RRM2 as a drug target [72].…”

Section: Resultsmentioning

confidence: 99%

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Turki,

Taguchi

2023

Preprint

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Understanding breast cancer drug response mechanism can play a crucial role in improving the treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient version of support vector machines (esvm) working as follows. First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to the treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in the dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, results demonstrate superiority and efficiency of esvm achieving high performance results and having more expressed genes in (1) well-established breast cancer cell lines including MD-MB231, MCF7, and HS578T; and (2) breast tissues. Moreover, esvm is able to identify (1) various drugs including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanism, progression, and metastasizing. Our method is available publicly in the maGENEgerZ web server at https://aibio.shinyapps.io/maGENEgerZ/.

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Section: Resultsmentioning

confidence: 99%

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Turki,

Taguchi

2023

Preprint

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“…Studies have found that RRM2 plays a crucial role in cell death, affecting autoimmune disorders such as rheumatoid arthritis (RA), as well as several types of cancer [ 59 ]. [ 60 ]. In particular, the inhibition of RRM2 results in a decrease in cellular proliferation and in increase in apoptosis, thus representing a potential chemotherapeutic target and treatment for RA.…”

Section: Discussionmentioning

confidence: 99%

Epigenome-wide association study of long-term psychosocial stress in older adults

Opsasnick,

Zhao,

Schmitz

et al. 2024

Epigenetics

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Long-term psychosocial stress is strongly associated with negative physical and mental health outcomes, as well as adverse health behaviours; however, little is known about the role that stress plays on the epigenome. One proposed mechanism by which stress affects DNA methylation is through health behaviours. We conducted an epigenome-wide association study (EWAS) of cumulative psychosocial stress ( n = 2,689) from the Health and Retirement Study (mean age = 70.4 years), assessing DNA methylation (Illumina Infinium HumanMethylationEPIC Beadchip) at 789,656 CpG sites. For identified CpG sites, we conducted a formal mediation analysis to examine whether smoking, alcohol use, physical activity, and body mass index (BMI) mediate the relationship between stress and DNA methylation. Nine CpG sites were associated with psychosocial stress (all p < 9E–07; FDR q < 0.10). Additionally, health behaviours and/or BMI mediated 9.4% to 21.8% of the relationship between stress and methylation at eight of the nine CpGs. Several of the identified CpGs were in or near genes associated with cardiometabolic traits, psychosocial disorders, inflammation, and smoking. These findings support our hypothesis that psychosocial stress is associated with DNA methylation across the epigenome. Furthermore, specific health behaviours mediate only a modest percentage of this relationship, providing evidence that other mechanisms may link stress and DNA methylation.

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“…Moreover, the 13 genes (AGT, BCL6, CDKN2C, FGFR3, GJA1, TNC, MT1X, S100A8, S100A9, SYT1, Table 5 shows drugs and drug targets within the IDG Drug Targets 2022. Hydroxycarbamide and gemcitabine are ribonucleotide reductase enzyme (RNR) inhibitors that have been used for breast cancer treatment (see Figure 5) [78,79]. The two drugs are associated with RRM2 as a drug target [80].…”

Section: Dataset2mentioning

confidence: 99%

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Turki,

Taguchi

2024

Mathematics

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Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server.

show abstract

Ribonucleotide reductase M2 (RRM2): Regulation, function and targeting strategy in human cancer

Cited by 37 publications

References 207 publications

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Epigenome-wide association study of long-term psychosocial stress in older adults

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

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