UBE2C affects breast cancer proliferation through the AKT/mTOR signaling pathway

Lu, Zinan; Song, Jia; Sun, Ting

doi:10.1097/cm9.0000000000001708

Cited by 30 publications

(27 citation statements)

References 38 publications

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“…Relevant studies have shown that ubiquitin conjugating enzyme 2C (UBE2C) is a biomarker of poor prognosis of breast cancer, 9 as UBE2C promotes proliferation of breast cancer cells by activating the AKT/mTOR signaling pathway. 10 Inhibition of UBE2C reduces the proliferation of breast cancer cells. 11 The use of UBE2C levels as a prognostic marker for lymph node-positive breast cancer has been validated, 12 and UBE2C mRNA expression in high-risk early breast cancer has prognostic significance.…”

Section: Introductionmentioning

confidence: 99%

Validation of a Disease-Free Survival Prediction Model Using UBE2C and Clinical Indicators in Breast Cancer Patients

Shen¹,

Yan²,

Yang³

et al. 2023

BCTT

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To explore the validation of a disease-free survival (DFS) model for predicting disease progression based on the combination of ubiquitin-conjugating enzyme E2 C (UBE2C) levels and clinical indicators in breast cancer patients. Methods: We enrolled 121 patients with breast cancer, collected their baseline characteristics and follow-up data, and analyzed the UBE2C levels in tumor tissues. We studied the relationship between UBE2C expression in tumor tissues and disease progression events of patients. We used the Kaplan-Meier method for identifying the disease-free survival rate of patients, and the multivariate Cox regression analysis to study the risk factors affecting the prognosis of patients. We sought to develop and validate a model for predicting disease progression. Results:We found that the level of expression of UBE2C could effectively distinguish the prognosis of patients. In the Receiver Operating Characteristic (ROC) curve analysis, the Area under the ROC Curve (AUC) = 0.826 (0.714-0.938) indicating that high levels of UBE2C was a high-risk factor for poor prognosis. After evaluating different models using the ROC curve, Concordance index (C-index), calibration curve, Net Reclassification Index (NRI), Integrated Discrimination Improvement Index (IDI), and other methods, we finally developed a model for the expression of Tumor-Node (TN) staging using Ki-67 and UBE2C, which had an AUC=0.870, 95% CI of 0.786-0.953. The traditional TN model had an AUC=0.717, and 95% CI of 0.581-0.853. Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC) analysis indicated that the model had good clinical benefits and it was relatively simple to use. Conclusion:We found that high levels of UBE2C was a high-risk factor for poor prognosis. The use of UBE2C in addition to other breast cancer-related indicators effectively predicted the possible disease progression, thus providing a reliable basis for clinical decision-making.

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Section: Introductionmentioning

confidence: 99%

Validation of a Disease-Free Survival Prediction Model Using UBE2C and Clinical Indicators in Breast Cancer Patients

Shen¹,

Yan²,

Yang³

et al. 2023

BCTT

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“…GO analysis can identify gene sets enriched in any of two groups, comparing one against the other but is almost always performed between tumor and normal tissue samples in order to find biological functions altered in tumors compared to healthy tissues [ 56 , 57 , 58 ]. In order to better characterize the tumor microenvironment, we extended that use to also identify the biological functions significantly altered in PP compared to GP samples.…”

Section: Discussionmentioning

confidence: 99%

Mining TCGA Database for Genes with Prognostic Value in Breast Cancer

Filippi

Mocanu²

2023

IJMS

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The aim of the study was to use transcriptomics data to identify genes associated with advanced/aggressive breast cancer and their effect on survival outcomes. We used the publicly available The Cancer Genome Atlas (TCGA) database to obtain RNA sequence data from patients with less than five years survival (Poor Prognosis, n = 101), patients with greater than five years survival (Good Prognosis, n = 200), as well as unpaired normal tissue data (normal, n = 105). The data analyses performed included differential expression between groups and selection of subsets of genes, gene ontology, cell enrichment analysis, and survival analyses. Gene ontology results showed significantly reduced enrichment in gene sets related to tumor immune microenvironment in Poor Prognosis and cell enrichment analysis confirmed significantly reduced numbers of macrophages M1, CD8 T cells, plasma cells and dendritic cells in samples in the Poor Prognosis samples compared with Good Prognosis. A subset of 742 genes derived from differential expression analysis as well as genes coding for immune checkpoint molecules was evaluated for their effect on overall survival. In conclusion, this study may contribute to the better understanding of breast cancer transcriptomics and provide possible targets for further research and eventual therapeutic interventions.

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“…[20][21][22] The hypoxic TME can induce the expression of hypoxiainducible factor (HIF)-1a, a momentous transcriptional factor that regulates the transcription of many genes involved in the glycolytic pathway or glucose transport in TAMs. [11,23,24] Two major pathways are significantly potentiated by HIF-1a transcription: Toll-like receptor/ nuclear factor-kB (NF-kB) and phosphatidylinositol 3kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR), which lead to an increase in glucose metabolism under oxygen-independent conditions. [25] Furthermore, HIF-1a promotes the transition of pyruvate to lactate via up-regulating the expression of these two enzymes: LDH (which catalyzes pyruvate to lactate) and pyruvate dehydrogenase kinase (which inactivates and restricts the entrance of pyruvate into the TCA cycle); further increasing lactate concentration.…”

Section: Glycolysismentioning

confidence: 99%

Metabolic reprogramming in the immunosuppression of tumor-associated macrophages

Wang

et al. 2022

Chinese Medical Journal

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Tumor-associated macrophages (TAMs) are an essential proportion of tumor-infiltrating immune cells in the tumor microenvironment (TME) and have immunosuppressive functions. The high plasticity and corresponding phenotypic transformation of TAMs facilitate oncogenesis and progression, and suppress antineoplastic responses. Due to the uncontrolled proliferation of tumor cells, metabolism homeostasis is regulated, leading to a series of alterations in the metabolite profiles in the TME, which have a commensurate influence on immune cells. Metabolic reprogramming of the TME has a profound impact on the polarization and function of TAMs, and can alter their metabolic profiles. TAMs undergo a series of metabolic reprogramming processes, involving glucose, lipid, and amino acid metabolism, and other metabolic pathways, which terminally promote the development of the immunosuppressive phenotype. TAMs express a pro-tumor phenotype by increasing glycolysis, fatty acid oxidation, cholesterol efflux, and arginine, tryptophan, glutamate, and glutamine metabolism. Previous studies on the metabolism of TAMs demonstrated that metabolic reprogramming has intimate crosstalk with anti-tumor or pro-tumor phenotypes and is crucial for the function of TAMs themselves. Targeting metabolism-related pathways is emerging as a promising therapeutic modality because of the massive metabolic remodeling that occurs in malignant cells and TAMs. Evidence reveals that the efficacy of immune checkpoint inhibitors is improved when combined with therapeutic strategies targeting metabolism-related pathways. In-depth research on metabolic reprogramming and potential therapeutic targets provides more options for anti-tumor treatment and creates new directions for the development of new immunotherapy methods. In this review, we elucidate the metabolic reprogramming of TAMs and explore how they sustain immunosuppressive phenotypes to provide a perspective for potential metabolic therapies.

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UBE2C affects breast cancer proliferation through the AKT/mTOR signaling pathway

Cited by 30 publications

References 38 publications

Validation of a Disease-Free Survival Prediction Model Using UBE2C and Clinical Indicators in Breast Cancer Patients

Validation of a Disease-Free Survival Prediction Model Using UBE2C and Clinical Indicators in Breast Cancer Patients

Mining TCGA Database for Genes with Prognostic Value in Breast Cancer

Metabolic reprogramming in the immunosuppression of tumor-associated macrophages

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