TMEM25 inhibits monomeric EGFR-mediated STAT3 activation in basal state to suppress triple-negative breast cancer progression

Bi, Jing; Wu, Zhihui; Zhang, Xin; Zeng, Taoling; Dai, Weizhong; Qiu, Ningyuan; Xu, M.; Qiao, Yikai; Lang, Ke; Zhao, Jianfeng; Cao, Xinyu; Lin, Qi; Chen, Xiao Lei; Xie, Liping; Ouyang, Zhengbiao; Guo, Jujiang; Zheng, Lin; Ma, Chao; Guo, Shiying; Chen, Kangmei; Mo, Wei; Fu, Guo; Zhao, Tong‐Jin; Wang, Hong-Rui

doi:10.1038/s41467-023-38115-2

Cited by 17 publications

(6 citation statements)

References 55 publications

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“…However, in both clinical and preclinical settings, the therapeutic efficacy of EGFR-targeted therapies, including monoclonal antibodies for TNBC treatment, has been unsatisfactory ( 47 ). Several studies have attempted to explain the mechanisms underlying the unsatisfactory effects of these strategies ( 48 , 49 ), suggesting the need for additional exploration. After the identification of the six key hypoxia-related factors, we conducted Pearson’s correlation analysis between six key hypoxic factors and 22 immune cell components in the TME and the interaction between the immune cells.…”

Section: Discussionmentioning

confidence: 99%

Analyses of hypoxia-related risk factors and clinical relevance in breast cancer

Li,

Yu,

Han

et al. 2024

Front. Oncol.

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IntroductionHypoxia plays an important role in the heterogeneity, relapse, metastasis, and drug resistance of breast cancer. In this study, we explored the hypoxia-related biological signatures in different subtypes of breast cancer and identified the key prognostic factors by bioinformatics methods.MethodsBased on The Cancer Genome Atlas (TCGA) Breast Cancer datasets, we divided the samples into immune-activated/suppressed populations by single-sample gene set enrichment analysis (ssGSEA) and then used hierarchical clustering to further identify hypoxic/non-hypoxic populations from the immune-suppressed samples. A hypoxia related risk model of breast cancer was constructed.ResultsNuclear factor interleukin-3 regulated (NFIL3), serpin family E member 1 (SERPINE1), FOS, biglycan (BGN), epidermal growth factor receptor (EGFR), and sushi-repeat-containing protein, X-linked (SRPX) were identified as key hypoxia-related genes. Margin status, American Joint Committee on Cancer (AJCC) stage, hypoxia status, estrogen receptor/progesterone receptor (ER/PR) status, NFIL3, SERPINE1, EGFR, and risk score were identified as independent prognostic indicators for breast cancer patients. The 3- and 5-year survival curves of the model and immunohistochemical staining on the breast cancer microarray verified the statistical significance and feasibility of our model. Among the different molecular types of breast cancer, ER/PR+ and HER2+ patients might have higher hypoxia-related risk scores. ER/PR-negative samples demonstrated more activated immune-related pathways and better response to most anticancer agents.DiscussionOur study revealed a novel risk model and potential feasible prognostic factors for breast cancer and might provide new perspectives for individual breast cancer treatment.

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Section: Discussionmentioning

confidence: 99%

Analyses of hypoxia-related risk factors and clinical relevance in breast cancer

Li,

Yu,

Han

et al. 2024

Front. Oncol.

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“…In Figure 4c, we report the following 18 transcription factors: SP1, RELA, NFKB1, HIF1A, STAT3, ZEB1, FOXO3, NFE2L2, JUN, NRC1, SP3, EP300, E2F1, CEBPB, USF1, HDAC1, ETS1, and STAT1. STAT3 is involved in breast cancer progression [71]. E2F1 and EP300 have been reported to be involved in breast cancer development and metastasis [72][73][74].…”

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.

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“…In cancer, EGFR contributes to tumor progression by promoting invasion and metastasis and stimulating tumor angiogenesis ( 40 ). EGFR activates complex signal transduction pathways with primary pathways including mitogen-activated protein kinase (MEK)/extracellular signal-regulated kinase (ERK) ( 41 ), JAK2/STAT3 ( 42 ), and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) ( 43 ). Dysregulation of these pathways is intricately linked to tumor development, invasion, and metastasis.…”

Section: Receptor Tyrosine Kinases In Tnbcmentioning

confidence: 99%

Potential therapeutic targets of the JAK2/STAT3 signaling pathway in triple-negative breast cancer

Long,

Fei,

Chen

et al. 2024

Front. Oncol.

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Triple-negative breast cancer (TNBC) poses a significant clinical challenge due to its propensity for metastasis and poor prognosis. TNBC evades the body’s immune system recognition and attack through various mechanisms, including the Janus Kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling pathway. This pathway, characterized by heightened activity in numerous solid tumors, exhibits pronounced activation in specific TNBC subtypes. Consequently, targeting the JAK2/STAT3 signaling pathway emerges as a promising and precise therapeutic strategy for TNBC. The signal transduction cascade of the JAK2/STAT3 pathway predominantly involves receptor tyrosine kinases, the tyrosine kinase JAK2, and the transcription factor STAT3. Ongoing preclinical studies and clinical research are actively investigating this pathway as a potential therapeutic target for TNBC treatment. This article comprehensively reviews preclinical and clinical investigations into TNBC treatment by targeting the JAK2/STAT3 signaling pathway using small molecule compounds. The review explores the role of the JAK2/STAT3 pathway in TNBC therapeutics, evaluating the benefits and limitations of active inhibitors and proteolysis-targeting chimeras in TNBC treatment. The aim is to facilitate the development of novel small-molecule compounds that target TNBC effectively. Ultimately, this work seeks to contribute to enhancing therapeutic efficacy for patients with TNBC.

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TMEM25 inhibits monomeric EGFR-mediated STAT3 activation in basal state to suppress triple-negative breast cancer progression

Cited by 17 publications

References 55 publications

Analyses of hypoxia-related risk factors and clinical relevance in breast cancer

Analyses of hypoxia-related risk factors and clinical relevance in breast cancer

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

Potential therapeutic targets of the JAK2/STAT3 signaling pathway in triple-negative breast cancer

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