BMP2 as a promising anticancer approach: functions and molecular mechanisms

Li, Tongtong; Lai, Yong-wei; Xu, Han; Niu, Xiamu; Zhang, Peng-xia

doi:10.1007/s10637-022-01298-4

Cited by 16 publications

(10 citation statements)

References 180 publications

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“…These genes included BMP2, IGFBP2, SPP1, SLC43A3, P2RY6, PTX3, and STEAP1. Among these, the role of SLC43A3 in tumors, especially gliomas, had hardly been reported, while the functions of the other genes in tumors were widely documented 79 – 84 . Interestingly, the expression of SLC43A3 increased with the pathological grade and was most highly expressed in the ME transcriptional subgroup, which has been associated with a malignant process and shorter median survival time in glioblastoma patients (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…BMP2 is a multifaceted gene that can exhibit both tumor-promoting and tumor-suppressive effects in different types of cancers. It can stimulate cell proliferation, angiogenesis, and metastasis in certain contexts, while also inducing cell cycle arrest, apoptosis (programmed cell death), and differentiation of cancer cells, leading to tumor growth inhibition 79 . IGFBP2 has been implicated in promoting tumor growth and progression in several types of cancer 80 .…”

Section: Discussionmentioning

confidence: 99%

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Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Zhang,

Dang,

Liu

et al. 2024

Sci Rep

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Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Zhang,

Dang,

Liu

et al. 2024

Sci Rep

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“…BMP2 (Bone morphogenetic protein 2) is a member of TGF-β (tumor growth factor-β). BMP2 is the most extensively investigated protein contributing to the development of bones, EMT, and multiple signaling pathways [ 60 ]. High BMP2 expression is a promising therapeutic target in lung cancer [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

Comprehensive DNA methylation profiling by MeDIP-NGS identifies potential genes and pathways for epithelial ovarian cancer

Gautam,

Gupta,

Sachan

2024

J Ovarian Res

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Ovarian cancer, among all gynecologic malignancies, exhibits the highest incidence and mortality rate, primarily because it is often presents with non-specific or no symptoms during its early stages. For the advancement of Ovarian Cancer Diagnosis, it is crucial to identify the potential molecular signatures that could significantly differentiate between healthy and ovarian cancerous tissues and can be used further as a diagnostic biomarker for detecting ovarian cancer. In this study, we investigated the genome-wide methylation patterns in ovarian cancer patients using Methylated DNA Immunoprecipitation (MeDIP-Seq) followed by NGS. Identified differentially methylated regions (DMRs) were further validated by targeted bisulfite sequencing for CpG site-specific methylation profiles. Furthermore, expression validation of six genes by Quantitative Reverse Transcriptase-PCR was also performed. Out of total 120 differentially methylated genes (DMGs), 68 genes were hypermethylated, and 52 were hypomethylated in their promoter region. After analysis, we identified the top 6 hub genes, namely POLR3B, PLXND1, GIGYF2, STK4, BMP2 and CRKL. Interestingly we observed Non-CpG site methylation in the case of POLR3B and CRKL which was statistically significant in discriminating ovarian cancer samples from normal controls. The most significant pathways identified were focal adhesion, the MAPK signaling pathway, and the Ras signaling pathway. Expression analysis of hypermethylated genes was correlated with the downregulation of the genes. POLR3B and GIGYF2 turned out to be the novel genes associated with the carcinogenesis of EOC. Our study demonstrated that methylation profiling through MeDIP-sequencing has effectively identified six potential hub genes and pathways that might exacerbate our understanding of underlying molecular mechanisms of ovarian carcinogenesis.

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“…PDCD4 is regulated by mir-21 targeting and promotes anoikis resistance in cancer cells [52]. BMP2 is a pluripotent factor and a member of the transforming growth factor-β (TGF-β) superfamily, associated with embryonic development and homeostasis of tissues and organs [53]. BMP2 and BMP9 inhibit anchoring non-dependent survival and promote anoikis [54].…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumour immune microenvironment

Abulaiti

Maimaiti

Wang

et al. 2022

Preprint

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Background: Lower-grade glioma (LGG) is a prevalent glial cell-derived brain tumor that is aggressive and infiltrative. Anoikis, a new and distinct form of cell death, is a catch-all phrase describing cells losing their ability to adhere to the extracellular matrix (ECM) and nearby cells, followed by the inducing of apoptosis. However, what role the mechanisms associated with anoikis play in LGG have not been thoroughly discovered. Methods: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Chinese Glioma Genome Atlas (CGGA) are three large databases that provide sequencing information for LGG patients, as well as the corresponding clinical data, were included in this study as the training set and multi-group validation set for the data. Application of ConsensusClusterPlus Consensus Clustering for molecular subtype classification of LGG patients based on anoikis-related genes (ARGs)with prognostic value. Subsequently, we screened genes significantly associated with patient prognosis using different machine learning algorithms. Risk profiles are constructed and assessed based on these screened genes. Results: Patients with LGG were classified into two distinct molecular subtypes based on a clustering approach, each characterized by their prognosis, clinical features, and tumor microenvironment. A 6-ARG prognostic signal (EGFR, SIX1, SP1, ANGPTL2, PDCD4, and BMP2) was subsequently constructed, and the signature genes showed good predictive performance not only in the training set but also in multiple validation sets. Additionally, we go into great depth about how high-risk and low-risk groups differ from one another in terms of attributes, including immune characteristics, tumor mutation characteristics, and drug sensitivity showing significant differences in the risk subgroups. Finally, this risk score is combined with multiple LGG clinicopathological features to create an at-a-glance nomogram for quantitatively predicting the probability of clinical survival in individuals with LGG, and the AUC values and decision curve analysis (DCA) of this nomogram suggest that the model can benefit patients from clinical treatment strategies. Conclusion: Overall, ARG signs can be used as a valid indicator of prognosis prediction and response to immunotherapy in patients with LGG.

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BMP2 as a promising anticancer approach: functions and molecular mechanisms

Cited by 16 publications

References 180 publications

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Comprehensive DNA methylation profiling by MeDIP-NGS identifies potential genes and pathways for epithelial ovarian cancer

Prognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumour immune microenvironment

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