ACER2 forms a cold tumor microenvironment and predicts the molecular subtype in bladder cancer: Results from real-world cohorts

Liu, Jinhui; Cheng, Chunliang; Qi, Tiezheng; Xiao, Jiatong; Zhou, Weimin; Deng, Dingshan; Dai, Yuanqing

doi:10.3389/fgene.2023.1148437

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…The DIAPH1 has its role in regulating the chromosomal instability of cancer cells as a growth inhibitor [35] and its overexpression was proven to be a predictive factor in breast cancer [36], and colorectal cancer [37]. The ACER2, as a key regulatory enzyme, promotes cancer cell survival [38] and can be used to predict molecular subtypes in bladder cancer [39]. The BDKRB2 role in cell angiogenesis was proven in cervical cancer [40], and breast cancer [41], and it was used as an epithelial-to-mesenchymal transition biomarker in glioma [42].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data

Jopek,

Pastuszak,

Cygert

et al. 2024

IEEE J. Transl. Eng. Health Med.

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The field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and therapy personalization. This study presents a multiclass approach based on deep learning to analyze and classify diseases based on blood platelet RNA. Its primary objective is to enhance cancer-type diagnosis in clinical settings by leveraging the power of deep learning combined with high-throughput sequencing of liquid biopsy. Ultimately, the study demonstrates the potential of this approach to accurately identify the patient's type of cancer. Methods: The developed method classifies patients using heatmap images, generated based on gene expression arranged according to the Kyoto Encyclopedia of Genes and Genomes pathways. The images represent samples of patients with ovarian cancer, endometrial cancer, glioblastoma, non-small cell lung cancer, and sarcoma, as well as cancer patients with brain metastasis. Results: Our deep learning-based models reached 66.51% balanced accuracy when distinguishing between those 6 sites of cancer origin and 90.5% balanced accuracy on a location-specific dataset where cancer types from close locations were grouped. The developed models were further investigated with an explainable artificial intelligence-based approach (XAI) -SHAP. They returned a set of 60 genes with the highest impact on the models' decision-making process. Conclusions: Our results show that deep-learning methods are a promising opportunity for cancer detection and could support clinicians' decisionmaking process in finding the solution for the black-box problem.

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Section: Discussionmentioning

confidence: 99%

Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data

Jopek,

Pastuszak,

Cygert

et al. 2024

IEEE J. Transl. Eng. Health Med.

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A “One Arrow Three Eagle” Strategy to Improve CM‐272 Primed Bladder Cancer Immunotherapy

Liu,

Yang,

et al. 2023

Advanced Materials

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Immunotherapy using an immune‐checkpoint blockade has significantly improved its therapeutic effects. CM‐272, which is a novel epigenetic inhibitor of G9a, induces immunogenic cell death (ICD) for recovering the sensitivity to anti‐PD‐1 antibodies; however, the efficacy of CM‐272 is greatly limited by promoting the transcription activity of HIF‐1α to form a hypoxic environment. Here, a Fe3+‐based nanoscale metal–organic framework (MIL‐53) is used to load CM‐272 (ultra‐high loading rate of 56.4%) for realizing an MIL‐53@CM‐272 nanoplatform. After entering bladder cancer cells, Fe3+ not only promotes the decomposition of H2O2 into O2 for O2‐compensated sonodynamic therapy but reduces the high level of glutathione in the tumor microenvironment (TME) for enhancing reactive oxygen species, including ferroptosis and apoptosis. MIL‐53 carriers can be degraded in response to the TME, accelerating the release of CM‐272, which helps achieve the maximum effectiveness in an O2‐sufficient TME by attenuating drug resistance. Furthermore, MIL‐53@CM‐272 enhances dendritic cell maturation and synergistically combines it with an anti‐programmed cell death protein 1 antibody during the study of immune‐related pathways in the transcriptomes of bladder cancer cells using RNA‐seq. This study presents the first instance of amalgamating nanomedicine with CM‐272, inducing apoptosis, ferroptosis, and ICD to achieve the “one arrow three eagle” effect.

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The role of ACER2 in intestinal sphingolipid metabolism and gastrointestinal cancers

Liu,

Zhou,

Jiang

et al. 2024

Front. Immunol.

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Sphingolipids, particularly sphingosine-1-phosphate (S1P), are bioactive lipids involved in regulating cellular processes such as proliferation, apoptosis, inflammation, and tumor progression. Alkaline ceramidase 2 (ACER2) plays a critical role in sphingolipid metabolism by catalyzing the hydrolysis of ceramide to sphingosine, which is subsequently converted to S1P. Dysregulation of ACER2 has been implicated in various gastrointestinal cancers, including colorectal cancer, gastric cancer, and hepatocellular carcinoma. ACER2-mediated sphingolipid signaling, particularly through the SphK/S1P pathway, influences cancer development by modulating immune responses, inflammation, and the balance between cell survival and death. This review examines the physiological functions of ACER2, and its role in sphingolipid metabolism, and its contribution to the pathogenesis of gastrointestinal cancers. Understanding the mechanisms by which ACER2 regulates tumor progression and immune modulation may open new avenues for targeted therapies in gastrointestinal malignancies.

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ACER2 forms a cold tumor microenvironment and predicts the molecular subtype in bladder cancer: Results from real-world cohorts

Cited by 3 publications

References 37 publications

Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data

Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data

A “One Arrow Three Eagle” Strategy to Improve CM‐272 Primed Bladder Cancer Immunotherapy

The role of ACER2 in intestinal sphingolipid metabolism and gastrointestinal cancers

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