Granulocyte colony-stimulating factor promotes an aggressive phenotype of colon and breast cancer cells with biochemical changes investigated by single-cell Raman microspectroscopy and machine learning analysis

Zhang, Wei; Karagiannidis, Ioannis; Vliet, Eliane De Santana Van; Yao, Ruoxin; Beswick, Ellen J.; Zhou, Anhong

doi:10.1039/d1an00938a

Cited by 4 publications

(3 citation statements)

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“…[ 169 ] Then Zhang et al. [ 170 ] investigated the long‐term treatment of cancer cells by G‐CSF. PCA was used to determine the Raman spectrum band with the most significant difference between normal control cells and cancer cells treated with G‐CSF.…”

Section: Application Of ML and Raman Spectroscopy In Biomedicinementioning

confidence: 99%

“…To evaluate the progress of G‐CSF treated cells, the concept of aggression score was derived using a posteriori probability based on the linear discriminant function. [ 170 ] This work may lead to identifying new targets for cancer treatment.…”

Section: Application Of ML and Raman Spectroscopy In Biomedicinementioning

confidence: 99%

“…A study showed granulocyte colony stimulating factor (G-CSF) was directly related to gastric cancer metastasis. [169] Then Zhang et al [170] investigated the long-term treatment of cancer cells by G-CSF. PCA was used to determine the Raman spectrum band with the most significant difference between normal control cells and cancer cells treated with G-CSF.…”

Section: Diagnosis Of Bacterial Pathogens At the Single-cell Levelmentioning

confidence: 99%

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Recent Progresses in Machine Learning Assisted Raman Spectroscopy

Jiang

et al. 2023

Advanced Optical Materials

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With the development of Raman spectroscopy and the expansion of its application domains, conventional methods for spectral data analysis have manifested many limitations. Exploring new approaches to facilitate Raman spectroscopy and analysis has become an area of intensifying focus for research. It has been demonstrated that machine learning techniques can more efficiently extract valuable information from spectral data, creating unprecedented opportunities for analytical science. This paper outlines traditional and more recently developed statistical methods that are commonly used in machine learning (ML) and ML‐algorithms for different Raman spectroscopy‐based classification and recognition applications. The methods include Principal Component Analysis, K‐Nearest Neighbor, Random Forest, and Support Vector Machine, as well as neural network‐based deep learning algorithms such as Artificial Neural Networks, Convolutional Neural Networks, etc. The bulk of the review is dedicated to the research advances in machine learning applied to Raman spectroscopy from several fields, including material science, biomedical applications, food science, and others, which reached impressive levels of analytical accuracy. The combination of Raman spectroscopy and machine learning offers unprecedented opportunities to achieve high throughput and fast identification in many of these application fields. The limitations of current studies are also discussed and perspectives on future research are provided.

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Section: Application Of ML and Raman Spectroscopy In Biomedicinementioning

confidence: 99%

Section: Application Of ML and Raman Spectroscopy In Biomedicinementioning

confidence: 99%

Section: Diagnosis Of Bacterial Pathogens At the Single-cell Levelmentioning

confidence: 99%

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Recent Progresses in Machine Learning Assisted Raman Spectroscopy

Jiang

et al. 2023

Advanced Optical Materials

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RAGE inhibitor TTP488 (Azeliragon) suppresses metastasis in triple-negative breast cancer

et al. 2023

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Triple-negative breast cancer (TNBC) is a highly aggressive and metastatic cancer subtype, which is generally untreatable once it metastasizes. We hypothesized that interfering with the Receptor for Advanced Glycation End-products (RAGE) signaling with the small molecule RAGE inhibitors (TTP488/Azeliragon and FPS-ZM1) would impair TNBC metastasis and impair fundamental mechanisms underlying tumor progression and metastasis. Both TTP488 and FPS-ZM1 impaired spontaneous and experimental metastasis of TNBC models, with TTP488 reducing metastasis to a greater degree than FPS-ZM1. Transcriptomic analysis of primary xenograft tumor and metastatic tissue revealed high concordance in gene and protein changes with both drugs, with TTP488 showing greater potency against metastatic driver pathways. Phenotypic validation of transcriptomic analysis by functional cell assays revealed that RAGE inhibition impaired TNBC cell adhesion to multiple extracellular matrix proteins (including collagens, laminins, and fibronectin), migration, and invasion. Neither RAGE inhibitor impaired cellular viability, proliferation, or cell cycle in vitro. Proteomic analysis of serum from tumor-bearing mice revealed RAGE inhibition affected metastatic driver mechanisms, including multiple cytokines and growth factors. Further mechanistic studies by phospho-proteomic analysis of tumors revealed RAGE inhibition led to decreased signaling through critical BC metastatic driver mechanisms, including Pyk2, STAT3, and Akt. These results show that TTP488 impairs metastasis of TNBC and further clarifies the signaling and cellular mechanisms through which RAGE mediates metastasis. Importantly, as TTP488 displays a favorable safety profile in human studies, our study provides the rationale for evaluating TTP488 in clinical trials to treat or prevent metastatic TNBC.

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