Polysulfide Serves as a Hallmark of Desmoplastic Reaction to Differentially Diagnose Ductal Carcinoma In Situ and Invasive Breast Cancer by SERS Imaging

Kubo, Akiko; Masugi, Yohei; Hase, Takeshi; Nagashima, Kengo; Kawai, Yuko; Takizawa, Minako; Hishiki, Takako; Shiota, Megumi; Wakui, Masatoshi; Kitagawa, Yuko; Kabe, Yoshio; Sakamoto, Michiie; Yachie‐Kinoshita, Ayako; Hayashida, Tetsu; Suematsu, Makoto

doi:10.3390/antiox12020240

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…The results shown in Fig. 6B are in good agreement with those of our previous report ( 18 ). Further investigation is necessary to decipher the mechanisms underlying the regulation of metabolic systems to provide specific metabolites to specific regions of cancer tissues.…”

Section: Discussionsupporting

confidence: 93%

“…6 and 7 ). Regarding the role of SSP in chemoresistance, our group has recently revealed that the same pathway contributes to the upregulation of reactive sulfur species, including polysulfides, which play a critical role in rendering cancer cells resistant to anticancer reagents in ovarian clear cell carcinoma ( 16 ) and invasive breast cancer ( 18 ). While the mechanisms remain unknown, reactive sulfur species and polysulfides are enriched in cancer-associated stromal regions of invasive breast cancer.…”

Section: Discussionmentioning

confidence: 99%

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PRMT1 Sustains De Novo Fatty Acid Synthesis by Methylating PHGDH to Drive Chemoresistance in Triple-Negative Breast Cancer

Yamamoto,

Hayashida,

Masugi

et al. 2024

Cancer Research

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Triple-negative breast cancer (TNBC) chemoresistance hampers the ability to effectively treat patients. Identification of mechanisms driving chemoresistance can lead to strategies to improve treatment. Here, we revealed that protein arginine methyltransferase-1 (PRMT1) simultaneously methylates D-3-phosphoglycerate dehydrogenase (PHGDH), a critical enzyme in serine synthesis, and the glycolytic enzymes PFKFB3 and PKM2 in TNBC cells. 13C metabolic flux analyses showed that PRMT1 methylation of these three enzymes diverts glucose toward intermediates in the serine-synthesizing and serine/glycine cleavage pathways, thereby accelerating the production of methyl donors in TNBC cells. Mechanistically, PRMT1-dependent methylation of PHGDH at R54 or R20 activated its enzymatic activity by stabilizing 3-phosphoglycerate binding and suppressing polyubiquitination. PRMT1-mediated PHGDH methylation drove chemoresistance independently of glutathione synthesis. Rather, activation of the serine synthesis pathway supplied α-ketoglutarate and citrate to increase palmitate levels through activation of fatty acid synthase (FASN). Increased palmitate induced protein S-palmitoylation of PHGDH and FASN to further enhance fatty acid synthesis in a PRMT1-dependent manner. Loss of PRMT1 or pharmacological inhibition of FASN or protein S-palmitoyltransferase reversed chemoresistance in TNBC. Furthermore, immunohistochemistry coupled with imaging mass spectrometry in clinical TNBC specimens substantiated that PRMT1-mediated methylation of PHGDH, PFKFB3, and PKM2 correlates with chemoresistance and that metabolites required for methylation and fatty acid synthesis are enriched in TNBC. Together, these results suggest that enhanced de novo fatty acid synthesis mediated by coordinated protein arginine methylation and protein S-palmitoylation is a therapeutic target for overcoming chemoresistance in TNBC.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

PRMT1 Sustains De Novo Fatty Acid Synthesis by Methylating PHGDH to Drive Chemoresistance in Triple-Negative Breast Cancer

Yamamoto,

Hayashida,

Masugi

et al. 2024

Cancer Research

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“…Kubo et al. ( 153 ) used SERS imaging based on gold nanoparticles to monitor and analyze polysulfides and proposed a strategy for automated diagnosis using machine learning. This provides a potential means for monitoring the hyperplasia of connective tissue occurring in cancer-related stromal regions.…”

Section: Sers Detection Of Tumor Biomarkersmentioning

confidence: 99%

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer

Liu,

Jia,

Zheng

2024

Front. Oncol.

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Breast cancer is the most commonly diagnosed cancer in women globally and a leading cause of cancer-related mortality. However, current detection methods, such as X-rays, ultrasound, CT scans, MRI, and mammography, have their limitations. Recently, with the advancements in precision medicine and technologies like artificial intelligence, liquid biopsy, specifically utilizing Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a promising approach to detect breast cancer. Liquid biopsy, as a minimally invasive technique, can provide a temporal reflection of breast cancer occurrence and progression, along with a spatial representation of overall tumor information. SERS has been extensively employed for biomarker detection, owing to its numerous advantages such as high sensitivity, minimal sample requirements, strong multi-detection ability, and controllable background interference. This paper presents a comprehensive review of the latest research on the application of SERS in the detection of breast cancer biomarkers, including exosomes, circulating tumor cells (CTCs), miRNA, proteins and others. The aim of this review is to provide valuable insights into the potential of SERS technology for early breast cancer diagnosis.

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“…Therefore, the characteristic information on BC can be acquired by SERS, enabling the BC diagnosis at an early stage . In previous studies, pathological sections or body fluids have been widely used to screen cancer by SERS. − Breast cancer (BC) diagnosis is still in a dilemma between the intrusive nature of pathological diagnosis and the elusive nature of liquid biopsy.…”

Section: Introductionmentioning

confidence: 99%

Robust Diagnosis of Breast Cancer Based on Silver Nanoparticles by Surface-Enhanced Raman Spectroscopy and Machine Learning

Wang,

Zhang,

Yue

et al. 2024

ACS Appl. Nano Mater.

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Due to the high intrusiveness of pathological diagnosis and the elusiveness of liquid biopsy, breast cancer (BC) is still in a dilemma between robustness and invasiveness. In our study, a molecular-specific diagnostic strategy was introduced for screening BC at an early stage, which utilizes surface-enhanced Raman spectroscopy (SERS) based on Ag NPs at 50–60 nm to acquire the fingerprint SERS spectra of fine needle aspiration (FNA) samples and machine learning for data mining. The SERS spectra of FNA samples from 78 patients were analyzed. Multiple machine learning algorithms including principal component analysis (PCA), principal component analysis–linear discriminant analysis (PCA-LDA), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) models were applied to deconstruct those SERS spectra for discrimination of different types of breast disease. Significant biochemical differences were found in SERS spectra of breast fibroadenoma, breast hyperplasia, and BC. With the SVM algorithm, the diagnostic sensitivity and specificity of BC, breast fibroadenomas, and breast hyperplasia can reach 94.74%, 83.33%, 81.82% and 86.96%, 100%, 94.00%, respectively. The hyphenated method of SERS and machine learning would re-energize FNA and enable FNA diagnosis of breast disease early and precisely, benefiting patients’ treatment efficacy and patient life cycle.

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Polysulfide Serves as a Hallmark of Desmoplastic Reaction to Differentially Diagnose Ductal Carcinoma In Situ and Invasive Breast Cancer by SERS Imaging

Cited by 5 publications

References 28 publications

PRMT1 Sustains De Novo Fatty Acid Synthesis by Methylating PHGDH to Drive Chemoresistance in Triple-Negative Breast Cancer

PRMT1 Sustains De Novo Fatty Acid Synthesis by Methylating PHGDH to Drive Chemoresistance in Triple-Negative Breast Cancer

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer

Robust Diagnosis of Breast Cancer Based on Silver Nanoparticles by Surface-Enhanced Raman Spectroscopy and Machine Learning

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