ArticleJournal of translational medicine2025
Raman micro-spectroscopy reveals the metabolic alterations in primary prostate tumor tissues of patients with metastases.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolomics and metabolites in cancer diagnosis and treatment.Molecular biomedicine · 2025Pooled it
- Raman spectroscopic signatures of prostate cancer progression: correlation with gleason score.Molecular and cellular biochemistry · 2026Article
- Si Ni Powder enhances chemotherapy efficacy in sleep-deprived breast cancer via modulation of the tumor microenvironment.Translational cancer research · 2026Article
- Lactate-mediated NK cell dysfunction as a prognostic marker and therapeutic target in breast cancer.Cell death discovery · 2026Article
- Development and validation of a plasma-urine metabolism diagnostic model for renal cell carcinoma using machine learning.World journal of urology · 2025Article
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Abstract
backgroundDistant metastases mainly influence the prognosis of patients with prostate cancer (PC), however, development of novel biomarkers for predicting metastatic PC and understanding of the molecular mechanisms remain essential. The objective of this study was to investigate the metabolic differences in the primary tumor tissues between localized PC and metastatic PC using Raman micro-spectroscopy and metabolomics analysis, and then explore potential biomarkers for predicting metastasis and the potential metabolic pathways during the progression from localized prostate cancer to metastasis.
methodsWe used confocal Raman microscopy (CRM) and liquid chromatography-mass spectrometry (LC-MS) based metabolomics to analyze the primary prostate tumor tissues of localized PC and metastatic PC. Subsequently, we used a convolutional neural network (CNN) structure to develop a classification model to predict metastatic PC based on the tissue Raman spectra, and then explored potential metabolic pathways via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.
resultsWe collected a total of 547 spontaneous Raman spectra and 2D Raman images of primary prostate tumor from 21 localized PC and 21 metastatic PC. Compared with localized PC, the Raman peak associated with unsaturated fatty acids was significantly higher in metastatic PC, while the Raman peaks associated with amino acids and proteins were significantly lower. Subsequently, we used a CNN structure to develop a classification model to predict metastatic PC based on the tissue Raman spectra and the model showed a testing accuracy of 81.3 ± 3.7%. The LC-MS based metabolomics results of tissues validated the CRM findings that the primary prostate tumor tissue of metastatic PC exhibited a similar changing trend in prenol lipids, linolenic acid, and multiple classes of amino acids.
conclusionThe CRM could be a potential tool for predicting metastases by analyzing prostate biopsy tissues at the time of diagnosis. Our study found that metabolic remodelling of primary tumors occurred during the metastasis process. The metabolic alterations in primary tumor tissue can help elucidate the underlying mechanisms of the metastasis process, leading to the development of new therapies.
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