Biofluorescence imaging-guided spatial metabolic tracing: In vivo tracking of metabolic activity in circulating tumor cell-mediated multi-organ metastases.

Circulating tumor cells (CTC) are considered metastatic precursors that are shed from the primary or metastatic deposits and navigate the bloodstream before undergoing extravasation to establish distant metastases. Metabolic reprogramming appears to be a hallmark of metastatic progression, yet current methods for evaluating metabolic heterogeneity within organ-specific metastases in vivo are limited. To overcome this challenge, we present Biofluorescence Imaging-Guided Spatial Metabolic Tracing (BIGSMT), a novel approach integrating in vivo biofluorescence imaging, stable isotope tracing, stain-free laser capture microdissection, and liquid chromatography-mass spectrometry. This innovative technology obviates the need for staining or intricate sample preparation, mitigating metabolite loss, and substantially enhances detection sensitivity and accuracy through chemical derivatization of polar metabolites in central carbon pathways. Application of BIGSMT to a preclinical CTC-mediated metastasis mouse model revealed significant heterogeneity in the in vivo carbon flux from glucose into glycolysis and the tricarboxylic acid (TCA) cycle across distinct metastatic sites. Our analysis indicates that carbon predominantly enters the TCA cycle through the enzymatic reaction catalyzed by pyruvate dehydrogenase. Thus, our spatially resolved BIGSMT technology provides fresh insights into the metabolic heterogeneity and evolution during melanoma CTC-mediated metastatic progression and points to novel therapeutic opportunities.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine.

Background: Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m6A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m6A modification-associated lncRNAs. Methods: Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m6A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs. Results: Six m6A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m6A effector-related lncRNA risk model (m6A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m6A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m6A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m6A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses. Conclusion: In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m6A-related lncRNAs in SOC. Incorporating the m6A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

Fatty acid metabolism-related lncRNA prognostic signature for serous ovarian carcinoma.

Background: To explore the role of fatty acid metabolism (FAM)-related lncRNAs in the prognosis and antitumor immunity of serous ovarian cancer (SOC). Materials & methods: A SOC FAM-related lncRNA risk model was developed and evaluated by a series of analyses. Additional immune-related analyses were performed to further assess the associations between immune state, tumor microenvironment and the prognostic risk model. Results: Five lncRNAs associated with the FAM genes were found and used to create a predictive risk model. The patients with a low-risk profile exhibited favorable prognostic outcomes. Conclusion: The established prognostic risk model exhibits better predictive capabilities for the prognosis of patients with SOC and offers novel potential therapy targets for SOC.

A Potential "Anti-Warburg Effect" in Circulating Tumor Cell-mediated Metastatic Progression?

Metabolic reprogramming is a defining hallmark of cancer metastasis, warranting thorough exploration. The tumor-promoting function of the "Warburg Effect", marked by escalated glycolysis and restrained mitochondrial activity, is widely acknowledged. Yet, the functional significance of mitochondria-mediated oxidative phosphorylation (OXPHOS) during metastasis remains controversial. Circulating tumor cells (CTCs) are considered metastatic precursors that detach from primary or secondary sites and harbor the potential to seed distant metastases through hematogenous dissemination. A comprehensive metabolic characterization of CTCs faces formidable obstacles, including the isolation of these rare cells from billions of blood cells, coupled with the complexities of ex vivo-culturing of CTC lines or the establishment of CTC-derived xenograft models (CDX). This review summarized the role of the "Warburg Effect" in both tumorigenesis and CTC-mediated metastasis. Intriguingly, bioinformatic analysis of single-CTC transcriptomic studies unveils a potential OXPHOS dominance over Glycolysis signature genes across several important cancer types. From these observations, we postulate a potential "Anti-Warburg Effect" (AWE) in CTCs-a metabolic shift bridging primary tumors and metastases. The observed AWE could be clinically important as they are significantly correlated with therapeutic response in melanoma and prostate patients. Thus, unraveling dynamic metabolic regulations within CTC populations might reveal an additional layer of regulatory complexities of cancer metastasis, providing an avenue for innovative anti-metastasis therapies.