Unraveling Autocrine Signaling Pathways through Metabolic Fingerprinting in Serous Ovarian Cancer Cells

Deciphering a GPCR-lncrna-miRNA nexus: Identification of an aberrant therapeutic target in ovarian cancer

Ovarian cancer ranks as a leading cause of mortality among gynecological malignancies, primarily due to the lack of early diagnostic tools, effective targeted therapy, and clear understanding of disease etiology. Previous studies have identified the pivotal role of Lysophosphatidic acid (LPA)-signaling in ovarian cancer pathobiology. Our earlier transcriptomic analysis identified Urothelial Carcinoma Associated-1 (UCA1) as an LPA-stimulated long non-coding RNA (lncRNA). In this study, we elucidate the tripartite interaction between LPA-signaling, UCA1, and let-7 miRNAs in ovarian cancer progression. Results show that the elevated expression of UCA1 enhances cell proliferation, invasive migration, and therapy resistance in high-grade serous ovarian carcinoma cells, whereas silencing UCA1 reverses these oncogenic phenotypes. UCA1 expression inversely correlates with survival outcomes and therapy response in ovarian cancer clinical samples, underscoring its prognostic significance. Mechanistically, UCA1 sequesters let-7 miRNAs, effectively neutralizing their tumor-suppressive functions involving key oncogenes such as Ras and c-Myc. More significantly, intratumoral delivery of UCA1-specific siRNAs inhibits the growth of cisplatin-refractory ovarian cancer xenografts, demonstrating the therapeutic potential of targeting LPAR-UCA1-let-7 axis in ovarian cancer. Thus, our results identify LPAR-UCA1-let-7 axis as a novel avenue for targeted treatment strategies.

Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering

BACKGROUND

Intracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) RESULTS: We proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SIGNIFICANCE: Our technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).

Understanding of Ovarian Cancer Cell-Derived Exosome Tropism for Future Therapeutic Applications

Exosomes, a subtype of extracellular vesicles, ranging from 50 to 200 nm in diameter, and mediate cell-to-cell communication in normal biological and pathological processes. Exosomes derived from tumors have multiple functions in cancer progression, resistance, and metastasis through cancer exosome-derived tropism. However, there is no quantitative information on cancer exosome-derived tropism. Such data would be highly beneficial to guide cancer therapy by inhibiting exosome release and/or uptake. Using two fluorescent protein (mKate2) transfected ovarian cancer cell lines (OVCA4 and OVCA8), cancer exosome tropism was quantified by measuring the released exosome from ovarian cancer cells and determining the uptake of exosomes into parental ovarian cancer cells, 3D spheroids, and tumors in tumor-bearing mice. The OVCA4 cells release 50 to 200 exosomes per cell, and the OVCA8 cells do 300 to 560 per cell. The uptake of exosomes by parental ovarian cancer cells is many-fold higher than by non-parental cells. In tumor-bearing mice, most exosomes are homing to the parent cancer rather than other tissues. We successfully quantified exosome release and uptake by the parent cancer cells, further proving the tropism of cancer cell-derived exosomes. The results implied that cancer exosome tropism could provide useful information for future cancer therapeutic applications.

Targeting Oncometabolites in Peritoneal Cancers: Preclinical Insights and Therapeutic Strategies

Peritoneal cancers present significant clinical challenges with poor prognosis. Understanding the role of cancer cell metabolism and cancer-promoting metabolites in peritoneal cancers can provide new insights into the mechanisms that drive tumor progression and can identify novel therapeutic targets and biomarkers for early detection, prognosis, and treatment response. Cancer cells dynamically reprogram their metabolism to facilitate tumor growth and overcome metabolic stress, with cancer-promoting metabolites such as kynurenines, lactate, and sphingosine-1-phosphate promoting cell proliferation, angiogenesis, and immune evasion. Targeting cancer-promoting metabolites could also lead to the development of effective combinatorial and adjuvant therapies involving metabolic inhibitors for the treatment of peritoneal cancers. With the observed metabolomic heterogeneity in cancer patients, defining peritoneal cancer metabolome and cancer-promoting metabolites holds great promise for improving outcomes for patients with peritoneal tumors and advancing the field of precision cancer medicine. This review provides an overview of the metabolic signatures of peritoneal cancer cells, explores the role of cancer-promoting metabolites as potential therapeutic targets, and discusses the implications for advancing precision cancer medicine in peritoneal cancers.

Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed cancer. Currently, mammography and breast ultrasonography are the main clinical screening methods for BC. Our study aimed to reveal the specific metabolic profiles of BC patients and explore the specific metabolic signatures in human plasma for BC diagnosis.

METHODS

This study enrolled 216 participants, including BC patients, benign patients, and healthy controls (HC) and formed two cohorts, one training cohort and one testing cohort. Plasma samples were collected from each participant and subjected to perform nontargeted metabolomics and proteomics. The metabolic signatures for BC diagnosis were identified through machine learning.

RESULTS

Metabolomics analysis revealed that BC patients showed a significant change of metabolic profiles compared to HC individuals. The alanine, aspartate and glutamate pathways, glutamine and glutamate metabolic pathways, and arginine biosynthesis pathways were the critical biological metabolic pathways in BC. Proteomics identified 29 upregulated and 2 downregulated proteins in BC. Our integrative analysis found that aspartate aminotransferase (GOT1), L-lactate dehydrogenase B chain (LDHB), glutathione synthetase (GSS), and glutathione peroxidase 3 (GPX3) were closely involved in these metabolic pathways. Support vector machine (SVM) demonstrated a predictive model with 47 metabolites, and this model achieved a high accuracy in BC prediction (AUC = 1). Besides, this panel of metabolites also showed a fairly high predictive power in the testing cohort between BC vs HC (AUC = 0.794), and benign vs HC (AUC = 0.879).

CONCLUSIONS

This study uncovered specific changes in the metabolic and proteomic profiling of breast cancer patients and identified a panel of 47 plasma metabolites, including sphingomyelins, glutamate, and cysteine could be potential diagnostic biomarkers for breast cancer.

Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer

Ovarian cancer (OC) is the third most common malignant tumor of women accompanied by alteration of systemic metabolism, yet the underlying interactions between the local OC tissue and other system biofluids remain unclear. In this study, we recruited 17 OC patients, 16 benign ovarian tumor (BOT) patients, and 14 control patients to collect biological samples including ovary plasma, urine, and hair from the same patient. The metabolic features of samples were characterized using a global and targeted metabolic profiling strategy based on Gas chromatography-mass spectrometry (GC-MS). Principal component analysis (PCA) revealed that the metabolites display obvious differences in ovary tissue, plasma, and urine between OC and non-malignant groups but not in hair samples. The metabolic alterations in OC tissue included elevated glycolysis (lactic acid) and TCA cycle intermediates (malic acid, fumaric acid) were related to energy metabolism. Furthermore, the increased levels of glutathione and polyunsaturated fatty acids (linoleic acid) together with decreased levels of saturated fatty acid (palmitic acid) were observed, which might be associated with the anti-oxidative stress capability of cancer. Furthermore, how metabolite profile changes across differential biospecimens were compared in OC patients. Plasma and urine showed a lower concentration of amino acids (alanine, aspartic acid, glutamic acid, proline, leucine, and cysteine) than the malignant ovary. Plasma exhibited the highest concentrations of fatty acids (stearic acid, EPA, and arachidonic acid), while TCA cycle intermediates (succinic acid, citric acid, and malic acid) were most concentrated in the urine. In addition, five plasma metabolites and three urine metabolites showed the best specificity and sensitivity in differentiating the OC group from the control or BOT groups (AUC > 0.90) using machine learning modeling. Overall, this study provided further insight into different specimen metabolic characteristics between OC and non-malignant disease and identified the metabolic fluctuation across ovary and biofluids.

Resveratrol Contrasts IL-6 Pro-Growth Effects and Promotes Autophagy-Mediated Cancer Cell Dormancy in 3D Ovarian Cancer: Role of miR-1305 and of Its Target ARH-I

Tumor dormancy is the extended period during which patients are asymptomatic before recurrence, and it represents a difficult phenomenon to target pharmacologically. The relapse of tumors, for instance arising from the interruption of dormant metastases, is frequently observed in ovarian cancer patients and determines poor survival. Inflammatory cytokines present in the tumor microenvironment likely contribute to such events. Cancer cell dormancy and autophagy are interconnected at the molecular level through ARH-I (DIRAS3) and BECLIN-1, two tumor suppressors often dysregulated in ovarian cancers. IL-6 disrupts autophagy in ovarian cancer cells via miRNAs downregulation of ARH-I, an effect contrasted by the nutraceutical protein restriction mimetic resveratrol (RV). By using three ovarian cancer cell lines with different genetic background in 2D and 3D models, the latter mimicking the growth of peritoneal metastases, we show that RV keeps the cancer cells in a dormant-like quiescent state contrasting the IL-6 growth-promoting activity. Mechanistically, this effect is mediated by BECLIN-1-dependent autophagy and relies on the availability of ARH-I. We also show that ARH-I (DIRAS3) is a bona fide target of miR-1305, a novel oncomiRNA upregulated by IL-6 and downregulated by RV. Clinically relevant, bioinformatic analysis of a transcriptomic database showed that the high expression of DIRAS3 and MAP1LC3B mRNAs together with that of CDKN1A, directing a cellular dormant phenotype, predicts better overall survival in ovarian cancer patients, and this correlates with MIR1305 downregulation. The possibility of maintaining a permanent cell dormancy in ovarian cancer by the chronic administration of RV should be considered as a therapeutic option to prevent the "awakening" of cancer cells in response to a permissive microenvironment, thus limiting the risk of tumor relapse and metastasis.