Metabolomic profiling identifies hair as a robust biological sample for identifying women with cervical cancer.

Metabolomics serves as a useful tool for identifying biomarkers of disease and uncovering pathogenic mechanisms. However, most metabolomic studies use biological fluids such as blood and urine as biospecimens, which could be dramatically influenced by daily activities and dietary variation, resulting in measurement fluctuations. In contrast, hair may serve as a robust source of stable longitudinal metabolite information. Here, we conducted a pilot study to investigate the possibility of using hair as a biospecimen for the metabolomic analysis of cervical cancer. Hair, plasma, urine, and cervical tissue samples from cervical cancer and benign tumor patients were collected. Biospecimens were then tested using a gas chromatography-mass spectrometry-based metabolomic platform. The expressions of enzymatic genes related to metabolic changes were validated using qPCR. Statistical analyses were calculated via the R-console platform. Metabolite profiles in both hair and cervical tissue samples were significantly different between cancer and control groups, while no difference was observed in plasma and urine samples. Further analysis showed that most of the altered metabolites in hair were upregulated, and they had a negative correlation with those in the cervical tissue. Eight common metabolites showed an area under the Receiver Operating Characteristic curve greater than 0.95. These metabolites primarily participated in amino acid metabolism, cofactor synthesis, ferroptosis, and glycolysis. The gene expressions (IDH1, OGDH, GLUD1, ENO1, GSS, and GPX4) associated with the shortlisted metabolic pathways were also upregulated. Our study is the first to reveal metabolomic changes of hair in cervical cancer patients and demonstrates the potential for the hair metabolome to be used for biomarker identification in cervical 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.

Ultrasound Enhances ZD2767P-Carboxypeptidase G2 against Chemoresistant Ovarian Cancer Cells by Altering the Intracellular Pharmacokinetics of ZD2767D.

Prodrug-carboxypeptidase G2 (e.g., ZD2767P+CPG2) can realize a targeted treatment where the specific advantage is a lack of CPG2 analogues in humans, but it is limited by low efficacy. Here ultrasound was employed to enhance ZD2767P+CPG2 (i.e., ZD2767P+CPG2+US) against chemoresistant human ovarian cancer cells. The release dynamics of ZD2767D (activated drug) by CPG2 were investigated. The in vitro efficacy was explored in SKOV3 and SKOV3/DDP (cisplatin-resistant subline) cells; spectrophotometry was established to quantify ZD2767P and ZD2767D, and then intracellular pharmacokinetics were evaluated. The in vivo efficacy was validated in both subcutaneous and orthotopic tumors. With insonation, the ZD2767D concentration was increased during an early period. Insonation synergized ZD2767P+CPG2 to enhance cell death and apoptosis, and efficacies in SKOV3 and SKOV3/DDP cells were similar. Intracellular pharmacokinetics of ZD2767D were nonproportional, and insonation increased the peak level, area under the level vs time curve, and mean residence time. In subcutaneous xenografts, ZD2767P+CPG2 and ZD2767P+CPG2+US resulted in volume-inhibitory rates of 20.4% and 26.5% in SKOV3 tumors and 36.8% and 81.6% in SKOV3/DDP tumors, respectively. In the orthotopic tumor model, the survival time in group ZD2767P+CPG2 or ZD2767P+CPG2+US was prolonged compared with group control, in SKOV3 (33.0 ± 3.5 or 39.2 ± 1.8 vs 25.0 ± 1.6 days, p < 0.0001) and SKOV3/DDP (16.2 ± 4.8 or 22.3 ± 7.3 vs 8.7 ± 3.9 days, p = 0.0015) tumors. These data indicated that ZD2767P+CPG2+US was effective against resistant ovarian cancer cells.