This study aims to determine ovarian cancer (OC) patients with platinum resistance for alternative treatment protocols by using metabolomic methodologies. Urine and serum samples of platinum-resistant and platinum-sensitive OC were analyzed using GC-MS. After data processing of GC-MS raw data, multivariate analyses were performed to interpret complex data for biologically meaningful information and to identify the biomarkers that cause differences between two groups. The biomarkers were verified after univariate, multivariate, and ROC analysis. Finally, metabolomic pathways related to group separations were specified. The results of biomarker analysis showed that 3,4-dihydroxyphenylacetic acid, 4-hydroxybutyric acid, L-threonine, D- mannose, and sorbitol metabolites were potential biomarkers in urine samples. In serum samples, L-arginine, linoleic acid, L-glutamine, and hypoxanthine were identified as important biomarkers. R2Y, Q2, AUC, sensitivity and specificity values of platinum-resistant and sensitive OC patients’ urine and serum samples were 0.85, 0.545, 0.844, 91.30%, 81.08 and 0.570, 0.206, 0.743, 77.78%, 74.28%, respectively. In metabolic pathway analysis of urine samples, tyrosine metabolism and fructose and mannose metabolism were found to be statistically significant (p < 0.05) for the discrimination of the two groups. While 3,4-dihydroxyphenylacetic acid, L-tyrosine, and fumaric acid metabolites were effective in tyrosine metabolism. D-sorbitol and D-mannose metabolites were significantly important in fructose and mannose metabolism. However, seven metabolomic pathways were significant (p < 0.05) in serum samples. In terms of p-value, L-glutamine in the nitrogen metabolic pathway from the first three pathways; L-glutamine and pyroglutamic acid metabolites in D-glutamine and D-glutamate metabolism. In the arginine and proline metabolic pathway, L-arginine, L-proline, and L-ornithine metabolites differed significantly between the two groups.

Aim The study aimed to determine the relationship between supportive care needs with coronavirus anxiety and death anxiety of women with gynecologic cancer during COVID‐19. Methods The population of the study was women with gynecologic cancer who received chemotherapy in a university hospital. The study sample was calculated using G*Power 3.1.9.4 analysis program and completed with 64 patients who agreed to participate and met the research criteria. The personal information form, supportive care needs survey‐short form (SCNS‐SF29Tr), coronavirus anxiety scale (CAS), and death anxiety scale (DAS) were used for data collection. Results The participants' SCNS‐SF29Tr mean score was 105.7 ± 17.26, CAS mean score was 11.19 ± 3.96, and DAS mean score was 40.13 ± 15.5. There was a positive, very high‐level correlation between the health system and information and psychological needs subscales of SCNS‐SF29Tr and CAS (r = 0.809, r = 0.878, respectively; p < 0.05). In addition, a high‐level relationship was found between the daily life subscale of SCNS‐SF29Tr and CAS (r = 0.674; p < 0.001). A positive low‐level relationship was determined between the health system and information, daily life, and psychological needs (except for the sexuality) subscales of SCNS‐SF29Tr and DAS (r = 0.357, r = 0.252, r = 0.353 respectively; p < 0.05). Conclusion Gynecologic cancer participants had unmet supportive care needs in all subscales except for the sexuality. The participants had higher supportive care needs, high‐level coronavirus anxiety, and medium‐level death anxiety. In addition, the participants' all supportive care needs have increased as their coronavirus anxiety levels have increased. The participants' supportive care needs have increased, except for sexuality, as their death anxiety levels have increased.