Cancer stem cells (CSCs) may contribute to an increased risk of recurrence in ovarian cancer (OC). Further research is needed to identify associations between CSC markers and OC patients’ clinical outcomes with greater certainty. If they prove to be correct, in the future, the CSC markers can be used to help predict survival and indicate new therapeutic targets. This study aimed to determine the CSC markers at mRNA and protein levels and their association with clinical presentation, outcome, and risk of recurrence in HGSOC (High-Grade Serous Ovarian Cancer). TCGA (The Cancer Genome Atlas) database with 558 ovarian cancer tumor samples was used for the evaluation of 13 CSC markers (ALDH1A1, CD44, EPCAM, KIT, LGR5, NES, NOTCH3, POU5F1, PROM1, PTTG1, ROR1, SOX9, and THY1). Data on mRNA and protein levels assessed by microarray and mass spectrometry were retrieved from TCGA. Models to predict chemotherapy response and survival were built using multiple variables, including epidemiological data, expression levels, and machine learning methodology. ALDH1A1 and LGR5 mRNA expressions indicated a higher platinum sensitivity (p = 3.50 × 10⁻³; p = 0.01, respectively). POU5F1 mRNA expression marked platinum-resistant tumors (p = 9.43 × 10⁻³). CD44 and EPCAM mRNA expression correlated with longer overall survival (OS) (p = 0.043; p = 0.039, respectively). THY1 mRNA and protein levels were associated with worse OS (p = 0.019; p = 0.015, respectively). Disease-free survival (DFS) was positively affected by EPCAM (p = 0.004), LGR5 (p = 0.018), and CD44 (p = 0.012). In the multivariate model based on CSC marker expression, the high-risk group had 9.1 months longer median overall survival than the low-risk group (p < 0.001). ALDH1A1, CD44, EPCAM, LGR5, POU5F1, and THY1 levels in OC may be used as prognostic factors for the primary outcome and help predict the treatment response.

Background: Identification of high-risk patients with endometrial cancer (EC) remains challenging despite advances in molecular profiling. Methods: We comprehensively analyzed the RNA expression of 18 amino acid transporters (AATs) in 373 EC samples from The Cancer Genome Atlas (TCGA). Results: Our clustering analysis based on AAT expression identified a patient group with a significantly worse prognosis. Sideroflexin family (SFXN1, SFXN5), glutamine (SLC38A1, SLC38A3), glycine (SLC6A9) transporters, and the heavy-chain subunit of heteromeric AATs (SLC3A2) were elevated in poorly differentiated tumors. In contrast, the transporter of small neutral amino acids (SLC1A4) and proline/imino-acid transporter (SLC6A20) were enriched in lower-grade disease. SFXN1 and SLC38A1 were also higher in advanced (III/IV) FIGO stages. AAT expression differed across molecular subtypes, with SFXN5, SLC38A1, SLC38A3, and SLC3A2 associated with the copy-number-high profile. In survival analysis, we demonstrated that SLC38A3, SFXN4, and SLC3A2 were associated with shorter disease-free survival, and SLC38A1, SLC38A3, and SFXN1 with worse overall survival, whereas SLC1A4, SLC6A20, and the taurine transporter (SLC6A6) were related to favorable outcomes. Conclusions: These findings highlight AAT expression as a potential marker of EC aggressiveness and prognosis and suggest that integrating metabolic features with molecular classification may improve risk stratification and identify potential therapeutic targets.