Purpose: Bladder cancer is the most common malignancy of the urinary tract and one of the most prevalent cancers worldwide. It represents a spectrum of diseases, from recurrent non-invasive tumors (NMIBCs) managed chronically, to muscle infiltrating and advanced-stage disease (MIBC) that requires multimodal and invasive treatment. Multiple studies have underlined the complexity of bladder tumors genome, highlighting many specific genetic lesions and genome-wide occurrences of copy-number alterations (CNAs). In this study, we analyzed CNAs of selected genes in our cohorts of cancer stem cells (CSCs) and in The Cancer Genome Atlas (TCGA-BLCA) cohort with the aim to correlate their frequency with patients’ prognosis. Methods: CNAs have been verified on our array-CGH data previously reported on 19 bladder cancer biopsies (10 NMIBCs and 9 MIBCs) and 16 matched isolated CSC cultures. In addition, CNAs data have been consulted on the TCGA database, to search correlations with patients’ follow-up. Finally, mRNA expression levels of LRP1B in TGCA cohort were obtained from The Human Protein Atlas. Results: We firstly identified CNAs differentially represented between TGCA data and CSCs derived from NMIBCs and MIBCs, and we correlated the presence of these CNAs with patients’ follow-up. LRP1B loss was significantly increased in CSCs and linked to short-term poor prognosis, both at genomic and transcriptomic level, confirming its pivotal role in bladder cancer tumorigenesis. Conclusion: Our study allowed us to identify potential "predictive" prognostic CNAs for bladder cancer, implementing knowledge for the ultimate goal of personalized medicine.

The lack of effective screening and successful treatment contributes to high ovarian cancer mortality, making it the second most common cause of gynecologic cancer death. Development of chemoresistance in up to 75% of patients is the cause of a poor treatment response and reduced survival. Therefore, identifying potential and effective biomarkers for its diagnosis and prognosis is a strong critical need. Copy number alterations are frequent in cancer, and relevant for molecular tumor stratification and patients’ prognoses. In this study, array-CGH analysis was performed in three cell lines and derived cancer stem cells (CSCs) to identify genes potentially predictive for ovarian cancer patients’ prognoses. Bioinformatic analyses of genes involved in copy number gains revealed that AhRR and PPP1R3C expression negatively correlated with ovarian cancer patients’ overall and progression-free survival. These results, together with a significant association between AhRR and PPP1R3C expression and ovarian cancer stemness markers, suggested their potential role in CSCs. Furthermore, AhRR and PPP1R3C’s increased expression was maintained in some CSC subpopulations, reinforcing their potential role in ovarian cancer. In conclusion, we reported for the first time, to the best of our knowledge, a prognostic role of AhRR and PPP1R3C expression in serous ovarian cancer.

Background: High dissemination potential and resistance to standard therapy significantly contribute to high mortality associated with ovarian cancer. Cancer stem cells (CSCs) drive tumor progression, metastasis, and recurrence after treatments’ failure. Here, we provide the first evidence of TIMP1 overexpression in ovarian CSCs, suggesting its potential role as a prognostic biomarker. Methods: Different ovarian cancer cell models were used to explore the potential link between TIMP1 and stem-like phenotypes. Experiments included spheroid formation, drug treatments, gene expression, functional assays, and zebrafish xenograft models to assess cell behavior and molecular changes. Results: TIMP1 was overexpressed in CSCs, and its expression was also upregulated in chemoresistant and anoikis-resistant cells. Our database analysis revealed a correlation between TIMP1 expression levels and poor patient prognosis. Overexpression of TIMP1 in ovarian cancer cell lines was able to recapitulate several features of the ovarian cancer stem cell phenotype, including treatment resistance, expression of stem cell markers, and anoikis resistance. TIMP1-overexpressing cells also exhibited enhanced migration potential in vitro and increased metastatic potential in vivo. Moreover, TIMP1 overexpression significantly altered the transcriptome landscape of cells, highlighting its role in modulating critical pathways associated with cell migration and inflammation. Conclusions: This study identifies the pivotal role of TIMP1 in ovarian CSCs and its contribution to therapy resistance, recurrence, and metastasis.

Background/Objectives: Aneuploidy is a prevalent cancer feature that occurs in many solid tumors. For example, high-grade serous ovarian cancer shows a high level of copy number alterations and genomic rearrangements. This makes genomic variants appealing as diagnostic or prognostic biomarkers, as well as for their easy detection. In this study, we focused on copy number (CN) losses shared by ovarian cancer stem cells (CSCs) to identify chromosomal regions that may be important for CSC features and, in turn, for patients’ prognosis. Methods: Array-CGH and bioinformatic analyses on three CSCs subpopulations were performed. Results: Pathway and gene ontology analyses on genes involved in copy number loss in all CSCs revealed a significant decrease in mRNA surveillance pathway, as well as miRNA-mediated gene silencing. Then, starting from these CN losses, we validated their potential prognostic relevance by analyzing the TCGA cohort. Notably, losses of 4q34.3-q35.2, 8p21.2-p21.1, and 18q12.2-q23 were linked to increased genomic instability. Loss of 18q12.2-q23 was also related to a higher tumor stage and poor prognosis. Finally, specific genes mapping in these regions, such as PPP2R2A and TPGS2A, emerged as potential biomarkers. Conclusions: Our findings highlight the importance of genomic alterations in ovarian cancer and their impact on tumor progression and patients’ prognosis, offering advance in understanding of the application of numerical aberrations as prognostic ovarian cancer biomarkers.