Ruolo Delle Alterazioni Della Proteina RPL8 Nel Carcinoma Ovarico Sieroso ad Alto Grado

Background and rationale of the study:

From our preliminary analyses of a dataset on patients with high-grade serous ovarian carcinoma (HGSOC), available in the online database The Cancer Genome Atlas, we found that the gene encoding ribosomal protein L8 (RPL8) is amplified at a high frequency (~30%) in HGSOC. Moreover, its mRNA expression is positively correlated with its genetic amplification-an observation not previously reported or studied in the literature. RPL8 is a structural component of the large ribosomal subunit, which is involved in protein synthesis. Based on this, and our preliminary data, we hypothesize that RPL8 amplification may play a role in ovarian cancer development. Understanding the impact of RPL8 amplification in ovarian cancer could provide new insights into the biology of this poorly understood cancer.

Study objectives:

The main objective of this project is to determine whether RPL8 can be used as a biomarker both for risk assessment and for patient stratification in choosing the most appropriate therapeutic option. Specifically, we aim to study:

1. The relationship between the genetic status of RPL8 and clinical outcomes.
2. The contribution of RPL8 amplification to treatment response.

Type of human tissue under study:

The analyses will be conducted on tumor tissue samples obtained from ovarian cancer resections. Some samples have already been collected and stored at the IRCCS, while others are yet to be gathered.

Type of investigation:

• Expression analysis of RPL8, C-MYC, and related genes (RT-PCR, ddPCR, WB, IHC).
• Gene copy number analysis and mutation screening (ddPCR and similar molecular techniques).
• Analysis of possible associations between pathological data, follow-up data, and therapeutic response outcomes in patients.

Analysis methodology:

Data on the genetic status, expression, and subcellular localization of RPL8 and C-MYC will be correlated with categorical and continuous variables related to the patients' medical history and clinical status. Differences between categorical variables will be analyzed using analysis of variance (ANOVA), the Mann-Whitney test, or the Kruskal-Wallis test, depending on whether data distribution is normal or not (assessed via the Kolmogorov-Smirnov test). Correlations between continuous variables will be evaluated using Pearson or Spearman tests, again based on data distribution.