Thesis etd-12312021-121410 |
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Thesis type
Tesi di laurea magistrale
URN
etd-12312021-121410
Thesis title
CHARACTERIZATION OF IMMUNE-RELATED GENE EXPRESSION IN THE TUMOR MICROENVIRONMENT OF TRIPLE NEGATIVE BREAST CANCERS
Department
BIOLOGIA
Course of study
BIOLOGIA APPLICATA ALLA BIOMEDICINA
Supervisors
.
relatore Prof. Scatena, Cristian
Keywords
- bioinformatics
- breast cancer
- next generation sequencing
- triple negative breast cancer
- tumor microenvironment
Graduation session start date
25/01/2022
Availability
Withheld
Release date
25/01/2092
Abstract (Inglese)
Abstract (Italiano)
Breast Cancer (BC) is a heterogeneous disease. Triple negative breast cancers (TNBC) are an aggressive subtype accounting for 20% of all BC. Notaby, treatment options for TNBC are limited due to the lack of suitable targets. Thus, this study aimed to explore the tumor microenvironment (TME) in TNBC to assess how it may influence the patients’ pathological response (PR) to treatments. To achieve that, in-silico prediction was performed to assess how differential expression of immune-related genes impacts the Progression Free Interval (PFI) and Overall Survival (OS), obtaining long-term outcome information. The UCSC Xena database was used to analyze the association between immune-related gene expression and OS or PFI. GEPIA2 was used to retrieve information on gene-specific cancer OS. As for the experimental lab validation, Next Generation Sequencing analysis was performed to assess the possible link between the TME and different PR to neoadjuvant chemothrapy, obtaining short-term outcome information. 20 FFPE biopsies specimens were retrospectively selected for this study. Cases have been classified in 2 groups: (i) “pCR”, composed of 11 patients who achieved a complete PR; (ii) “pPR”, including 9 patients who achieved a partial or absent PR. RNA was extracted and the AmpliSeq for Illumina Immune Response Panel was used to investigate 399 genes involved in TME and immune response system. Adequate statistical analyses were applied to evaluate differences between groups.
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