The aim of the present study is to perform a comprehensive molecular characterization of intrahepatic cholangiocarcinoma (ICC) in patients exposed to well-known or putative risk factors (such as asbestos) for this malignancy, in order to identify possible "molecular signatures" associated to such different risk factors.
Exposure to distinct risk factors of the enrolled ICC patients will be assessed by modified ReNaM questionnaire. Molecular characterization of ICC tissue samples will be carried out by RNAseq. Briefly, after surgical resection tissue samples will be immediately suspended in RNAlater. RNAseq analysis will be performed on the Illumina HiScanSQ platform. Any possible mutations identified by RNAseq will be validated by Sanger sequencing. Putative identified fusion transcripts will be confirmed by RT-PCR, using specific primers pairs located on the sequences from the exons of the two putative fusion genes. Variations in gene expression will be validated by the real-time PCR. The bioinformatic analysis will be made by using CentOS5 Server. For evaluation of asbestos fibers in tumor tissues, samples embedded in paraffin will be incinerated and then analyzed in a scanning electron microscope and by EDS spectroscopy.
Study Type
OBSERVATIONAL
Enrollment
45
Policlinico S.Orsola- Malpighi, S.S.D. Oncologia Medica- Biasco
Bologna, BO, Italy
RECRUITINGIdentification of molecular biomarkers in ICC patients exposed to different risk factors.
For each patient enrolled, molecular profile of ICC tissue samples will be correlated to the anamnestic data collected by modified ReNaM questionnaire. For bio-informatic analysis, the collected data will be analyzed in order to identify signals that significantly deviate from the expected SNVs distribution in the general population, then analyzing the presence of clustering for the selected genes group. A two-way unsupervised hierarchical clustering analysis will be run to assess gene expression in the study groups (exposed / unexposed to the different risk factors). Random permutation test will be also conducted to assess the presence of genes whose expression is different in the study groups. Statistical analyses will be conducted using the R software (R Foundation and for Statistical Computing).
Time frame: 3 years
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