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A functional biological network centered on XRCC3: a new possible marker of chemoradiotherapy resistance in rectal cancer patients

Authors :
Marco Agostini
Maura Digito
Claudia Mescoli
Andrea Zangrando
Chiara Bedin
Carlo Zanon
Igor Jurisica
Marialuisa Lavitrano
Giovanni Esposito
Roberto Giovannoni
Edoardo D'Angelo
Donato Nitti
Chiara Pastrello
Marco Giordan
Salvatore Pucciarelli
Flavio Rizzolio
Gabriele Romano
Antonio Giordano
Isacco Maretto
Agostini, M
Zangrando, A
Pastrello, C
D'Angelo, E
Romano, G
Giovannoni, R
Giordan, M
Maretto, I
Bedin, C
Zanon, C
Digito, M
Esposito, G
Mescoli, C
Lavitrano, M
Rizzolio, F
Jurisica, I
Giordano, A
Pucciarelli, S
Nitti, D
Publication Year :
2015

Abstract

Preoperative chemoradiotherapy is widely used to improve local control of disease, sphincter preservation and to improve survival in patients with locally advanced rectal cancer. Patients enrolled in the present study underwent preoperative chemoradiotherapy, followed by surgical excision. Response to chemoradiotherapy was evaluated according to Mandard’s Tumor Regression Grade (TRG). TRG 3, 4 and 5 were considered as partial or no response while TRG 1 and 2 as complete response. From pretherapeutic biopsies of 84 locally advanced rectal carcinomas available for the analysis, only 42 of them showed 70% cancer cellularity at least. By determining gene expression profiles, responders and non-responders showed significantly different expression levels for 19 genes (P < 0.001). We fitted a logistic model selected with a stepwise procedure optimizing the Akaike Information Criterion (AIC) and then validated by means of leave one out cross validation (LOOCV, accuracy D 95%). Four genes were retained in the achieved model: ZNF160, XRCC3, HFM1 and ASXL2. Real time PCR confirmed that XRCC3 is overexpressed in responders group and HFM1 and ASXL2 showed a positive trend. In vitro test on colon cancer resistant/susceptible to chemoradioterapy cells, finally prove that XRCC3 deregulation is extensively involved in the chemoresistance mechanisms. Protein-protein interactions (PPI) analysis involving the predictive classifier revealed a network of 45 interacting nodes (proteins) with TRAF6 gene playing a keystone role in the network. The present study confirmed the possibility that gene expression profiling combined with integrative computational biology is useful to predict complete responses to preoperative chemoradiotherapy in patients with advanced rectal cancer.

Subjects

Subjects :
Oncology
Male
Pathology
Cancer Research
Colorectal cancer
Drug Resistance
RC, Rectal cancer
Preoperative chemoradiotherapy
DSB
HT
CRT, Chemoradiotherapy
Carcinoembryonic antigen
CEA
Protein-protein interaction
XRCC3
Tumor Cells, Cultured
High throughput
Rectal cancer
pCRT
Gy
SNP, Single nucleotide polymorphism
Tumor Regression Grade
biological network
Single-strand breaks
Tumor
Cultured
biology
Chemoradiotherapy
Small interfering RNA
Middle Aged
DSB, Double-strand break
integrated approach
Tumor Cells
HT, High throughput
Gene Expression Regulation, Neoplastic
DNA-Binding Proteins
Treatment Outcome
Gene Knockdown Techniques
siRNA, Small interfering RNA
Adenocarcinoma
Molecular Medicine
CRT
Female
Biological network
Integrated approach
Microarray
Treatment response
microarray
Adult
PPI, Protein-protein interaction
RIN, RNA integrity number
medicine.medical_specialty
Double-strand breaks
PPI
mRNA
SNP
Settore BIO/11 - Biologia Molecolare
Young Adult
Internal medicine
medicine
Biomarkers, Tumor
Humans
SSB
Aged
Pharmacology
CEA, carcinoembryonic antigen
DSB, Double-strand breaks
Gy, Gray
SSB, Single-strand breaks
mRNA, mRNA
pCRT, Preoperative chemoradiotherapy
preoperative chemoradiotherapy
rectal cancer
treatment response
Drug Resistance, Neoplasm
Gene Expression Profiling
Multivariate Analysis
Rectal Neoplasms
Neoplastic
carcinoembryonic antigen
RIN
medicine.disease
Molecular medicine
Gray
RC
RNA integrity number
Single nucleotide polymorphism
siRNA
Gene expression profiling
Gene Expression Regulation
SSB, Single-strand break
biology.protein
Clinical Study
Neoplasm
Biomarkers

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....bae09628b598868f97091c4ff26c4648