Gene and MicroRNA Expression Are Predictive of Tumor Response in Rectal Adenocarcinoma Patients Treated With Preoperative Chemoradiotherapy
Year: 2016
Type: article
Study design: Observational Study
Abstract: Preoperative chemoradiotherapy (pCRT) followed by surgery is the standard treatment for locally advanced rectal cancer (LARC). However, tumor response to pCRT is not uniform, and there are no effective predictive methods. This study investigated whether specific gene and miRNA expression are associated with tumor response to pCRT. Tissue biopsies were obtained from patients before pCRT and resection. Gene and miRNA expression were analyzed using a one-color microarray technique that compares signatures between responders (R) and non-responders (NR), as measured based on tumor regression grade. Two groups composed of 38 "exploration cohort" and 21 "validation cohort" LARC patients were considered for a total of 32 NR and 27 R patients. In the first cohort, using SAM Two Class analysis, 256 genes and 29 miRNAs that were differentially expressed between the NR and R patients were identified. The anti-correlation analysis showed that the same 8 miRNA interacted with different networks of transcripts. The miR-630 appeared only with the NR patients and was anti-correlated with a single transcript: RAB5B. After PAM, the following eight transcripts were strong predictors of tumor response: TMEM188, ITGA2, NRG, TRAM1, BCL2L13, MYO1B, KLF7, and GTSE1. Using this gene set, an unsupervised cluster analysis was applied to the validation cohort and correctly assigned the patients to the NR or R group with 85.7% accuracy, 90% sensitivity, and 82% specificity. All three parameters reached 100% when both cohorts were considered together. In conclusion, gene and miRNA expression profiles may be helpful for predicting response to pCRT in LARC patients. J. Cell. Physiol. 232: 426-435, 2017. © 2016 Wiley Periodicals, Inc.
Source: Journal of Cellular Physiology
DOI: 10.1002/jcp.25441
Authors: Caterina Millino, Isacco Maretto, Beniamina Pacchioni, Maura Digito, Antonino De Paoli, Vincenzo Canzonieri, Edoardo D'Angelo, Marco Agostini, Flavio Rizzolio, Antonio Giordano, Andrea Barina, Senthilkumar Rajendran, Giovanni Esposito, Gerolamo Lanfranchi, Donato Nitti, Salvatore Pucciarelli
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Award: G1084545485
OpenAlex ID: W2401553788
