Dataset Information

MRNA expression profiles of colorectal liver metastases as a novel biomarker for early recurrence after partial hepatectomy

ABSTRACT: Identification of specific risk groups for recurrence after surgery for colorectal liver metastases (CRLM) remains challenging due to the heterogeneity of the disease. Classical clinicopathologic parameters have limited prognostic and predictive value. The aim of this study was to identify a gene expression signature measured in CRLM discriminating early from late recurrence after surgery for CRLM. Methods: Tumour samples of CRLM from two patient groups were collected: I) patients with recurrent disease within 12 months after surgery (N=33), and II) patients without recurrences and disease free for at least 36 months (N=30). The included patients were clinically homogeneous; all patients had a low risk profile (clinical risk score 0-2) and did not receive (neo-) adjuvant chemotherapy. Extracted total RNA from both groups was hybridized to Illumina arrays, and processed for analysis. A leave-one-out cross validation (LOOCV) analysis was performed to identify a potentially prognostic gene expression signature. Results: The LOOCV yielded an 11-gene profile with prognostic value in relation to recurrent disease within 12 months after partial hepatectomy. This gene expression profile had a sensitivity of 81.8%, with a specificity of 66.7% for predicting early recurrences (within 12 months) versus no recurrences for at least 36 months after surgery (X2 P<0.0001). Conclusion: The current study yielded an 11-gene signature at mRNA level in CRLM discriminating early from late or no relapse after partial hepatectomy.

ORGANISM(S): Homo sapiens

PROVIDER: GSE81423 | GEO | 2017/01/01

SECONDARY ACCESSION(S): PRJNA321522

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Background: With the incidence of papillary thyroid carcinoma rising worldwide, the decision about lobectomy versus total thyroidectomy will become relevant for an increasing number of patients. There exists no reliable biomarker for metastatic potential or tendency of recurrence that could assist in the risk stratification of patients. Objective: To develop a gene expression classifier for metastatic potential by measuring RNA expression in the primary tumor at the time of cancer surgery. Further, to investigate the ability of the gene expression classifier to identify metastatic and recurrent cases. Method: Genome-wide expression analyses. The development cohort consisted of freshly frozen tissues from 38 patients collected between the years 1986 and 2009. Validation was performed on a consecutive cohort of formalin fixated paraffin embedded surgical tissue specimens from 183 patients treated at Odense and Aarhus University Hospitals. Results: A 17 gene classifier (ADAMTS1, ANTXR1, C7, CXCL12, EBF1, FBLN2, FOSL2, GGT5, GPR124, JAM3, LRIG1, NDRG1, PRRX1, ROBO1, SORL1, TCF4, and ZEB1) was identified based on the expression values of these genes in the groups with and without metastasis in the development cohort. The 17 gene classifier for regional and/or distant metastasis identified was tested against the clinical status in the validation cohort. Sensitivity was 51.6% (95% CI 41.2%-61.8) and specificity 61.6 % (95% CI 50.5%-71.9%). Further, the Kaplan-Meyer method was used to estimate whether the classifier was useful as a prognostic marker for recurrences. Log-rank testing failed to identify any significance (p=0.32). Conclusion: A 17 gene classifier for metastatic potential was developed, and the results showed a clear biological difference between groups. However, through validation, the prognostic significance of this classifier could not be shown in identifying metastatic cases or in the ability of dichotomizing patients according to risk of recurrence after primary treatment. 38 patients with papillary thyroid carcinoma from a consecutive cohort of patients, no replicates

Dataset Information

MRNA expression profiles of colorectal liver metastases as a novel biomarker for early recurrence after partial hepatectomy

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure