Project description:lncRNAs contributes to the development of colorectal cancer (CRC). Analysis of tumor tissues and adjacent non-tumor tissues from 6 colorectal cancer patients was conducted. Results indicate insight into molecular signature of the tumorigenesis of CRC.
Project description:Microarray analyses for the identification of differences in gene expression patterns have increased our understanding of the molecular genetic events in colorectal cancer. We used gene expression analysis data from recurrent and non-recurrent patients with colorectal cancer to identify differentially expressed probes. Tumor tissues were taken from 81 patients with colorectal cancer, rapidly frozen in RNAlater, and isolated using Trizol. Gene expression pro?les were determined using Affymetrix HG-U133 Plus 2.0 GeneChips.We aimed to identify a molecular signature that can reliably identify colorectal cancer patients at high risk for recurrence.
Project description:Relapse and metastatic progression is a frequent event in colorectal cancer patients detected at early stages. The risk of recurrence requires the development of new biomarkers to correctly predict biological behavior of early stage II and stage III patients and their response to adjuvant chemotherapy. Here, we combined the proteomic quantification of secreted proteins involved in metastasis with a transcriptional analysis to develop a risk score algorithm based on the expression of six genes (SEC6). The SEC6 signature was predictive of survival and recurrence for stage II and III patients in four different datasets including a total of 1534 patients and was also associated with deficient mismatch repair, CpG-island methylator positive status and BRAF mutation. SEC6 was also predictive of beneficial or detrimental effects from 5-Fluorouracil-containing regimes and the improved response to more aggressive chemotherapies based on FOLFOX and FOLFIRI. In summary, the SEC6 risk-score algorithm may constitute a new tool for decision-making in colorectal cancer management.
Project description:The aim of this study is to identify prognostic gene expression signatures associated with two molecularly distinct subtypes of colorectal cancer. Samples were taken from colorectal cancers in surgically resected specimens in 96 colorectal cancer patients. The expression profiles were determined using Affymetrix Human Genome U133Plus 2.0 arrays. This is a test set for validation of prognostic gene expression signature that was developed from GSE14333. All data were normalized by using the RMA method (affy package in R/Bioconductor).
Project description:Microarray analyses for the identification of differences in gene expression patterns have increased our understanding of the molecular genetic events in colorectal cancer. We used gene expression analysis data from recurrent and non-recurrent patients with colorectal cancer to identify differentially expressed probes.
Project description:We developed an experimentally derived molecular signature from mouse tumor models that is closely associated with survival of colorectal cancer (CRC) patients.
Project description:The goal of this experiment was to build gene expression signature associated with long-term outcomes of patients with hepatic metastatic colorectal cancer. The samples were corrected from surgically resected liver metastasis and extracted RNA was subjected to Illumina expression gene chip analysis.
Project description:In patients with advanced colorectal cancer, leucovorin, fluorouracil, and irinotecan (FOLFIRI) is considered as one of the reference first-line treatments. However, only about half of treated patients respond to this regimen, and there is no clinically useful marker that predicts response. A major clinical challenge is to identify the subset of patients who could benefit from this chemotherapy. We aimed to identify a gene expression profile in primary colon cancer tissue that could predict chemotherapy response. Patients and Methods:- Tumor colon samples from 21 patients with advanced colorectal cancer were analyzed for gene expression profiling using Human Genome GeneChip arrays U133. At the end of the first-line treatment, the best observed response, according to WHO criteria, was used to define the responders and nonresponders. Discriminatory genes were first selected by the significance analysis of microarrays algorithm and the area under the receiver operating characteristic curve. A predictor classifier was then constructed using support vector machines. Finally, leave-one-out cross validation was used to estimate the performance and the accuracy of the output class prediction rule. Results:- We determined a set of 14 predictor genes of response to FOLFIRI. Nine of nine responders (100% specificity) and 11 of 12 nonresponders (92% sensitivity) were classified correctly, for an overall accuracy of 95%. Conclusion:- After validation in an independent cohort of patients, our gene signature could be used as a decision tool to assist oncologists in selecting colorectal cancer patients who could benefit from FOLFIRI chemotherapy, both in the adjuvant and the first-line metastatic setting.