Project description:Samples were taken from colorectal cancers in surgically resected specimens in 36 colorectal cancer patients. The expression profiles were determined using Affymetrix Human Genome U133 Plus 2.0 arrays. Comparison between the sample groups allow to identify a set of discriminating genes that can be used for molecular markers for predicting recurrence. Keywords: repeat Thirty-six colorectal cancer patients who had undergone surgical resection of colorectal cancer were studied. In all patients, curative resection was performed and no patients had any distant metastasis at the time of operation (stage III patients). Among the 36 patients, 23 patients did not develop recurrence. On the other hand, 13 patients developed rucurrence such as liver metastases, lung metastases and distant lymph node metastases. The median follow up period was 4.5 years.
Project description:Tumor environment is of vital importance for the incidence and development of colorectal cancer. In order to decipher tumor environment feature of colorectal cancer and explore the role of its immune context composition in cancer classification, we performed gene expression microarray on microdissection processed stroma compartments of colorectal cancer and adjacent normal tissues. Integrated analysis of our data with public gene expression microarray data of microdissection processed stromal and epithelial tissues of colorectal, we identified three immune subtypes — active immune, active stroma and mixed type. These immune subtypes differed by immune cell infiltration pattern, expression of immune checkpoint inhibitors mutation landscape, extent of mutation burden, extent of copy number burden and prognosis. Together, these results suggested that dividing colorectal cancer characterized by tumor environment is of vital important in predicting patients clinical outcome and help to guide precision and personalized management.
Project description:Mucinous adenocarcinoma (MuC), a subtype of colorectal cancer (CRC), exhibits distinct molecular features and a poorer prognosis compared to non-mucinous CRC (NMuC). Standard CRC treatments often fail to address MuC's unique characteristics, especially in stage II, where the benefit of adjuvant chemotherapy is unclear. Biomarkers that improve risk assessment and guide personalized treatment are needed. Based on RNA-seq data and Cox regression models, we developed a signature reflecting the characteristics of MuC which showed a strong prediction ability and clinical utility in both MuC and NMuC. Our signature represents a valuable tool for predicting recurrence and guiding personalized treatment in CRC.
Project description:Mucinous adenocarcinoma (MuC), a subtype of colorectal cancer (CRC), exhibits distinct molecular features and a poorer prognosis compared to non-mucinous CRC (NMuC). Standard CRC treatments often fail to address MuC's unique characteristics, especially in stage II, where the benefit of adjuvant chemotherapy is unclear. Biomarkers that improve risk assessment and guide personalized treatment are needed. Based on RNA-seq data and Cox regression models, we developed a signature reflecting the characteristics of MuC which showed a strong prediction ability and clinical utility in both MuC and NMuC. Our signature represents a valuable tool for predicting recurrence and guiding personalized treatment in CRC.
Project description:Samples were taken from colorectal cancers in surgically resected specimens in 36 colorectal cancer patients. The expression profiles were determined using Affymetrix Human Genome U133 Plus 2.0 arrays. Comparison between the sample groups allow to identify a set of discriminating genes that can be used for molecular markers for predicting recurrence. Keywords: repeat
Project description:Identification of molecular features that predict the malignant potential of a polyp is a major clinical step in individualizing polyp patient management. Why does one polyp develop into cancer while another does not? Our first aim was to validate our original findings on differences between polyps based on transformation in an expanded and independent cohort of patients. Our next aim was to identify the molecular events that define the transition point of polyps to cancer based on the aggressiveness of the polyp (polyp outcome phenotype) to develop a cancer risk prediction model for polyps.
Project description:We report the RNA-seq data of 40 advanced colorectal adenoma patients form Dongguk University Ilsan International Hospital. The polyps with a diameter of 1cm or greater were regarded as advenced colorectal adenoma and obtained through colonoscopy. The data consist of 22 tublar adenoma, 6 tublovillous adenoma, 5 sessile serrated adenoma/polyp, 1 traditional serrated adenoma, intramucosal adenocarcinoma, neuroendocrine tumor, hyperplastic polyp, inflammatory polyp, high grade dysplasia, and atypical glands with adjacent hyperplastic mucosa.
Project description:<p>Colorectal cancer (CRC) is a highly heterogeneous disease, for which prognosis has been relegated to clinic-pathologic staging for decades. There is a need to molecularly stratify subpopulations of CRC to better predict outcome and assign therapies. Here we report targeted exome sequencing of 1,321 cancer-related genes on 468 tumor specimens, which identified a subset of 17 genes that best classify CRC, with APC (Gene ID: 324) playing a central role in predicting overall survival. APC may assume 0, 1, or 2 truncating mutations, each with a striking differential impact on survival. Tumors lacking any APC mutation carry a worse prognosis than single APC mutation tumors, but tumors with two APC mutations and KRAS (Gene ID: 3845) and TP53 (Gene ID: 7157) mutations confer the poorest survival among all the subgroups examined. Our study demonstrates a substantial prognostic role for APC and suggests that sequencing of APC may have clinical utility in the routine staging and potential therapeutic assignment for CRC.</p>
