A circRNA signature predicts postoperative recurrence in stage II/III colon cancer
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ABSTRACT:
SUBMITTER: Prof. Rui-hua Xu
PROVIDER: S-SCDT-EMM-2018-10168 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Accurate risk stratification for patients with stage II/III colon cancer is pivotal for postoperative treatment decisions. Here, we aimed to identify and validate a circRNA-based signature that could improve postoperative prognostic stratification for these patients. In current retrospective analysis, we included 667 patients with R0 resected stage II/III colon cancer. Using RNA-seq analysis of 20 paired frozen tissues collected postoperation, we profiled differential circRNA expression between patients with and without recurrence, followed by quantitative validation. With clinical information, we generated a four-circRNA-based cirScore to classify patients into high-risk and low-risk groups in the training cohort. The patients with high cirScores in the training cohort had a shorter disease-free survival (DFS) and overall survival (OS) than patients with low cirScores. The prognostic capacity of the classifier was validated in the internal and external cohorts. Loss-of-function assays indicated that the selected circRNAs played functional roles in colon cancer progression. Overall, our four-circRNA-based classifier is a reliable prognostic tool for postoperative disease recurrence in patients with stage II/III colon cancer.
| S-EPMC6783650 | biostudies-literature
Project description:BackgroundOur recent study observed that the expression of ubiquitin D (UBD), a member of ubiquitin-like modifier family, was upregulated in colon cancer parenchymal cells. The present study further investigated the clinical signicance of UBD in colon cancer.MethodsUsing quantitative PCR, tissue microarray (TMA), western blot analysis and immunohistochemical stain, we evaluated UBD mRNA and protein levels in tumour tissues from patients with colon cancer at different stages and in paired adjacent normal epithelium.ResultsImmunohistochemical detection of UBD on a TMA containing 203 paired specimens showed that increased cytoplasmic UBD was signicantly associated with depth of cancer invasion, lymph node metastasis, distant metastasis, tumour histologic grade, advanced clinical stage and Ki-67 proliferative index. Patients with UBD-positive tumours had a significantly higher disease recurrence rate and poorer survival than patients with UBD-negative tumours after the radical surgery. Stratification analysis according to tumour stage revealed UBD as an independent predictor for tumour recurrence in patients with stage II and III tumours.ConclusionUBD may contribute to the progression of colon carcinogenesis and function as a novel prognostic indicator of forecasting recurrence of stage II and III patients after curative operations.
| S-EPMC2965875 | biostudies-literature
Project description:About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset -142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = -0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients.
| S-EPMC3264655 | biostudies-literature
Project description:Tumor recurrence is usually detected within one year after radical resection of stage III gastric cancer. This study aimed to establish the expression profile and determine potential circular RNAs (circRNAs) and predict the early recurrence of stage III gastric cancer. We identified 46 differently expressed circRNAs between cancer and adjacent normal tissues through circRNA microarray. We further screened eight indicators related to early recurrence. We subsequently divided the remaining cases into two cohorts. qRT-PCR results demonstrated a significantly different outcome between low and high expressed groups of four circRNAs in the training cohort. We then constructed a four-circRNA-based classifier to evaluate the risk of early recurrence and distinguished patients with a high risk from those with a low risk. The areas under the receiver operator characteristic curve (ROC) of this classifier were 0.763 and 0.711 in the two cohorts, respectively. A new formula could be established by combined the circRNA classifier with TNM stages. The areas under the ROC curve were 0.866 and 0.818 of the two cohorts, respectively. Our study suggested that this four-circRNA-based classifier yielded a predictive ability to the early recurrence of stage III gastric cancer after radical surgery.
| S-EPMC5410275 | biostudies-literature
Project description:Purpose: The present study aimed to develop a classification model to predict recurrence in stage II and III colon cancer, using a previously published 128-gene signature on external and independent material. Experimental Design: Microarray gene expression data from 148 patients (37 Danish patients and 111 patients retrieved from the Gene Expression Omnibus, GSE17536) with stage II and III colon cancer were analyzed using Affymetrix Arrays (Affymetrix, Santa Clara, USA). Based on a known 128-gene signature, a classification model was created with the random forest method, using a training set consisting of stage I colon cancers (with localized disease and a good prognosis) and stage IV colon cancers (with metastasis and a poor prognosis). The classifier were built to predict stage II and III colon cancers as either stage I-like (good prognosis) or stage IV-like (poor prognosis). Results: The 3-year relapse-free survival probability (RFS) of stage III patients predicted to have a good prognosis was 79% compared to 55% of patients with a poor prognosis (P = 0.177, log-rank test). The classification model could not stratify stage II colon cancer. The complete dataset representing: (1) the 37 Danish patients (2) the 111 patients retrieved from Series GSE17536 (re-used data), is linked below as a supplementary file. Tumor samples were obtained from 37 patients with stage II and III colon cancer, who underwent colon resection at the Department of Surgery, Roskilde Hospital, Denmark and the Department of Surgery, Bispebjerg Hospital, Denmark between 2001 and 2008. Purified tumor RNA was reverse-transcribed, labelled and hybridized to Affymetrix Human Genome U133 Plus 2.0 GeneChip Array (Affymetrix, Santa Clara, USA) according to the manufacturers instructions and scanned at the RH Microarray Center, Rigshospitalet, University of Copenhagen.
2011-08-31 | E-GEOD-31595 | biostudies-arrayexpress
Project description:Objective: To evaluate whether a radiomics signature could improve stratification of postoperative risk and prediction of chemotherapy benefit in stage II colorectal cancer (CRC) patients. Material and Methods: This retrospective study enrolled 299 stage II CRC patients from January 2010 to December 2015. Based on preoperative portal venous-phase CT scans, radiomics features were generated and selected to build a radiomics score (Rad-score) using the Least Absolute Shrinkage and Selection Operator (LASSO) method. The minority group was balanced by the synthetic minority over-sampling technique (SMOTE). Predictive models were built with the Rad-score and clinicopathological factors, and the area under the curve (AUC) was used to evaluate their performance. A nomogram was also constructed for predicting 3-year disease-free survival (DFS). The performance of the nomogram was assessed with a concordance index (C-index) and calibration plots. Results: Overall, 114 features were selected to construct the Rad-score, which was significantly associated with the 3-year DFS. Multivariate analysis demonstrated that the Rad-score, CA724 level, mismatch repair status, and perineural invasion were independent predictors of recurrence. Results showed that the Rad-score can classify patients into high-risk and low-risk groups in the training cohort (AUC 0.886) and the validation cohort (AUC 0.874). On this basis, a nomogram that integrated the Rad-score and clinical variables demonstrated superior performance (AUC 0.954, 0.906) than the clinical model alone (AUC 0.765, 0.705) in the training and validation cohorts, respectively. The C-index of the nomogram was 0.872, and the performance was acceptable. Conclusion: Our radiomics-based model can reliably predict recurrence risk in stage II CRC patients and potentially provide complementary prognostic value to the traditional clinicopathological risk factors for better identification of patients who are most likely to benefit from adjuvant therapy. The proposed nomogram promises to be an effective tool for personalized postoperative surveillance for stage II CRC patients.
| S-EPMC8017337 | biostudies-literature
Project description:Despite advances in colon cancer research and novel therapies, high risk of recurrence remains a major challenge. This study reports miRNA expression profiling as a biomarker for the prognosis of TNM stage II and III colon cancer. Fresh frozen biopsies from the study cohort (N=111) were analyzed for miRNA by RT-qPCR and LASSO regression analysis was used to build a classifier of miRNAs. The prognostic accuracy was tested and the classifier was validated in an independent colon cohort (TCGA-COAD, N=209). The LASSO regression analysis identified a 16-miRNA signature including miR-143-5p, miR-27a-3p, miR-31-5p, miR-181a-5p, miR-30b-5p, miR-30d-5p, miR-146a-5p, miR-23a-3p, miR-150-5p, miR-210-3p, miR-25-3p, miR-196a-5p, miR-148a-3p, miR-222-3p, miR-30c-5p and miR-223-3p. A low 16-miRNA signature was associated with better 5-year disease-free survival (DFS) in the study cohort than a high signature (93 % versus 58 %; p< 0.001). The signature was an independent prognostic factor for better 5-year DFS in multivariate analyses (HR 21.4; 95% CI: 4.21-108.7; p< 0.001). The results in the validation cohort were consistent with the study cohort in univariate (77 % versus 65 %; p= 0.045) and multivariate analyses (HR 2.0; 95% CI: 1.04-3.89; p=0.039). We identified a 16-miRNA signature as a reliable prognostic biomarker for classification of colon cancer stage II and III patients into groups with low and high risk for recurrence.
| S-EPMC5675676 | biostudies-literature
Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration.
2009-07-02 | GSE12032 | GEO
Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration. Of 198 samples, 129 represented the discovery phase and 69 represented the validation phase.
2009-07-02 | E-GEOD-12032 | biostudies-arrayexpress
Project description:Colorectal cancer (CRC) is the third most common cancer worldwide. Accurately identifying stage II CRC patients at risk for recurrence is an unmet clinical need. KCNQ1 was previously identified as a tumour suppressor gene and loss of expression was associated with poor survival in patients with CRC liver metastases. In this study the prognostic value of KCNQ1 in stage II and stage III colon cancer patients was examined.KCNQ1 mRNA expression was assessed in 90 stage II colon cancer patients (AMC-AJCCII-90) using microarray gene expression data. Subsequently, KCNQ1 protein expression was evaluated in an independent cohort of 386 stage II and stage III colon cancer patients by immunohistochemistry of tissue microarrays.Low KCNQ1 mRNA expression in stage II microsatellite stable (MSS) colon cancers was associated with poor disease-free survival (DFS) (P=0.025). Loss of KCNQ1 protein expression from epithelial cells was strongly associated with poor DFS in stage II MSS (P<0.0001), stage III MSS (P=0.0001) and stage III microsatellite instable colon cancers (P=0.041). KCNQ1 seemed an independent prognostic value in addition to other high-risk parameters like angio-invasion, nodal stage and microsatellite instability-status.We conclude that KCNQ1 is a promising biomarker for prediction of disease recurrence and may aid stratification of patients with stage II MSS colon cancer for adjuvant chemotherapy.
| S-EPMC5155368 | biostudies-literature