The recurrence risk monitoring and prediction model of colorectal cancer patients was established based on Digital Twin
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Cancer
PROVIDER: 113709 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:Expression profiling of superficial bladder tumours to delineate the expression pattern differences between non-recurring and recurring tumours. Keywords = bladder cancer, superficial, recurrence, prediction Keywords: other
2002-12-08 | GSE88 | GEO
Project description:211 FFPE NSLC surgical samples were used to generate recurrence prediction models
2018-02-13 | GSE90623 | GEO
Project description:Background: One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results: The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good results of the proposed model (p=0.0314, 0.0305, respectively). The predicted median recurrence-free survival times for each patient were reflected real ones well. Conclusions: Our method of individualized recurrence risk prediction is accurate, technically simple and reproducible to be used in clinical practice. Therefore, it would be useful in customizing the lung cancer management strategies. Keywords: Recurrence Free Survival Analysis
2007-11-20 | GSE8894 | GEO
Project description:The molecular profile of endometrial cancer has become an important tool in determining patient prognosis and their optimal adjuvant treatment. In addition to The Cancer Genome Atlas (TCGA), simpler tools have been developed, such as the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE). We attempted to determine a genetic signature to build a recurrence risk score in patients diagnosed with low- and intermediate-risk endometrial cancer. A case-control study was conducted. The eligible patients were women diagnosed with recurrence low- and intermediate-risk endometrial cancer between January 2009 and December 2014 at a single institution; the recurrence patients were matched to two nonrecurrence patients with the same diagnosis by age and surgical staging. Following RNA isolation of 51 cases, 17 recurrence and 34 nonrecurrence patients, the expression profile was determined using the nCounter® PanCancer Pathways Panel, which contains 770 genes. The expression profile was successfully characterized in 49/51 (96.1%) cases. We identified 12 genes differentially expressed between the recurrence and nonrecurrence groups. The ROC curve for each gene was generated, and all had AUCs higher than 0.7. After backward stepwise logistic regression, four genes were highlighted: FN1, DUSP4, LEF1, and SMAD9. The recurrence risk score was calculated, leading to a ROC curve of the 4-gene model with an AUC of 0.93, sensitivity of 100%, and specificity of 72.7%. We identified a four-gene signature that is associated the risk of recurrence in patients with low- and intermediate-risk endometrial cancer. This finding suggests a new prognostic factor in this poorly explored group of patients with endometrial cancer.
2021-07-27 | GSE178671 | GEO
Project description:Interventions: Case series:none
Primary outcome(s): recurrence of adenoma
Study Design: Cohort study
| 2681212 | ecrin-mdr-crc
Project description:We analysed a cohort of pure DCIS cases treated only with wide local excision for genome-wide copy number and loss of heterozygosity using Affymetrix OncoScan® MIP arrays. Cases included those without recurrence within 6 years (n=25) and with recurrence between 1-5 years after diagnosis (n=15). Additional cases were excluded for low grade (n=6) or non-clonal recurrence (n=2). Recurrence tumour was available for 8 cases. Pure DCIS were broadly similar in copy number changes compared to invasive breast cancer, with the consistent exception of a greater frequency of ERBB2 amplification in DCIS. There were no significant differences in age or ER status between the cases with a recurrence versus those without. Overall, the DCIS cases with recurrence had more copy number events than the DCIS without recurrence. The increased copy number appeared non-random with several genomic regions showing an increase in frequency in recurrent cases including 20q gain, ERBB2 amplification and 15q loss. Copy number changes may provide prognostic information for DCIS recurrence but validation in additional cohorts is required.
2015-12-01 | GSE66746 | GEO
Project description:Treatment of late-stage melanoma patients with immune checkpoint blockade (ICB) is currently one of the most effective standard therapies. The response rates upon neoadjuvant ICB in stage III melanoma are higher as compared to stage IV disease. Given that successful ICB depends on systemic immune response, we hypothesized that systemic immune suppression might be a mechanism responsible for lower response rates in late-stage disease, and also potentially with disease recurrence in early-stage disease.
2024-01-09 | PXD040399 | Pride
Project description:We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared. Adequate DASL assay profiles were obtained in 300 patients and we selected 12 genes for the gene set model. When the proportions of patients were categorized as having a low or high risk by the prognostic scores using these genes from LOOCV procedure, the Kaplan-Meier curve showed significant different recurrence rate between two groups. Clinical model was developed using FIGO stage as well as post-surgical pathological findings.
2013-05-01 | E-GEOD-44001 | biostudies-arrayexpress
Project description:Purpose: Several risk factors for local recurrence of breast cancer after breast conserving therapy (BCT) have been identified. The identification of additional risk factors would be very useful in guiding optimal therapy and also improve understanding of the mechanisms underlying local recurrence. We used cDNA microarray analysis to identify gene expression profiles associated with local recurrence. Experimental Design: Using 18K cDNA microarrays, gene expression profiles were obtained from 50 patients who underwent BCT. Of these 50 patients 19 developed a local recurrence; the remaining 31 patients were selected as controls as they were free of local recurrence at least 11 years after treatment. For 9/19 patients also the local recurrence was available for gene expression profiling. Unsupervised and supervised methods of classification were used to separate patients in groups corresponding to disease outcome and to study the overall gene expression pattern of primary tumors and their recurrences. Results: Hierarchical clustering of patients did not show any grouping reflecting local recurrence status. Supervised analysis revealed no significant set of genes that was able to distinguish recurring tumors from non-recurring tumors. Paired-data analysis of primary tumors and local recurrences showed a remarkable similarity in gene expression profile between primary tumors and their recurrences. Conclusions: No significant differences in gene expression between primary breast cancer tumors in patients with or without local recurrence after breast conserving therapy were identified. Furthermore, analyses of primary tumors and local recurrences show a preservation of the overall gene expression pattern in the local recurrence, even after radiotherapy. Keywords: gene expression profiling
2006-09-24 | GSE4913 | GEO
Project description:We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared.
2013-05-01 | GSE44001 | GEO