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
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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:Cholangiocarcinoma (CCA) has high recurrence rates that severely limit long-term survival. Effective tools for accurate recurrence monitoring and diagnosis remain lacking. Metabolic reprogramming, a key driver of CCA growth and recurrence, is underutilized in cancer screening and management. This study aimed to identify metabolite-based biomarkers to evaluate recurrence severity, enhance disease management, and elucidate the molecular mechanisms underlying CCA recurrence.
2025-03-19 | PXD060214 | JPOST Repository
Project description:Circulating microRNAs (miRNAs) may be able to serve as prognostic biomarkers. However, identifying the accurate screening method/s for miRNA detection/quantification from serum is crucial for any further downstream analysis. Here, we compared two high-throughput methods, small RNA sequencing (sRNA-seq) and TaqMan Low-Density Array (TLDA), to quantify miRNAs at baseline (time of primary surgery) in the serum from ER+ BC patients for whom the long-term clinical follow-up information (10 years) and subsequent metastasis status are known. For this study, 48 serum samples were selected based on the outcome of breast cancer patients, and therefore sub-divided in: patients with no recurrence within a 10-years period (Cohort 1, n=16), patients with early recurrence within 5 years (Cohort 2, <5 Years, n=16), or patients with late recurrence after 5 years up to 10 years (Cohort 3, >5 Years, n=16). For each serum sample, the miRNome analysis was performed by Next Generation Sequencing (NGS) using 20 µl of the 45 µl total RNA extracted from 200 µl serum by the miRCURY Biofluids RNA Isolation Kit. The NGS libraries were constructed and sequenced at the ProfileXpert platform (www.profilexpert.fr). The data was quality checked by assigning Q-scores after the intensity correction and base calling. The raw data were treated with the NCS 2.0.2 and RTA 2.4.11 software’s to generate the Base Calling File. The Bcl2factq v2.17.1.14 was used to demultiplexing using 1 mismatch.
2025-01-01 | E-MTAB-10555 | biostudies-arrayexpress
Project description:BackgroundAt present, there is a lack of established treatment protocols for recurrent medulloblastoma. The assessment of recurrence risk prior to treatment is of utmost importance in determining the most suitable treatment modality and intensity for medulloblastoma. Consequently, the creation of a predictive model for medulloblastoma recurrence is imperative in aiding clinical decision-making. The objective of this study is to construct an enhanced risk prediction model for relapse in medulloblastoma by integrating molecular subtyping and straightforward immune markers, such as neutrophil-to-lymphocyte ratio (NLR), into a nomogram.MethodsA retrospective analysis was conducted on the clinical data of 273 patients diagnosed with medulloblastoma. The NLR was calculated prior to radiotherapy, and various clinical characteristics including age, gender, molecular subtype, dissemination, and residual lesions after resection were collected. Survival analysis was performed utilizing the Kaplan-Meier method, while Cox regression models were employed to identify independent prognostic factors. Furthermore, a column chart illustrating all independent prognostic factors was generated using R. The nomogram's prognostic predictive ability was evaluated using the Concordance Index (C-index), area under the curve (AUC), and calibration curve.ResultsThe median progression-free survival (PFS) for the entire cohort was determined to be 63.8 months. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors that were associated with PFS in patients diagnosed with medulloblastoma. These factors included age, residual tumor volume exceeding 1.5 cm3, NLR exceeding 4.5, dissemination occurring prior radiotherapy, and molecular subtype classified as Group 3. These identified factors were then utilized to construct a column chart. The nomogram C-index for the predicted PFS in the training and validation cohorts was 0.749 and 0.736, respectively. The AUC for predicting the 3-year PFS exhibited satisfactory accuracy in the validation cohort (AUC =0.71). Furthermore, the calibration curve indicated a strong concordance between the predicted and ideal values. Additionally, the Kaplan-Meier curve, based on PFS, demonstrated a statistically significant distinction between the low-risk and high-risk groups as predicted by the nomogram (P<0.001).ConclusionsOur study revealed that the NLR prior to treatment serves as an autonomous prognostic determinant for the recurrence or metastasis of medulloblastoma subsequent to treatment. By integrating NLR with clinical variables, the utilization of a nomogram demonstrates the capability to anticipate PFS following radiotherapy in medulloblastoma patients. This nomogram exhibits potential in facilitating more accurate risk stratification, thereby guiding the implementation of personalized treatment strategies for individuals with medulloblastoma.
| S-EPMC11811586 | biostudies-literature
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:Glioblastoma remains incurable and, due to its infiltrative growth, high levels of treatment resistance and population of glioma initiating/stem cells (GICs), recurs in all patients. Here we design and characterize a novel induced-recurrence model in which mice xenografted with primary patient-derived GICs are treated with a therapeutic regimen closely recapitulating patient standard of care, followed by monitoring until tumours recur (IR-PDX). By tracking in vivo tumour growth, we confirm the patient specificity and initial efficacy of treatment prior to recurrence. Availability of longitudinally matched pairs of primary and recurrent GICs enabled patient-specific evaluation of the faithfulness with which the model recapitulated phenotypes associated with the true recurrence. Through comprehensive multi-omic analyses, we showed that the IR-PDX model recapitulated genomic, epigenetic, and transcriptional state heterogeneity changes upon recurrence in a patient-specific manner. The accuracy of the IR-PDX enabled both novel biological insights, including the positive association between glioblastoma recurrence and levels of ciliated neural stem cell-like tumour cells, and the identification of druggable patient-specific therapeutic vulnerabilities. This proof-of-concept study opens the possibility for prospective precision medicine approaches, in which the IR-PDX model is developed between first diagnosis and disease progression to identify target-drug candidates for use as second line of treatment at recurrence.
2025-03-20 | GSE271620 | GEO
Project description:Glioblastoma remains incurable and, due to its infiltrative growth, high levels of treatment resistance and population of glioma initiating/stem cells (GICs), recurs in all patients. Here we design and characterize a novel induced-recurrence model in which mice xenografted with primary patient-derived GICs are treated with a therapeutic regimen closely recapitulating patient standard of care, followed by monitoring until tumours recur (IR-PDX). By tracking in vivo tumour growth, we confirm the patient specificity and initial efficacy of treatment prior to recurrence. Availability of longitudinally matched pairs of primary and recurrent GICs enabled patient-specific evaluation of the faithfulness with which the model recapitulated phenotypes associated with the true recurrence. Through comprehensive multi-omic analyses, we showed that the IR-PDX model recapitulated genomic, epigenetic, and transcriptional state heterogeneity changes upon recurrence in a patient-specific manner. The accuracy of the IR-PDX enabled both novel biological insights, including the positive association between glioblastoma recurrence and levels of ciliated neural stem cell-like tumour cells, and the identification of druggable patient-specific therapeutic vulnerabilities. This proof-of-concept study opens the possibility for prospective precision medicine approaches, in which the IR-PDX model is developed between first diagnosis and disease progression to identify target-drug candidates for use as second line of treatment at recurrence.
2025-03-20 | GSE271619 | GEO
Project description:Interventions: Case series:none
Primary outcome(s): recurrence of adenoma
Study Design: Cohort study
| 2681212 | ecrin-mdr-crc