Circulation cell-free DNA as a biomarker applied in clinical practice of gastrointestinal tumor
Ontology highlight
ABSTRACT:
DISEASE(S): Gastric Cancer, Colorectal Carcinoma
PROVIDER: 2662278 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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2024-02-06 | GSE255074 | GEO
Project description:Tuberculosis (TB) remains a leading cause of death from an infectious disease worldwide. This is partly due to a lack of tools to effectively screen and triage individuals with potential TB. Whole blood RNA signatures have been extensively studied as potential biomarkers for TB, but they have failed to meet the World Health Organization's (WHOs) optimal target product profiles (TPPs) for a non-sputum triage or diagnostic test. In this study, we investigated the utility of plasma cell-free RNA (cfRNA) as a host response biomarker for TB. We used RNA profiling by sequencing to analyze plasma samples from individuals with a cough lasting at least two weeks, who were seen at outpatient TB clinics in Uganda, Vietnam, and the Philippines. We split the samples into a discovery set for model training and testing, and reserved a validation set from a separate cohort to validate the model performance. We trained 15 machine learning classification models and developed a 6-gene signature that has a high performance in differentiating TB positive and negative individuals (Area Under the Curve, AUC = 0.95, 0.92, 0.95 for the test, training and validation sets respectively). This 6-gene signature exceeds the optimal WHO TPPs for a TB triage test (sensitivity: 97.1% [95% CI: 80.9-100%], specificity: 85.2% [95% CI: 72.4-100%]) and was robust to differences in geographic location, sample collection, and HIV status. Analysis of matched whole blood samples from the validation cohort highlighted the differences in origin of plasma and whole blood RNA. Overall, our results demonstrate the utility of plasma cfRNA for the detection of TB and suggest the potential for a point-of-care, gene expression-based assay to aid in early detection of TB.
2024-02-06 | GSE255071 | GEO
Project description:Tuberculosis (TB) remains a leading cause of death from an infectious disease worldwide. This is partly due to a lack of tools to effectively screen and triage individuals with potential TB. Whole blood RNA signatures have been extensively studied as potential biomarkers for TB, but they have failed to meet the World Health Organization's (WHOs) optimal target product profiles (TPPs) for a non-sputum triage or diagnostic test. In this study, we investigated the utility of plasma cell-free RNA (cfRNA) as a host response biomarker for TB. We used RNA profiling by sequencing to analyze plasma samples from individuals with a cough lasting at least two weeks, who were seen at outpatient TB clinics in Uganda, Vietnam, and the Philippines. We split the samples into a discovery set for model training and testing, and reserved a validation set from a separate cohort to validate the model performance. We trained 15 machine learning classification models and developed a 6-gene signature that has a high performance in differentiating TB positive and negative individuals (Area Under the Curve, AUC = 0.95, 0.92, 0.95 for the test, training and validation sets respectively). This 6-gene signature exceeds the optimal WHO TPPs for a TB triage test (sensitivity: 97.1% [95% CI: 80.9-100%], specificity: 85.2% [95% CI: 72.4-100%]) and was robust to differences in geographic location, sample collection, and HIV status. Analysis of matched whole blood samples from the validation cohort highlighted the differences in origin of plasma and whole blood RNA. Overall, our results demonstrate the utility of plasma cfRNA for the detection of TB and suggest the potential for a point-of-care, gene expression-based assay to aid in early detection of TB.
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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
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Project description:Interventions: Group 1: The project aims to:
Statistically compare the fragment length of cell-free DNA in the blood plasma of 80 patients with a confirmed diagnosis of colorectal carcinoma in stages I-IV with the cfDNA fragment lengths of 50 healthy subjects.
For this purpose, residual amounts of blood plasma samples are used, which are collected anyway as part of theroutine examinations.
Cell-free DNA was isolated from the blood plasma and the fragment lengths are measured using qPCR.
The project ends with the publication of the data.
Group 2: Statistically compare the fragment length of cell-free DNA in the blood plasma of 50 healthy subjects with 80 patients with a confirmed diagnosis of colorectal carcinoma in stages I-IV.
The plasma samples from healthy volunteers were purchased (Central Biohub GmbH).
Primary outcome(s): The aim of this study is: 1) to analyze total cfDNA concentration in CRC patients with different histopathological stages (UICC I-IV). 2) to address the question whether the integrity index of cfDNA increases in CRC patients as a result of elevated necrotic degradation processes. Therefore, we intended to quantify short and long fragments of cfDNA via qPCR independent from total cfDNA levels by using equal template concentrations for CRC patients and healthy individuals. This approach may help to understand whether previously described diagnostic markers are either increased, decreased or unaltered in blood plasma of CRC patients compared to healthy individuals.
Study Design: Allocation: ; Masking: ; Control: ; Assignment: ; Study design purpose: basic science
| 2754663 | ecrin-mdr-crc
Project description:FinaleMe: Predicting DNA Methylation by the Fragmentation Patterns of Plasma Cell-free DNA
| PRJNA965897 | ENA
Project description:FinaleMe: Predicting DNA Methylation by the Fragmentation Patterns of Plasma Cell-free DNA
| PRJNA965896 | ENA
Project description:Using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq), we analyzed methylation profiles in 93 plasma samples from 77 pediatric brain tumor patients and 16 non-neoplastic patients. Binomial GLMnet classifiers of tumor and tumor subtypes were built in training sets, and performance was evaluated in test sets. The methylation profiles from plasma cfMeDIP-seq discriminated tumor from non-tumor patients with 0.83 accuracy (precision = 0.93, sensitivity = 0.86, and specificity = 0.67). Among major tumor subtypes vs. nontumors, circumscribed astrocytic glioma showed an accuracy of 0.86 (precision = 0.88, sensitivity = 0.88, and specificity = 0.83), and glioneuronal tumors had an accuracy of 0.83 (precision, sensitivity, and specificity = 0.83). Circumscribed astrocytic glioma and glioneuronal tumors could be discriminated from other tumor subtypes with 0.79 and 0.82 accuracy, respectively.
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