Dataset Information

The clinical study of predicting the chemotherapy resistance of colorectal cancer cells based on autophagy theory by gene variation combined with miRNA detection

ABSTRACT: Interventions: experimental group:chemotherapy Primary outcome(s): Tumor regression Study Design: Sequential

DISEASE(S): Colon And Rectal Cancer

PROVIDER: 2705047 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

ACCESS DATA

Similar Datasets

Project description:Treatment options and reliable predictive marker to determine patients at risk to develop postoperative LD and to define the optimal time point of liver resection are limited. Accordingly, there is an urgent need for an easily assessable preoperative test to predict postoperative liver function recovery, specifically as current markers are often expensive, time consuming and sometimes invasive. Emerging evidence suggests that microRNA (miRNA) signatures represent potent diagnostic, prognostic and treatment response biomarkers for several diseases. Using next-generation sequencing as an unbiased systematic approach 554 miRNAs were detected in preoperative plasma of 21 patients suffering from postoperative LD after liver resection and 27 matched controls. Subsequently, we identified a miRNA signature - consisting of miRNAs 151a-5p, 192-5p and 122-5p - that highly correlated with patients developing postoperative LD after liver resection. The predictive potential for postoperative LD was subsequently confirmed using real-time PCR in an independent validation cohort of 98 patients. Ultimately, a regression model of the two miRNA ratios 151a-5p to 192-5p and 122-5p to 151a-5p was found to reliably predict postoperative LD, severe morbidity, prolonged intensive care unit and hospital stay and even mortality prior to surgery with a remarkable accuracy, thereby outperforming established markers of postoperative LD. Ultimately, we documented that miRNA ratios closely followed liver function recovery after partial hepatectomy. Conclusion: Given the clinical relevance of predicting potentially fatal postoperative clinical outcome after liver resection, our data demonstrate the clinical utility of a novel miRNA-based biomarker to support the selection of patients undergoing partial hepatectomy. The dynamical changes during liver function recovery indicate a possible role in tailoring the optimal time point of surgery to each individual patient. Thereby, our data might help to tailor surgical strategies to the specific risk profile of individual patients.

Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT, RDX and HMX. One important goal of microarray experiments is to discover novel biomarker genes for quantitative phenotypic prediction. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can be used to quantitatively predict earthworm tissue residues of the explosives compounds that they were exposed to and took in from the HMX-spiked soil. Results: We collected a large microarray gene expression and earthworm tissue residue dataset. First, differentially expressed genes were identified for each exposure duration (4, 14 and 28 days). These genes were used in multivariate regression modeling for HMX residue prediction. Eighteen different regression models were tested and compared. The best performing model was able to achieve very high prediction accuracies with R2 values of 0.715, 0.728 and 0.822 for 4 days, 14 days and 28 days exposures, separately. Conclusions: This study demonstrated that multivariate regression coupled with high throughput microarray gene expression was a promising approach to quantitative phenotypic prediction. Adult earthworms (Eisenia fetida) were exposed in soil spiked with HMX (0, 8, 16, 32, 64, or 128 mg/kg) for 4, 14 or 28 days. Each treatment originally had 10 replicate worms with all 10 worms survived at the end of exposure. Total RNA was isolated from the surviving worms. A total of 120 worm RNA samples were hybridized to a custom-designed oligo array using AgilentM-bM-^@M-^Ys one-color Low RNA Input Linear Amplification Kit. The array contains 15,208 non-redundant 60-mer probes, each targeting a unique E. fetida transcript. After hybridization and scanning, gene expression data were acquired using AgilentM-bM-^@M-^Ys Feature Extraction Software (v.9.1.3). The 120-array dataset consists of three exposure groups (4 day, 14 day and 28 day) with each group having the following 8 treatments: day 0 control, blank control, solvent control, 8, 16, 32, 64, and 128 mg HMX/g soil. Each treatment had 5 biological replicates (i.e., five individual worms).

Dataset Information

The clinical study of predicting the chemotherapy resistance of colorectal cancer cells based on autophagy theory by gene variation combined with miRNA detection

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets