Gene expression profiling of surgically treated pancreatic cancer patients using NanoString nCounter.
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE300858 | GEO | 2025/12/31
REPOSITORIES: GEO
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Project description:Gene expression profiling of surgically treated pancreatic cancer patients using NanoString nCounter.
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Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of a total of 8,644 genes in 20 pancreatic cancer cases, which had been consecutively operated.
2024-11-02 | GSE246880 | GEO
Project description:BackgroundDespite genomic simplicity, recent studies have reported at least 3 major atypical teratoid rhabdoid tumor (ATRT) subgroups with distinct molecular and clinical features. Reliable ATRT subgrouping in clinical settings remains challenging due to a lack of suitable biological markers, sample rarity, and the relatively high cost of conventional subgrouping methods. This study aimed to develop a reliable ATRT molecular stratification method to implement in clinical settings.MethodsWe have developed an ATRT subgroup predictor assay using a custom genes panel for the NanoString nCounter System and a flexible machine learning classifier package. Seventy-one ATRT primary tumors with matching gene expression array and NanoString data were used to construct a multi-algorithms ensemble classifier. Additional validation was performed using an independent gene expression array against the independently generated dataset. We also analyzed 11 extra-cranial rhabdoid tumors with our classifier and compared our approach against DNA methylation classification to evaluate the result consistency with existing methods.ResultsWe have demonstrated that our novel ensemble classifier has an overall average of 93.6% accuracy in the validation dataset, and a striking 98.9% accuracy was achieved with the high-prediction score samples. Using our classifier, all analyzed extra-cranial rhabdoid tumors are classified as MYC subgroups. Compared with the DNA methylation classification, the results show high agreement, with 84.5% concordance and up to 95.8% concordance for high-confidence predictions.ConclusionsHere we present a rapid, cost-effective, and accurate ATRT subgrouping assay applicable for clinical use.
| S-EPMC10825849 | biostudies-literature
Project description:We identified differentially expressed microRNAs between MIF-high and MIF-low tumor groups. These microRNAs were then investigated for their potential downstream targets using an integrative strategy, which combines miR-Walk target prediction module and mRNA expression array data to identify key MIF-induced signalling pathways driving tumor progresion and disease aggressiveness. Genes that associated with prognostic significance was then subjected to mechanistic study. miRNA expression profiling of 69 pancreatic ductual adenocarcinoma was performed in two sets. The batch effect between the two sets of data was removed using Partek Genomic Suite
2016-06-01 | E-GEOD-62498 | biostudies-arrayexpress
Project description:Gene expression characteristics associated with different recurrence patterns were successfully identified through expression profiling of a total of 8,644 genes in 16 pancreatic cancer cases, which had been consecutively operated on within a defiend short period of time
2025-11-01 | GSE281762 | GEO
Project description:SummaryThe NanoTube is an open-source pipeline that simplifies the processing, quality control, normalization and analysis of NanoString nCounter gene expression data. It is implemented in an extensible R library, which performs a variety of gene expression analysis techniques and contains additional functions for integration with other R libraries performing advanced NanoString analysis techniques. Additionally, the NanoTube web application is available as a simple tool for researchers without programming expertise.Availability and implementationThe NanoTube R package is available on Bioconductor under the GPL-3 license (https://www.bioconductor.org/packages/NanoTube/). The R-Shiny application can be downloaded at https://github.com/calebclass/Shiny-NanoTube, or a simplified version of this application can be run on all major browsers, at https://research.butler.edu/nanotube/.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9805552 | biostudies-literature
Project description:The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different datasets. We also offer suggestions on the design and analysis of studies involving this technology.
| S-EPMC6614807 | biostudies-literature
Project description:The NanoString nCounter System has been widely used in basic science and translational science research for the past decade. The System consists of two instruments: the PrepStation and the Digital Analyzer, and both have been continuously improved with evolving technologies. A great amount of research data have been generated at multiple research laboratories with the employment of different generations of the System. With the need of integrating multiple datasets, researchers are interested to know whether signals are comparable between different generations of the System. Toward this end, we designed a profiling study to compare performance of two generations of the NanoString nCounter System using a common set of biological samples. Using graphical tools and statistical models, we found that two different generations of NanoString nCounter System produced equivalent signals and signal deviations are in the range of random background noises for the medium-high expression levels.
| S-EPMC6874068 | biostudies-literature
Project description:BackgroundBlood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers.MethodsThe NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker.ResultsGene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB.ConclusionsThe NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.
| S-EPMC9522394 | biostudies-literature
Project description:The medium-throughput mRNA abundance platform NanoString nCounter has gained great popularity in the past decade, due to its high sensitivity and technical reproducibility as well as remarkable applicability to ubiquitous formalin fixed paraffin embedded (FFPE) tissue samples. Based on RCRnorm developed for normalizing NanoString nCounter data and Bayesian LASSO for variable selection, we propose a fully integrated Bayesian method, called RCRdiff, to detect differentially expressed (DE) genes between different groups of tissue samples (eg, normal and cancer). Unlike existing methods that often require normalization performed beforehand, RCRdiff directly handles raw read counts and jointly models the behaviors of different types of internal controls along with DE and non-DE gene patterns. Doing so would avoid efficiency loss caused by ignoring estimation uncertainty from the normalization step in a sequential approach and thus can offer more reliable statistical inference. We also propose clustering-based strategies for DE gene selection, which do not require any external dataset and are free of any arbitrary cutoff. Empirical evidence of the attractiveness of RCRdiff is demonstrated via extensive simulation and data examples.
| S-EPMC8795478 | biostudies-literature