Risk stratification of gastrointestinal stromal tumors by Nanostring gene expression profiling

Nowak, Klaudia; Formenti, Kim; Huang, Jing‐Yang; Bigras, Gilbert; Chu, Quincy Siu-Chung; Adam, Benjamin; Izevbaye, Iyare

doi:10.1007/s00432-022-03924-3

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…NanoString technology has been widely applied in studies of human disease where expression profiling for large numbers of genes or samples was conducted. For instance, 231 genes were investigated in 34 patients with gastrointestinal stromal tumors [ 28 ], 50 genes were tested in over 500 patients with breast cancer [ 29 ], and 477 genes were assessed in patients with metastatic gastric cancer [ 30 ]. Recently, the NanoString nCounter System has also been used to study gene regulation in plant–pathogen interactions.…”

Section: Introductionmentioning

confidence: 99%

Application of the NanoString nCounter System as an Alternative Method to Investigate Molecular Mechanisms Involved in Host Plant Responses to Plasmodiophora brassicae

Zhou

Galindo‐González

Hwang

et al. 2022

IJMS

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Clubroot, caused by the soilborne pathogen Plasmodiophora brassicae, is an important disease of canola (Brassica napus) and other crucifers. The recent application of RNA sequencing (RNA-seq) technologies to study P. brassicae–host interactions has generated large amounts of gene expression data, improving knowledge of the molecular mechanisms of pathogenesis and host resistance. Quantitative PCR (qPCR) analysis has been widely applied to examine the expression of a limited number of genes and to validate the results of RNA-seq studies, but may not be ideal for analyzing larger suites of target genes or increased sample numbers. Moreover, the need for intermediate steps such as cDNA synthesis may introduce variability that could affect the accuracy of the data generated by qPCR. Here, we report the validation of gene expression data from a previous RNA-seq study of clubroot using the NanoString nCounter System, which achieves efficient gene expression quantification in a fast and simple manner. We first confirm the robustness of the NanoString system by comparing the results with those generated by qPCR and RNA-seq and then discuss the importance of some candidate genes for resistance or susceptibility to P. brassicae in the host. The results show that the expression of genes measured using NanoString have a high correlation with the values obtained using the other two technologies, with R > 0.90 and p < 0.01, and the same expression patterns for most genes. The three methods (qPCR, RNA-seq, and NanoString) were also compared in terms of laboratory procedures, time, and cost. We propose that the NanoString nCounter System is a robust, sensitive, highly reproducible, and simple technology for gene expression analysis. NanoString could become a common alternative to qPCR to validate RNA-seq data or to create panels of genes for use as markers of resistance/susceptibility when plants are challenged with different P. brassicae pathotypes.

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Section: Introductionmentioning

confidence: 99%

Application of the NanoString nCounter System as an Alternative Method to Investigate Molecular Mechanisms Involved in Host Plant Responses to Plasmodiophora brassicae

Zhou

Galindo‐González

Hwang

et al. 2022

IJMS

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Advancing gastrointestinal stromal tumor management: The role of imagomics features in precision risk assessment

Pan,

Zhou,

Chen

et al. 2024

World J Gastrointest Surg

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BACKGROUND Gastrointestinal stromal tumors (GISTs) vary widely in prognosis, and traditional pathological assessments often lack precision in risk stratification. Advanced imaging techniques, especially magnetic resonance imaging (MRI), offer potential improvements. This study investigates how MRI imagomics can enhance risk assessment and support personalized treatment for GIST patients. AIM To assess the effectiveness of MRI imagomics in improving GIST risk stratification, addressing the limitations of traditional pathological assessments. METHODS Analyzed clinical and MRI data from 132 GIST patients, categorizing them by tumor specifics and dividing into risk groups. Employed dimension reduction for optimal imagomics feature selection from diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), and contrast enhanced T1WI with fat saturation (CE-T1WI) fat suppress (fs) sequences. RESULTS Age, lesion diameter, and mitotic figures significantly correlated with GIST risk, with DWI sequence features like sphericity and regional entropy showing high predictive accuracy. The combined T1WI and CE-T1WI fs model had the best predictive efficacy. In the test group, the DWI sequence model demonstrated an area under the curve (AUC) value of 0.960 with a sensitivity of 80.0% and a specificity of 100.0%. On the other hand, the combined performance of the T1WI and CE-T1WI fs models in the test group was the most robust, exhibiting an AUC value of 0.834, a sensitivity of 70.4%, and a specificity of 85.2%. CONCLUSION MRI imagomics, particularly DWI and combined T1WI/CE-T1WI fs models, significantly enhance GIST risk stratification, supporting precise preoperative patient assessment and personalized treatment plans. The clinical implications are profound, enabling more accurate surgical strategy formulation and optimized treatment selection, thereby improving patient outcomes. Future research should focus on multicenter studies to validate these findings, integrate advanced imaging technologies like PET/MRI, and incorporate genetic factors to achieve a more comprehensive risk assessment.

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Risk stratification of gastrointestinal stromal tumors by Nanostring gene expression profiling

Cited by 2 publications

References 48 publications

Application of the NanoString nCounter System as an Alternative Method to Investigate Molecular Mechanisms Involved in Host Plant Responses to Plasmodiophora brassicae

Application of the NanoString nCounter System as an Alternative Method to Investigate Molecular Mechanisms Involved in Host Plant Responses to Plasmodiophora brassicae

Advancing gastrointestinal stromal tumor management: The role of imagomics features in precision risk assessment

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