MicroRNA expression profiling in gastric cancers plasmas
Ontology highlight
ABSTRACT:
ORGANISM(S): synthetic construct Homo sapiens
PROVIDER: GSE86822 | GEO | 2016/09/13
SECONDARY ACCESSION(S): PRJNA342626
REPOSITORIES: GEO
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I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
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Project description:Circulating microRNAs (miRNAs) in serum may serve as promising diagnostic biomarkers for patients with gastric cancer (GC). Using quantitative reverse transcription polymerase chain reaction (qRT-PCR) based Exiqon panel, we identified 58 differentially expressed miRNAs from 3 GC pool samples and 1 normal control (NC) pool in the initial screening phase. Identified miRNAs were further validated in the training (49 GC VS. 47 NCs) and validation phases (154 GC VS. 120 NCs) using qRT-PCR. Consequently, six serum miRNAs (miR-10b-5p, miR-132-3p, miR-185-5p, miR-195-5p, miR-20a-3p and miR-296-5p) were significantly overexpressed in GC compared with NCs. The areas under the receiver operating characteristic (ROC) curve of the six-miRNA panel were 0.764 and 0.702 for the training and validation phases, respectively. In conclusion, we identified a six-miRNA panel in serum for the detection of GC.
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