Global Pan-cancer serum miRNA classifier across 13 cancer types: Analysis of 46,349 clinical samples
Krishnamoorthy, P.; Parthasarathy, M.; Das, N.; S Raj, A.; Kumar, A.; Gupta, V.; Das, S.; Kumar, H.
Show abstract
ABSTRACTLiquid biopsy offers the minimally-invasive way of early cancer diagnosis. MicroRNAs (miRNAs) are small non-coding RNAs that show promising diagnostic potential due to their stability and their dysregulation upon different physiological conditions. However, existing cancer classifiers often rely on cohort-based comparisons, limiting their clinical utility. Extensive analyses in this study present a pan-cancer miRNA-based single-sample classifier, trained on 16,190 samples, tested across 9 independent datasets, and further validated on 8 distinct disease cohorts. The classifier leverages miRNA expression signatures to classify cancer and non_cancer samples including healthy, other diseases with high sensitivity and specificity, enabling personalized predictions. The classifier identifies cancer by evaluating the relative expression patterns of specific miRNAs, capturing neoplasm-specific dysregulation patterns independent of cohort effects. This study highlights the potential of miRNAs in robust cancer classification, offering a minimally invasive, scalable, and clinically adaptable miRNA serum resource for early cancer detection across diverse populations and malignancies.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Novel cancer subtyping method based on patient-specific gene regulatory network 95%
- Candidate genes associated with neurological manifestations of COVID-19: Meta-analysis using multiple computational approaches 94%
- Discovering Key Transcriptomic Regulators in Pancreatic Ductal Adenocarcinoma using Dirichlet Process Gaussian Mixture Model 94%
Similar papers in this journal
- The repertoire of copy number alteration signatures in human cancer 96%
- SPCS: A Spatial and Pattern Combined Smoothing Method of Spatial Transcriptomic Expression 94%
- Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing 94%
Similar papers in this journal
- Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data 95%
- Systems biomedicine of primary and metastatic colorectal cancer reveals potential therapeutic targets 94%
- BNIP3 upregulation characterizes cancer cell subpopulation with increased fitness and proliferation 94%
Similar papers in this journal
- Predicting cancer origins with a DNA methylation-based deep neural network model 95%
- Exploring the Diagnostic Potential of miRNA Signatures in the Fabry Disease Serum: A Comparative Study of Automated and Manual Sample Isolations 95%
- Identification of cuproptosis and ferroptosis-related subtypes and development of a prognostic signature in colon cancer 94%
Similar papers in this journal
- Analytical performance of a highly sensitive system to detect gene variants using next-generation sequencing for lung cancer companion diagnostics 92%
- Identification of prognostic biomarkers for suppressing tumorigenesis and metastasis of Hepatocellular carcinoma through transcriptome analysis 92%
- Deep learning models for poorly differentiated colorectal adenocarcinoma classification in whole slide images using transfer learning 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.