ArticleJNCI cancer spectrum2023
Prediction of tissue-of-origin of early stage cancers using serum miRNomes.
Article in JNCI cancer spectrum, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 31 citations in OpenAlex.
- Serum microRNAs for the assessment of symptomatically refractory gastroesophageal reflux disease despite acid suppression.Esophagus : official journal of the Japan Esophageal Society · 2026Article
- Machine Learning-Based Identification of Candidate Serum miRNA Features for Pan-Cancer and Cancer Type Classification.Life (Basel, Switzerland) · 2026Article
- Comparison of Comprehensive Serum miRNA Sequencing and Apolipoprotein A2 Isoforms for Early Detection of Pancreatic Cancer.Cancers · 2026Article
- MicroRNAs in oncology: a translational perspective in the era of AI.Nature reviews. Clinical oncology · 2026Review
- Enzymatic colorimetric encoding-based digital medicine for pancreatic cancer diagnosis.Nature communications · 2026Article
- miRBiT: a rules-based single-sample serum miRNA classifier for pan-cancer detection with multi-cohort validation.Briefings in bioinformatics · 2026Article
- Glioma identification from microRNA biomarkers using machine learning.Frontiers in systems biology · 2026Article
- Discovering Anticancer Effects of Phytochemicals on MicroRNA in the Context of Data Mining.Nutrients · 2025Article
- Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.RNA biology · 2025Article
- Preanalytical variables and analytes in liquid biopsy approach for brain tumors: A comprehensive review and recommendations from the RANO Group and the Brain Liquid Biopsy Consortium.Neuro-oncology · 2025Review
- Determination of miRNA in tear extracellular vesicles significantly associated with treatment-requiring retinopathy of prematurity: a pilot study.Scientific reports · 2025Article
- Environmental factors in gastric carcinogenesis and preventive intervention strategies.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2025Review
- Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.Frontiers in bioinformatics · 2025Article
- Biologically-informed regional subset analysis with CatBoost for robust tissue-of-origin prediction.PloS one · 2025Article
- Prospects for liquid biopsy using microRNA and extracellular vesicles in breast cancer.Breast cancer (Tokyo, Japan) · 2025Review
- Evaluation of Plasma microRNA-222 as a Biomarker for Gastric Cancer.Journal of clinical medicine · 2024Article
- Biomarkers in Cancer Screening: Promises and Challenges in Cancer Early Detection.Hematology/oncology clinics of North America · 2024Review
- Article
- MicroRNA expression profiling of urine exosomes in children with congenital cytomegalovirus infection.Scientific reports · 2024Article
- An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction.NAR genomics and bioinformatics · 2024Article
Corrections and comments
- Commented on by
Authors and funding
45 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNoninvasive detection of early stage cancers with accurate prediction of tumor tissue-of-origin could improve patient prognosis. Because miRNA profiles differ between organs, circulating miRNomics represent a promising method for early detection of cancers, but this has not been shown conclusively.
methodsA serum miRNA profile (miRNomes)-based classifier was evaluated for its ability to discriminate cancer types using advanced machine learning. The training set comprised 7931 serum samples from patients with 13 types of solid cancers and 5013 noncancer samples. The validation set consisted of 1990 cancer and 1256 noncancer samples. The contribution of each miRNA to the cancer-type classification was evaluated, and those with a high contribution were identified.
resultsCancer type was predicted with an accuracy of 0.88 (95% confidence interval [CI] = 0.87 to 0.90) in all stages and an accuracy of 0.90 (95% CI = 0.88 to 0.91) in resectable stages (stages 0-II). The F1 score for the discrimination of the 13 cancer types was 0.93. Optimal classification performance was achieved with at least 100 miRNAs that contributed the strongest to accurate prediction of cancer type. Assessment of tissue expression patterns of these miRNAs suggested that miRNAs secreted from the tumor environment could be used to establish cancer type-specific serum miRNomes.
conclusionsThis study demonstrates that large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system. Further investigations of the regulating mechanisms of the miRNAs that contributed strongly to accurate prediction of cancer type could pave the way for the clinical use of circulating miRNA diagnostics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.