ArticleOncotarget2017
Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Is There a Role for Machine Learning in Liquid Biopsy for Brain Tumors? A Systematic Review.International journal of molecular sciences · 2023Pooled it
- TriCLFF: a multi-modal feature fusion framework using contrastive learning for spatial domain identification.Briefings in bioinformatics · 2025Article
- Deconvolution of the tumor-educated platelet transcriptome reveals activated platelet and inflammatory cell transcript signatures.JCI insight · 2024Article
- Assessing the Influence of Climate Change and Environmental Factors on the Top Tick-Borne Diseases in the United States: A Systematic Review.Microorganisms · 2023Review
- Prognostic prediction of lung adenocarcinoma by integrative analysis of RHOH expression and methylation.The clinical respiratory journal · 2023Article
- Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization.BMC bioinformatics · 2023Article
- EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer.Scientific reports · 2022Article
- Comparative whole transcriptome analysis of gene expression in three canine soft tissue sarcoma types.PloS one · 2022Article
- A diagnostic classifier for gene expression-based identification of early Lyme disease.Communications medicine · 2022Article
- Learning sparse log-ratios for high-throughput sequencing data.Bioinformatics (Oxford, England) · 2021Article
- Liquid biopsy approach to pancreatic cancer.World journal of gastrointestinal oncology · 2021Review
- Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma.Scientific reports · 2021Article
- Bimodal expression ofEJHaem · 2021Article
- Morphologic and Molecular Landscape of Pancreatic Cancer Variants as the Basis of New Therapeutic Strategies for Precision Oncology.International journal of molecular sciences · 2020Review
- Identification and Analysis of Dysfunctional Genes and Pathways in CD8Frontiers in genetics · 2020Article
- The Methylation Pattern for Knee and Hip Osteoarthritis.Frontiers in cell and developmental biology · 2020Article
- The Key Genes for Perineural Invasion in Pancreatic Ductal Adenocarcinoma Identified With Monte-Carlo Feature Selection Method.Frontiers in genetics · 2020Article
- A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
- A phenotypic and genomics approach in a multi-ethnic cohort to subtype systemic lupus erythematosus.Nature communications · 2019Article
- Clinical metagenomics.Nature reviews. Genetics · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.
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.