ReviewJournal of Cancer2025
Technical and Biological Biases in Bulk Transcriptomic Data Mining for Cancer Research.
Review in Journal of Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 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
121 citing papers in PubMed.
- Multi-omics mapping of TIMP1-associated stromal-myeloid remodeling in autoimmune gastritis and gastric cancer.Hereditas · 2026Article
- Shared pathogenic genes and therapeutic targets in periodontitis and systemic juvenile idiopathic arthritis: An integrative bioinformatics and machine learning study.PLoS computational biology · 2026Article
- RNA Sequencing Technologies in Acute Lymphoblastic Leukemia: A Comparative Technical Review.Current issues in molecular biology · 2026Review
- Identification of key genes associated with T cell exhaustion in HIV infection based on transcriptomic data.Virology journal · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Steroidogenesis is associated with bladder cancer progression via reprogramming the metabolic landscape of the tumor immune microenvironment.Discover oncology · 2026Article
- The alternative splicing events and the function of TLN1 in clear cell renal cell carcinoma.Discover oncology · 2026Article
- Metal ion-amplified phototherapy for tumors: Mechanisms, nanomaterial design, and synergistic strategies.Materials today. Bio · 2026Review
- Comprehensive Analysis of Genomic and Phenomic Data Reveals Context-Dependent Function of A20 (Cancers · 2026Article
- Identification of serum 5'tRF-Val as a promising non-invasive biomarker for early detection of esophageal cancer.Scientific reports · 2026Article
- Bioinformatics analysis and preliminary clinical validation of ITGA9 and NPTX2 as prognosis-related genes in acute myeloid leukemia.Discover oncology · 2026Article
- LncRNA HMGA2-AS1 promotes postoperative recurrence in non-muscle-invasive bladder cancer by sponging miR-367-3p to activate YAP1.World journal of surgical oncology · 2026Article
- ETV4 promotes colorectal cancer progression through SLC7A11-mediated ferroptosis inhibition.Biology direct · 2026Article
- Nomogram for predicting cancer-specific mortality risk in endometrial cancer after postoperative radiotherapy.Translational cancer research · 2026Article
- VCAN promotes the progression and recurrence of esophageal squamous cell carcinoma by remodeling the tumor microenvironment.Scientific reports · 2026Article
- Comprehensive Bioinformatic Analysis of TONSL Expression in Pan-Cancer.Cancer reports (Hoboken, N.J.) · 2026Article
- Artificial Intelligence and Prostate-Breast Cancer Biology: Recent Advances in Treatments and Perspectives - A Comprehensive Review.Health science reports · 2026Article
- Review
- RNA sequence analysis of differentially expressed genes in left atrial appendage thrombus.Journal of thrombosis and thrombolysis · 2026Article
- IRF1 suppresses gastric tumorigenesis via dual PI3K/AKT-ERK pathway modulation and functional antagonism of oncogenic MX2.Cellular oncology (Dordrecht, Netherlands) · 2026Article
61 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer research has been significantly advanced by the integration of transcriptomic data through high-throughput sequencing technologies like RNA sequencing (RNA-seq). This paper reviews the transformative impact of transcriptomics on understanding cancer biology, focusing on the use of extensive datasets such as The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). While transcriptomic data provides crucial insights into gene expression patterns and disease mechanisms, the analysis is fraught with technical and biological biases. Technical biases include issues related to microarray, RNA-seq, and nanopore sequencing methods, while biological biases arise from factors like tumor heterogeneity and sample purity. Additionally, misinterpretations often occur when correlational data is erroneously assumed to imply causality or when bulk data is misattributed to specific cell types. This review emphasizes the need for researchers to understand and mitigate these biases to ensure accurate data interpretation and reliable clinical outcomes. By addressing these challenges, the paper aims to enhance the robustness of cancer research and improve the application of transcriptomic data in developing effective therapies and diagnostic tools.
Identifiers
What Socratic 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 Socratic graph.