ReviewComputational and structural biotechnology journal2021
Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 188 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
188 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prognostic factors and outcomes in pediatric acute myeloid leukemia: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2025Pooled it
- Hashimoto's thyroiditis reduces central lymph node metastasis risk in papillary thyroid microcarcinoma: an integrated meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis.Cell communication and signaling : CCS · 2023Pooled it
- Influence of polyfunctional TbetFrontiers in immunology · 2025Trial
- Review
- Evaluating statistical models for overdispersed multiomics data: a multiplex immunofluorescence case study.American journal of epidemiology · 2026Article
- Upper Tract Urothelial Carcinoma: Molecular Pathogenesis and Current Treatment Strategies-A Narrative Review.Cancers · 2026Review
- Oncogenic cell fate decision in breast epithelial cells I: growth factors and mechanisms.Research square · 2026Article
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Artificial intelligence and multi-omics integration in liquid biopsy for genitourinary cancers: a systematic scoping review.International urology and nephrology · 2026Article
- Construction of Rheumatoid Arthritis-Associated Interstitial Lung Disease diagnostic model and identification of biomarkers based on a multi-omics integration strategy of machine learning.Clinics (Sao Paulo, Brazil) · 2026Article
- MOGANet: A Multi-omics Graph Attention Network for Cancer Diagnosis and Biomarker Identification.Interdisciplinary sciences, computational life sciences · 2026Article
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- Review
- Low GBX2 expression is associated with histological type B thymoma: a multi-omics analysis.Discover oncology · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Integrative transcriptomic and structural modeling reveal CASP1, TLR3, PYCARD, and CD274 as immune-modulatory drivers in breast cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Harnessing biomarkers to guide immunotherapy in esophageal cancer: toward precision oncology.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Bridging expression profiles and clinical outcomes in HCC target selection.Hepatology international · 2026Article
- Robust graph structure learning to improve multi-omics cancer subtype classification.BMC bioinformatics · 2026Article
128 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.
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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.