ArticleHeliyon2022
Multi-omics analysis to screen potential therapeutic biomarkers for anti-cancer compounds.
Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 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
4 citing papers in PubMed.
- Multi-omics Quality Assessment in Personalized Medicine Through European Infrastructure for Translational Medicine (EATRIS): An Overview.Phenomics (Cham, Switzerland) · 2025Review
- Solute carrier family 6 member 3 promotes the development of clear cell renal cell carcinoma by enhancing glycolysis and inhibiting ferroptosis.CytoJournal · 2025Article
- Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers.ACS pharmacology & translational science · 2024Review
- Anti-Tumor Secondary Metabolites Originating from Fungi in the South China Sea's Mangrove Ecosystem.Bioengineering (Basel, Switzerland) · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discover potential biomarkers of the response for anti-cancer therapies, including traditional Chinese medicine (TCM), is a critical but much different task in the field of cancer research. Based on accumulated data and sophisticated methods, multi-omics analysis provides a feasible strategy for the discovery of potential therapeutic biomarkers. Here, we screened the potential therapeutic biomarkers for anti-cancer compounds in TCM through multi-omics data analysis. Firstly, compounds in TCM were collected from the public databases. Then, the molecules that those compounds can intervene on cell lines were carefully filtered out from existing drug bioactivity datasets. Finally, multi-omics analysis including gene mutation analysis, differential expression gene analysis, copy number variation analysis and clinical survival analysis for pan-cancer were conducted to screen potential therapeutic biomarkers for compounds in TCM. 13 molecules of compounds in TCM namely ERBB2, MYC, FLT4, TEK, GLI1, TOP2A, PDE10A, SLC6A3, GPR55, TERT, EGFR, KCNA3 and HDAC4 are differentially expressed, high frequently mutated, obtain high copy number variation rate and also significant in survival, are considered as the potential therapeutic biomarkers.
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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.