ReviewHolistic integrative oncology.2026
Advances in plasma metabolomics detection technology and its clinical applications in lung cancer and other malignancies.
Review in Holistic integrative oncology., 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Application of NMR-based metabolomics and machine learning for non-invasive disease screening in dogs.Frontiers in veterinary science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic reprogramming is a fundamental hallmark distinguishing tumor cells from their normal counterparts. This process leads to pronounced alterations in the types and concentrations of metabolites present in the bodily fluids of cancer patients via liquid biopsy approaches. Plasma and serum, owing to their minimally invasive collection, repeatability, and ability to reflect systemic metabolic status, have emerged as optimal sample types for clinical metabolomics. These metabolic changes serve as valuable indicators for inferring disease progression and predicting patient prognosis. Lung cancer, particularly non-small cell lung cancer (NSCLC), with its high global incidence and mortality, represents a critical area where plasma metabolomics can address unmet clinical needs in prognostic prediction and therapeutic stratification. This review focuses primarily on lung cancer. However, the scarcity of studies investigating the prognostic value of metabolite alterations in lung cancer promoted the inclusion of research from other malignancies as well. This review first summarizes the current liquid biopsy metabolomics detection technologies and associated biological materials, followed by an overview of tumor-related metabolic pathway alterations. It then discusses the clinical applications of these principles in prognostic prediction and therapeutic evaluation. The aim is to provide a comprehensive overview and inspire future research directions in this field.
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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.