ReviewDiscover oncology2026
The progress of biomarkers detection to lung cancer.
Review in Discover 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.
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
backgroundLung cancer is a leading cause of cancer mortality, largely due to late-stage diagnosis. Biomarker detection is critical for early screening, molecular subtyping, and personalized therapy. This review summarizes the categories of lung cancer biomarkers, evaluates recent advances in detection technologies, and assesses their clinical potential and associated challenges.
methodsBiomarkers were systematically categorized into three categories: molecular (DNA, RNA, proteins), epigenetic (e.g., DNA methylation), and liquid biopsy-based (e.g., ctDNA, CTCs, exosomes). Detection platforms were analyzed, including gene-based techniques (PCR, NGS, FISH), protein-based methods (IHC, ELISA, MS), liquid biopsy workflows, and emerging biosensors. The principles, applications, and limitations of each technology were critically examined.
resultsBiomarkers and detection technologies serve complementary roles. Tissue-based assays (e.g., IHC, PCR) remain foundational for molecular profiling. Liquid biopsies (e.g., NGS, dPCR) enable non-invasive monitoring of therapy and resistance analysis. Novel biosensors provide ultra-high sensitivity (fg/mL to aM level) for early detection. Challenges include low abundance of early-stage biomarkers, lack of standardization, tumor heterogeneity, and clinical translation hurdles.
conclusionsThe field is moving towards multi-omics integration, ultra-sensitive detection, and standardization. Combining multiple biomarkers, utilizing complementary technologies, and facilitating the clinical adoption of innovative platforms are essential for enhancing early diagnosis and enabling precision oncology in lung cancer.
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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.