ReviewCancers2017
Current and Prospective Protein Biomarkers of Lung Cancer.
Review in Cancers, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 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
70 citing papers in PubMed.
- Leveraging Commercially Available Protein Assays as Biomarkers for Lung Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Overview of Optical Fiber Biosensors for the Detection of Cancer Biomarkers in the Past Decade.ACS omega · 2026Review
- Soluble Major Histocompatibility Complex I-Related Chain A (sMICA)*008 Levels Associate with Smoking, Presence of Chronic Obstructive Pulmonary Disease, and Prevalence of Lung Cancer.Journal of clinical medicine · 2026Article
- The progress of biomarkers detection to lung cancer.Discover oncology · 2026Review
- Environmental Pollutants and Protein Destabilization in Lung Cancer: Anticancer Drug Strategies for Structural Stability.ACS omega · 2026Article
- A stacked cavity-backed metamaterial antenna for confined hyperthermia of subcutaneous fat in ISM band.Frontiers in medical technology · 2026Article
- Article
- RNA-binding proteins and autophagy in lung cancer: mechanistic insights and therapeutic perspectives.Discover oncology · 2025Review
- Predicting survival in small cell lung cancer patients undergoing various treatments: a machine learning approach.Translational lung cancer research · 2025Article
- Innovative Theranostics Strategies in the Fight Against Lung Cancer.Mini reviews in medicinal chemistry · 2025Review
- Repurposing of sericin combined with dactolisib or vitamin D to combat non-small lung cancer cells through computational and biological investigations.Scientific reports · 2024Article
- A Multi-Omics Study of Epigenetic Changes in Type II Alveolar Cells of A/J Mice Exposed to Environmental Tobacco Smoke.International journal of molecular sciences · 2024Article
- Small-Cell Lung Cancer: Is Liquid Biopsy a New Tool Able to Predict the Efficacy of Immunotherapy?Biomolecules · 2024Review
- PD-L1 on large extracellular vesicles is a predictive biomarker for therapy response in tissue PD-L1-low and -negative patients with non-small cell lung cancer.Journal of extracellular vesicles · 2024Article
- Emerging Multifunctional Carbon-Nanomaterial-Based Biosensors for Cancer Diagnosis.Small science · 2024Article
- Overexpression of SLC35F2 is a potential prognostic biomarker for lung adenocarcinoma.Heliyon · 2024Article
- Advances and Challenges in Nanomaterial-Based Electrochemical Immunosensors for Small Cell Lung Cancer Biomarker Neuron-Specific Enolase.ACS omega · 2024Review
- Liquid biopsy for early detection of lung cancer.Chinese medical journal pulmonary and critical care medicine · 2023Review
- Biomarkers of lung cancer for screening and in never-smokers-a narrative review.Translational lung cancer research · 2023Review
- The Usefulness of the Ratio of Antigen-Autoantibody Immune Complexes to Their Free Antigens in the Diagnosis of Non-Small Cell Lung Cancer.Diagnostics (Basel, Switzerland) · 2023Article
10 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is a malignant lung tumor with various histological variants that arise from different cell types, such as bronchial epithelium, bronchioles, alveoli, or bronchial mucous glands. The clinical course and treatment efficacy of lung cancer depends on the histological variant of the tumor. Therefore, accurate identification of the histological type of cancer and respective protein biomarkers is crucial for adequate therapy. Due to the great diversity in the molecular-biological features of lung cancer histological types, detection is impossible without knowledge of the nature and origin of malignant cells, which release certain protein biomarkers into the bloodstream. To date, different panels of biomarkers are used for screening. Unfortunately, a uniform serum biomarker composition capable of distinguishing lung cancer types is yet to be discovered. As such, histological analyses of tumor biopsies and immunohistochemistry are the most frequently used methods for establishing correct diagnoses. Here, we discuss the recent advances in conventional and prospective aptamer based strategies for biomarker discovery. Aptamers like artificial antibodies can serve as molecular recognition elements for isolation detection and search of novel tumor-associated markers. Here we will describe how these small synthetic single stranded oligonucleotides can be used for lung cancer biomarker discovery and utilized for accurate diagnosis and targeted therapy. Furthermore, we describe the most frequently used in-clinic and novel lung cancer biomarkers, which suggest to have the ability of differentiating between histological types of lung cancer and defining metastasis rate.
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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.