ReviewInternational journal of molecular sciences2012
Phosphoproteomics and lung cancer research.
Review in International journal of molecular sciences, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 12 citations in OpenAlex.
- Phosphoproteomics of Retinoblastoma: A Pilot Study Identifies Aberrant Kinases.Molecules (Basel, Switzerland) · 2018Article
- Study on Attenuating Angiogenesis and Epithelial-Mesenchymal Transition (EMT) of Non-Small Cell Lung Carcinoma (NSCLC) by Regulating MAGEC2.Technology in cancer research & treatment · 2018Article
- Study of phosphorylation events for cancer diagnoses and treatment.Clinical and translational medicine · 2015Article
- Investigating co-evolution of functionally associated phosphosites in human.Molecular genetics and genomics : MGG · 2014Article
- Integrating omics technologies to study pulmonary physiology and pathology at the systems level.Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology · 2014Review
- Systems biology approaches to identify developmental bases for lung diseases.Pediatric research · 2013Review
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Massive evidence suggests that genetic abnormalities contribute to the development of lung cancer. These molecular abnormalities may serve as diagnostic, prognostic and predictive biomarkers for this deadly disease. It is imperative to search these biomarkers in different tumorigenesis pathways so as to provide the most appropriate therapy for each individual patient with lung malignancy. Phosphoproteomics is a promising technology for the identification of biomarkers and novel therapeutic targets for cancer. Thousands of proteins interact via physical and chemical association. Moreover, some proteins can covalently modify other proteins post-translationally. These post-translational modifications ultimately give rise to the emergent functions of cells in sequence, space and time. Phosphoproteomics clinical researches imply the comprehensive analysis of the proteins that are expressed in cells or tissues and can be employed at different stages. In addition, understanding the functions of phosphorylated proteins requires the study of proteomes as linked systems rather than collections of individual protein molecules. In fact, proteomics approaches coupled with affinity chromatography strategies followed by mass spectrometry have been used to elucidate relevant biological questions. This article will discuss the relevant clues of post-translational modifications, phosphorylated proteins, and useful proteomics approaches to identify molecular cancer signatures. The recent progress in phosphoproteomics research in lung cancer will be also discussed.
Indexed as
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What OpenQuestion 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 OpenQuestion graph.