ReviewNature clinical practice. Gastroenterology & hepatology2009
Proteomic and metabolic prediction of response to therapy in gastrointestinal cancers.
Review in Nature clinical practice. Gastroenterology & hepatology, 2009. 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, 19 citations in OpenAlex.
- Relevance of Circulating Tumor Cells as Predictive Markers for Cancer Incidence and Relapse.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Neoadjuvant treatment for advanced esophageal cancer: response assessment before surgery and how to predict response to chemoradiation before starting treatment.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2015Review
- Proteomic and metabolic prediction of response to therapy in gastric cancer.World journal of gastroenterology · 2014Review
- The proteomics of colorectal cancer: identification of a protein signature associated with prognosis.PloS one · 2011Article
- Heat shock protein: hard worker or bad offender for gastric diseases.International journal of proteomics · 2010Article
- Mining novel biomarkers for prognosis of gastric cancer with serum proteomics.Journal of experimental & clinical cancer research : CR · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite substantial improvements in the diagnosis and treatment of many gastrointestinal cancers, particularly colorectal cancer, numerous patients are only diagnosed in advanced stages of disease, which can preclude curative treatment. Screening and early diagnosis of high-risk individuals might be the most promising approach to improve prognosis; however, molecular biomarkers for early diagnosis of most gastrointestinal cancers are not yet available. The prognosis of patients with advanced gastrointestinal cancers has improved through the development of multimodal treatments and the introduction of targeted therapies. Nonetheless, not all patients benefit equally from these treatment approaches, and toxicity can be substantial. The ability to predict whether a patient will respond to therapy early in their treatment for gastrointestinal cancer may be of particular value to stratify and individualize patient treatment strategies. Despite improvement in the understanding of cancer pathogenesis and progression at the molecular level, the molecular changes that underlie treatment response and/or drug resistance are still largely unknown. PET is the first technique to show promise in prediction of response to therapy, and has resulted in promising advancements, particularly in esophageal and gastric cancers. Tissue-based and blood-based molecular biomarkers are still subject to validation. Prediction of response to treatment could ultimately lead to an overall improvement in prognosis.
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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.