| Title |
Employing proteomics to understand the effects of nutritional intervention in cancer treatment
|
|---|---|
| Published in |
Analytical & Bioanalytical Chemistry, July 2018
|
| DOI | 10.1007/s00216-018-1219-z |
| Pubmed ID | |
| Authors |
Monica M. Schroll, Amanda B. Hummon |
| Abstract |
Lifestyle optimizations are implementable changes that can have an impact on health and disease. Nutrition is a lifestyle optimization that has been shown to be of great importance in cancer initiation, progression, and metastasis. Dozens of clinical trials are currently in progress that focus on the nutritional modifications that cancer patients can make prior to and during medical care that increase the efficacy of treatment. In this review, we discuss various nutritional inventions for cancer patients and the analytical approaches to characterize the downstream molecular effects. We first begin by briefly explaining the many different forms of nutritional intervention currently being used in cancer treatment as well as their motivating biology. The forms of nutrient modulation described in this review include calorie restriction, the different practices of fasting, and carbohydrate restriction. The review then shifts to explain how proteomics is used to determine biomarkers of cancer and how it can be utilized in the future to determine the metabolic phenotype of a tumor, and inform physicians if nutritional intervention should be recommended for a cancer patient. Nutrigenomics aims to understand the relationship of nutrients and gene expression and can be used to understand the downstream molecular effects of nutrition restriction, partially through proteomic analysis. Proteomics is just beginning to be used as cancer diagnostic and predictive tools. However, these approaches have not been used to their full potential to understand nutritional intervention in cancer. Graphical abstract ᅟ. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 44 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 14% |
| Student > Postgraduate | 6 | 14% |
| Student > Master | 6 | 14% |
| Other | 4 | 9% |
| Student > Bachelor | 3 | 7% |
| Other | 8 | 18% |
| Unknown | 11 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 10 | 23% |
| Nursing and Health Professions | 8 | 18% |
| Medicine and Dentistry | 6 | 14% |
| Agricultural and Biological Sciences | 3 | 7% |
| Chemistry | 2 | 5% |
| Other | 5 | 11% |
| Unknown | 10 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 September 2018.
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#20,663,600
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Outputs from Analytical & Bioanalytical Chemistry
#6,602
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Outputs of similar age from Analytical & Bioanalytical Chemistry
#107
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