↓ Skip to main content

Mitochondria, cholesterol and cancer cell metabolism

Overview of attention for article published in Clinical and Translational Medicine, July 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users

Citations

dimensions_citation
123 Dimensions

Readers on

mendeley
215 Mendeley
Title
Mitochondria, cholesterol and cancer cell metabolism
Published in
Clinical and Translational Medicine, July 2016
DOI 10.1186/s40169-016-0106-5
Pubmed ID
Authors

Vicent Ribas, Carmen García-Ruiz, José C. Fernández-Checa

Abstract

Given the role of mitochondria in oxygen consumption, metabolism and cell death regulation, alterations in mitochondrial function or dysregulation of cell death pathways contribute to the genesis and progression of cancer. Cancer cells exhibit an array of metabolic transformations induced by mutations leading to gain-of-function of oncogenes and loss-of-function of tumor suppressor genes that include increased glucose consumption, reduced mitochondrial respiration, increased reactive oxygen species generation and cell death resistance, all of which ensure cancer progression. Cholesterol metabolism is disturbed in cancer cells and supports uncontrolled cell growth. In particular, the accumulation of cholesterol in mitochondria emerges as a molecular component that orchestrates some of these metabolic alterations in cancer cells by impairing mitochondrial function. As a consequence, mitochondrial cholesterol loading in cancer cells may contribute, in part, to the Warburg effect stimulating aerobic glycolysis to meet the energetic demand of proliferating cells, while protecting cancer cells against mitochondrial apoptosis due to changes in mitochondrial membrane dynamics. Further understanding the complexity in the metabolic alterations of cancer cells, mediated largely through alterations in mitochondrial function, may pave the way to identify more efficient strategies for cancer treatment involving the use of small molecules targeting mitochondria, cholesterol homeostasis/trafficking and specific metabolic pathways.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 215 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 <1%
Chile 1 <1%
Brazil 1 <1%
Unknown 212 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 48 22%
Student > Ph. D. Student 44 20%
Student > Bachelor 21 10%
Student > Master 19 9%
Student > Doctoral Student 11 5%
Other 25 12%
Unknown 47 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 84 39%
Agricultural and Biological Sciences 27 13%
Medicine and Dentistry 25 12%
Immunology and Microbiology 7 3%
Chemistry 4 2%
Other 15 7%
Unknown 53 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 03 October 2016.
All research outputs
#14,600,874
of 25,374,917 outputs
Outputs from Clinical and Translational Medicine
#377
of 1,060 outputs
Outputs of similar age
#204,699
of 379,946 outputs
Outputs of similar age from Clinical and Translational Medicine
#9
of 16 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,060 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 379,946 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.