Introduction:
Cachexia is a major clinical complication that affects up to 80% of patients with advanced cancer, and by some estimates is responsible for 20% of cancer deaths [1]. Despite its role in cancer mortality, no approved therapies are yet available, and the mechanisms underlying cachexia remain to be fully elucidated.
Methods:
Gene expression profiling provides a quantitative means to systematically assess biological differences between tissue from patients who develop cachexia and from those who do not. Here, we report cachexia-related findings from computational analysis of publicly accessible gene expression datasets from various biological contexts, including a dataset with adipose tissue from cancer patients with and without cachectic weight loss (E-GEOD-51931) [2].
Results:
Genes and pathways identified by these analyses include currently known cachexia mechanisms, and also emerging mechanisms that have not historically been emphasized in cachexia research. Some of these emerging mechanisms may help to more fully explain the consequences of the well-known association between inflammation and cachexia and support the previously described importance of Pax7 in cachexia [3].
Conclusions:
Transcriptomics is a powerful approach for dissecting the molecular underpinnings of cancer cachexia and prioritizing areas for drug development. Our findings suggest new avenues for experimental follow-up and highlight new possibilities for the treatment of cachexia in cancer patients.
