Does a Mushroom Used in Traditional Chinese Medicine to Treat Cancer Actually Influence How Cancer Cells Behave?
Huaier is a mushroom found on hardwood tree trunks that has been used in traditional Chinese medicine to treat cancers. Researchers have observed that it causes programmed cell death, or apoptosis, and regeneration of tissues at the cancer site, all without the toxic effects of chemotherapy. However, there has been limited clinical evidence about the molecular mechanisms by which Huaier acts on cancer and bodily tissues.
In a 2021 preprint, BGI and collaborators in China and Japan presented a first of its kind clinical study to learn more the transcriptional landscape in patients who elected to receive Huaier therapy and no conventional anti-cancer chemotherapy. Their study examined the transcriptome of 31 volunteers, 3 of whom were healthy and 28 of whom were living with cancer. Patients suffered from a variety of cancers at the onset of the study: of the lung, pancreas, esophagus, stomach, colorectal tract, prostate, breast, bile ducts, brain, and spinal cord. Volunteers who took Huaier ingested 20 grams each day for 90 days and gave blood samples for RNA sequencing one day before the study, 30 days and 90 days into the course of taking Huaier.
Amidst 7.0 Gb of data, researchers discovered more than 24,000 novel coding transcripts. There was huge spread in the number of genes that were differentially expressed – meaning change in the amount of transcripts for that one gene – with ranges of 21 - 5,671 upregulated genes and 28 - 4,227 downregulated genes. Because cancers involve transcriptional dysregulation, supplements that can induce dynamic changes in transcription are promising candidates for therapies. This collaboration has provided valuable insights into what tools and strategies are needed to understand the complexities and variability of transcription during different stages of cancer and treatment. These insights should inform how researchers and clinicians design future studies that further resolve relationships between transcription, disease, demographics of human patients.
