SCIENCE
Unraveling Cancer's Complex Web: Genes, Pathways, and New Treatment Ideas
Wed Nov 19 2025
Cancer is a tricky puzzle. Scientists have been using a special tool called the Reactome Graph Database to understand it better. This tool helps them see how different genes work together in the body. They looked at 862 genes that cause cancer and 324 genes that cause other diseases but often show up in cancer patients too.
These genes are like players in a big game. Each one has a special role in different parts of the body. Scientists made a kind of map, called a pathway fingerprint, to show where each gene plays its biggest role. This map helped them see that most cancer genes are very important in something called Signal Transduction. That's like a messaging system in the body.
Some of these genes are also found in people with rare diseases. These genes can sometimes cause cancer too. Scientists found that these genes often change a part of the body's messaging system called R. This change happens a lot in cancer patients.
The scientists also compared the maps of the cancer genes and the other disease genes. They found that both types of genes often work in the same messaging system. This means that the other diseases might make cancer worse by messing up this system.
Some of these genes are targeted by cancer drugs. By looking at these targets and the genes that work with them, scientists found new combinations of drugs that might work better together. For example, they found that Desmopressin could help Dasatinib work better.
The scientists also looked at how cancer affects different parts of the body's messaging system. They found that cancer mostly messes up the messaging system, the way genes are turned on and off, and the way cells grow and develop. They also found new targets for drugs that could help fix these problems.
Finally, they made maps of how these genes work together in different cancers. They found that some genes work in similar ways in different cancers. This could help scientists find new treatments that work for many types of cancer.
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questions
How does the infrequent occurrence of OMIM-catalogued deleterious AVs in cancers challenge the notion of random passenger mutations?
Is the frequent mutation of the residue R a coincidence, or is there a hidden agenda involving the amino acid R?
What methodologies could be employed to validate the gene networks generated from the pathway fingerprints, ensuring their accuracy and relevance?
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