Genes have been found to be associated with most aggressive kidney cancers

HSE researchers identified genetic signatures
for the most aggressive subtype of clear cell kidney cancer.
After studying data from tumor samples from 456 patients, Grigory Puzanov, a researcher at the International Bioinformatics Laboratory at HSE's School of Computer Science, identified cancer subtypes
associated with favorable or adverse processes of the disease.
The paper was published in
Scientific Reports.

Clear cell kidney cancer (ccRCC) is the most common subtype of kidney cancer, with a 5-year survival rate of 60% to 70%, and the number of new cases has been on
the rise in recent decades.
Despite the abundance of data available on the disease, there is little information about specific human genes that can help predict its clinical course
.

Puzhanov's findings reveal which ccRCC subtypes are more dangerous than others, and which human genes appear to be responsible
for the progression of the disease.
This new information has important implications
for the early detection of aggressive tumors and the design of personalized treatment options for ccRCC patients.

The authors analyzed data
from 456 tumor samples from the Cancer Genome Atlas (TCGA) that were not treated with radiation or additional drug therapy.
The k-means method was used to cluster the samples into subgroups with similar characteristics, and subtypes
with different survival rates were identified.
To perform gene clustering, Puzanov selected 2,000 genes
with highly variable expression patterns in ccRCC.

Gene expression is the process by which genes are read and copied to produce messenger RNA (mRNA), which is then used to synthesize proteins
.

The bioinformatics algorithm ran 100 times, each time classifying
tumor samples based on similarity in 2,000 gene expression patterns.
Three clusters (subtypes)
with different survival rates were identified.
The cluster with the lowest survival rate was associated with metastasis and the worst
response to subsequent treatment.

The study was conducted
in several phases.
In the first phase, the characteristics of each cluster were studied to better understand the genetic factors that may influence the course of
the disease.
The study authors then identified key genes for the high survival cluster and the low survival cluster and constructed an interaction network
that synthesized the proteins encoded by these genes.

Puzanov's analysis determined which genes encode proteins with the most network connections
.
The study found that the clusters with the lowest survival rates were associated with the MFI2, CP, APOB, and ENAM genes, which are known to be involved in the transport of insulin-like growth factors, a protein structurally similar to insulin, and post-translational modification
of proteins.
In addition, specific low-survival subtypes are genes encoding fibrinogen and prothrombin associated with coagulation (FGA, FGG, and F2).

"Some of these key genes may affect the efficacy
of anti-tumor therapies.
For example, increased activity of CP, FGA, and FGG genes was associated with adverse reactions to nivolumab, and high expression of APOB and ENAM predicted a non-response
to sunitinib.
This knowledge can help develop the most appropriate targeted treatment regimen for patients with malignant tumors" – Grigory Puzanov, Research Fellow, International Bioinformatics Laboratory, School of Computer Science, HSE University

According to the researchers, a combination of traditional antitumor drugs and anticoagulants (drugs that help prevent blood clotting) can improve the effectiveness of
cancer treatment.
Therefore, there is evidence that heparin, which is commonly used to treat thromboembolic events in cancer patients, contributes to patient survival and has antimetastatic activity
.