New single nucleotide polymorphisms (SNPs), that have never before been implicated in cancer, were identified in a recent bioinformatics study, offering opportunities for lung cancer treatment.
The study, published in Genomics, is the first of its kind. Online gene data associated with lung adenocarcinomas and lung squamous cells was mined from over 2,000 samples and functional impacts of the genes were identified.
The results provided a list of deleterious non-synonymous somatic mutations that can be considered as a promising target for future drug design and therapy for patients with lung adenocarcinomas and squamous cell carcinomas.
Researchers from Mohanlal Sukhadia University Udaipur stated, “There is an urgent need for analysing increasing online genomic data so as to provide the researchers and medical practitioners meaningful information that can assist them in differentiating different lung cancer types.”
Non-synonymous single nucleotide polymorphisms
Lung cancer is the second most common cancer and has become one of the most common and leading causes of mortality caused due to diseases around the world.
However, patients can become resistant to standard therapies such as platinum-based chemotherapy, so alternative effective approaches are needed. Medical practitioners have started adopting alternate therapies in which therapeutic targets are patient’s molecular oncogenic drivers.
Genetic variations caused by non-synonymous single nucleotide polymorphisms (nsSNPs) result in the altered amino acid at the mutated site that, in turn, may have many structural and functional consequences in the mutated protein. Understanding how non-synonymous SNPs regulate growth and survival pathways could play a critical role in understanding the pathobiology of cancer.
The authors added that an increased understanding could also “Unveil many other medical and pharmacological relevant facets of cancer such as early/poor prognosis, progression to metastasis, overall survival rate, sensitivity to drugs and selection of appropriate treatment therapy.”
Leveraging online data
First, the team selected candidate genes and retrieved the related missense nsSNPs mutation data from online databases. The study utilised patients’ online mutation data and 2,059 NSCLC samples were assessed. They extracted a set of high-confidence nsSNPs based on sequence and structural homology-based prediction.
The researchers then scored the conservation of amino acid at SNP sites. This allowed them to identify molecular mechanisms for possible structural and functional effects caused by nsSNPs.
Novel non-synonymous SNPs identified
Using this integrated bioinformatics approach, the oncogenic potential of 661 nsSNPs in 16 genes was established. A set of 29 nsSNPs conserved high confidence mutations in 10 of these 16 relevant genes. Out of these 29, 4 nsSNPs were suggested to be novel rare genetic markers for NSCLC.
In addition, 6 missense nsNSPs were identified that have never before been implicated in cancer.
The authors said, “These represent novel targets for drug resistance studies as well for early cancer patient’s genotyping/screening.”
Written by Poppy Jayne Morgan, Front Line Genomics
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