Low-Penetrance Susceptibility Variants in Colorectal Cancer-Current Outlook in the Field.
Marcin SzumanIwona Krela-KaźmierczakSzymon Tytus HryhorowiczAlicja KryszczyńskaNatalia GrotAndrzej PławskiPublished in: International journal of molecular sciences (2024)
Colorectal cancer (CRC) is one of the most frequent and mortality-causing neoplasia, with various distributions between populations. Strong hereditary predispositions are the causatives of a small percentage of CRC, and most cases have no transparent genetic background. This is a vast arena for exploring cancer low-susceptibility genetic variants. Nonetheless, the research that has been conducted to date has failed to deliver consistent conclusions and often features conflicting messages, causing chaos in this field. Therefore, we decided to organize the existing knowledge on this topic. We screened the PubMed and Google Scholar databases. We drew up markers by gene locus gathered by hallmark: oncogenes, tumor suppressor genes, genes involved in DNA damage repair, genes involved in metabolic pathways, genes involved in methylation, genes that modify the colonic microenvironment, and genes involved in the immune response. Low-penetration genetic variants increasing the risk of cancer are often population-specific, hence the urgent need for large-scale testing. Such endeavors can be successful only when financial decision-makers are united with social educators, medical specialists, genetic consultants, and the scientific community. Countries' policies should prioritize research on this subject regardless of cost because it is the best investment. In this review, we listed potential low-penetrance CRC susceptibility alleles whose role remains to be established.
Keyphrases
- decision making
- genome wide
- healthcare
- dna damage
- copy number
- immune response
- papillary thyroid
- dna methylation
- mental health
- squamous cell
- public health
- genome wide identification
- oxidative stress
- transcription factor
- risk assessment
- dna repair
- high grade
- type diabetes
- machine learning
- risk factors
- deep learning
- monte carlo