The Implication of X-Linked Genetic Polymorphisms in Susceptibility and Sexual Dimorphism of Cancer.

The X-chromosome is implicated in cancer development through various mechanisms, including X-inactivation defects, loss of heterozygosity, and germline and somatic alterations of X-linked genes. Sex is a key factor which influences cancer susceptibility as many cancer types show sexual dimorphism in their incidence. The aim of this review was to summarize the germline genetic polymorphisms lying on the X-chromosome that have been associated with cancer susceptibility and to evaluate their possible implication in cancer-related sexual dimorphism. PubMed and Web of Science were searched using the terms "X-chromosome", "polymorphism" and "cancer". The literature review revealed 39 articles reporting 33 genetic variants in 22 X-linked genes as being associated with cancer risk. Most of these genes interact with each other in a direct or indirect way, as GeneMANIA software revealed, demonstrating the complication of the mechanisms through which they are involved in tumorigenesis. Polymorphisms in eight genes [androgen receptor (AR), fibroblast growth factor 13 (FGF13), forkhead box P3 (FOXP3), L1 cell adhesion molecule (L1CAM), nudix hydrolase 11 (NUDT11), Shroom family member 2 (SHROOM2), transcription elongation factor A-like 7 (TCEAL7) and TIMP metallopeptidase inhibitor 1 (TIMP1)] are reported to have a sex-specific association with cancer susceptibility, which might explain the sexual dimorphism of certain cancer types. All of the above eight mentioned genes, with the exception of L1CAM, exhibit differences in their expression pattern between breast tumor (sex-specific)/thyroid tumor (sex-influenced) vs. normal tissues according to our analysis using GENT2 software. Additionally, differences in breast or thyroid tumor compared with normal tissues were also observed in five genes analyzed with GENT2 software that were previously related to sex-influenced cancer according to literature. Finally, the present review points out the need for the development of appropriate free and user-friendly statistical software in order to reduce bias/errors in statistical analyses and overcome researchers' reluctance to include X-chromosome variants in their genetic-association studies.