Identification of Potential Driver Genes Based on Multi-Genomic Data in Cervical Cancer - Xu, Yuexun; Luo, Hui; Hu, Qunchao; Zhu, Haiyan (Frontiers in Genetics, 2021)
Background: Cervical cancer became the third most common cancer among women and genome characterization of cervical cancer patients has revealed the extensive complexity of molecular alterations. However, identifying driver mutation and depicting molecular classification in cervical cancer remains a challenge. Methods: We performed an integrative, multi-platform analysis of the TCGA cervical cancer based on 284 clinical cases and identified the driver genes of cervical cancer. Results: Multi-platform integration showed that cervical cancer exhibited a wide range of mutation. The top 10 mutated genes were TTN ,PIK3CA, MUC4, KMT2C, MUC16, KMT2D, SYNE1, FLG, DST, and EP300 with a mutation rate from 12% to 33%. Applying GISTIC to detect copy number variation(CNV), the most frequent chromosome arm-level CNVs included losses in 4p, 11p, 11q, and gains in 20q, 3q, 1q. Then, we performed unsupervised consensus clustering of tumor CNV profiles and methylation profile, and detected four statistically significant expression subtypes. Finally, by combining the multidimensional datasets, we identified eleven potential driver genes, including GPR107, CHRNA5, ZBTB20, Rb1, NCAPH2, SCA1, SLC25A5, RBPMS, DDX3X, and H2BFM. Conclusions: This comprehensive analysis described the genetic characteristic of cervical cancer and identified novel driver genes in cervical cancer. These results provide insight into developing precision treatment in cervical cancer.