Construction and validation of a histone acetylation-related lncRNA prognosis signature for ovarian cancer - Hu, Xiao-Qian; Zhang, Xiao-Chong; Li, Shao-Teng; Hua, Tian (Frontiers in Genetics, 2022)
Ovarian cancer (OC) leads to the most deaths across the gynecological malignancies. The various epigenetic regulatory mechanisms of histone acetylation on cancer have attracted increasing attentions from scientists. Long non-coding RNA (lncRNA) also plays an important role in multiple biology processes linked to OC. This study aimed to identify the histone acetylation-related lncRNAs (HARlncRNAs) with respect to the prognosis in OC. We obtained the transcriptome data from Genotype-Tissue Expression Project (GTEx) and the Cancer Genome Atlas (TCGA), HARlncRNAs were firstly identified by co-expression and differential expression analyses, then univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to construct the HARlncRNAs risk signature. Kaplan-Meier analysis, time-dependent receiver operating characteristics (ROC), univariate Cox regression, multivariate Cox regression, nomogram, and calibration were made to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in risk groups were conducted to explore the tightly correlated pathways with risk group. A risk signature with 14 HARlncRNAs in OC was finally established and furtherly validated in International Cancer Genome Consortium (ICGC) cohort, the 1-, 3-, and 5-year ROC value, nomogram, and calibration results confirmed the good prediction power of this model. The patients were grouped into high and low-risk subgroups according to the risk score by median value. The low-risk group patients exhibited the higher homologous recombination deficiency (HRD) score, LOH_frac_altered and mutLoad_nonsilent. Furthermore, consensus clustering analysis was employed to divide OC patients into three clusters based on expression of the 14 HARlncRNAs, presented different survival probability. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (tSNE) were also analyzed to evaluate the three clusters. In conclusion, the risk signature composed of 14 HARlncRNAs might function as biomarkers and prognostic indicators with respect to a predicting for the reponse to the anti-cancer drugs in OC.