Development and validation of prognostic markers in sarcomas base on a multi-omics analysis

Abstract

Background: In sarcomas, the DNA copy number and DNA methylation exhibit genomic aberrations. Transcriptome imbalances play a driving role in the heterogeneous progression of sarcomas. However, it is still unclear whether abnormalities of DNA copy numbers are systematically related to epigenetic DNA methylation, thus, a comprehensive analysis of sarcoma occurrence and development from the perspective of epigenetic and genomics is required.

Methods: RNASeq, copy number variation (CNV), methylation data, clinical follow-up information were obtained from The Cancer Genome Atlas (TCGA) and GEO database. The association between methylation and CNV was analyzed to further identify methylation-related genes (MET-Gs) and CNV abnormality-related genes (CNV-Gs). Subsequently DNA copy number, methylation, and gene expression data associated with the MET-Gs and CNV-Gs were integrated to determine molecular subtypes and clinical and molecular characteristics of molecular subtypes. Finally, key biomarkers were determined and validated in independent validation sets.

Results: A total of 5354 CNV-Gs and 4042 MET-Gs were screened and showed a high degree of consistency. Four molecular subtypes (iC1, iC2, iC3, and iC4) with different prognostic significances were identified by multiomics cluster analysis, specifically, iC2 had the worst prognosis and iC4 indicated an immune-enhancing state. Three potential prognostic markers (ENO1, ACVRL1 and APBB1IP) were determined after comparing the molecular characteristics of the four molecular subtypes. The expression of ENO1 gene was significantly correlated with CNV, and was noticeably higher in iC2 subtype with the worst prognosis than any other subtypes. The expressions of ACVRL1 and APBB1IP were negatively correlated with methylation, and were high-expressed in the iC4 subtype with the most favorable prognosis. In addition, the number of silent/nonsilent mutations and neoantigens in iC2 subtype were significantly more than those in iC1/iC3/iC4 subtype, and the same trend was also observed in CNV Gain/Loss.

Conclusion: The current comprehensive analysis of genomic and epigenomic regulation provides new insights into multilayered pathobiology of sarcomas. Four molecular subtypes and three prognostic markers developed in this study improve the current understanding of the molecular mechanisms underlying sarcoma.

Keywords: Bioinformatics; CNV; Methylation; Sarcomas; TCGA.

Fig. 1

a Z-value distribution of correlation between CNVCor and METcor. b CNVs and METCor overlap. c Distribution of CNVCor on chromosomes (top panel) and correlation (bottom panel). d The distribution of METCor on chromosomes. e The type of METCor gene. f The distribution of MET sites

Fig. 2

a NMF clustering results of CNVCor. b NMF clustering results of METCor. c KM survival curve of CNVCor subtype. d KM survival curve of METCor. e The correlation between subtypes of CNVCor cluster, subtypes of METCor cluster and pathological subtypes. f The overlap between subtypes of CNVCor cluster and subtypes of METCor cluster

Fig. 3

a Heatmap of the expression of subtype CNVCor identified by iCluste. b Heatmap of the expression of subtype METCor identified by iCluste. c The PFS KM curve between the subtypes identified by iCluster. d PFS survival curve between iC1 and iC2 subtypes. e PFS survival curve between iC2 and iC3 subtypes. f PFS survival curve between iC2 and iC4 subtypes. g The corresponding relationship between iC subtype and pathological subtype

Fig. 4

a Frequency distributions of Gain and Loss in CNV. b Frequency distributions of Gain and MetHyper. c Frequency distributions of Gain and MetHypo. d Frequency distributions of CNV Loss and MetHyper. e Frequency distributions of CNV Loss and MetHypo. f Frequency distributions of MetHyper and MetHypo

Fig. 5

a The scores of six immune cells in all the samples. b The scores of the six immune cells were in the three subtypes of iCluster. c Immunosignature scores of 5 types

Fig. 6

a Distribution of CNV in the iCluster subtype. b Distribution of methylation in the iCluster subtype. c Heatmap of differentially expressed genes in iCluster subtypes

Fig. 7

a–d Correlation between NO1 gene methylation and expression, expression of NO1 in iC subtype, OS KM curve of samples from high-expression group and low-expression group in TCGA data, and OS KM curve of samples from high-expression group and low-expression group of GSE21050 verification set. e–h Correlation between ACVRL1 gene methylation and expression, expression of ACVRL1 in iC subtype, OS KM curve of samples from high-expression group and low-expression group of GSE21050 data and GSE7118 data. i–l Correlation between APBB1IP gene methylation and expression, expression of APBB1IP in iC subtype, OS KM curve of samples from high-expression group and low-expression group of GSE21050 data and GSE7118 data

Fig. 8

a The top 50 mutated genes between iC subtypes. b The number of mutations of the top 50 genes in the iC subtype. c Distribution of silent, nonsilent and neoantigens on iC subtypes. d Distribution of CNV Gain/Loss and methylated MetHyper/MetHypo on iC subtypes

Fig. 9

a Distribution of 7 histological types in four molecular subtypes. b The expression relationship between AMPD2/TLE2 genes and three potential prognostic markers (ENO1, ACVRL1 and APBB1IP). c Prognostic ROC curves of three potential Prognostic Models (ENO1, ACVRL1 and APBB1IP) in four categories of samples (DL, LMS, MFS and UPS)