Genome-wide screening of abberant methylated drivers combined with relative risk loci in bladder cancer

Identification and Validation of Three-Gene Signature in Lung Squamous Cell Carcinoma by Integrated Transcriptome and Methylation Analysis

Since DNA methylation (DNAm) is associated with the carcinogenesis of various cancers, this study aimed to explore potential DNAm prognostic signatures of lung squamous cell carcinoma (LUSC). First, transcriptomic and methylation profiles of LUSC were obtained from The Cancer Genome Atlas database (TCGA). DNAm-related genes were screened by integrating DNAm and transcriptome profiles via MethylMix package. Subsequently, a prognostic signature was conducted with the least absolute shrinkage and selector operation (LASSO) Cox analysis. This signature combined with the clinicopathological parameters was then utilized to construct a prognostic nomogram via the rms package. A signature based on three DNAm-related genes claudin 1 (CLDN1), ATP-binding cassette subfamily C member 5 (ABCC5), and cystatin A (CSTA) that were hypomethylated and upregulated in LUSC was constructed. Univariate and multivariate Cox regression analysis suggested that this signature, combined with age and TNM.N stage, was significantly correlated with survival rate. Time-dependent receiver operating characteristics and calibration curves suggested the nomogram constructed with age and TNM.N stage variables could accurately evaluate the 3- and 5-year outcome of LUSC. Finally, the average mRNA and protein expression levels of CLDN1, ABCC5, and CSTA in LUSC were verified to be significantly higher than those in paracancerous tissues. Moreover, silencing CLDN1, ABCC5, and CSTA expressions could significantly reduce the carcinogenesis of the A549 cell line. The DNAm-driven prognostic signature consists of CLDN1, ABCC5, and CSTA incorporated with age and TNM. N stage could facilitate the prediction outcome of LUSC.

Bioinformatics analysis to screen DNA methylation-driven genes for prognosis of patients with bladder cancer

Background

Bladder cancer (BLCA) is the most prevalent tumor affecting the urinary system, and has contributed to a rise in morbidity and mortality rates. Herein, we sought to identify the methylation-driven genes (MDGs)of BLCA in an effort to develop prognostic biomarkers suitable for the individualized assessment of patients with this particular cancer.

Methods

The Cancer Genome Atlas (TCGA) dataset was distributed into training set (n=272) and testing set(n=117). The ConsensusClusterPluspackage was used to identify BLCA subtypes. The ChAMP package was used to analyze differential methylation probe (DMP) and differential methylation region (DMR). The differentially expressed genes (DEGs) were detected using DESeq2. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to identify the pathways enriched of DEGs. Correlation analysis between 5’-C-phosphate-G-3’s (CpGs) and DEGs was employed to identify the MDGs. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to build the protein-protein interaction (PPI) network of MDGs. Screening for BLCA prognosis-related MDGs and clinical features was conducted via the Cox regression model. A prognosis-related nomogram was developed and validated for prediction of the BLCA patients’ survival.

Results

We identified 2 BLCA clusters. Differential methylations at CpGs sites (dm-CpGs) were observed between cluster2 and cluster1, with 14,189 of them hypermethylated and 878 hypomethylated, predominantly in the CpG islands. In addition, a total 4,234 DEGs were identified between cluster2 and cluster1. The KEGG pathway and GO term enrichment analyses found that some DEGs were significantly enriched in multiple cancer-related pathways. A total of 33 MDGs were detected from correlation analysis between CpGs and DEGs. We selected BLCA-specific prognostic DMGs signatures for risk model development. The nomogram comprised a risk model to predict survival for BLCA patients. The efficiency of the prognostic prediction model was validated in the training and testing set.

Conclusions

This study discovered differential methylation patterns and MDGs in BLCA patients, which provided a bioinformatics basis for guiding BLCA early diagnosis and prognosis analyses.

Genome-wide screening of abberant methylated drivers combined with relative risk loci in bladder cancer

Background

To explore important methylation‐driven genes (MDGs) and risk loci to construct risk model for prognosis of bladder cancer (BCa).

Methods

We utilized TCGA‐Assembler package to download 450K methylation data and corresponding transcriptome profiles. MethylMix package was used for identifying methylation‐driven genes and functional analysis was mainly performed based on ConsensusPathDB database. Then, Cox regression method was utilized to find prognostic MDGs, and we selected 17 hub genes via stepwise regression and multivariate Cox models. Kruskal‐Wallis test was implemented for comparisons between risk with other clinical variables. Moreover, we constructed the risk model and validated it in http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507. Gene set enrichment analysis was performed using the levels of risk score as the phenotype. Additionally, we further screened out the relative methylation sites associated with the 17 hub genes. Cox regression and Survival analysis were conducted to find the specifically prognostic sites.

Results

Two hundred and twenty‐eight MDGs were chosen by ConsensusPathDB database. Results revealed that most conspicuous pathways were transcriptional mis‐regulation pathways in cancer and EMT. After Cox regression analysis, 17 hub epigenetic MDGs were identified. We calculated the risk score and found satisfactory predictive efficiency by ROC curve (AUC = 0.762). In the validation group from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507, 17 hub genes remained higher predictive value with AUC = 0.723 and patients in high‐risk group. Meanwhile, Kruskal‐Wallis test revealed that higher risk score correlated with a higher level of TNM stage, tumor grade, and advanced pathological stages. Then, identified 38 risk methylated loci that highly associated with prognosis. Last, gene set enrichment analysis revealed that high‐risk level of MDGs may correlate with several important pathways, including MAPK signaling pathway and so on.

Conclusion

Our study indicated several hub‐MDGs, calculated novel risk score and explored the prognostic value in BCa, which provided a promising approach to BCA prognosis assessment.