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Integrative Analysis of DNA Methylation Data and Transcriptome Data Identified a DNA Methylation-Dysregulated Four-LncRNA Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma.

ABSTRACT: Increasing evidence has demonstrated the crosstalk between DNA epigenetic alterations and aberrant expression of long non-coding RNAs (lncRNAs) during carcinogenesis. However, epigenetically dysregulated lncRNAs and their functional and clinical roles in Head and Neck Squamous Cell Carcinoma (HNSCC) are still not explored. In this study, we performed an integrative analysis of DNA methylation data and transcriptome data and identified a DNA methylation-dysregulated four-lncRNA signature (DNAMeFourLncSig) from 596 DNA methylation-dysregulated lncRNAs using a machine-learning-based feature selection method, which classified the patients of the discovery cohort into two risk groups with significantly different survival including overall survival, disease-specific survival, and progression-free survival. Then the DNAMeFourLncSig was implemented to another two HNSCC patient cohorts and showed similar prognostic values in both. Results from multivariable Cox regression analysis revealed that the DNAMeFourLncSig might be an independent prognostic factor. Furthermore, the DNAMeFourLncSig was substantially correlated with the complete response rate of chemotherapy and may predict chemotherapy response. Functional in silico analysis found that DNAMeFourLncSig-related mRNAs were mainly enriched in cell differentiation, tissue development and immune-related pathways. Overall, our study will improve our understanding of underlying transcriptional and epigenetic mechanisms in HNSCC carcinogenesis and provided a new potential biomarker for the prognosis of patients with HNSCC.

SUBMITTER: Wang Q

PROVIDER: S-EPMC8047109 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC. Methods: Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5'-C-phosphate-G- 3' (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were then subject to gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes. Results: Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (CETN3, DCAF7, GPX4, HNRNPA0, NUP54, SERPINB1, STARD5, TRIM52, C4orf3, C12orf51, and C17orf65) were identified in the TCGA database by multivariate Cox regression analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients. Conclusion: Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.

Project description:Background:Hepatocellular carcinoma (HCC) is an aggressive cancer with a poor prognosis and a high incidence. The molecular changes and novel biomarkers of HCC need to be identified to improve the diagnosis and prognosis of this disease. We investigated the current research concentrations of HCC and identified the transcriptomics-related biomarkers of HCC from The Cancer Genome Atlas (TGCA) database. Methods:We investigated the current research concentrations of HCC using literature metrology analysis for studies conducted from 2008 to 2018. We identified long noncoding RNAs (lncRNAs) that correlated with the clinical features and survival prognoses of HCC from The Cancer Genome Atlas (TGCA) database. Differentially expressed genes (lncRNAs, miRNAs, and mRNAs) were also identified by TCGA datasets in HCC tumor tissues. A lncRNA competitive endogenous RNA (ceRNA) network was constructed from lncRNAs based on intersected lncRNAs. Survival times and the association between the expression levels of the key lncRNAs of the ceRNA network and the clinicopathological characteristics of HCC patients were analyzed using TCGA. Real-time polymerase chain reaction (qRT-PCR) was used to validate the reliability of the results in tissue samples from 20 newly-diagnosed HCC patients. Results:Analysis of the literature pertaining to HCC research revealed that current research is focused on lncRNA functions in tumorigenesis and tumor development. A total of 128 HCC dysregulated lncRNAs were identified; 66 were included in the co-expressed ceRNA network. We analyzed survival times and the associations between the expression of 66 key lncRNAs and the clinicopathological features of the HCC patients identified from TCGA. Twenty-six lncRNAs were associated with clinical features of HCC (P < 0.05) and six key lncRNAs were associated with survival time (log-rank test P < 0.05). Six key lncRNAs were selected for the validation of their expression levels in 20 patients with newly diagnosed HCC using qRT-PCR. Consistent fold changes in the trends of up and down regulation between qRT-PCR validation and TCGA proved the reliability of our bioinformatics analysis. Conclusions:We used integrative bioinformatics analysis of the TCGA datasets to improve our understanding of the regulatory mechanisms involved with the functional features of lncRNAs in HCC. The results revealed that lncRNAs are potential diagnostic and prognostic biomarkers of HCC.

Dataset Information

Integrative Analysis of DNA Methylation Data and Transcriptome Data Identified a DNA Methylation-Dysregulated Four-LncRNA Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma.

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