Dataset Information

Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis.

ABSTRACT: As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomarker identification based on an unbalanced dataset of LIHC. Then, we obtained 34 differentially expressed genes (DEGs). The ability of the 34 DEGs to discriminate tumor samples from normal samples was evaluated by classification, and a high area under the curve (AUC) was achieved in our studied dataset and in two external validation datasets (AUC = 0.997, 0.973, and 0.949, respectively). Additionally, we also found three subtypes of LIHC, and revealed different biological mechanisms behind the three subtypes. Mutation enrichment analysis showed that subtype 3 had many enriched mutations, including tumor protein p53 (TP53) mutations. Overall, our study suggested that the 34 DEGs could serve as diagnostic biomarkers, and the three subtypes could help with precise treatment for LIHC.

SUBMITTER: Ouyang X

PROVIDER: S-EPMC7566011 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis.

Ouyang Xiao X Fan Qingju Q Ling Guang G Shi Yu Y Hu Fuyan F

Genes 20200906 9

As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomar ...[more]

PMID: 32899915

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Project description:BackgroundTumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets.MethodThe discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA.ResultsBased on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction.ConclusionThe present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

Project description:Background: Although current immunotherapies have achieved some successes for hepatocellular carcinoma (HCC) patients, their benefits are limited for most HCC patients. Therefore, the identification of biomarkers for promoting immunotherapeutic responses in HCC is urgently needed. Methods: Using the TCGA HCC cohort, we investigated correlations of various molecular features with antitumor immune signatures (CD8+ T cell infiltration and cytolytic activity) and an immunosuppressive signature (PD-L1 expression) in HCC. These molecular features included mRNAs, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), proteins, and pathways. Results: We found that the mutations of several oncogenes and tumor suppressor genes significantly correlated with reduced antitumor immune signatures, including TTN, CTNNB1, RB1, ZFHX4, and TP53. It indicates that these genes' mutations may inhibit antitumor immune responses in HCC. Four proteins (Syk, Lck, STAT5, and Caspase-7) had significant positive expression correlations with CD8+ T cell enrichment, cytolytic activity, and PD-L1 expression in HCC. It suggests that these proteins' expression could be useful biomarkers for the response to immune checkpoint inhibitors Similiarly, we identified other types of biomarkers potentially useful for predicting the response to ICIs, including miRNAs (hsa-miR-511-5p, 150-3p, 342-3p, 181a-3p, 625-5p, 4772-3p, 155-3p, 142-5p, 142-3p, 155-5p, 625-3p, 1976, 7702), many lncRNAs, and pathways (apoptosis, cytokine-cytokine receptor interaction, Jak-STAT signaling, MAPK signaling, PI3K-AKT signaling, HIF-1 signaling, ECM receptor interaction, focal adhesion, and estrogen signaling). Further, tumor mutation burden showed no significant correlation with antitumor immunity, while tumor aneuploidy levels showed a significant negative correlation with antitumor immunity. Conclusion: The molecular features significantly associated with HCC immunity could be predictive biomarkers for immunotherapeutic responses in HCC patients. They could also be potential intervention targets for boosting antitumor immunity and immunotherapeutic responses in HCC.

Dataset Information

Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis.

Publications

Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis.

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