Dataset Information

Liquid biopsy by combining 5-hydroxymethylcytosine signatures of plasma cell-free DNA and protein biomarkers for diagnosis and prognosis of hepatocellular carcinoma.

ABSTRACT:

Background

Liquid biopsy based on 5-hydroxymethylcytosine (5hmC) signatures of plasma cell-free DNA (cfDNA) originating from tumor cells provides a novel approach for early diagnosis in hepatocellular carcinoma (HCC). Here, we sought to develop a reliable model using cfDNA 5hmC signatures and protein biomarkers for diagnosis and prognosis of HCC.

Patients and methods

We carried out genome-wide 5hmC sequencing of cfDNA samples collected from 165 healthy volunteers, 62 liver cirrhosis (LC) patients and 135 HCC patients. A sensitive 5hmC diagnostic model was developed based on 5hmC signatures selected by sparse Partial Least Squares Discriminant Analysis and cross-validation to define the weighted diagnostic score (wd-score). Then, we combined protein biomarkers with the wd-score to build a more robust score (HCC score) by logistic regression.

Results

The distribution pattern of differential 5hmC regions could clearly distinguish HCC patients, LC patients and healthy volunteers. The wd-score based on 64 5hmC signatures in cfDNA achieves 93.24% of area under the curve (AUC) to distinguish HCC patients from non-HCC patients, and the HCC score by combing protein biomarkers achieves 92.75% of AUC to distinguish HCC patients from LC patients. Meanwhile, the HCC score showed high capacity for screening high recurrence risk patients after receiving surgical resection, and appeared to be an independent indicator for both relapse-free survival (P = 0.00865) and overall survival (P = 0.000739). Furthermore, the values of the HCC score in patients' longitudinal plasma samples were positively associated with tumor burden dynamics during follow-up.

Conclusion

We have developed and validated a novel non-invasive liquid biopsy strategy for HCC diagnosis, prognosis and surveillance during HCC progression.

SUBMITTER: Cai Z

PROVIDER: S-EPMC7841321 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Liquid biopsy by combining 5-hydroxymethylcytosine signatures of plasma cell-free DNA and protein biomarkers for diagnosis and prognosis of hepatocellular carcinoma.

Cai Z Z Zhang J J He Y Y Xia L L Dong X X Chen G G Zhou Y Y Hu X X Zhong S S Wang Y Y Chen H H Xie D D Liu X X Liu J J

ESMO open 20210125 1

<h4>Background</h4>Liquid biopsy based on 5-hydroxymethylcytosine (5hmC) signatures of plasma cell-free DNA (cfDNA) originating from tumor cells provides a novel approach for early diagnosis in hepatocellular carcinoma (HCC). Here, we sought to develop a reliable model using cfDNA 5hmC signatures and protein biomarkers for diagnosis and prognosis of HCC.<h4>Patients and methods</h4>We carried out genome-wide 5hmC sequencing of cfDNA samples collected from 165 healthy volunteers, 62 liver cirrhos ...[more]

PMID: 33508734

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Project description:BackgroundLiver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.MethodsBig data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values.ResultsA list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, 'pathways in cancer,' was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982-0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences.DiscussionThe study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, 'pathways in cancer.' The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

Project description:Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide due to poor survival outcome. Thus, there is an urgent need to identify effective biomarkers for early diagnosis and prognosis prediction. Methods: A total of 389 differentially expressed genes (DEGs) between HCC samples and normal were selected based on the Robust Rank Aggregation (RRA) method. We combined DEGs expression and clinical traits to construct a gene co-expression network through WGCNA. Forty hub genes were selected from the key module. Among them, YWHAB, PPAT, NOL10 were eventually identified as prognostic biomarkers using multivariate Cox regression model. Biomarkers expression pattern was investigated by informatic analysis and verified by RNA-seq of 32 patients with HCC. DiseaseMeth 2.0, MEXPRESS, and Tumor Immune Estimation Resource (TIMER) were used to assess the methylation and immune status of biomarkers. GSVA, CCK8, colony formation assay, Edu imaging kit, wound-healing assay, and xenograft tumor model were utilized to investigate the effects of biomarkers on proliferation, metastasis of HCC cells in vitro, and in vivo. The Kaplan-Meier (KM) plotter and ROC curves were used to validate the prognostic and diagnostic value of biomarker expression. Results: All the selected biomarkers were upregulated in HCC samples and higher expression levels were associated with advanced tumor stages and T grades. The regulation of YWHAB, PPAT, NOL10 promoter methylation varied in tumors, and precancerous normal tissues. Immune infiltration analysis suggested that the abnormal regulations of these biomarkers were likely attributed to B cells and dendritic cells. GSVA for these biomarkers showed their great contributions to proliferation of HCC. Specific inhibition of their expression had strong effects on tumorigenesis in vitro and in vivo. ROC and KM curves confirmed their usefulness of diagnosis and prognosis of HCC. Conclusions: These findings identified YWHAB, PPAT, and NOL10 as novel biomarkers and validated their diagnostic and prognostic value for HCC.

Dataset Information

Liquid biopsy by combining 5-hydroxymethylcytosine signatures of plasma cell-free DNA and protein biomarkers for diagnosis and prognosis of hepatocellular carcinoma.

Background

Patients and methods

Results

Conclusion

Publications

Liquid biopsy by combining 5-hydroxymethylcytosine signatures of plasma cell-free DNA and protein biomarkers for diagnosis and prognosis of hepatocellular carcinoma.

Similar Datasets

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