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Weighted miRNA co-expression networks analysis identifies circulating miRNA predicting overall survival in hepatocellular carcinoma patients.

ABSTRACT: The weighted gene co-expression network analysis (WGCNA) has been used to explore gene expression datasets by constructing biological networks based on the likelihood expression profile among genes. In recent years, WGCNA found application in biomarker discovery studies, including miRNA. Serum samples from 20 patients with hepatocellular carcinoma (HCC) were profiled through miRNA 3.0 gene array and miRNAs biomarker candidates were identified through WGCNA. Results were validated by qRT-PCR in 102 HCC serum samples collected at diagnosis. WGCNA identified 16 miRNA modules, nine of them were significantly associated with the clinical characteristics of the patient. The Red module had a significant negative correlation with patients Survival (-?0.59, p?=?0.007) and albumin (-?0.52, p?=?0.02), and a positive correlation with PCR (0.61, p?=?0.004) and alpha-fetoprotein (0.51, p?=?0.02). In the red module, 16 circulating miRNAs were significantly associated with patient survival. MiR-3185 and miR-4507 were identified as predictors of patient survival after the validation phase. At diagnosis, high expression of circulating miR-3185 and miR-4507 identifies patients with longer survival (HR 2.02, 95% CI 1.10-3.73, p?=?0.0086, and HR of 1.75, 95% CI 1.02-3.02, p?=?0.037, respectively). Thought a WGCNA we identified miR-3185 and miR-4507 as promising candidate biomarkers predicting a longer survival in HCC patients.

SUBMITTER: Pascut D

PROVIDER: S-EPMC7609726 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Weighted miRNA co-expression networks analysis identifies circulating miRNA predicting overall survival in hepatocellular carcinoma patients.

Pascut Devis D Pratama Muhammad Yogi MY Gilardi Francesca F Giuffrè Mauro M Crocè Lory Saveria LS Tiribelli Claudio C

Scientific reports 20201103 1

The weighted gene co-expression network analysis (WGCNA) has been used to explore gene expression datasets by constructing biological networks based on the likelihood expression profile among genes. In recent years, WGCNA found application in biomarker discovery studies, including miRNA. Serum samples from 20 patients with hepatocellular carcinoma (HCC) were profiled through miRNA 3.0 gene array and miRNAs biomarker candidates were identified through WGCNA. Results were validated by qRT-PCR in 1 ...[more]

PMID: 33144628

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Project description:BackgroundFew studies have focused on the prognosis of patients with hepatocellular carcinoma (HCC) of Barcelona Clinic Liver Cancer (BCLC) stage 0‒C in terms of early recurrence and 5-years overall survival (OS). We sought to develop nomograms for predicting 5-year OS and early recurrence after curative resection of HCC, based on a clinicopathological‒radiological model. We also investigated whether different treatment methods influenced the OS of patients with early recurrence.MethodsRetrospective data, including clinical pathology, radiology, and follow-up data, were collected for 494 patients with HCC who underwent hepatectomy. Nomograms estimating OS and early recurrence were constructed using multivariate Cox regression analysis, based on the random survival forest (RSF) model. We evaluated the discrimination and calibration abilities of the nomograms using concordance indices (C-index), calibration curves, and Kaplan‒Meier curves. OS curves of different treatments for patients who had recurrence within 2 years after curative surgery were depicted and compared using the Kaplan-Meier method and the log-rank test.ResultsMultivariate Cox regression revealed that BCLC stage, non-smooth margin, maximum tumor diameter, age, aspartate aminotransferase levels, microvascular invasion, and differentiation were prognostic factors for OS and were incorporated into the nomogram with good predictive performance in the training (C-index: 0.787) and testing cohorts (C-index: 0.711). A nomogram for recurrence-free survival was also developed based on four prognostic factors (BCLC stage, non-smooth margin, maximum tumor diameter, and microvascular invasion) with good predictive performance in the training (C-index: 0.717) and testing cohorts (C-index: 0.701). In comparison to the BCLC staging system, the C-index (training cohort: 0.787 vs. 0.678, 0.717 vs. 0.675; external cohort 2: 0.748 vs. 0.624, 0.729 vs. 0.587 respectively, for OS and RFS; external cohort1:0.716 vs. 0.627 for RFS, all p value<0.05), and model calibration curves all showed improved performance. Patients who underwent surgery after tumor recurrence had a higher reOS than those who underwent comprehensive treatments and supportive care.ConclusionsThe nomogram, based on clinical, pathological, and radiological factors, demonstrated good accuracy in estimating OS and recurrence, which can guide follow-up and treatment of individual patients. Reoperation may be the best option for patients with recurrence in good condition.

Project description:BackgroundHepatocellular carcinoma (HCC) remains a major challenge for public health worldwide. Considering the great heterogeneity of HCC, more accurate prognostic models are urgently needed. To identify a robust prognostic gene signature, we conduct this study.Materials and methodsLevel 3 mRNA expression profiles and clinicopathological data were obtained in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). GSE14520 dataset from the gene expression omnibus (GEO) database was downloaded to further validate the results in TCGA. Differentially expressed mRNAs between HCC and normal tissue were investigated. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, multivariate Cox regression analysis, nomogram, and decision curve analysis (DCA) were used to assess the prognostic capacity of the six-gene signature. The prognostic value of the gene signature was further validated in independent GSE14520 cohort. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. The performance of the prognostic signature in differentiating between normal liver tissues and HCC were also investigated.ResultsA novel six-gene signature (including CSE1L, CSTB, MTHFR, DAGLA, MMP10, and GYS2) was established for HCC prognosis prediction. The ROC curve showed good performance in survival prediction in both the TCGA HCC cohort and the GSE14520 validation cohort. The six-gene signature could stratify patients into a high- and low-risk group which had significantly different survival. Cox regression analysis showed that the six-gene signature could independently predict OS. Nomogram including the six-gene signature was established and shown some clinical net benefit. Furthermore, GSEA revealed several significantly enriched oncological signatures and various metabolic process, which might help explain the underlying molecular mechanisms. Besides, the prognostic signature showed a strong ability for differentiating HCC from normal tissues.ConclusionsOur study established a novel six-gene signature and nomogram to predict overall survival of HCC, which may help in clinical decision making for individual treatment.

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Weighted miRNA co-expression networks analysis identifies circulating miRNA predicting overall survival in hepatocellular carcinoma patients.

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Weighted miRNA co-expression networks analysis identifies circulating miRNA predicting overall survival in hepatocellular carcinoma patients.

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