Integrative multi-platform meta-analysis of hepatocellular carcinoma gene expression profiles for identifying prognostic and diagnostic biomarkers
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ABSTRACT:
SUBMITTER: Gholizadeh M
PROVIDER: S-EPMC10311049 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
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Project description:Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.
| S-EPMC5884535 | biostudies-literature
Project description:The systemic therapy landscape for hepatocellular carcinoma is rapidly evolving, as the recent approvals of checkpoint inhibitor-based regimens such as atezolizumab-bevacizumab and durvalumab-tremelimumab in advanced disease have led to an expanding therapeutic armamentarium. The development of biomarkers, however, has not kept up with the approvals of new agents. Nevertheless, biomarker research for hepatocellular carcinoma has recently been growing at a rapid pace. The most active areas of research are biomarkers for early detection and screening, accurate prognostication, and detection of minimal residual disease following curative intent therapies, and, perhaps most importantly, predictive markers to guide selection and sequencing of the individual agents, including tyrosine kinase inhibitors and immunotherapy. In this review, we briefly summarize the recent developments in systemic therapeutics for hepatocellular carcinoma, introduce the key completed and ongoing prospective and retrospective studies evaluating diagnostic, prognostic, and predictive biomarkers with high clinical relevance, highlight several potentially important areas of future research, and share our insights for each biomarker.
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Project description:To establish serum microRNA profiles as prognostic biomarkers in hepatocellular carcinoma patients (HCCs), we used deep sequencing to screen serum microRNAs in a discovery set .Twelve up-regulated serum miRNAs were selected for qPCR analysis in a training set. MiR-192-5p and miR-29a-3p were identified and associated with HCC prognosis. HCCs with high concentrations of miR-192-5p and miR-29a-3p had poorer overall survival (OS) and progression-free survival (PFS) than those with low concentrations. We calculated a prognostic index (PI) score and classified patients into low-, medium- and high-risk groups. OS and PFS among the 3 groups from the training set were significantly different (all P < 0.05). PI (PIOS, PIPFS) score was the only independent prognostic predictor for OS and PFS of HCCs in the training set. These results were further confirmed in a validation set. In conclusion, differentially expressed serum miRNAs can be helpful for predicting survival in HCCs.
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Project description:Hepatocellular carcinoma (HCC) is the fifth most common cancer with high mortality, due to late diagnosis and limited treatment options. Blood miRNAs, which circulate in a highly stable, cell-free form, show promise as novel potential biomarkers for early detection of HCC. Whole miRNome profiling was performed to identify deregulated miRNAs between HCC and normal healthy (NH) volunteers. These deregulated miRNAs were validated in an independent cohort of HCC, NH and chronic Hepatitis B (CHB) volunteers and finally in a 3rd cohort comprising NH, CHB, cirrhotic and HCC volunteers to evaluate miRNA changes during disease progression. The associations between circulating miRNAs and liver-damage markers, clinicopathological characteristics and survival outcomes were analysed to identify prognostic markers. Twelve miRNAs are differentially expressed between HCC and NH individuals in all three cohorts. Five upregulated miRNAs (miR-122-5p, miR-125b-5p, miR-885-5p, miR-100-5p and miR-148a-3p) in CHB, cirrhosis and HCC patients are potential biomarkers for CHB infection, while miR-34a-5p can be a biomarker for cirrhosis. Notably, four miRNAs (miR-1972, miR-193a-5p, miR-214-3p and miR-365a-3p) can distinguish HCC from other non-HCC individuals. Six miRNAs are potential prognostic markers for overall survival.
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Project description:BackgroundThe cell cycle pathway genes are comprised of 113 members which are critical to the maintenance of cell cycle and survival of tumor cells. This study was performed to investigate the diagnostic and prognostic values of cell cycle gene expression in hepatocellular carcinoma (HCC) patients.MethodsClinical features and cell cycle pathway gene expression data were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Differentially expressed genes (DEGs) were determined by the student t-test between HCC and noncancerous samples. Kaplan-Meier survival, univariate, and multivariate survival analyses and validation analysis were performed to characterize the associations between cell cycle gene expression and patients' overall survival and recurrence-free survival.Results47 and 5 genes were significantly upregulated and downregulated genes in HCC samples, respectively. The high expression of BUB3, CDK1, and CHEK1 was associated with increased mortality (adjusted P value = 0.04, odds ratio (OR): 1.89 (95% confidence interval (CI): 1.04-3.46); adjusted P value = 0.02, OR: 2.06 (95% CI:1.15-3.75); and adjusted P value = 0.04, OR: 1.84 (%95 CI: 1.03-3.32), respectively). The expression of PTTG2 and RAD21 was significantly associated with cancer recurrence (adjusted P value = 0.01, OR: 2.17 (95% CI: 1.24-3.86); adjusted P value = 0.03, OR: 1.88[95% CI:1.08-3.28], respectively), while the low expression of MAD1L1 was associated with cancer recurrence (adjusted P value = 0.03, OR: 0.53 (%95 CI: 0.3-0.93)).ConclusionsThe present study demonstrated that BUB3, CDK1, and CHEK1 may serve as a prognostic biomarker for HCC patients. PTTG2, RAD21, and MAD1L1 expression is a major factor affecting the recurrence of HCC patients.
| S-EPMC7298261 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is the one of the most common cancers and lethal diseases in the world. DNA methylation alteration is frequently observed in HCC and may play important roles in carcinogenesis and diagnosis.MethodsUsing the TCGA HCC dataset, we classified HCC patients into different methylation subtypes, identified differentially methylated and expressed genes, and analyzed cis- and trans-regulation of DNA methylation and gene expression. To find potential diagnostic biomarkers for HCC, we screened HCC-specific CpGs by comparing the methylation profiles of 375 samples from HCC patients, 50 normal liver samples, 184 normal blood samples, and 3780 samples from patients with other cancers. A logistic regression model was constructed to distinguish HCC patients from normal controls. Model performance was evaluated using three independent datasets (including 327 HCC samples and 122 normal samples) and ten newly collected biopsies.ResultsWe identified a group of patients with a CpG island methylator phenotype (CIMP) and found that the overall survival of CIMP patients was poorer than that of non-CIMP patients. Our analyses showed that the cis-regulation of DNA methylation and gene expression was dominated by the negative correlation, while the trans-regulation was more complex. More importantly, we identified six HCC-specific hypermethylated sites as potential diagnostic biomarkers. The combination of six sites achieved ~ 92% sensitivity in predicting HCC, ~ 98% specificity in excluding normal livers, and ~ 98% specificity in excluding other cancers. Compared with previously published methylation markers, our markers are the only ones that can distinguish HCC from other cancers.ConclusionsOverall, our study systematically describes the DNA methylation characteristics of HCC and provides promising biomarkers for the diagnosis of HCC.
| S-EPMC5977535 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and is the one of the few cancers in which a continued increase in incidence has been observed over several years. HCC associated with chronic liver disease evolves from precancerous lesion and early HCC to overt cancer, and identifying key molecules contributing to early stage HCC is an urgent need. We aim to determine transcriptome-based molecular signature of multistep hepatocarcinogenesis, and to identify novel biomarkers to diagnose and predict early stage HCC.
2017-12-31 | GSE89377 | GEO
Project description:Background: The aim of the present study was to develop an improved diagnostic and prognostic model for HBV-associated HCC by combining AFP with PIVKA-II and other potential serum/plasma protein biomarkers. Methods: A total of 578 patients, including 352 patients with HBV-related HCC, 102 patients with HBV-associated liver cirrhosis (LC), 124 patients with chronic HBV, and 127 healthy subjects (HS), were enrolled in the study. The serum levels of AFP, PIVKA-II, and other laboratory parameters were collected. Univariate and multivariate logistic regression and Cox regression analyses were performed to identify independent diagnostic and prognostic factors, respectively. The diagnostic efficacy of the nomogram was evaluated using receiver operator curve (ROC) analysis and the prognostic performance was measured by Harrell’s concordance index (C-index). Results: AFP and PIVKA-II levels were significantly increased in HBV-related HCC, compared with those in HBV-associated LC and chronic HBV participants (p < 0.05 and p < 0.001, respectively). The diagnostic nomogram, which included age, gender, AFP, PIVKA-II, prothrombin time (PT), and total protein (TP), discriminated patients with HBV-HCC from those with HBV-LC or chronic HBV with an AUC of 0.970. In addition, based on the univariate and multivariate Cox regression analysis, PIVKA-II, γ-glutamyl transpeptidase, and albumin were found to be significantly associated with the prognosis of HBV-related HCC and were incorporated into a nomogram. The C-index of the nomogram for predicting 3-year survival in the training and validation groups was 0.75 and 0.78, respectively. The calibration curves for the probability of 3-year OS showed good agreement between the nomogram prediction and the actual observation in the training and the validation groups. Furthermore, the nomogram had a higher C-index (0.74) than that of the Child−Pugh grade (0.62), the albumin−bilirubin (ALBI) score (0.64), and Barcelona Clinic Liver Cancer (0.56) in all follow-up cases. Conclusion: Our study suggests that the nomograms based on AFP, PIVKA-II, and potential serum protein biomarkers showed a better performance in the diagnosis and prognosis of HCC, which may help to guide therapeutic strategies and assess the prognosis of HCC.
| S-EPMC10138067 | biostudies-literature
Project description:BACKGROUND: SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry) has become an attractive approach for cancer biomarker discovery due to its ability to resolve low mass proteins and high-throughput capability. However, the analytes from mass spectrometry are described only by their mass-to-charge ratio (m/z) values without further identification and annotation. To discover potential biomarkers for early diagnosis of osteosarcoma, we designed an integrative workflow combining data sets from both SELDI-TOF-MS and gene microarray analysis. METHODS: After extracting the information for potential biomarkers from SELDI data and microarray analysis, their associations were further inferred by link-test to identify biomarkers that could likely be used for diagnosis. Immuno-blot analysis was then performed to examine whether the expression of the putative biomarkers were indeed altered in serum from patients with osteosarcoma. RESULTS: Six differentially expressed protein peaks with strong statistical significances were detected by SELDI-TOF-MS. Four of the proteins were up-regulated and two of them were down-regulated. Microarray analysis showed that, compared with an osteoblastic cell line, the expression of 653 genes was changed more than 2 folds in three osteosarcoma cell lines. While expression of 310 genes was increased, expression of the other 343 genes was decreased. The two sets of biomarkers candidates were combined by the link-test statistics, indicating that 13 genes were potential biomarkers for early diagnosis of osteosarcoma. Among these genes, cytochrome c1 (CYC-1) was selected for further experimental validation. CONCLUSION: Link-test on datasets from both SELDI-TOF-MS and microarray high-throughput analysis can accelerate the identification of tumor biomarkers. The result confirmed that CYC-1 may be a promising biomarker for early diagnosis of osteosarcoma.
| S-EPMC2691408 | biostudies-literature
Project description:The potential significance of plasma extracellular vesicle-derived miRNAs in non-hepatitis B-, non-hepatitis C-related hepatocellular carcinoma as biomarker for the diseases was explored.
2023-10-11 | GSE244605 | GEO