Dataset Information

Identification and Validation of HCC-specific Gene Transcriptional Signature for Tumor Antigen Discovery.

ABSTRACT: A novel two-step bioinformatics strategy was applied for identification of signatures with therapeutic implications in hepatitis-associated HCC. Transcriptional profiles from HBV- and HCV-associated HCC samples were compared with non-tumor liver controls. Resulting HCC modulated genes were subsequently compared with different non-tumor tissue samples. Two related signatures were identified, namely "HCC-associated" and "HCC-specific". Expression data were validated by RNA-Seq analysis carried out on unrelated HCC samples and protein expression was confirmed according to The Human Protein Atlas" (http://proteinatlas.org/), a public repository of immunohistochemistry data. Among all, aldo-keto reductase family 1 member B10, and IGF2 mRNA-binding protein 3 were found strictly HCC-specific with no expression in 18/20 normal tissues. Target peptides for vaccine design were predicted for both proteins associated with the most prevalent HLA-class I and II alleles. The described novel strategy showed to be feasible for identification of HCC-specific proteins as highly potential target for HCC immunotherapy.

SUBMITTER: Petrizzo A

PROVIDER: S-EPMC4937235 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Identification and Validation of HCC-specific Gene Transcriptional Signature for Tumor Antigen Discovery.

Petrizzo Annacarmen A Caruso Francesca Pia FP Tagliamonte Maria M Tornesello Maria Lina ML Ceccarelli Michele M Costa Valerio V Aprile Marianna M Esposito Roberta R Ciliberto Gennaro G Buonaguro Franco M FM Buonaguro Luigi L

Scientific reports 20160708

A novel two-step bioinformatics strategy was applied for identification of signatures with therapeutic implications in hepatitis-associated HCC. Transcriptional profiles from HBV- and HCV-associated HCC samples were compared with non-tumor liver controls. Resulting HCC modulated genes were subsequently compared with different non-tumor tissue samples. Two related signatures were identified, namely "HCC-associated" and "HCC-specific". Expression data were validated by RNA-Seq analysis carried out ...[more]

PMID: 27387388

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Project description:Background: Hepatocellular carcinoma (HCC) is the world's second most deadly cancer, and metabolic reprogramming is its distinguishing feature. Among metabolite profiling, variation in amino acid metabolism supports tumor proliferation and metastasis to the most extent, yet a systematic study on the role of amino acid metabolism-related genes in HCC is still lacking. An effective amino acid metabolism-related prediction signature is urgently needed to assess the prognosis of HCC patients for individualized treatment. Materials and Methods: RNA-seq data of HCC from the TCGA-LIHC and GSE14520 (GPL3921) datasets were defined as the training set and validation set, respectively. Amino acid metabolic genes were extracted from the Molecular Signature Database. Univariate Cox and LASSO regression analyses were performed to build a predictive risk signature. K-M curves, ROC curves, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of this risk signature. Functional enrichment was analyzed by GSEA and CIBERSORTx software. Results: A nine-gene amino acid metabolism-related risk signature including B3GAT3, B4GALT2, CYB5R3, GNPDA1, GOT2, HEXB, HMGCS2, PLOD2, and SEPHS1 was constructed to predict the overall survival (OS) of HCC patients. Patients were separated into high-risk and low-risk groups based on risk scores and low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for HCC. ROC curves showed that this risk signature can effectively predict the 1-, 2-, 3- and 5-year survival times of patients with HCC. Additionally, prognostic nomograms were established based on the training set and validation set. These genes were closely correlated with the immune regulation. Conclusion: Our study identified a nine-gene amino acid metabolism-related risk signature and built predictive nomograms for OS in HCC. These findings will help us to personalize the treatment of liver cancer patients.

Project description:Background: Hepatocellular carcinoma (HCC) is a typical inflammatory-related malignant tumor with complex immune tolerance microenvironment and poor prognosis. In this study, we aimed to construct a novel immune-related gene signature for the prognosis of HCC patients, exploring tumor microenvironment (TME) cell infiltration characterization and potential mechanisms. Methods: A total of 364 HCC samples with follow-up information in the TCGA-LIHC dataset were analyzed for the training of the prognostic signature. The Least Absolute Shrinkage and Selector Operation (LASSO) regression based on the IRGs was conducted to identify the prognostic genes and establish an immune risk signature. The immune cell infiltration in TME was estimated via the CIBERSORT method. Gene Set Variation Analysis (GSVA) was conducted to compare the biological pathways involved in the low-risk and high-risk groups. Furthermore, paraffin sections of HCC tissue microarrays containing 77 patients from Fudan University Shanghai Cancer Center were used for IHC staining. The clinical characteristics of the 77 HCC patients were collected and summarized for survival analysis validation via the Kaplan-Meier (KM) method. Results: Three-gene signature with close immune correlation (Risk score = EPO * 0.02838 + BIRC5 * 0.02477 + SPP1 * 0.0002044) was constructed eventually and proven to be an effective prognostic factor for HCC patients. The patients were divided into a high-risk and a low-risk group according to the optimal cutoff, and the survival analysis revealed that HCC samples with high-risk immuno-score had significantly poorer outcomes than the low-risk group (p < 0.0001). The results of CIBERSORT suggested that the immune cell activation was relatively higher in the low-risk group with better prognosis. Besides, GSVA analysis showed multiple signaling differences between the high- and low-risk group, indicating that the three-gene prognostic model can affect the prognosis of patients by affecting immune-related mechanisms. Tissue microarray (TMA) results further confirmed that the expression of three genes in HCC tissues was closely related to the prognosis of patients, respectively. Conclusion: In this study, we constructed and validated a robust three-gene signature with close immune correlation in HCC, which presented a reliable performance in the prediction of HCC patients' survival.

Project description:BackgroundCurrently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis.MethodThe multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis.ResultsWe obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients.ConclusionsWe constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients.

Dataset Information

Identification and Validation of HCC-specific Gene Transcriptional Signature for Tumor Antigen Discovery.

Publications

Identification and Validation of HCC-specific Gene Transcriptional Signature for Tumor Antigen Discovery.

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