CT-based radiomic analysis of hepatocellular carcinoma patients to predict key genomic information.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15623-e15623 ◽  
Author(s):  
Derek L West ◽  
Aikaterini Kotrotsou ◽  
Andrew Scott Niekamp ◽  
Tagwa Idris ◽  
Dunia Giniebra Camejo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Texture Features ◽  
Ct Images ◽  
The Cancer Genome Atlas ◽  
Tumor Segmentation ◽  
Full Potential ◽  
Imaging Features ◽  
Treatment Naïve

e15623 Background: The utilization of computed tomography (CT) has virtually replaced the need for tissue diagnosis in hepatocellular carcinoma (HCC). Imaging features (e.g. size, shape and vascularity) have been associated with patient survival. However, the full potential of CT in HCC diagnosis may not be reached, as high-throughput computing allows for extraction of quantitative features that are not part of radiologists’ lexicon. The purpose of this study was to investigate the ability of radiomic analysis to successfully identify specific doxorubicin chemoresistant genes on CT images of treatment-naïve hepatocellular carcinoma (HCC). Methods: We identified 27 treatment-naïve patients with a single HCC tumor from The Cancer Genome Atlas (TCGA) whom had gene expression profiles. Baseline CT images were obtained from The Cancer Imaging Archive (TCIA). 3D Slicer software was used for manual tumor segmentation and final segmented images were reviewed by a board-certified radiologist. Following tumor segmentation, texture analysis was performed on MATLAB environment. A total of 310 rotation invariant texture features, which measure tumor heterogeneity, were obtained (first-order histogram and grey level co-occurrence matrix). The mRMR method was used to select the most relevant radiomic features. ROC analysis and LOOCV were used to assess the performance of five specific genes known to confer doxorubicin chemoresistance (TP53, TOP2A, CTNNB1, CDKN2A and AKT1). Results: Radiomic analysis identified TP53 (AUC = 86.61%, Specificity = 92.31%, Sensitivity = 92.9%), TOP2A (AUC = 78.0%, Specificity = 69%, Sensitivity = 85.7%), CTNNB1 (AUC = 86.8%, Specificity = 92.3%, Sensitivity = 85.7%), CDKN2A (AUC = 76.9%, Specificity = 76.9%, Sensitivity = 78.6%) and AKT1 (AUC = 72.5%, Specificity = 69.2%, Sensitivity = 85.7%) in treatment-naïve HCC CT studies. Conclusions: The identification of specific genes that confer chemoresistance to doxorubicin can be reliably ascertained via the use of radiomic analysis. This study may help tailor future treatment paradigms via the ability to categorize HCC tumors on genetic level and identify tumors which may not have a favorable response to doxorubicin based therapies.

scholarly journals Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Shenglan Cai ◽  
Xingwang Hu ◽  
Ruochan Chen ◽  
Yiya Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Immune Genes ◽  
Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

scholarly journals Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yan ◽  
Zuotian Huang ◽  
Tong Mou ◽  
Yunhai Luo ◽  
Yanyao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Competing Endogenous Rna ◽  
Tissue Samples ◽  
Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

scholarly journals Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Binghao Zhao ◽  
Yuekun Wang ◽  
Yaning Wang ◽  
Congxin Dai ◽  
Yu Wang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinicopathological Characteristics ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Phenotype ◽  
Immune Phenotypes ◽  
Glioma Microenvironment ◽  
Genome Atlas

The immunosuppressive mechanisms of the surrounding microenvironment and distinct immunogenomic features in glioblastoma (GBM) have not been elucidated to date. To fill this gap, useful data were extracted from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, GSE43378, GSE23806, and GSE12907. With the ssGSEA method and the ESTIMATE and CIBERSORT algorithms, four microenvironmental signatures were used to identify glioma microenvironment genes, and the samples were reasonably classified into three immune phenotypes. The molecular and clinical features of these phenotypes were characterized via key gene set expression, tumor mutation burden, fraction of immune cell infiltration, and functional enrichment. Exhausted CD8+ T cell (GET) signature construction with the predictive response to commonly used antitumor drugs and peritumoral edema assisted in further characterizing the immune phenotype features. A total of 2,466 glioma samples with gene expression profiles were enrolled. Tumor purity, ESTIMATE, and immune and stromal scores served as the 4 microenvironment signatures used to classify gliomas into immune-high, immune-middle and immune-low groups, which had distinct immune heterogeneity and clinicopathological characteristics. The immune-H phenotype had higher expression of four immune signatures; however, most checkpoint molecules exhibited poor survival. Enriched pathways among the subtypes were related to immunity. The GET score was similar among the three phenotypes, while immune-L was more sensitive to bortezomib, cisplatin, docetaxel, lapatinib, and rapamycin prescriptions and displayed mild peritumor edema. The three novel immune phenotypes with distinct immunogenetic features could have utility for understanding glioma microenvironment regulation and determining prognosis. These results contribute to classifying glioma subtypes, remodeling the immunosuppressive microenvironment and informing novel cancer immunotherapy in the era of precision immuno-oncology.

scholarly journals Integrating Genetic and Transcriptomic Data to Reveal Pathogenesis and Prognostic Markers of Pancreatic Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Kaisong Bai ◽  
Tong Zhao ◽  
Yilong Li ◽  
Xinjian Li ◽  
Zhantian Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Adenocarcinoma ◽  
Somatic Mutation ◽  
Somatic Mutations ◽  
Prognostic Markers ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genomic Variation ◽  
The Cancer Genome Atlas ◽  
Driver Genes

Pancreatic adenocarcinoma (PAAD) is one of the deadliest malignancies and mortality for PAAD have remained increasing under the conditions of substantial improvements in mortality for other major cancers. Although multiple of studies exists on PAAD, few studies have dissected the oncogenic mechanisms of PAAD based on genomic variation. In this study, we integrated somatic mutation data and gene expression profiles obtained by high-throughput sequencing to characterize the pathogenesis of PAAD. The mutation profile containing 182 samples with 25,470 somatic mutations was obtained from The Cancer Genome Atlas (TCGA). The mutation landscape was generated and somatic mutations in PAAD were found to have preference for mutation location. The combination of mutation matrix and gene expression profiles identified 31 driver genes that were closely associated with tumor cell invasion and apoptosis. Co-expression networks were constructed based on 461 genes significantly associated with driver genes and the hub gene FAM133A in the network was identified to be associated with tumor metastasis. Further, the cascade relationship of somatic mutation-Long non-coding RNA (lncRNA)-microRNA (miRNA) was constructed to reveal a new mechanism for the involvement of mutations in post-transcriptional regulation. We have also identified prognostic markers that are significantly associated with overall survival (OS) of PAAD patients and constructed a risk score model to identify patients’ survival risk. In summary, our study revealed the pathogenic mechanisms and prognostic markers of PAAD providing theoretical support for the development of precision medicine.

scholarly journals Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Xiaofei Wang ◽  
Jie Qiao ◽  
Rongqi Wang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

scholarly journals Prognostic Significance of NBEAL2 in Liver hepatocellular carcinoma

2021 ◽  
Author(s):  
Yanghui Wen ◽  
Hui Su ◽  
Wuke Wang ◽  
Feng Ren ◽  
Haitao Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Chi Square ◽  
Kaplan Meier ◽  
Chi Square Test ◽  
Liver Hepatocellular Carcinoma ◽  
The Relationship

Abstract Background: NBEAL2 is a member of the BEACH domain–containing protein (BDCP) family and little is known about the relationship between NBEAL2 and malignancy.Methods: We downloaded the Gene expression profiles and clinical data of Liver hepatocellular carcinoma(LIHC) form the Cancer Genome Atlas (TCGA) dataset. The expression difference of NBEAL2 in LIHC tissues and adjacent nontumor tissues was analyzed by R software. The relationship between NBEAL2 expression and clinicopathological parameters was evaluate by Chi-square test. The effect of NBEAL2 expression on survival were assessed by Kaplan–Meier survival analysis and Cox proportional hazards regression model. GSEA was used to explore the potential molecular mechanism of NBEAL2 in LIHC.Results: Up-regulation of NBEAL2 expression was detected in the LIHC tissue compared with adjacent nontumor tissues(P < 0.001). The chi-square test showed that no significant correlation between the expression level of NBEAL2 and various clinicopathological parameters (including T, N and M classifications) were detected. The Kaplan–Meier curves suggested that lower NBEAL2 expression was related with poor prognosis. The results of Multivariate analysis revealed that a lower expression of NBEAL2 in LIHC was an independent risk of poor overall survival (HR, 8.873; 95% CI, 1.159-67.936; P = 0.035). GSEA suggested that multiple tumor-related metabolic pathways were evidently enriched in samples with the low-NBEAL2 expression phenotype. Conlusion: NBEAL2 might act as an tumor suppressor gene in the progression of LIHC but the precise role of NBELA2 in LIHC needs further vertification.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

scholarly journals CancerLivER: a database of liver cancer gene expression resources and biomarkers

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Harpreet Kaur ◽  
Sherry Bhalla ◽  
Dilraj Kaur ◽  
Gajendra PS Raghava
Keyword(s):  
Gene Expression ◽  
Liver Cancer ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Full Potential ◽  
Individual Gene ◽  
Comprehensive Information ◽  
Tremendous Amount ◽  
Disease Specific

Abstract Liver cancer is the fourth major lethal malignancy worldwide. To understand the development and progression of liver cancer, biomedical research generated a tremendous amount of transcriptomics and disease-specific biomarker data. However, dispersed information poses pragmatic hurdles to delineate the significant markers for the disease. Hence, a dedicated resource for liver cancer is required that integrates scattered multiple formatted datasets and information regarding disease-specific biomarkers. Liver Cancer Expression Resource (CancerLivER) is a database that maintains gene expression datasets of liver cancer along with the putative biomarkers defined for the same in the literature. It manages 115 datasets that include gene-expression profiles of 9611 samples. Each of incorporated datasets was manually curated to remove any artefact; subsequently, a standard and uniform pipeline according to the specific technique is employed for their processing. Additionally, it contains comprehensive information on 594 liver cancer biomarkers which include mainly 315 gene biomarkers or signatures and 178 protein- and 46 miRNA-based biomarkers. To explore the full potential of data on liver cancer, a web-based interactive platform was developed to perform search, browsing and analyses. Analysis tools were also integrated to explore and visualize the expression patterns of desired genes among different types of samples based on individual gene, GO ontology and pathways. Furthermore, a dataset matrix download facility was provided to facilitate the users for their extensive analysis to elucidate more robust disease-specific signatures. Eventually, CancerLivER is a comprehensive resource which is highly useful for the scientific community working in the field of liver cancer.Availability: CancerLivER can be accessed on the web at https://webs.iiitd.edu.in/raghava/cancerliver.

scholarly journals Identification, using cDNA macroarray analysis, of distinct gene expression profiles associated with pathological and virological features of hepatocellular carcinoma

Oncogene ◽  
2002 ◽  
Vol 21 (18) ◽  
pp. 2926-2937 ◽  
Author(s):  
Oona Delpuech ◽  
Jean-Baptiste Trabut ◽  
Françoise Carnot ◽  
Jean Feuillard ◽  
Christian Brechot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cdna Macroarray ◽  
Distinct Gene ◽  
Macroarray Analysis
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