scholarly journals Computational Analysis of Transcriptomic and Proteomic Data for Deciphering Molecular Heterogeneity and Drug Responsiveness in Model Human Hepatocellular Carcinoma Cell Lines

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 623
Author(s):  
Panagiotis C. Agioutantis ◽  
Heleni Loutrari ◽  
Fragiskos N. Kolisis
Keyword(s):  
Hepatocellular Carcinoma ◽  
Correlation Analysis ◽  
Cell Lines ◽  
Computational Approach ◽  
Gene Set Enrichment Analysis ◽  
Molecular Heterogeneity ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Well Differentiated ◽  
Drug Responsiveness

Hepatocellular carcinoma (HCC) is associated with high mortality due to its inherent heterogeneity, aggressiveness, and limited therapeutic regimes. Herein, we analyzed 21 human HCC cell lines (HCC lines) to explore intertumor molecular diversity and pertinent drug sensitivity. We used an integrative computational approach based on exploratory and single-sample gene-set enrichment analysis of transcriptome and proteome data from the Cancer Cell Line Encyclopedia, followed by correlation analysis of drug-screening data from the Cancer Therapeutics Response Portal with curated gene-set enrichment scores. Acquired results classified HCC lines into two groups, a poorly and a well-differentiated group, displaying lower/higher enrichment scores in a “Specifically Upregulated in Liver” gene-set, respectively. Hierarchical clustering based on a published epithelial–mesenchymal transition gene expression signature further supported this stratification. Between-group comparisons of gene and protein expression unveiled distinctive patterns, whereas downstream functional analysis significantly associated differentially expressed genes with crucial cancer-related biological processes/pathways and revealed concrete driver-gene signatures. Finally, correlation analysis highlighted a diverse effectiveness of specific drugs against poorly compared to well-differentiated HCC lines, possibly applicable in clinical research with patients with analogous characteristics. Overall, this study expanded the knowledge on the molecular profiles, differentiation status, and drug responsiveness of HCC lines, and proposes a cost-effective computational approach to precision anti-HCC therapies.

scholarly journals Importance of collection in gene set enrichment analysis of drug response in cancer cell lines

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Alain R. Bateman ◽  
Nehme El-Hachem ◽  
Andrew H. Beck ◽  
Hugo J. W. L. Aerts ◽  
Benjamin Haibe-Kains
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Enrichment Analysis ◽  
Cancer Cell Lines ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set

scholarly journals Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis ◽  
Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

scholarly journals SNHG3/miR-148a-3p Axis–mediated High Expression of DNMT1 Correlates with Poor Prognosis and Tumor Immune Infiltration of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Ninghua Yao ◽  
Wei Jiang ◽  
Jie Sun ◽  
Chen Yang ◽  
Wenjie Zheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Immune Cell Infiltration ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Immune Infiltration ◽  
Computer Analyses ◽  
Dnmt1 Expression

Abstract Background Epigenetic reprogramming plays an important role in the occurrence, development, and prognosis of hepatocellular carcinoma (HCC). DNA methylation is a key epigenetic regulatory mechanism, and DNA methyltransferase 1 (DNMT1) is the major enzyme responsible for maintenance methylation. Nevertheless, the role and mechanism of DNMT1 in HCC remains poorly defined. Methods In the current study, we conducted pan-cancer analysis for DNMT1’s expression and prognosis using The Cancer Genome Atlas (TCGA) data set. We conducted gene Set Enrichment Analysis (GSEA) between high-and-low DNMT1 expression groups to identify DNMT1-related functional significance. We also investigated the relationship between DNMT1 expression and tumor immune microenvironment, including immune cell infiltration and the expression of immune checkpoints. Through a combination series of computer analyses (including expression analyses, correlation analyses, and survival analyses), the noncoding RNAs (ncRNAs) that contribute to the overexpression of DNMT1 were ultimately identified. Results We found that DNMT1 was upregulated in 16 types of human carcinoma including HCC, and DNMT1 might be a biomarker predicting unfavorable prognosis in HCC patients. DNMT1 mRNA expression was statistically associated with age, histological grade, and the level of serum AFP. Moreover, DNMT1 level was significantly and positively linked to tumor immune cell infiltration, immune cell biomarkers, and immune checkpoint expression. Meanwhile, Gene Set Enrichment Analysis (GSEA) revealed that high-DNMT1 expression was associated with epithelial mesenchymal transition (EMT), E2F target, G2M checkpoint, and inflammatory response. Finally, through a combination series of computer analyses the SNHG3/hsa-miR-148a-3p/DNMT1 axis was confirmed as the potential regulatory pathway in HCC. Conclusion SNHG3/miR-148a-3p axis upregulation of DNMT1 may be related to poor outcome, tumor immune infiltration, and regulated malignant properties in HCC.

scholarly journals Identification METTL18 as a Potential Prognosis Biomarker and Associated With Immune Infiltrates in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Tian-Hao Li ◽  
Cheng Qin ◽  
Bang-Bo Zhao ◽  
Hong-Tao Cao ◽  
Xiao-Ying Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Multivariate Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Wilcoxon Test ◽  
Hepatic Tissue ◽  
Gene Set Enrichment ◽  
Poor Overall Survival ◽  
Gene Set

Methyltransferase-like 18 (METTL18), a METTL family member, is abundant in hepatocellular carcinoma (HCC). Studies have indicated the METTL family could regulate the progress of diverse malignancies while the role of METTL18 in HCC remains unclear. Data of HCC patients were acquired from the cancer genome atlas (TCGA) and gene expression omnibus (GEO). The expression level of METTL18 in HCC patients was compared with normal liver tissues by Wilcoxon test. Then, the logistic analysis was used to estimate the correlation between METTL18 and clinicopathological factors. Besides, Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA) were used to explore relevant functions and quantify the degree of immune infiltration for METTL18. Univariate and Multivariate Cox analyses and Kaplan–Meier analysis were used to estimate the association between METTL18 and prognosis. Besides, by cox multivariate analysis, a nomogram was conducted to forecast the influence of METTL18 on survival rates. METTL18-high was associated with Histologic grade, T stage, Pathologic stage, BMI, Adjacent hepatic tissue inflammation, AFP, Vascular invasion, and TP53 status (P < 0.05). HCC patients with METTL18-high had a poor Overall-Survival [OS; hazard ratio (HR): 1.87, P < 0.001), Disease-Specific Survival (DSS, HR: 1.76, P = 0.015), and Progression-Free Interval (PFI, HR: 1.51, P = 0.006). Multivariate analysis demonstrated that METTL18 was an independent factor for OS (HR: 2.093, P < 0.001), DSS (HR: 2.404, P = 0.015), and PFI (HR: 1.133, P = 0.006). Based on multivariate analysis, the calibration plots and C-indexes of nomograms showed an efficacious predictive effect for HCC patients. GSEA demonstrated that METTL18-high could activate G2M checkpoint, E2F targets, KRAS signaling pathway, and Mitotic Spindle. There was a positive association between the METTL18 and abundance of innate immunocytes (T helper 2 cells) and a negative relation to the abundance of adaptive immunocytes (Dendritic cells, Cytotoxic cells etc.). Finally, we uncovered knockdown of METTL18 significantly suppressed the proliferation, invasion, and migration of HCC cells in vitro. This research indicates that METTL18 could be a novel biomarker to evaluate HCC patients’ prognosis and an important regulator of immune responses in HCC.

scholarly journals Consensus molecular subtype differences linking colon adenocarcinoma and obesity revealed by a cohort transcriptomic analysis

2021 ◽  
Author(s):  
Michael W. Greene ◽  
Peter T. Abraham ◽  
Peyton C. Kuhlers ◽  
Elizabeth A. Lipke ◽  
Martin J. Heslin ◽  
...  
Keyword(s):  
Molecular Subtype ◽  
Colon Adenocarcinoma ◽  
The United States ◽  
Gene Set Enrichment Analysis ◽  
Health Concern ◽  
Molecular Heterogeneity ◽  
Dependent Manner ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Consensus Molecular Subtype

AbstractBackgroundColorectal cancer (CRC) is the third-leading cause of cancer-related deaths in the United States and worldwide. Obesity - a worldwide public health concern - is a known risk factor for cancer including CRC. However, the mechanisms underlying the link between CRC and obesity have yet to be fully elucidated in part because of the molecular heterogeneity of CRC. We hypothesized that obesity modulates CRC in a consensus molecular subtype (CMS)-dependent manner.MethodsRNA-seq data and associated tumor and patient characteristics including body weight and height data for 232 patients were obtained from The Cancer Genomic Atlas – Colon Adenocarcinoma (TCGA-COAD) database. Tumor samples were classified into the four CMSs with the CMScaller R package; Body mass index (BMI) was calculated and categorized as normal, overweight, and obese.ResultsWe observed a significant difference in CMS categorization between BMI categories. Differentially expressed genes (DEGs) between obese and overweight samples and normal samples differed across the CMSs, and associated prognostic analyses indicated that the DEGs had differing effects on survival. Using Gene Set Enrichment Analysis, we found differences in Hallmark gene set enrichment between obese and overweight samples and normal samples across the CMSs. We constructed Protein-Protein Interaction networks and observed differences in obesity-regulated hub genes for each CMS. Finally, we analyzed and found differences in predicted drug sensitivity between obese and overweight samples and normal samples across the CMSs.ConclusionsThus, we conclude that obesity has CMS-specific effects on the CRC tumor transcriptome.

scholarly journals Differential Gene Set Enrichment Analysis: A statistical approach to quantify the relative enrichment of two gene sets

2019 ◽  
Author(s):  
James H. Joly ◽  
William E. Lowry ◽  
Nicholas A. Graham
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Enrichment Analysis ◽  
Cancer Cell Lines ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Relative Enrichment ◽  
Gene Sets ◽  
Differential Gene

AbstractGene Set Enrichment Analysis (GSEA) is an algorithm widely used to identify statistically enriched gene sets in transcriptomic data. However, to our knowledge, there exists no method for examining the enrichment of two gene sets relative to one another. Here, we present Differential Gene Set Enrichment Analysis (DGSEA), an adaptation of GSEA that assesses the relative enrichment of two gene sets. Using the metabolic pathways glycolysis and oxidative phosphorylation as an example, we demonstrate that DGSEA accurately captures the hypoxia-induced shift towards glycolysis. We also show that DGSEA is more predictive than GSEA of the metabolic state of cancer cell lines, including lactate secretion and intracellular concentrations of lactate and AMP. Furthermore, we demonstrate that DGSEA identifies novel metabolic dependencies not found by GSEA in cancer cell lines. Together, these data demonstrate that DGSEA is a novel tool to examine the relative enrichment of two gene sets.

scholarly journals Interaction with the DNA Binding Domain of MLL and a Small Molecular Drug Induces Proteolysis of MLL-Fusion Proteins and Ablates Leukemic Stem Cell Activity

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1549-1549
Author(s):  
Jasper de Boer ◽  
Sandra Cantilena
Keyword(s):  
Fusion Protein ◽  
Cell Lines ◽  
Fusion Proteins ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Self Renewal ◽  
Gene Set ◽  
Cellular Machinery

Abstract Introduction Leukemias harbouring 11q23 abnormalities causing mixed-lineage leukaemia gene (MLL) rearrangements are associated with poor clinical outcomes. Despite being an aggressive leukaemia, the MLL rearranged infant ALL has among the lowest mutation rates reported for any cancer. This means that to improve survival for patients with this aggressive leukaemia we need drugs that target the abnormal proteins produced by the MLL fusion gene or that interact with the abnormal MLL fusion protein to shut down the cellular machinery that drives these leukemias. Indeed, targeting MLL fusion dependent gene pathways has become a major focus. Our previous studies have shown that inhibition of MLL-fusions, in a conditional mouse model of MLL-ENL driven acute myeloid leukaemia, resulted in a block in self-renewal of the leukemic cells and ablated the leukaemia in the mice. This led us to hypothesise that if, we could achieve pharmacological inactivation of the MLL fusion proteins, we could achieve improved clinical outcomes. To achieve this, we set out a drug screening programme in acute leukaemia with the aim to discover drugs that can inactivate MLL-fusion oncoproteins. Results Our drug discovery pipeline screened clinical approved drugs for their ability to inhibit the function of the MLL fusion protein. This lead to the discovery of a drug that interacts with the DNA binding domain within the MLL fusion protein. This interaction destabilises the MLL fusion protein so that the fusion protein gets degraded within 24 hours of addition of the drug. So far, we have shown that we can inhibit and induce the degradation of MLL-AF9, MLL-AF6 and MLL-AF4 (and WT MLL) in the human MLL rearranged cell lines (THP-1, SHI-I and MV4-11), in primary immortalized cells in which the MLL-AF9 is overexpressed from a lentiviral backbone and in patient derived leukemic samples. Inactivation/degradation of the MLL fusion protein should shut down the cellular machinery that drives these leukemias. It is well established that MLL-fusions lead to abhorrent upregulation of its target genes HOXA9, MEIS1 and c-MYB. Treatment of MLL rearranged cells resulted in the downregulation of these MLL-fusion target genes within 24hrs of addition of the drug. Furthermore, Gene Set Enrichment Analysis of drug treated MLL-AF9 cells showed strong negative enrichment to various published MLL fusion target gene sets. Inactivation of MLL fusion protein should also result in block in self-renewal as we have previously shown in our conditional mouse model. Indeed, Gene Set Enrichment Analysis showed negative enrichment to published Leukemic Stem Cell gene set. To analyse the impact of drug treatment on self-renewal, we used a well-established self-renewal assay, whereby self-renewal is assessed by their ability to form colonies derived from single cells in methylcellulose. While treatment had no significant impact on the colony formation of CD34 positive cord blood progenitors, the drug was able to block the colony formation ability of MLL rearranged cell lines while only slowing a slight reduction in in the colony numbers of non MLL rearranged cell lines. Conclusion Overall, the data indicates that we may have discovered a new targeted treatment for MLL rearranged leukemia, which shows excellent clinical properties. We have successfully generated Patient Derived Xenografts (PDX) models and we are currently testing this drug to verify its effectiveness in the treatment on PDX. We will include this data in our presentation. Disclosures No relevant conflicts of interest to declare.

Expression of NOTCH receptors and ligands and prognosis of hepatocellular carcinoma

2020 ◽  
Vol 14 (17) ◽  
pp. 1631-1639
Author(s):  
Binyu Zhao ◽  
Shanshan Hu ◽  
Qingqing Xiao ◽  
Sinuo Fan ◽  
Xizhi Yu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Notch Receptor ◽  
Gene Set Enrichment Analysis ◽  
Rank Test ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Notch Receptors ◽  
Tumor Node Metastasis Stage

Aim: To elucidate potential prognostic significance of NOTCH receptor and ligand expression in hepatocellular carcinoma. Materials & methods: NOTCH receptors and ligands were divided into increased and decreased expression groups by X-tile program. The association between NOTCH receptors/ligands and prognosis was analyzed by Kaplan–Meier method and log-rank test. Gene set enrichment analysis was performed to explore NOTCH receptors/ligands-related pathways via gsea-3.0. Results: DLL3 and DLL4 were independent prognostic factors for overall survival. Further studies showed that only DLL3 was significantly associated with tumor, node, metastasis stage. Gene set enrichment analysis analysis demonstrated that retinol metabolism, drug metabolism cytochrome P450 and tryptophan metabolism were significantly enriched in DLL3 expression phenotype. Conclusion: We demonstrate that DLL3 may be a prognostic biomarker in hepatocellular carcinoma.

scholarly journals ZCCHC17 Served as a Predictive Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Fahui Liu ◽  
Jiadong Liang ◽  
Puze Long ◽  
Lilan Zhu ◽  
Wanyun Hou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Mrna Levels ◽  
Gene Set Enrichment ◽  
Prognostic Evaluation ◽  
Gene Set ◽  
Public Data

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. The prognosis and five-year survival rate of HCC are not promising due to tumor recurrence and metastasis. Exploring markers that contribute to the early diagnosis of HCC, markers for prognostic evaluation of HCC patients, and effective targets for treating HCC patients are in the spotlight of HCC therapy. Zinc Finger CCHC-Type Containing 17 (ZCCHC17) encodes the RNA binding protein ZCCHC17, but its role in HCC is still unclear. Here, 90 paraffin-embedded specimens combined with bioinformatics were used to comprehensively clarify the value of ZCCHC17 in the diagnosis and prognosis of HCC and its potential functions. Paraffin-embedded specimens were used to assess ZCCHC17 protein expression and its correlation with prognosis in 90 HCC patients. the public data sets of HCC patients from TCGA, ICG, and GEO databases were also used for further analysis. It was found that protein and mRNA levels of ZCCHC17 in HCC tissues were significantly higher than those in normal tissues. The abnormally high expression may be related to the abnormal DNA methylation of ZCCHC17 in tumor tissues. The high expression of ZCCHC17 is related to AFP, histologic grade, tumor status, vascular invasion, and pathological stage. Multi-data set analysis showed that patients with high ZCCHC17 expression had a worse prognosis, and multivariate cox regression analysis showed an independent prognostic significance of ZCCHC17. The results of functional analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), indicate that ZCCHC17 is mainly involved in immune regulation. Subsequently, further single-sample gene set enrichment analysis (ssGSEA) showed that the expression of ZCCHC17 was related to the infiltration of immune cells. Importantly, we also analyzed the relationship between ZCCHC17 and immune checkpoint genes, tumor mutation burden (TMB), microsatellite instability (MSI) and TP53 status in HCC patients and evaluated the role of ZCCHC17 in cancer immunotherapy. In summary, ZCCHC17 is a novel marker for the diagnosis and prognostic evaluation of HCC. Concurrently, it regulates immune cells in the tumor microenvironment (TME) of HCC patients, which has a specific reference value for the immunotherapy of HCC.

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