Large integrative analysis for the identification of novel ... · Panagiotis Mokos1, Fotis E...
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Hepatocellular carcinoma (HCC) is the third leading cause of cancer related death worldwide. Over the past years many efforts has been made to elucidate the molecular basis of the initiation and progression of (HCC). However, most of these studies have been focused on individual genes or single type of data which may lack the power to detect the complex mechanisms of cancer formation by overlooking the interaction networks. To gain insight into the molecular driver events, the combined effect of miRNA and mRNA expression was investigated in relation to cancer staging. We utilised the raw data of miRNA- and RNA- sequencing datasets from The Cancer Genome Atlas (TCGA). We performed filtering of the data with cpm values, scaling normalization and log2 transformation for the use of linear modeling. We then identified differentially expressed genes/miRNAs from three comparisons: Stage I/Normal, Stage II/ Normal and Stage III/ Normal. Genes and miRNAs with adjusted p-value < 0.05 and log2 fold change > 2 and 1 respectively, were considered significant. For each comparison type, we found the negative correlations between differentially expressed genes and miRNAs and the corresponding p-values. We combined Targetscan and microCosm databases in order to obtain a curated list of potential miRNA-mRNA interaction pairs. For each pair, the corresponding p-value was computed, adjusted for multiple testing using the BH method, using an adjusted p-value < 0.05 to identify and retain only the significant interactions. A pathway enrichment analysis, using KEGG annotation, revealed that up-regulated genes during the HCC progression are significantly correlated with the Cell cycle, Oocyte meiosis and p53 signaling pathways. Down-regulated genes are respectively correlated with Retinol metabolism and the Drug metabolism-cytochrome P450. Furthermore, we performed survival analyses with log rank test of patients of each stage in order to assess the relationship between differential gene/miRNA expression and overall survival. In the future, it will be interesting to correlate the differential mRNA/miRNA expression profiles with DNA methylation events, somatic mutations and CNVs of each stage in order to elucidate both genetic and epigenetic mechanisms underlying the development of HCC. Panagiotis Mokos 1 , Fotis E Psomopoulos 2,3 , Chrysanthi Ainali 4 , Alexandra Charalampidou 5 , Margarita Hadzopoulou-Cladaras 1 , Dimitra Dafou 1 1 Department of Genetics Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, GR54 124 Thessaloniki, Greece; 2 Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR54 124 Thessaloniki, Greece; 3 Center for Research & Technology Hellas, GR57 001 Thessaloniki, Greece; 4 Department of Informatics, School of Natural and Mathematical Sciences, King's College London, WC2R 2LS London, UK; 5 Information Technology (IT) Center, Aristotle University of Thessaloniki, GR54 124 Thessaloniki, Greece . *e-mail: [email protected] Large integrative analysis for the identification of novel molecular targets in hepatocellular carcinoma Abstract miRNA-mRNA interaction network analysis across all clinical stages Figure 4. Kaplan- Meier survival analysis of HCC patient samples. Patients with (a, b) Stage I (c, d) Stage II and (e, f) Stage III were classified into high- or low- expressing groups based on whether the expression of gene signature A (upper lane) and B (bottom lane) was greater than the median expression level. The P values from Logrank tests comparing two KM curves are shown in the bottom right side of each figure. Figure 2. Network analysis of 100 most significant predicted interactions between miRNAs and mRNAs in HCC development. For each comparison type: (a) Stage I/Normal, (b) Stage II/ Normal and (c) Stage III/ Normal negative correlations were computed between differentially expressed genes and miRNAs with corresponding p- values. Targetscan and microCosm databases were utilised in order to obtain a curated list of potential miRNA- mRNA interaction pairs. For each pair p- value was computed and adjusted for multiple testing using BH method and only the interactions with adjusted p- value < 0.05 were considered significant. Red-colored diamonds and blue-colored circles indicate miRNAs and mRNAs respectively. Node size represents node degree (number of edges connected to the node). Figure 3. Pathway enrichment analysis. (a) Gene signature A and (b) B based on KEGG database. Gene signature A and B consists of genes with down- and up-regulation during HCC progression. Barplots represent the 5 more enriched KEGG terms of each signature with color coding: red indicates high enrichment, blue indicates low enrichment. The length of the bars represent the percentage of each row (KEGG category). The analysis was performed using the hypergeometric test and the adjustment for the estimated significance level to account for multiple hypothesis testing. a b Conclusions: (1) We established differential expression profiles of miRNAs/mRNAs distinctive of each HCC clinical stage (2) We identified significant ‘hubs’ in HCC disease progression (3) We identified prognostic expression signatures during HCC progression. a c e Gene signature B Stage I Stage I Stage II Stage II Stage III Stage III Time (days) Time (days) Time (days) Time (days) Time (days) Time (days) Survival probability Survival probability Survival probability Survival probability Survival probability Survival probability Gene signature A Kaplan- Meier Survival analysis across all clinical stages b d f a Pathway enrichment analysis Gene signature A Gene signature Β Retinol metabolism Drug metabolism- cytochrome P450 Metabolism of xenobiotics by cytochrome P450 Drug metabolism – other enzymes Metabolic pathways Cell cycle Oocyte meiosis p53 signaling pathway Progesterone – mediated oocyte maturation Homologous recombination a b There is great genetic and genomic diversity in HCC and in the need to gain insight into its molecular alterations, we performed • Identification of differentially expressed genes and miRNAs profiles distinctive of disease staging • Construction of miRNA-mRNA interaction networks in relation to disease staging • Assessment of the clinical significance of newly identified expression profiles during HCC progression Differential expression and heatmap cluster analyses across all clinical stages RNAseqV2, miRNAseq and clinical data of Liver Hepatocellular Carcinoma (LIHC) patients were retrieved from the TCGA data portal (http://cancergenome.nih.gov/). The full clinical dataset were downloaded (up to July, 2015) and checked for the assessment of eligibility. The exclusion criteria were set to histologic diagnosis not being HCC. Overall, a total of 342 HCC patients were included in our study with the corresponding clinical data including age, gender, race, the American Joint Committee on Cancer staging system (also be called AJCC TNM staging system), tumor status, vital status, risk factors, vascular invasion, Child-Pugh classification and surgical types. Among the 367 patients (Cohort T), the adjacent non-tumor tissues were retrieved from 50 subjects (Cohort N). RNA-Seq version 2 were used in this meta-analysis generated by Illumina HighSeq. Normal samples were defined as pathologically non- cancerous tissue samples. Methodology Results Figure 1. Cluster analysis. Heatmaps of differentially expressed mRNAs (a, b, c) and miRNAs (d, e, f) of 3 comparison types: Stage I/Normal, Stage II/ Normal and Stage III/ Normal. Genes/miRNAs with adj.p-value < 0.05 and logFC > 2/logFC > 1 were considered differentially expressed. Multiple testing correction was performed using Benjamini – Hochberg (BH) method. Hierarchical clustering analysis was performed using the ward.D2 linkage and Euclidean distance method. Each column represents a specimen and each row represents a gene. Green color indicates genes that are up-regulated and red color indicates genes that are down-regulated. Black indicates genes whose expression is unchanged in tumors compared to normal. Blue and orange sample labels indicate normal and tumor samples respectively. Gene signature A Stage I Stage II Stage III mRNA miRNA Stage I Stage II Stage III Data Workflow a b c f e d Aim of the study c
Transcript of Large integrative analysis for the identification of novel ... · Panagiotis Mokos1, Fotis E...
Hepatocellular carcinoma (HCC) is the third leading cause of cancer related death worldwide. Over the past years many efforts has been made to elucidate the molecular basis of the initiation and progression of (HCC). However, most of these studies have been focused on individual genes or single type of data which may lack the power to detect the complex mechanisms of cancer formation by overlooking the interaction networks. To gain insight into the molecular driver events, the combined effect of miRNA and mRNA expression was investigated in relation to cancer staging. We utilised the raw data of miRNA- and RNA- sequencing datasets from The Cancer Genome Atlas (TCGA). We performed filtering of the data with cpm values, scaling normalization and log2 transformation for the use of linear modeling. We then identified differentially expressed genes/miRNAs from three comparisons: Stage I/Normal, Stage II/ Normal and Stage III/ Normal. Genes and miRNAs with adjusted p-value < 0.05 and log2 fold change > 2 and 1 respectively, were considered significant. For each comparison type, we found the negative correlations between differentially expressed genes and miRNAs and the corresponding p-values. We combined Targetscan and microCosm databases in order to obtain a curated list of potential miRNA-mRNA interaction pairs. For each pair, the corresponding p-value was computed, adjusted for multiple testing using the BH method, using an adjusted p-value < 0.05 to identify and retain only the significant interactions. A pathway enrichment analysis, using KEGG annotation, revealed that up-regulated genes during the HCC progression are significantly correlated with the Cell cycle, Oocyte meiosis and p53 signaling pathways. Down-regulated genes are respectively correlated with Retinol metabolism and the Drug metabolism-cytochrome P450. Furthermore, we performed survival analyses with log rank test of patients of each stage in order to assess the relationship between differential gene/miRNA expression and overall survival. In the future, it will be interesting to correlate the differential mRNA/miRNA expression profiles with DNA methylation events, somatic mutations and CNVs of each stage in order to elucidate both genetic and epigenetic mechanisms underlying the development of HCC.
Panagiotis Mokos1, Fotis E Psomopoulos2,3, Chrysanthi Ainali4, Alexandra Charalampidou5, Margarita Hadzopoulou-Cladaras1, Dimitra Dafou1
1 Department of Genetics Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, GR54 124 Thessaloniki, Greece; 2 Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR54 124 Thessaloniki, Greece; 3 Center for Research & Technology Hellas, GR57 001 Thessaloniki, Greece; 4Department of Informatics, School of Natural and Mathematical Sciences, King's College London, WC2R 2LS London, UK; 5Information Technology (IT) Center, Aristotle University of Thessaloniki, GR54 124 Thessaloniki, Greece . *e-mail: [email protected]
Large integrative analysis for the identification of novel molecular targets in hepatocellular carcinoma
Abstract
miRNA-mRNA interaction network analysis across all clinical stages
Figure 4. Kaplan- Meier survival analysis of HCC patient samples. Patients with (a, b) Stage I (c, d) Stage II and (e, f) Stage III were classified into high- or low- expressing groups based on whether the expression of gene signature A (upper lane) and B (bottom lane) was greater than the median expression level. The P values from Logrank tests comparing two KM curves are shown in the bottom right side of each figure.
Figure 2. Network analysis of 100 most significant predicted interactions between miRNAs and mRNAs in HCC development. For each comparison type: (a) Stage I/Normal, (b) Stage II/ Normal and (c) Stage III/ Normal negative correlations were computed between differentially expressed genes and miRNAs with corresponding p-values. Targetscan and microCosm databases were utilised in order to obtain a curated list of potential miRNA-mRNA interaction pairs. For each pair p-value was computed and adjusted for multiple testing using BH method and only the interactions with adjusted p-value < 0.05 were considered significant. Red-colored diamonds and blue-colored circles indicate miRNAs and mRNAs respectively. Node size represents node degree (number of edges connected to the node).
Figure 3. Pathway enrichment analysis. (a) Gene signature A and (b) B based on KEGG database. Gene signature A and B consists of genes with down- and up-regulation during HCC progression. Barplots represent the 5 more enriched KEGG terms of each signature with color coding: red indicates high enrichment, blue indicates low enrichment. The length of the bars represent the percentage of each row (KEGG category). The analysis was performed using the hypergeometric test and the adjustment for the estimated significance level to account for multiple hypothesis testing.
a
b
c
Conclusions: (1) We established differential expression profiles of miRNAs/mRNAs distinctive of each HCC clinical stage (2) We identified significant ‘hubs’ in HCC disease progression (3) We identified
prognostic expression signatures during HCC progression.
a c e
Gen
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gnat
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B
Stage I
Stage I
Stage II
Stage II
Stage III
Stage III
Time (days) Time (days)
Time (days) Time (days)
Time (days)
Time (days)
Surv
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bab
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rviv
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rob
abili
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Surv
ival
pro
bab
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rviv
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rob
abili
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Surv
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pro
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Gen
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Kaplan- Meier Survival analysis across all clinical stages
b d f
a
Pathway enrichment analysis Gene signature A Gene signature Β
Retinol metabolism
Drug metabolism- cytochrome P450
Metabolism of xenobiotics by cytochrome P450
Drug metabolism – other enzymes
Metabolic pathways
Cell cycle
Oocyte meiosis
p53 signaling pathway
Progesterone – mediated oocyte maturation
Homologous recombination
a b
There is great genetic and genomic diversity in HCC and in the need to gain insight into its molecular alterations, we performed • Identification of differentially expressed genes and miRNAs profiles distinctive of disease staging • Construction of miRNA-mRNA interaction networks in relation to disease staging • Assessment of the clinical significance of newly identified expression profiles during HCC progression
Differential expression and heatmap cluster analyses across all clinical stages
RNAseqV2, miRNAseq and clinical data of Liver Hepatocellular Carcinoma (LIHC) patients were retrieved from the TCGA data portal (http://cancergenome.nih.gov/).
The full clinical dataset were downloaded (up to July, 2015) and checked for the assessment of eligibility. The exclusion criteria were set to histologic diagnosis not being HCC. Overall, a total of 342 HCC patients were included in our study with the corresponding clinical data including age, gender, race, the American Joint Committee on Cancer staging system (also be called AJCC TNM staging system), tumor status, vital status, risk factors, vascular invasion, Child-Pugh classification and surgical types. Among the 367 patients (Cohort T), the adjacent non-tumor tissues were retrieved from 50 subjects (Cohort N). RNA-Seq version 2 were used in this meta-analysis generated by Illumina HighSeq. Normal samples were defined as pathologically non-cancerous tissue samples.
Methodology
Results
Figure 1. Cluster analysis. Heatmaps of differentially expressed mRNAs (a, b, c) and miRNAs (d, e, f) of 3 comparison types: Stage I/Normal, Stage II/ Normal and Stage III/ Normal. Genes/miRNAs with adj.p-value < 0.05 and logFC > 2/logFC > 1 were considered differentially expressed. Multiple testing correction was performed using Benjamini – Hochberg (BH) method. Hierarchical clustering analysis was performed using the ward.D2 linkage and Euclidean distance method. Each column represents a specimen and each row represents a gene. Green color indicates genes that are up-regulated and red color indicates genes that are down-regulated. Black indicates genes whose expression is unchanged in tumors compared to normal. Blue and orange sample labels indicate normal and tumor samples respectively.
Gen
e si
gnat
ure
A
Stage I Stage II Stage III
mR
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iRN
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Stage I Stage II Stage III
Data Workflow
a b c
f e d
Aim of the study
c
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