Identification of survival‑associated key genes and long non‑coding RNAs in glioblastoma multiforme by weighted gene co‑expression network analysis
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- Published online on: February 19, 2019 https://doi.org/10.3892/ijmm.2019.4101
- Pages: 1709-1722
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Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Glioblastoma multiforme (GBM) is an aggressive carcinoma that was first described in 1800 (1). According to the World Health Organisation classification of tumours of the central nervous system (CNS), GBM is classified as a grade IV malignant glial neoplasm with astrocytic differentiation (2). As one of the most commonly diagnosed malignant CNS tumours, it accounts for 45.6% of primary malignant brain tumours, with an annual incidence of 3.1 cases per 100,000 individuals in the United States (3). GBM is considered to be one of the most malignant primary intracranial tumours and has a dismal prognosis, <5% of patients surviving 5 years after diagnosis (3). Even after microsurgery and adjuvant temozolomide-based radiochemotherapy or radiotherapy alone, the prognosis of patients remains poor, with a median survival of 14 months (4). In recent years, clinical trials of angiogenesis inhibitor therapies have been performed on patients with recurrent GBM; however, no survival benefit was achieved (5). Furthermore, a randomised phase III clinical trial of epidermal growth factor receptor (EGFR) variant III-targeted drugs failed to confirm any survival benefit compared with the control group (6). Therefore, searching for novel candidate genes and studying the underlying mechanisms driving tumourigenesis are important to generate new therapeutic targets.
The development of microarray and high-throughput sequencing technology has provided new methods for investigating the molecular mechanisms underlying tumour behaviour and for screening drug targets. In 2012, the Chinese Glioma Genome Atlas (CGGA) was built, which provides genomic and clinical data for hundreds of samples that have been examined by whole-genome sequencing, whole-exome sequencing, DNA methylation microarray detection, as well as microRNA (miRNA), circular RNA, long non-coding RNA (lncRNA) and mRNA sequencing (7). This database represents a landmark achievement in glioma research in China.
Weighted gene co-expression network analysis (WGCNA) is a powerful method for describing the correlations among genes using microarray or RNA sequencing data. WGCNA can be applied to search for candidate biomarkers or therapeutic targets, as well as phenotype-associated modules or gene clusters based on co-expression networks (8). Compared with other analytical methods, WGCNA has the advantage of summarising and standardising the methods and functions in the R software package. Currently, this systematic method is widely used to study a number of complex diseases, particularly cancer. For instance, Lv et al (9) observed that LINC01314 functions as a tumour suppressor in hepatoblastoma. Furthermore, Clarke et al (10) indicated that KCNK5 was associated with poor outcomes of the basal-like molecular subtype in breast cancer. Using this analysis, the pathways involved in the co-expression network of cancer models and target genes can be identified. However, relevant research is currently lacking in GBM.
The present study aimed to explore the underlying molecular mechanisms, and identify novel prognostic biomarkers and treatment targets of GBM. The expression profiles of lncRNAs and mRNAs in GBM compared with those of normal controls were investigated, and differentially expressed RNAs were identified from the CGGA database. Next, the enriched pathways participating in the tumourigenesis of GBM were determined, and protein-protein interaction (PPI) networks were constructed. Finally, using public databases, potential prognostic biomarkers were confirmed, and the clinical significance and biological functions of survival-associated lncRNAs were identified.
Materials and methods
Data collection
RNA sequencing and clinical data of GBM patients were obtained from the CGGA database (http://www.cgga.org.cn). The gene expression levels were measured in terms of fragments per kilobase of transcript per million mapped reads. Various clinical data were also downloaded from the database, which included the patient gender, age, The Cancer Genome Atlas (TCGA) subtype, overall survival (OS), radiotherapy and chemotherapy details, and the mutation status of the genes isocitrate dehydrogenase (IDH), tumour protein p53 (TP53), EGFR, ATRX and enhancer of zeste homolog 2 (EZH2) (11,12). All cases with pathological diagnosis of GBM were included in the analysis. The exclusion criteria applied in the present study were as follows: i) Histologic confirmation of the diagnosis of any brain tumour type other than primary GBM; ii) history of radiotherapy or chemotherapy prior to histologic diagnosis; iii) patients with missing follow-up records; and iv) missing mutation information for the five aforementioned genes. According to these criteria, a total of 88 GBM samples were selected for inclusion in the current study (Table I). Furthermore, another gene expression and clinical dataset of GBM patients was downloaded from the TCGA database (https://cancergenome.nih.gov/), from which 162 GBM cases were selected. The dataset obtained from TCGA was analysed, and served as the validation dataset.
Construction of the gene co-expression network and identification of preserved modules
First, the expression data profiles from the CGGA were tested to confirm that they were suitable for the analysis. The standard deviation value for all samples of each gene was calculated, and the top 5,000 genes with the lowest standard deviation values were selected for subsequent analysis. Next, the co-expression network of genes was constructed using the WGCNA package in R (8). To calculate the scale-free topology fitting index r2 that corresponded to different soft-thresholding parameter β values, functional pickSoftThreshold was used, and the β value was selected if r2 reached 0.9. The soft-thresholding power β value was then set to 6, and the minModuleSize was set to 30. Subsequently, the gene expression profile was transformed into an adjacency matrix and a topological overlap matrix (TOM), which was defined as the sum of adjacency between the gene and all other genes for network generation. Next, the corresponding dissimilarity of TOM (dissTOM) was calculated, and dissTOM-based hierarchical clustering was used to produce a hierarchical clustering dendrogram of genes. Modules of clustered genes were then generated using the Dynamic Tree Cut algorithm. The module eigengene was calculated using the function moduleEigengenes, and a number of modules were merged according to a cut-off line for the module dendrogram. The interactions (correlations) of each module were analysed and visualised by heatmaps. To identify modules associated with patient characteristics, the Pearson's correlation test was used to evaluate the correlation of module eigengenes with the clinical traits, OS and mutation status, and correlations with P-values of <0.05 were considered to be statistically significant.
Gene ontology (GO) and pathway enrichment analysis
The functional enrichment of the genes of the identified module was assessed based on GO terms (13) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway (14) annotations. GO term analyses were performed using the DAVID database (https://david.ncifcrf.gov/) and Panther database (http://www.pantherdb.org) (15), which are essential tools for the success of high-throughput gene function analysis. Pathway analysis was also conducted using multiple online databases, including the DAVID database, KEGG pathway database (http://www.genome.jp/kegg) and STRING online database (http://string-db.org) (16). P-values of <0.05 were considered to denote statistically significant differences in GO term enrichment and KEGG pathway analyses, and the false discovery rate was utilised to correct the P-values.
PPI network construction and analysis
To identify the gene-encoded proteins and construct the PPI network of the identified module, the genes were mapped to the STRING database. The results obtained from this database were then imported into Cytoscape software (version 3.6.0; https://cyto-scape.org/) to analyse the interactional associations among the gene-encoding proteins and their degrees in GBM (17). In addition, significant genes from the PPI network complex were selected according to their degree of importance. The corresponding proteins may be the core proteins or key candidate genes that have significant physiological regulatory functions.
Survival analysis and validation of the genes in the TCGA dataset
To confirm the reliability of the identified genes from the CGGA data, GBM data from TCGA were then used to perform validation with the GEPIA database (http://gepia.cancer-pku.cn) (18). Through this database, the expression levels of all genes of interest in GBM and other tumours can be obtained. Furthermore, Kaplan-Meier curves were generated based on the GEPIA database. The OS was estimated using the log-rank test, and P<0.05 was considered to denote statistically significant data.
KEGG analysis of lncRNA-correlated mRNAs in GBM
In TCGA data, mRNAs having a Spearman's correlation with lncRNA of >0.4 were considered to be lncRNA-correlated mRNAs. These were then analysed by KEGG pathway enrichment analysis. A P-value of <0.05 was applied to identify the significant pathways.
Further analysis of candidate lncRNAs in GBM
The genes nearby lncRNAs were analysed by genomic region enrichment of annotations tool (GREAT version 3.0.0) (19). The potential targets of lncRNA were predicted by searching the miRDB database (http://www.mirdb.org).
Results
Gene co-expression network of GBM
To detect and explore the possible biological function of the key survival-associated genes, WGCNA was performed based on the mRNA and lncRNA profiles derived from the CGGA database. According to the exclusion criteria mentioned earlier, RNA sequencing results and the clinical data of 88 GBM samples were downloaded from the CGGA database. For module detection, 5,000 coding and non-coding RNAs were selected for further analysis from the original 21,000 genes according to the standard deviation values. One outlier sample was removed from the sample network. The TCGA subtype, gender, age, OS, radiotherapy and chemotherapy information, and the mutation status of IDH, TP53, EGFR, ATRX and EZH2 were defined as clinical traits (Fig. 1A). Analysis of the network topology was first performed for various soft-thresholding power β values to determine the relative balanced scale independence and mean connectivity of the weighted gene co-expression network. As shown in Fig. 1B, power 6 was the lowest power at which the scale-free topology fitting indices r2 reached 0.90; thus, this power was selected in order to produce a hierarchical clustering tree (dendrogram) of the 5,000 genes. In total, 21 modules were identified by hierarchical clustering and dynamic branch cutting, and each module was assigned a unique colour as an identifier (Fig. 1C and D). The largest module contained 826 genes, while the smallest contained 58 genes. The grey module represented a gene set that was not assigned to any of the modules.
Identification of the modules significantly associated with survival
To explore the survival significance of the selected modules, correlations between the OS and module eigengenes were analysed. It was observed that three modules (black, brown and light cyan) were positively correlated with OS (P<0.05). Among them, the black module had the lowest P-value. Another five modules (turquoise, blue, purple, tan and grey) were negatively correlated with OS (Fig. 2; P<0.05), with the purple module exhibiting the lowest P-value. According to the previous analysis of the survival significance of modules, the black and purple modules were selected for further analysis with OS, since they had the lowest P-values. Limited progress in targeted therapy for GBM has been made in recent years, and the majority of the therapeutic targets for this disease are oncogenes (20); thus, the purple module was further analysed in the present study, since the genes in purple module are considered to be oncogenes. In total, 195 genes, including 193 protein-coding and 2 non-coding genes, were identified in the purple module. The locations of certain of these identified genes on human chromosomes are displayed in the Circos plot in Fig. 3.
Enrichment analysis of coding genes in the purple module
The functions and pathway enrichment of the candidate genes in GBM identified from the purple module were analysed using multiple online databases, including the DAVID, Panther, KEGG pathway and STRING databases. The GO functional enrichments of genes were determined with the DAVID and Panther databases, with a P-value of <0.05 indicating statistical significance of the data. The protein-coding genes in the purple module were mapped to the GO database to determine their potential functions. GO terms were divided into three functional groups, including biological processes (BP), cell composition (CC) and molecular function (MF). The enriched GO terms for the genes are presented in Fig. 4. For the candidate genes in the purple module, the top three enriched GO terms in each category were as follows: Extracellular structure organisation, extracellular matrix (ECM) organisation and multicellular organismal macromolecule metabolic process in the BP category; ECM, extracellular region and extracellular space in the CC category; and collagen binding, ECM structural constituent and growth factor binding in the MF category (Fig. 4A and B). These results revealed that the majority of the genes were significantly enriched in extracellular structure, binding, cell parts and cell growth.
Subsequently, functional and signalling pathway enrichment of genes in the purple module was performed using the online databases DAVID, STRING and KEGG. The top enriched KEGG pathways for the candidate genes included ECM organisation, collagen formation, integrin cell surface interactions, degradation of the ECM, collagen biosynthesis and modifying enzymes, ECM proteoglycans, assembly of collagen fibrils and other multimeric structures, ECM-receptor interaction, collagen degradation and signalling by receptor tyrosine kinases (Fig. 5). The majority of the identified pathways were cancer-associated signalling pathways.
PPI network analysis of survival-associated coding genes in GBM
Data from the STRING database revealed that several of the genes interacted with each other. In total, 113 of the 193 candidate protein-coding genes were filtered to form the PPI network complex (Fig. 6). The network contained 113 nodes and 648 edges. Among the 113 nodes, the most significant 19 hub node genes were identified using a degree of ≥10 as the filtering criterion. These genes were COL1A1, COL1A2, DCN, COL3A1, FN1, MMP9, COL6A1, COL5A2, COL6A2, ITGA5, FBN1, CTGF, ITGA4, PDGFRB, LUM, PPIB, MMP14, ITGA11 and COPB1 (Table II). Among these genes, COL1A1 exhibited the highest node degree (degree =30), and FN1 shares the highest degree with 33 validated coding genes.
Validation of the survival-associated genes in TCGA dataset
To confirm the reliability of the 193 survival-associated coding genes identified from the CGGA, GBM datasets were also downloaded from TCGA database (including 162 GBM samples), and the RNA sequencing data and survival information of these datasets were subjected to Kaplan-Meier survival analysis. The results revealed significantly different OS between the high and low expression groups for 33 genes in the TCGA GBM, including ADAM12, B4GALT7, CD248, CHPF2, COL6A1, COL6A2, CYGB, DCBLD2, DERL2, DUSP6, EFNB2, EMILIN1, EPHA2, FAP, FBLN1, FN1, FZD1, HOXB2, HSP90B1, IGFBP4, LAMB1, LOXL1, MMP11, NID2, P4HB, PCOLCE, PDIA3, PLOD1, PMAIP1, PPIB, RARRES1, TBL2 and THY1 (P<0.05; Fig. 7). Therefore, the expression levels of these 33 genes may be used as predictors of OS in GBM patients, indicating that they may be candidate genes involved in GBM that deserve further investigation.
Validation and survival analysis of candidate lncRNAs in GBM
According to the co-expression network, two lncRNAs, namely LOC541471 and LOC284494, were identified as survival-associated key non-coding genes in GBM. Next, the genes nearby these two lncRNAs were analysed. The results demonstrated that 8 genes were located near the transcription start site (Fig. 8A and B). To confirm the reliability of the identified lncRNAs, TCGA GBM data were subsequently used to perform validation by GEPIA. As shown in Fig. 8C, LOC541471 was significantly overexpressed in the GBM data-sets obtained from TCGA. Next, the expression levels of these two lncRNAs in other types of cancer were explored using TCGA. It was observed that LOC541471 was overexpressed in 17 other cancer types (Fig. 8D), while LOC284494 was overexpressed in 3 other cancer types, as compared with their corresponding normal tissues (data not shown). Subsequently, survival analysis was performed in the TCGA GBM data, and a significant association with survival was observed only for LOC541471 (Fig. 8E). Therefore, LOC541471 was regarded as a core lncRNA in the network and warrants further investigation.
Functional analysis of lncRNA LOC541471 in GBM
As mentioned earlier, LOC541471 was found to be the core lncRNA in the co-expression network. To understand how this lncRNA is involved in GBM, a deeper insight into the GBM expression data from TCGA was required. Spearman's correlation of LOC541471 was calculated with ~24,300 coding genes in 156 GBM patients. It was observed that pleckstrin-2 (PLEK2) is the mRNA exhibiting the highest correlation index (r=0.68) with lncRNA LOC541471, and this gene is located on human chromosome 3p24.1 (Fig. 9A and B). According to the TCGA data, PLEK2 is significantly overexpressed in GBM tissues as compared with the normal tissue (Fig. 9C). Next, a total of 436 mRNAs that had a Spearman's correlation with LOC541471 of >0.4 were further examined by KEGG pathway analysis. Pathways were considered as enriched at a P-value of <0.05 (Fig. 9D), and the most enriched pathway was oxidative phosphorylation (OXPHOS). Finally, the expression data of GBM samples obtained from TCGA and CGGA were analysed, and the expression of LOC541471 in IDH1 wild-type was observed to be much higher compared with that in the IDH1 mutant group (Fig. 10).
Cytoplasmic lncRNAs can act as competing endogenous RNAs to modulate the functions of miRNAs (21). By searching the miRDB database (22), it was observed that LOC541471 was located in the cytoplasm, and 37 possible targets miRNAs were identified (Table III). The top 5 target miRNAs were hsa-miR-548t-3p, hsa-miR-548ap-3p, hsa-miR-548aa, hsa-miR-4288 and hsa-miR-3138. Taken together, these results predicted the potential mechanism of LOC541471; however, further studies are required to demonstrate this additional mechanism of LOC541471 in GBM.
Discussion
GBM is the most common and aggressive primary brain tumour type. The therapeutic options consist of microsurgery, and treatment with radiotherapy plus adjuvant chemotherapy with temozolomide and targeted drugs. Despite a large number of basic and clinical studies that have revealed the mechanisms underlying the formation and development of GBM in the past decades, the prognosis for this disease remains considerably poor (23-25). Therefore, searching for new candidate genes is an important part of studying the disease tumourigenesis and would help identify novel therapeutic targets. A number of previous studies have used regulatory network methods to analyse gene expression data, and these approaches focus not only on the differences, but also on the correlations between gene expression datasets (26-28). Such studies have provided numerous relevant molecular mechanisms and valuable biomarkers for GBM. However, these methods use a hard threshold to determine the correlation between genes and do not consider the changes in the correlation intensity between genes under different conditions. Therefore, more appropriate methods are required.
WGCNA is a novel statistical method analysing gene correlations that is based on scale-free topology and construction of a weighted network via soft thresholds. It is not only used to construct gene networks and detect sub-networks, but also to identify hub genes and select candidate biomarker genes (8). In general, module checking in WGCNA requires a knowledge-independent process, and has been widely and successfully applied in biological function analysis in various diseases (29-31). Furthermore, it is well known that the mutation of genes contributes to GBM tumourigenesis; however, the role of lncRNAs has not yet been fully researched.
In the present study, a comprehensive analysis of lncRNA and mRNA profiling data of GBM patients obtained from the public database CGGA was performed. Subsequently, the modules associated with the OS of GBM cases were identified, the coding genes of the module were analysed by GO and KEGG enrichment analyses, and the PPI network was constructed. Next, genes associated with survival were identified by Kaplan-Meier survival analysis. Finally, the core lncRNAs and their biological functions were identified, and their expression was validated using the GBM dataset obtained from TCGA. In conclusion, our work has identified a gene set involving the survival and tumourigenesis of GBM in which lncRNAs play a critical role.
The gene expression profile of GBM obtained from CGGA was initially examined in the present study. A total of 5,000 genes with the lowest standard deviation values were selected, since these were considered as more likely to provide good data quality. Next, the purple module was identified by WGCNA, in which genes were significantly associated with OS of GBM patients.
Subsequent GO and KEGG pathway analyses of mRNAs in the purple module revealed that the majority of the genes were significantly enriched in extracellular structure, binding, cell part and cell growth. They were also involved in pathways relevant to tumour progression and migration, such as ECM organisation, collagen formation, integrin cell surface interactions, degradation of the ECM, collagen biosynthesis and modifying enzymes, and ECM-receptor interactions. These results can explain the correlations between the purple module and the OS of GBM patients.
According to the present study analysis, the top enriched GO terms were associated with ECM. It is well known that ECM is considered to be the key component in the spread of gliomas throughout the brain (32). In addition, the ECM micro-environmental composition (33) and its mechanical force (34) influence the migration of glioma cells. ECM-binding proteins were considered to be indispensable elements of GBM migration (35). Furthermore, the degradation of ECM-components involves various proteases and hyaluronidases, which are important not only in promoting glioma cell migration by ECM decomposition, but also in releasing various growth and chemotactic factors that stimulate growth, survival and migration of GBM (36).
The current study identified 195 genes in the purple module, which were markedly associated with OS. Additionally, there were 33 candidate genes associated with the OS of patients with GBM according to TCGA data, and these may serve as prognostic biomarkers for GBM. Data from the STRING database further revealed that 113 genes of the 193 candidate protein-coding genes were filtered into the PPI network complex. Among these candidate genes, COL1A1 exhibited the highest degree in the network. COL1A1 encodes the major component of type I collagen, which is the fibrillar collagen found in the majority of connective tissues. Previous studies have reported that COL1A1 is upregulated in the microvasculature of proliferating GBMs (37). In addition, COL1A1 may be considered for use in stratifying patients with GBM into subgroups according to the risk of recurrence at diagnosis, as well as for prognostic and therapeutic evolution (38). These previous observations are consistent with the current research findings, suggesting that COL1A1 may also be a potential target for GBM therapy.
The current study also identified that FN1 shares the highest degree with 33 validated coding genes. Fibronectin is a high-molecular-weight (~440 kDa) glycoprotein of the ECM that binds to membrane-spanning receptor proteins, known as integrins (39). Researchers have proven that FN1 is upregulated by TWIST1, which is known to promote epithelial-mesenchymal transition and/or GBM invasion (40). Furthermore, FN1 is associated with glioblastoma recurrence and can be regarded as a target for antiangiogenic therapy (41). The current study findings suggest that COL1A1 and FN1 are associated with migration, invasion, angiogenesis, recurrence and OS in GBM patients. Thus, these genes may serve important roles in the tumourigenesis of GBM.
The important roles of certain lncRNAs in GBM were clearly demonstrates in the current study. However, understanding the functions of these lncRNAs is challenging, since numerous lncRNAs are not included in the public databases. In the present study, it was demonstrated that LOC541471 was highly correlated with PLEK2 in GBM. Therefore, the biological functions of the lncRNA LOC541471 were further examined, and it was observed that it participates in the regulation of biological networks. KEGG pathway enrichment analysis for all protein-coding genes exhibiting Spearman's correlations with lncRNA LOC541471 of >0.4 revealed that the most enriched pathway was OXPHOS. OXPHOS is the metabolic pathway in which cells use enzymes to oxidise nutrients, thereby releasing energy that is used to produce adenosine triphosphate (42). This pathway serves an important role in the energy supply of GBM. Researchers have reported that IMP2 controls oxidative phosphorylation and is crucial for preserving glioblastoma cancer stem cells (43). Additionally, OXPHOS complexes may be clearly altered in GBM compared with normal brain tissue (44). Research progress in targeted therapies also indicates that the simultaneous targeting of glycolysis and OXPHOS is highly effective in blocking GBM tumourigenic phenotypes (45). Therefore, the present study results revealed that LOC541471 may serve a core role in the tumourigenesis of GBM, and may be a novel oncogene worth further study.
It should be noted that the current study examined a limited number of cases. The CGGA database only discloses a small part of the data, and these include gliomas from Grade I to Grade IV, as well as certain recurrent GBM cases. Therefore, only 88 cases met the requirements of the present study. It was attempted to add more data from other databases, however, standardisation of data from different sequencing platforms is not possible. Therefore, these 88 cases were selected for analysis. As more data become available in the CGGA database, more cases will be included in future studies.
In conclusion, using GBM data from the CGGA database and integrated WGCNA, a survival-associated gene module was identified, and 195 candidate key genes were obtained from this gene module. Among them, 33 key genes were proven to be correlated with OS, and the majority of the genes were involved in pathways associated with the ECM, ECM proteoglycans and tyrosine kinases receptor signalling. Furthermore, the lncRNA LOC541471 was identified as an OS-associated lncRNA, and appeared to serve a role in the OXPHOS of GBM through the PLEK2 gene. These findings may significantly enhance our understanding on the aetiology and underlying molecular events of GBM, and these candidate genes and pathways may serve as novel prognostic markers and potential therapeutic targets for GBM.
Funding
This study was supported by the National Natural Science Foundation of China (grant nos. 81472661 and 81872048), the National Key Research and Development Program of China (grant no. 2016YFA0500303), the Beijing Municipal Natural Science Foundation (grant no. 7161004) and the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (grant no. ZYLX201608).
Availability of data and materials
The analysed datasets generated during the study are available from the corresponding author on reasonable request.
Authors' contributions
XC, YSo and LZ conceived and designed the experiments. CP, LK and XX performed the GO and PPI analyses. CX, YSu, YG collected and performed the WGCNA. ZZ, WZ and LH constructed the Circos plot and other figures. XC, YSo and LZ wrote the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Abbreviations:
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Abbreviations: WGCNA |
weighted gene co-expression network analysis; GBM, glioblastoma multiforme; lncRNA, long non-coding RNA; GO, Gene Ontology; CNS, central nervous system; CGGA, Chinese Glioma Genome Atlas; PPI, protein-protein interaction; OS, overall survival; TCGA, The Cancer Genome Atlas; TOM, topological overlap matrix; KEGG, Kyoto Encyclopaedia of Genes and Genomes; BP, biological processes; CC, cell composition; MF, molecular function; ECM, extracellular matrix; OXPHOS, oxidative phosphorylation |
Acknowledgments
Not applicable.
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