Screening of Critical Genes and MicroRNAs in Blood Samples of Patients with Ruptured Intracranial Aneurysms by Bioinformatic Analysis of Gene Expression Data

Next-generation sequencing profiling of miRNAs in individuals with 22q11.2 deletion syndrome revealed altered expression of miR-185-5p

BACKGROUND

The 22q11.2 deletion syndrome (22q11.2DS) is a microdeletion syndrome with highly variable phenotypic manifestations, even though most patients present the typical 3 Mb microdeletion, usually affecting the same ~ 106 genes. One of the genes affected by this deletion is DGCR8, which plays a crucial role in miRNA biogenesis. Therefore, the haploinsufficiency of DGCR8 due to this microdeletion can alter the modulation of the expression of several miRNAs involved in a range of biological processes.

RESULTS

In this study, we used next-generation sequencing to evaluate the miRNAs profiles in the peripheral blood of 12 individuals with typical 22q11DS compared to 12 healthy matched controls. We used the DESeq2 package for differential gene expression analysis and the DIANA-miTED dataset to verify the expression of differentially expressed miRNAs in other tissues. We used miRWalk to predict the target genes of differentially expressed miRNAs. Here, we described two differentially expressed miRNAs in patients compared to controls: hsa-miR-1304-3p, located outside the 22q11.2 region, upregulated in patients, and hsa-miR-185-5p, located in the 22q11.2 region, which showed downregulation. Expression of miR-185-5p is observed in tissues frequently affected in patients with 22q11DS, and previous studies have reported its downregulation in individuals with 22q11DS. hsa-miR-1304-3p has low expression in blood and, thus, needs more validation, though using a sensitive technology allowed us to identify differences in expression between patients and controls.

CONCLUSIONS

Thus, lower expression of miR-185-5p can be related to the 22q11.2 deletion and DGCR8 haploinsufficiency, leading to phenotypic consequences in 22q11.2DS patients, while higher expression of hsa-miR-1304-3p might be related to individual genomic variances due to the heterogeneous background of the Brazilian population.

Holistic exploration of CHGA and hsa-miR-137 in colorectal cancer via multi-omic data Integration

Colorectal cancer (CRC) ranks among the most widespread malignancies globally, with early detection significantly influencing prognosis. Employing a systems biology approach, we aimed to unravel the intricate mRNA-miRNA network linked to CRC pathogenesis, potentially yielding diagnostic biomarkers. Through an integrative analysis of microarray, Bulk RNA-seq, and single-cell RNA-seq data, we explored CRC-related transcriptomes comprehensively. Differential gene expression analysis uncovered crucial genes, while Weighted Gene Co-expression Network Analysis (WGCNA) identified key modules closely linked to CRC. Remarkably, CRC manifested its strongest correlation with the turquoise module, signifying its pivotal role. From the cohort of genes showing high Gene Significance (GS) and Module Membership (MM), and Differential Expression Genes (DEGs), we highlighted the downregulated Chromogranin A (CHGA) as a notable hub gene in CRC. This finding was corroborated by the Human Protein Atlas database, which illustrated decreased CHGA expression in CRC tissues. Additionally, CHGA displayed elevated expression in primary versus metastatic cell lines, as evidenced by the CCLE database. Subsequent RT-qPCR validation substantiated the marked downregulation of CHGA in CRC tissues, reinforcing the significance of our differential expression analysis. Analyzing the Space-Time Gut Cell Atlas dataset underscored specific CHGA expression in epithelial cell subclusters, a trend persisting across developmental stages. Furthermore, our scrutiny of colon and small intestine Enteroendocrine cells uncovered distinct CHGA expression patterns, accentuating its role in CRC pathogenesis. Utilizing the WGCNA algorithm and TargetScan database, we validated the downregulation of hsa-miR-137 in CRC, and integrated assessment highlighted its interplay with CHGA. Our findings advocate hsa-miR-137 and CHGA as promising CRC biomarkers, offering valuable insights into diagnosis and prognosis. Despite proteomic analysis yielding no direct correlation, our multifaceted approach contributes comprehensive understanding of CRC's intricate regulatory mechanisms. In conclusion, this study advances hsa-miR-137 and CHGA as promising CRC biomarkers through an integrated analysis of diverse datasets and network interactions.

Transcriptomic Studies on Intracranial Aneurysms

Intracranial aneurysm (IA) is a relatively common vascular malformation of an intracranial artery. In most cases, its presence is asymptomatic, but IA rupture causing subarachnoid hemorrhage is a life-threating condition with very high mortality and disability rates. Despite intensive studies, molecular mechanisms underlying the pathophysiology of IA formation, growth, and rupture remain poorly understood. There are no specific biomarkers of IA presence or rupture. Analysis of expression of mRNA and other RNA types offers a deeper insight into IA pathobiology. Here, we present results of published human studies on IA-focused transcriptomics.

Screening and Biological Function Analysis of miRNA and mRNA Related to Lung Adenocarcinoma Based on Bioinformatics Technology

Objective. To screen the differentially expressed miRNAs (DEMs) and the differentially expressed gene mRNAs (DEGs) in lung adenocarcinoma (LUAD) from the TCGA database and to explore the relationship between miRNAs and the prognosis of lung adenocarcinoma and their biological functions. Methods. The RNA-seq and miRNA-seq data of lung adenocarcinoma samples were downloaded from the TCGA database for analysis, and the R program was used to screen for differentially expressed miRNAs and mRNAs. Then, the molecular functions, biological processes, cellular components, and signaling pathways involved in the occurrence and development of LUAD were analyzed using the functional accumulation analysis software of GSEA. The relationship between the integrated differentially expressed RNAs was analyzed by miRcode, TargetScan, and miRTarbase databases, and the miRNA-mRNA network was constructed. Result. A total of 516 differentially expressed miRNAs and 5464 differentially expressed mRNAs were identified in LUAD. The GSEA enrichment analysis showed that miRNAs and mRNAs were mainly enriched in extracellular structure organization, external encapsulating structure organization, extracellular matrix organization, and gated channel activity. They were mainly involved in neuroactive ligand-receptor interaction signaling pathway. Some miRNAs and mRNAs in clustering modules were found to be associated with the prognosis of LUAD. Four targeting networks consisting of 22 miRNAs and 531 mRNAs were constructed. Conclusion. The miRNA and mRNA related to the prognosis of LUAD were screened out, which provided a valuable preliminary basis for the follow-upin-depth clinical research and basic experimental research of LUAD.

Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease; however, there are no comprehensive therapeutic interventions. Therefore, this study is aimed at identifying novel molecular targets that may improve the diagnosis and treatment of patients with AD.

METHODS

In our study, GSE5281 microarray dataset from the GEO database was collected and screened for differential expression analysis. Genes with a P value of <0.05 and ∣log2FoldChange | >0.5 were considered differentially expressed genes (DEGs). We further profiled and identified AD-related coexpression genes using weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis was performed to determine the characteristics and pathways of the key modules. We constructed an AD-related model based on hub genes by logistic regression and least absolute shrinkage and selection operator (LASSO) analyses, which was also verified by the receiver operating characteristic (ROC) curve.

RESULTS

In total, 4674 DEGs were identified. Nine distinct coexpression modules were identified via WGCNA; among these modules, the blue module showed the highest positive correlation with AD (r = 0.64, P = 3e - 20), and it was visualized by establishing a protein-protein interaction network. Moreover, this module was particularly enriched in "pathways of neurodegeneration-multiple diseases," "Alzheimer disease," "oxidative phosphorylation," and "proteasome." Sixteen genes were identified as hub genes and further submitted to a LASSO regression model, and six genes (EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF) were identified based on the model index. Additionally, we assessed the accuracy of the LASSO model by plotting an ROC curve (AUC = 0.940).

CONCLUSIONS

Using the WGCNA and LASSO models, our findings provide a better understanding of the role of biomarkers EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF and provide a basis for further studies on AD progression.

Identification of upregulated NF-κB inhibitor alpha and IRAK3 targeting lncRNA following intracranial aneurysm rupture-induced subarachnoid hemorrhage

Background

This study was performed to identify genes and lncRNAs involved in the pathogenesis of subarachnoid hemorrhage (SAH) from ruptured intracranial aneurysm (RIA).

Methods

Microarray GSE36791 was downloaded from Gene Expression Omnibus (GEO) database followed by the identification of significantly different expressed RNAs (DERs, including lncRNA and mRNA) between patients with SAH and healthy individuals. Then, the functional analyses of DEmRNAs were conducted and weighted gene co-expression network analysis (WGCNA) was also performed to extract the modules associated with SAH. Following, the lncRNA-mRNA co-expression network was constructed and the gene set enrichment analysis (GSEA) was performed to screen key RNA biomarkers involved in the pathogenesis of SAH from RIA. We also verified the results in a bigger dataset GSE7337.

Results

Totally, 561 DERs, including 25 DElncRNAs and 536 DEmRNAs, were identified. Functional analysis revealed that the DEmRNAs were mainly associated with immune response-associated GO-BP terms and KEGG pathways. Moreover, there were 6 modules significantly positive-correlated with SAH. The lncRNA-mRNA co-expression network contained 2 lncRNAs (LINC00265 and LINC00937) and 169 mRNAs. The GSEA analysis showed that these two lncRNAs were associated with three pathways (cytokine-cytokine receptor interaction, neurotrophin signaling pathway, and apoptosis). Additionally, IRAK3 and NFKBIA involved in the neurotrophin signaling pathway and apoptosis while IL1R2, IL18RAP and IL18R1 was associated with cytokine-cytokine receptor interaction pathway. The expression levels of these genes have the same trend in GSE36791 and GSE7337.

Conclusion

LINC00265 and LINC00937 may be implicated with the pathogenesis of SAH from RIA. They were involved in three important regulatory pathways. 5 mRNAs played important roles in the three pathways.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02156-1.

Circular RNA expression profile in human primary multiple intracranial aneurysm

Primary multiple intracranial aneurysm (MIA) is a vascular disease that frequently leads to fatal vascular rupture and subarachnoid hemorrhage. However, the epigenetic regulation associated with MIA has remained largely elusive. Circular RNAs (circRNAs) serve important roles in cardiovascular diseases; however, their association with MIA has remained to be investigated. The present study initially aimed to explore novel mechanisms of MIA through examining circRNA expression profiles. Comprehensive circRNA expression profiles were detected by RNA sequencing (RNA-Seq) in human peripheral blood mononuclear cells. The RNA-Seq results were validated by reverse transcription-quantitative PCR. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested the functions of these circRNAs. A competing endogenous RNA network was constructed to reveal the circRNA-microRNA-mRNA relationship. Among the 3,328 differentially expressed circRNAs between the MIA and matched control groups, 60 exhibited significant expression changes (|log2 fold change|≥2; P<0.05). Among these 60 circRNAs, 20 were upregulated, while the other 40 were downregulated. A number of downregulated circRNAs were involved in inflammation. The most significant KEGG pathway was ‘leukocyte transendothelial migration’. The circRNAs Homo sapiens (hsa)_circ_0135895, hsa_circ_0000682 and hsa_circ_0000690, which were also associated with the above-mentioned pathway, were indicated to be able to regulate protein tyrosine kinase 2, protein kinase Cβ and integrin subunit αL, respectively. To the best of our knowledge, the present study was the first to perform a circRNA sequencing analysis of MIA. The results specifically predicted the regulatory role of circRNAs in the pathogenesis of MIA. ‘Leukocyte transendothelial migration’ may be critical for the pathogenesis of MIA.

Identification and validation of key genes mediating intracranial aneurysm rupture by weighted correlation network analysis

Background

Rupture of intracranial aneurysm (IA) is the leading cause of subarachnoid hemorrhage. However, there are few pharmacological therapies available for the prevention of IA rupture. Therefore, exploring the molecular mechanisms which underlie IA rupture and identifying the potential molecular targets for preventing the rupture of IA is of vital importance.

Methods

We used the Gene Expression Omnibus (GEO) datasets GSE13353, GSE15629, and GSE54083 in our study. The 3 datasets were merged and normalized. Differentially expressed gene (DEG) screening and weighted correlation network analysis (WGCNA) were conducted. The co-expression patterns between ruptured IA samples and unruptured IA samples were compared. Then, the DEGs were mapped into the whole co-expression network of ruptured IA samples, and a DEG co-expression network was generated. Molecular Complex Detection (MCODE) (http://baderlab.org/Software/MCODE) was used to identify key genes based on the DEG co-expression network. Finally, key genes were validated using another GEO dataset (GSE122897), and their potential diagnostic values were shown using receiver operating characteristic (ROC) analysis.

Results

In our study, 49 DEGs were screened while 8 and 6 gene modules were detected based on ruptured IA samples and unruptured IA samples, respectively. Pathways associated with inflammation and immune response were clustered in the salmon module of ruptured IA samples. The DEG co-expression network with 35 nodes and 168 edges was generated, and 14 key genes were identified based on this DEG co-expression network. The gene with the highest degree in the key gene cluster was CXCR4. All key genes were validated using GSE122897, and they all showed the potential diagnostic value in predicting IA rupture.

Conclusions

Using a weighted gene co-expression network approach, we identified 8 and 6 modules for ruptured IA and unruptured IA, respectively. After that, we identified the hub genes for each module and key genes based on the DEG co-expression network. All these key genes were validated by another GEO dataset and might serve as potential targets for pharmacological therapies and diagnostic markers in predicting IA rupture. Further studies are needed to elucidate the detailed molecular mechanisms and biological functions of these key genes which underlie the rupture of IA.

Identification of potential key pathways, genes and circulating markers in the development of intracranial aneurysm based on weighted gene co-expression network analysis

Background: Intracranial aneurysm (IA) is a disease resulted from weak brain control, characterized by local expansion or dilation of brain artery. This study aimed to construct a gene co-expression network by Weighted Gene Correlation Network Analysis (WGCNA) to explore the potential key pathways and genes for the development of IA.Method: Six IA-related gene expression data sets were downloaded from the Gene Expression Omnibus (GEO) database for identifying differentially expressed genes (DEGs). WGCNA was used to identify modules associated with IA. Functional enrichment analysis was used to explore the potential biological functions. ROC analysis was used to find markers for predicting IA.Results: Purple, greenyellow and yellow modules were significantly associated with unruptured intracranial aneurysms, while blue and turquoise modules were significantly associated with ruptured intracranial aneurysms. Functional modules significantly related to IA were enriched in Ribosome, Glutathione metabolism, cAMP signalling pathway, Lysosome, Glycosaminoglycan degradation and other pathways. CD163, FCEREG, FPR1, ITGAM, NLRC4, PDG, and TYROBP were up-regulated ruptured intracranial aneurysms and serum, these genes were potential circulating markers for predicting IA rupture.Conclusions: Potential IA-related key pathways, genes and circulating markers were identified for predicting IA rupture by WGCNA analysis.

Identification of Key Biological Processes, Pathways, Networks, and Genes with Potential Prognostic Values in Hepatocellular Carcinoma Using a Bioinformatics Approach

Aim: Hepatocellular carcinoma (HCC), as one primary liver cancer type, accounts for 75%-85% of liver cancer cases. HCC is the second leading cause of cancer death in East Asia and sub-Saharan Africa and the sixth most common in western countries. Identification of key genes would facilitate the development of therapies and improve the prognosis outcomes of HCC patients. This study was to identify the key biological processes, pathways, and key genes in HCC. Methods: Data were downloaded from Broad GDAC. Differentially expressed genes (DEGs) and weighted gene coexpression network (WGCNA) were analyzed by DESeq2 and WGCNA, respectively. Gene ontology (GO) and KEGG enrichment analyses were performed on all DEGs and the coexpressed genes in two significant modules. Kaplan-Meier plotter online database was used to identify the potential prognostic genes in HCC. Finally, GEO database was used to validate the analysis of gene expression of Broad GDAC data. Results: The authors identified the dark gray and red modules as the significant modules in HCC based on WGCNA. GO and KEGG enrichment of the two significant modules identified the mitochondrion-mediated metabolic processes and pathways, and the cell cycle as the key biological processes and pathways in HCC. Subsequently, 28 hub genes were screened out by constructing protein-protein interaction network using Metascape. Finally, three genes (NDUFAF6, CKAP5, and DSN1 genes) were identified to be potential prognostic and key genes in HCC based on Kaplan-Meier survival analysis, GEO dataset validation, and literature review. Conclusions: The authors found that mitochondrion-mediated metabolic processes and the cell cycle were the key biological processes and pathways in HCC. NDUFAF6, CKAP5, and DSN1 genes were valuable genes with the potential to be prognosis biomarkers and targeted therapies in HCC.

Dysregulation of microRNA Modulatory Network in Abdominal Aortic Aneurysm

Abdominal artery aneurysm (AAA) refers to abdominal aortic dilatation of 3 cm or greater. AAA is frequently underdiagnosed due to often asymptomatic character of the disease, leading to elevated mortality due to aneurysm rupture. MiRNA constitute a pool of small RNAs controlling gene expression and is involved in many pathologic conditions in human. Targeted panel detecting altered expression of miRNA and genes involved in AAA would improve early diagnosis of this disease. In the presented study, we selected and analyzed miRNA and gene expression signatures in AAA patients. Next, generation sequencing was applied to obtain miRNA and gene-wide expression profiles from peripheral blood mononuclear cells in individuals with AAA and healthy controls. Differential expression analysis was performed using DESeq2 and uninformative variable elimination by partial least squares (UVE-PLS) methods. A total of 31 miRNAs and 51 genes were selected as the most promising biomarkers of AAA. Receiver operating characteristics (ROC) analysis showed good diagnostic ability of proposed biomarkers. Genes regulated by selected miRNAs were determined in silico and associated with functional terms closely related to cardiovascular and neurological diseases. Proposed biomarkers may be used for new diagnostic and therapeutic approaches in management of AAA. The findings will also contribute to the pool of knowledge about miRNA-dependent regulatory mechanisms involved in pathology of that disease.

A two-miRNA signature (miR-21 and miR-92) in peripheral whole blood as a potential biomarker for diagnosis of human cerebral aneurysms

Introduction

microRNAs (miRs) have been reported as blood-based noninvasive indicators for the diagnosis of various diseases. However, the utility of whole blood-based miRs in the diagnosis of intracranial aneurysm (IA) is still not clear. The present study aimed to examine miR expression profiling in the peripheral whole blood of IA patients and healthy controls.

Material and methods

Seventy-three IA patients, including 34 unruptured and 39 ruptured, and 28 healthy subjects, were recruited for diagnostic analysis. microRNA (miR) expression profiling in whole blood from healthy controls and IA patients was evaluated using miRNA microarray assay. RT-qPCR was used to evaluate miR expression. Receiver operating characteristics (ROC) curves and the area under the ROC curves (AUC) were used to calculate the diagnostic power of miRs in whole blood of IA.

Results

We observed significantly higher miR-21 and miR-92 expression levels in aneurysmal tissues and whole blood of IA patients as compared to healthy subjects. miR-21 expression level was significantly positively correlated with miR-92 in IA tissues and whole blood of IA patients. ROC analysis revealed that miR-21 (AUC = 0.843, sensitivity = 0.849, specificity = 0.750) and miR-92 (AUC = 0.892, sensitivity = 0.945, specificity = 0.786) were promising in diagnosis of IA with high detectability. Intriguingly, miR-21 combined with miR-92 markedly improved the diagnostic power of IA (AUC = 0.920, sensitivity = 1.000, specificity = 0.786).

Conclusions

miR-21 combined with miR-92 could be considered as a potential biomarker for IA screening.

Current status of clinical proteogenomics in lung cancer

Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.

MicroRNA-125b in vascular diseases: An updated systematic review of pathogenetic implications and clinical applications

Epigenetic changes, particularly non‐coding RNAs, have been implicated extensively in the pathogenesis of vascular diseases. Specific miRNAs are involved in the differentiation, phenotypic switch, proliferation, apoptosis, cytokine production and matrix deposition of endothelial cells and/or vascular smooth muscle cells. MicroRNA‐125b has been studied in depth for its role in carcinogenesis with a double‐edged role; that is, it can act as an oncogene in some cancer types and as a tumour suppressor gene in others. However, cumulative evidence from the use of advanced miRNA profiling techniques and bioinformatics analysis suggests that miR‐125b can be a potential mediator and useful marker of vascular diseases. Currently, the exact role of miR‐125b in vascular diseases is not known. In this systematic review, we intend to provide an updated compilation of all the recent findings of miR‐125b in vascular diseases, using a systematic approach of retrieving data from all available reports followed by data summarization. MiR‐125b serves as a pathogenic player in multiple vascular pathologies involving endothelia and vascular smooth muscle cells and also serves as a diagnostic marker for vascular diseases. We further provide a computational biologic presentation of the complex network of miR‐125b and its target genes within the scope of vascular diseases.

Identification of key modules and hub genes for small-cell lung carcinoma and large-cell neuroendocrine lung carcinoma by weighted gene co-expression network analysis of clinical tissue-proteomes

Small-cell lung carcinoma (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC) are high-grade lung neuroendocrine tumors (NET). However, comparative protein expression within SCLC and LCNEC remains unclear. Here, protein expression profiles were obtained via mass spectrometry-based proteomic analysis. Weighted gene co-expression network analysis (WGCNA) identified co-expressed modules and hub genes. Of 34 identified modules, six were significant and selected for protein-protein interaction (PPI) network analysis and pathway enrichment. Within the six modules, the activation of cellular processes and complexes, such as alternative mRNA splicing, translation initiation, nucleosome remodeling and deacetylase (NuRD) complex, SWItch/Sucrose Non-Fermentable (SWI/SNF) superfamily-type complex, chromatin remodeling pathway, and mRNA metabolic processes, were significant to SCLC. Modules enriched in processes, including signal recognition particle (SRP)-dependent co-translational protein targeting to membrane, nuclear-transcribed mRNA catabolic process of nonsense-mediated decay (NMD), and cellular macromolecule catabolic process, were characteristically activated in LCNEC. Novel high-degree hub genes were identified for each module. Master and upstream regulators were predicted via causal network analysis. This study provides an understanding of the molecular differences in tumorigenesis and malignancy between SCLC and LCNEC and may help identify potential therapeutic targets.

Identification of Key Pathways and Genes in the Dynamic Progression of HCC Based on WGCNA

Hepatocellular carcinoma (HCC) is a devastating disease worldwide. Though many efforts have been made to elucidate the process of HCC, its molecular mechanisms of development remain elusive due to its complexity. To explore the stepwise carcinogenic process from pre-neoplastic lesions to the end stage of HCC, we employed weighted gene co-expression network analysis (WGCNA) which has been proved to be an effective method in many diseases to detect co-expressed modules and hub genes using eight pathological stages including normal, cirrhosis without HCC, cirrhosis, low-grade dysplastic, high-grade dysplastic, very early and early, advanced HCC and very advanced HCC. Among the eight consecutive pathological stages, five representative modules are selected to perform canonical pathway enrichment and upstream regulator analysis by using ingenuity pathway analysis (IPA) software. We found that cell cycle related biological processes were activated at four neoplastic stages, and the degree of activation of the cell cycle corresponded to the deterioration degree of HCC. The orange and yellow modules enriched in energy metabolism, especially oxidative metabolism, and the expression value of the genes decreased only at four neoplastic stages. The brown module, enriched in protein ubiquitination and ephrin receptor signaling pathways, correlated mainly with the very early stage of HCC. The darkred module, enriched in hepatic fibrosis/hepatic stellate cell activation, correlated with the cirrhotic stage only. The high degree hub genes were identified based on the protein-protein interaction (PPI) network and were verified by Kaplan-Meier survival analysis. The novel five high degree hub genes signature that was identified in our study may shed light on future prognostic and therapeutic approaches. Our study brings a new perspective to the understanding of the key pathways and genes in the dynamic changes of HCC progression. These findings shed light on further investigations.