Abstract

Background and Aim

   The hepatitis B virus (HBV) is one of the most common causes of liver
   cancer in the world. This study aims to provide a better understanding
   of the mechanisms involved in the development and progression of
   HBV‐associated hepatocellular carcinoma (HCC) by identifying hub genes
   and the pathways related to their functions.

Methods

   [32]GSE83148 and [33]GSE94660 were selected from the Gene Expression
   Omnibus (GEO) database, differentially expressed genes (DEGs) with an
   adjusted p‐value < 0.05 and a |logFC| ≥1 were identified. Common DEGs
   of two data sets were identified using the GEO2R tool. The Kyoto
   Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO)
   databases were used to identify pathways. Protein−protein interactions
   (PPIs) analysis was performed by using the Cytoscap and Gephi. A Gene
   Expression Profiling Interactive Analysis (GEPIA) analysis was carried
   out to confirm the target genes.

Results

   One hundred and ninety‐eight common DEGs and 49 hub genes have been
   identified through the use of GEO and PPI, respectively. The GO and
   KEGG pathways analysis showed DEGs were enriched in the G1/S transition
   of cell cycle mitotic, cell cycle, spindle, and extracellular matrix
   structural constituent. The expression of four genes (TOP2A, CDK1,
   CCNA2, and CCNB2) with high scores in module 1 were more in tumor
   samples and have been identified by GEPIA analysis.

Conclusion

   In this study, the hub genes and their related pathways involved in the
   development of HBV‐associated HCC were identified. These genes, as
   potential diagnostic biomarkers, may provide a potent opportunity to
   detect HBV‐associated HCC at the earliest stages, resulting in a more
   effective treatment.

   Keywords: diagnostic biomarkers, HBV, hepatocellular carcinoma, PPI
   network

1. INTRODUCTION

   Hepatitis B virus (HBV) infection constitutes a prominent global health
   issue with a prevalence of about two billion. Despite the widespread
   availability of efficacious HBV vaccines, the persistence of
   approximately 350 million individuals as chronic HBV carriers worldwide
   underscores the enduring challenge posed by this infectious agent.[34]
   ^1 , [35]^2 Moreover, infection with chronic HBV is known as the most
   common cause of hepatocellular carcinoma (HCC).[36] ^3 HCC is the fifth
   most common cancer and the third cause of cancer‐related death in the
   world.[37] ^4 In individuals afflicted by HBV infection and subsequent
   hepatic fibrosis, the potential emergence of HCC becomes a
   consequential clinical consideration.[38] ^2 Several diagnostic
   methods, including serum alpha‐fetoprotein (AFP) levels and abdominal
   ultrasound tests, are utilized to detect early‐stage HCC. However,
   these methods frequently exhibit limited sensitivity in their ability
   to identify HCC during its initial phases, potentially due to the
   influence of environmental factors. Furthermore, liver biopsy, being an
   invasive procedure with inherent limitations in interpretation,
   frequently results in the diagnosis of HBV‐related HCC at advanced
   stages, consequently impeding the effectiveness of treatment.[39] ^5

   It has been predicted that by 2030, HCC will remain the third leading
   cause of cancer‐related death, and it is a disease with a poor
   prognosis.[40] ^6 As a result, identification of the mechanisms
   responsible for the oncogenic activity of HBV could assist in the
   development of effective treatments for HBV‐related HCC. Understanding
   these mechanisms paves the way for further progress in the field of
   diagnosis and treatment in the future. Nowadays, the advancement of
   novel technologies, particularly in the realm of biomarker development,
   has significantly augmented the capacity for prognostication and
   prediction in HCC patient care. It is conceivable that these biomarkers
   can be used to detect HBV‐related HCC at very early stages without the
   need for imaging studies or a tumor biopsy.[41] ^2

   Therefore, utilizing molecular biomarkers with high sensitivity is
   considered an effective method for detecting HCC in its early stages,
   predicting disease progression, definitively diagnosing the disease,
   providing timely treatment, reducing treatment costs, and enhancing
   survivability. For example, Huang et al. indicated that the ACBD4 gene
   is significantly downregulated in HBV‐associated HCC patients and is
   correlated with a decrease in overall survival rate. They revealed that
   ACBD4 expression can serve as biomarkers of OS in HBV‐related HCC
   patients after hepatectomy.[42] ^7 In addition, it is essential to use
   various bioinformatics methods to identify the primary genes and
   biological pathways that control tumor growth and advancement to
   uncover the molecular processes involved in cancer formation. This
   information may be used to create novel biomarkers or therapy
   approaches to enhance the prognosis of individuals with liver cancer.
   Gene expression analysis in cancer may independently predict survival
   and impact therapy decision.[43] ^8 In this regard, data analysis was
   used to identify the key genes and pathways involved in the development
   of HBV‐related HCC in the present study. The hub genes that were highly
   expressed in HBV infection and HCC, the molecular pathways involved,
   and the target genes whose high expression reduced the survival rate
   were identified and discussed. Consequently, enhanced comprehension of
   the mechanisms implicated in the evolution and advancement of
   HBV‐related HCC empowers us to administer efficacious treatments. By
   promptly detecting the disease and intervening in a timely manner,
   prognosis and diagnosis through the utilization of these genes and
   their associated pathways have the potential to reduce mortality rates
   and optimize treatment strategies.

2. METHODS AND MATERIALS

2.1. Microarray data

   Using [44]GSE83148, which included gene expression data from 122 liver
   samples from chronic hepatitis B patients and six liver samples from
   healthy controls which was based on the [45]GPL570 (Affymetrix Human
   Genome U133 Plus 2.0 Array) and [46]GSE94660, an RNA‐seq data set which
   contains 21 HCC samples and 21 non‐neoplastic liver samples which was
   retrieved platform [47]GPL16791 (Illumina HiSeq. 2500 [Homo sapiens])
   in the Gene Expression Omnibus (GEO)
   ([48]http://www.ncbi.nlm.nih.gov/geo/) were downloaded. GEO can be a
   useful database, since it provides hundreds of thousands of microarray
   gene expression data sets freely available for download and usage. The
   two data sets meet the following criteria: this experiment all met
   three criteria: (1) samples from human HBV‐infected liver tissue; (2)
   with case‐control group; and (3) sample number >40.

2.2. Common differentially expressed genes (DEGs) in HCC patients

   An analysis of common DEGs was performed between HCC specimens and
   normal specimens was performed in GEO2R
   ([49]https://www.ncbi.nlm.nih.gov/geo/geo2r/). The DEGs were screened
   according to adjusted p values < 0.05 and |log FC| (fold change) ≥1.
   Then, the common DEGs expressed jointly in the two data files were
   identified with the Venn diagram web tool
   (bioinformatics.psb.ugent.be/webtools/Venn/).

2.3. Functional enrichment analysis

   Gene ontology (GO) ([50]http://www.geneontology.org) is one of the most
   widely used methods in bioinformatics to annotate genes and their
   products on a large scale and is widely used in research. In this
   course, we will be covering three different aspects of biology, as well
   as biological process (BP), molecular function (MF), and cellular
   component (CC). By using the Kyoto Encyclopedia of Genes and Genomes
   (KEGG) ([51]https://www.kegg.jp/), which is an extremely practical
   database resource, polymer experiments and genome sequencing can be
   done with ease. To determine which pathways a particular gene is
   enriched in based on the molecular information that is available, such
   as macromolecular data sets, one must use molecular information. To
   explore the functions of these DEGs, the DAVID database
   ([52]https://david.ncifcrf.gov/) was used to perform GO term analysis
   at first. Then, we submitted 198 common DEGs into the Enrichr database
   to perform a KEGG pathway enrichment analysis. p < 0.05 was set to
   indicate a statistically significant difference.

2.4. Protein−protein interactions (PPI) network construction and performance
analysis

   For the purpose of determining the hub genes based on their
   PPI network, DEGs were imported to the STRING server
   ([53]https://string-db.org; version 11.5). A PPI network with
   centrality parameters such as degree, betweenness, and closeness was
   used to determine the centrality parameters for the PPI network. To
   construct the PPI network, Cytoscape (version 3.6.0) was utilized as a
   tool, so the output file generated by STRING was imported into
   Cytoscape as a source for the analysis of significant genes in the
   network. As a result of using Cytoscape, hub genes were identified
   based on centrality parameters, including degree, betweenness, and
   closeness. Hub genes can be identified by using these genes as
   candidates, the output file was imported into the Gephi package to help
   screen and cluster the hub genes.

2.5. Verification and survival analysis

   To enrich the characterization of these genes, we employed the
   “Enrichr” software‐a tool facilitating the analysis of modules derived
   from Gephi software. This utilization enables us to delve into the
   attributes of hub genes and explore their values effectively. Gene
   Expression Profiling Interactive Analysis (GEPIA), an interactive web
   server developed by Peking University, serves the purpose of analyzing
   and visualizing RNA sequencing expression data. To identify biomarkers
   associated with HCC, we conducted a differential mRNA expression
   analysis with the “single gene analysis” module of GEPIA.

2.6. Statistical analysis

   We were able to calculate the values of the DEGs taken from the GEO
   data sets. Adjusted p‐values less than 0.05 were considered
   statistically significant and were counted as significant data. For GO
   and KEGG enrichment analyses, the threshold for significance is
   considered to be a p‐value < 0.05, which is considered the cut‐off
   point for significant results. In the Analysis‐Box Plots module of the
   GEPIA with the settings of p‐values < 0.05, log FC < 1, and a Match
   TCGA Normal to GTEx Data, allowing us to explore expression levels of
   genes associated with LIHC. Our biomarkers were selected with degree
   >45 and p‐value < 0.05.

3. RESULTS

3.1. Common DEGs in HCC patients

   [54]GSE83148 and [55]GSE94660 were selected from the GEO database.
   Using the GEO2R online tool, we obtained 927 and 2114 DEGs from
   [56]GSE83148 and [57]GSE94660, respectively, adjusted p value < 0.05
   and |log FC| ≥1 as the cutoff criteria to identify DEGs, then, we
   applied Venn diagram software to detect the common DEGs among the two
   data sets (Figure [58]1). Results showed that 198 common DEGs were
   obtained (a list of the 198 common DEGs is provided in Supporting
   Information S1: Table [59]1).

Figure 1.

   Figure 1
   [60]Open in a new tab

   Identification of 198 common DEGs among [61]GSE83148 and [62]GSE94660
   data sets by Venn diagram software. Different colors represent
   different data sets. DEGs, differentially expressed genes.

3.2. GO and KEGG pathway enrichment the common DEGs

   GO annotation and KEGG pathways enrichment analysis were conducted
   through the DAVID database and Enrichr database, respectively. The top
   10 enriched GO term and KEGG pathways are shown in Table [63]1. As
   shown in Table [64]1, GO BP analysis revealed that these 198 DEGs were
   significantly enriched in the G1/S transition of the mitotic cell
   cycle, mitotic sister chromatid segregation, chromosome segregation,
   and mitotic cell cycle phase transition, The top four significantly
   enriched CCs terms included spindle, kinetochore, cyclin‐dependent
   protein kinase holoenzyme complex, and mitotic spindle. For GO MF
   analysis, the top four significantly enriched terms were extracellular
   matrix (ECM) structural constituent, protein binding, chemokine
   activity, and platelet‐derived growth factor binding. Additionally, the
   top four markedly enriched pathways for these 198 DEGs were cell cycle,
   human T‐cell leukemia virus 1 infection, ECM‐receptor interaction, and
   viral protein interaction with cytokine and cytokine receptors.

Table 1.

   Functional and pathway enrichment analysis of the common DEGs.
   Category Term Count p Value
   GOTERM_BP_DIRECT GO:0000082~G1/S transition of mitotic cell cycle 8
   3.56605035609663E−06
   GOTERM_BP_DIRECT GO:0000070~mitotic sister chromatid segregation 6
   2.58200905140327E−05
   GOTERM_BP_DIRECT GO:0007059~chromosome segregation 8
   3.41219711633171E−05
   GOTERM_BP_DIRECT GO:0044772~mitotic cell cycle phase transition 5
   7.87113248359031E−05
   GOTERM_BP_DIRECT GO:0000079~regulation of cyclin‐dependent protein
   serine/threonine kinase activity 6 1.44251564141268E−04
   GOTERM_BP_DIRECT GO:0001503~ossification 7 1.61574155502457E−04
   GOTERM_BP_DIRECT GO:0006935~chemotaxis 8 1.70314932774602E−04
   GOTERM_BP_DIRECT GO:0031640~killing of cells of other organisms 7
   2.19580406966191E−04
   GOTERM_BP_DIRECT GO:0007155~cell adhesion 16 2.75080469937276E−04
   GOTERM_BP_DIRECT GO:0007267~cell−cell signaling 10 3.53841839991809E−04
   GOTERM_CC_DIRECT GO:0005819~spindle 11 1.24320674807025E−06
   GOTERM_CC_DIRECT GO:0000776~kinetochore 10 1.97418504817842E−05
   GOTERM_CC_DIRECT GO:0000307~cyclin‐dependent protein kinase holoenzyme
   complex 6 5.67366371540544E−05
   GOTERM_CC_DIRECT GO:0072686~mitotic spindle 9 6.03463363997726E−05
   GOTERM_CC_DIRECT GO:0000775~chromosome, centromeric region 6
   3.30619625206371E−04
   GOTERM_CC_DIRECT GO:0030496~midbody 9 3.7391894954945E−04
   GOTERM_CC_DIRECT GO:0005634~nucleus 77 0.0012684941777735500
   GOTERM_CC_DIRECT GO:0005604~basement membrane 6 0.0019456534558584200
   GOTERM_CC_DIRECT GO:0015630~microtubule cytoskeleton 8
   0.002367388970954160
   GOTERM_CC_DIRECT GO:0005788~endoplasmic reticulum lumen 10
   0.0024280317810708000
   GOTERM_MF_DIRECT GO:0005201~extracellular matrix structural constituent
   11 0.0000009454919970515
   GOTERM_MF_DIRECT GO:0005515~protein binding 147 3.75883910263759E−05
   GOTERM_MF_DIRECT GO:0008009~chemokine activity 6 1.14979330928152E−04
   GOTERM_MF_DIRECT GO:0048407~platelet‐derived growth factor binding 4
   1.35498266777386E−04
   GOTERM_MF_DIRECT GO:0048248~CXCR3 chemokine receptor binding 3
   8.98486873086342E−04
   GOTERM_MF_DIRECT GO:1990837~sequence‐specific double‐stranded DNA
   binding 15 0.0010465795548514000
   GOTERM_MF_DIRECT GO:0019901~protein kinase binding 14
   0.0016022040556511200
   GOTERM_MF_DIRECT GO:0008201~heparin binding 8 0.0017349747635131200
   GOTERM_MF_DIRECT GO:0000987~core promoter proximal region
   sequence‐specific DNA binding 5 0.0023238664167645900
   GOTERM_MF_DIRECT GO:0016538~cyclin‐dependent protein serine/threonine
   kinase regulator activity 4 0.003512810556673530
   KEGG_PATHWAY hsa04110:Cell cycle 13 0.000000178738363661
   KEGG_PATHWAY hsa05166:Human T‐cell leukemia virus 1 infection 11
   1.98104521944018E−04
   KEGG_PATHWAY hsa04512:ECM‐receptor interaction 7 4.95605218722635E−04
   KEGG_PATHWAY hsa04061:Viral protein interaction with cytokine and
   cytokine receptor 6 0.005446431697476530
   KEGG_PATHWAY hsa05169:Epstein‐Barr virus infection 8
   0.00789823594609545
   KEGG_PATHWAY hsa04115:p53 signaling pathway 5 0.00975613396479264
   KEGG_PATHWAY hsa05165:Human papillomavirus infection 10
   0.012532645726405700
   KEGG_PATHWAY hsa04114:Oocyte meiosis 6 0.016379614160842600
   KEGG_PATHWAY hsa05215:Prostate cancer 5 0.024147672961561200
   KEGG_PATHWAY hsa04914:Progesterone‐mediated oocyte maturation 5
   0.028386977938644300
   [65]Open in a new tab

   Abbreviations: DEGs, differentially expressed genes; GO, gene ontology;
   KEGG, Kyoto encyclopedia of genes and genomes.

   To learn more about the common DEGs found in HCC patients, GO and KEGG
   pathways associated with these DEGs were analyzed by Enrichr. It has
   been shown that several common DEGs play a significant role in the
   onset and progression of HCC, such as the cell cycle (p‐value:
   4.219e−10), human T‐cell leukemia virus 1 infection (p‐value:
   0.00001543), cellular response to chemokine (p‐value: 1.396e−7),
   mitotic cell cycle phase transition (p‐value: 1.967e−7), spindle
   (p‐value: 3.286e−8), collagen‐containing ECM (p‐value: 2.830e−7),
   chemokine activity (p‐value: 0.000006782), and cyclin‐dependent protein
   serine/threonine kinase regulator activity (p‐value: 0.000006782)
   (Supporting Information S1: Figure [66]1).

3.3. PPI network and Hub genes

   To construct a PPI network, we used the STRING database as a foundation
   (Figure [67]2A). To draw the PPI network, Cytoscape software was
   employed (Figure [68]2B). It is possible to identify strong molecular
   interactions that are influencing the progression of disease by
   analyzing PPI networks. A total of 134 nodes were identified as DEGs in
   the network (number of nodes: 134, clustering coefficient: 0.502,
   network centralization: 0.253). We ranked the hub genes according to
   their degree, closeness, and betweenness centrality (Supporting
   Information S1: Table [69]2). Using the STRING server and the
   parameters applied to identify the key hubs in the network, the common
   genes found in the top 49 genes were identified as being the network's
   key hubs (number of nodes: 49, clustering coefficient: 0.457, network
   centralization: 0.209) (Supporting Information S1: Table [70]3).

Figure 2.

   Figure 2
   [71]Open in a new tab

   Protein–protein interaction (PPI) analysis. The size and the color of
   nodes represent the degree and betweenness, respectively. (A) PPI
   networks of the common differentially expressed genes (DEGs) from the
   [72]GSE83148 and [73]GSE94660, (B) PPI network of Hub genes of the
   common DEGs from the [74]GSE83148 and [75]GSE94660.

3.4. Clustering of Hub genes

   Gephi 0.9.2 ([76]www.gephi.com) was used to reconstruct the PPI network
   of the hub genes, and hub genes were clustered as a result of this
   process (Figure [77]3A).

Figure 3.

   Figure 3
   [78]Open in a new tab

   (A) Network visualization and analysis of hub genes from the PPI
   network of hub genes by Gephi. The size and the color of nodes
   represent the degree and betweenness, respectively. (B) Network
   visualization and analysis of hub genes in module 1.
   PPI, Protein−protein interactions.

   Table [79]2 shows a graph of the interactions between hub genes. A
   total of five modules were identified as clusters within the network.
   The centrality parameters were then calculated for each module to
   determine its centrality. The interaction network of hub genes in
   module 1 had a strong interaction and high degree among each other
   (Figure [80]3B).

Table 2.

   The list of top ranked genes computed by Gephi.
   Module 1 Module 2 Module 3 Module 4 Module 5
   Gene symbol Degree Gene symbol Degree Gene symbol Degree Gene symbol
   Degree Gene symbol Degree
   TOP2A 49 SNAP25 4 COL1A1 21 ANKRD22 4 FBXO32 2
   CDK1 48 DTNA 2 SPP1 19 SLAMF8 3 NEDD4L 2
   CCNA2 47 STX6 2 COL1A2 16
   CCNB2 46 SSPN 2 SOX9 14
   AURKA 46 BHLHE41 2 THBS2 13
   ZWINT 44 JAG1 11
   HMMR 44 LEF1 11
   ANLN 43 CXCL10 11
   TYMS 42 CXCL9 10
   NEK2 42 CD24 9
   ASPM 42 LGR5 9
   CCNE2 35 CCL20 8
   E2F7 32 EPCAM 8
   BARD1 13 MCAM 8
   CCND2 11 VCAN 7
   CDKN2C 7 SOX4 7
   USP21 2 CD3D 7
   ENAH 2 LY75 6
   PFKP 2 MGP 6
   NPNT 3
   [81]Open in a new tab

3.5. Verification of the hub genes

   It has been shown by the GEPIA that some genes have significant
   prognostic value in HCC. The interaction network of hub genes in module
   1 had a strong interaction and high degree among each other
   (Figure [82]3B). The expression of figure genes with high degrees in
   module 1 was higher in HCC samples than in normal samples such as DNA
   Topoisomerase 2‐Alpha (TOP2A), Cyclin‐dependent kinase 1 (CDK1), Cyclin
   A2 (CCNA2), and Cyclin B2 (CCNB2) in module 1 (Table [83]2). Therefore,
   these genes may be introduced as potential biomarkers for HCC,
   including TOP2A (p < 0.05), CDK1 (p < 0.05), CCNA2 (p < 0.05), and
   CCNB2 (p < 0.05) among other ones (Figure [84]4).

Figure 4.

   Figure 4
   [85]Open in a new tab

   The Box plots of the core genes. TOP2A, CDK1, CCNA2, and CCNB2 showed a
   significant difference between normal and tumor samples.
   (*p < 0.05). CCNA2, cyclin A2; CCNB2, cyclin B2; CDK1, cyclin‐dependent
   kinase 1; TOP2A, topoisomerase 2‐alpha.

4. DISCUSSION

   HCC is the predominant form of primary liver cancer in adults and is a
   leading cause of mortality in people with liver cirrhosis. HCC is
   strongly associated with HBV infection worldwide. Individuals with HBV
   are checked for HCC using AFP blood levels and abdominal
   ultrasonography every 6 months, yet HBV‐related HCC is still the
   primary cause of death in these individuals. Discovering novel
   biomarkers and regulatory mechanisms associated with HCC development in
   HBV‐infected individuals may improve the early detection, diagnosis,
   and treatment of HCC.[86] ^9 In this regard, we identified 198 common
   DEGs between two gene expression data sets ([87]GSE83148 and
   [88]GSE94660) related to HBV‐associated HCC. GO and pathway analysis
   revealed these DEGs were significantly enriched in processes like G1/S
   transition of cell cycle mitotic, cell cycle, spindle, and ECM
   structural constituent. In addition, from the PPI network analysis, 49
   hub genes were identified as potentially important regulators. Further
   analysis highlighted four specific hub genes—CCNA2, CCNB2, CDK1, and
   TOP2A—that showed significantly higher expression in HBV‐associated HCC
   tumor samples compared to normal samples in the GEPIA database. The
   results suggest CCNA2, CCNB2, CDK1, and TOP2A may serve as potential
   diagnostic biomarkers for early detection of HBV‐associated HCC.

   Biomarkers may be found in tumor tissues, serum, and other body fluids.
   Several biomarker detection techniques using extremely specific
   recognition biomarkers have been created. ELISA, gel electrophoresis,
   SPR, Mass‐sensing BioCD protein array, SERS, colorimetric test,
   electrochemical assay, and fluorescence approaches are some biomarker
   detection techniques. Several approaches rely on traditional
   immunoassays where a capture antibody is attached to a solid support to
   capture the target, and a reporter antibody is used for assay read‐out.
   These methods are affected by the common issue of nontarget proteins
   adsorbing onto the surface of a biosensor.[89] ^10 Most existing
   approaches have limitations like insufficient accuracy, sensitivity,
   and specificity needed for clinical diagnostic purposes. Moreover,
   blood‐based biomarkers show promise in cancer screening and might be
   used not only for assessing general population risk but also for
   evaluating treatment response and tracking recurrence.[90] ^11 The
   primary objective of biomarker detection is to create a dependable,
   economical, and potent detection instrument for forecasting,
   identification, and tracking the reappearance of a certain illness.
   Several studies have used extensive bioinformatics analysis to discover
   central genes from PPI networks established using DEGs in several
   cancer types, including non‐small cell lung cancer (NSCLC),[91] ^12
   breast cancer,[92] ^13 and colorectal cancer.[93] ^14 It is worth
   noting that in the field of bioinformatics analysis, distinct research
   groups have the potential to uncover identical prognostic biomarkers
   utilizing disparate data sets.[94] ^13 , [95]^15 This occurrence might
   potentially enhance the importance of data mining. The process of
   hepatocarcinogenesis triggered by HBV is intricate and influenced by
   several factors. In recent years, the use of bioinformatics techniques,
   such as microarray and next‐generation sequencing, has been prevalent
   in the analysis of large‐scale data. These approaches can be employed
   to investigate the underlying processes of HBV‐related HCC.

   CCNA2, a cyclin family member, has a crucial function in the cell cycle
   and tumor development.[96] ^16 In compatible with our findings, recent
   studies also showed that overexpression of CCNA2 is associated with
   progression of HCC linked to HBV infection.[97] ^17 , [98]^18 CCNA2
   interacts with CDK2 in the S phase and CDK1 at the G2/M transition to
   aid in DNA synthesis and chromosomal condensation. Research has shown
   that HBV often integrates into the CCND1 gene, which encodes a cyclin
   important in the cell cycle, and that CCNA2 is often elevated in
   HBV‐associated HCC.[99] ^19 Dysregulation of CCNA2 may result in
   uncontrolled cell proliferation and promote tumor formation.[100] ^20
   As indicated in our results obtained through GO annotation and KEGG
   pathways enrichment analysis, cell cycle and G1/S transition of cell
   cycle mitotic are critical pathways, which were dysregulated through
   HCC progression affected by HBV infection. Indeed, the CCNA2 gene
   dysregulation is linked to the development of HCC in HBV‐infected
   individuals.[101] ^18 HBV infection may trigger the activation of CCNA2
   and other genes involved in the cell cycle, which enhances viral
   replication and contributes to the development of a premalignant
   phenotype.[102] ^21 The upregulation of CCNA2 and other genes involved
   in the cell cycle is linked to the dysregulation of the Wnt/β‐catenin
   signaling system, which plays a crucial role in the growth, stem cell
   characteristics, and resistance to drugs in HCC.[103] ^21 HBV infection
   may stimulate the production of host proviral factors such as HNF4A,
   PPARA, RXRA, and CEBPB, which enhance HBV gene transcription and
   replication.[104] ^22 In addition, HBV's deregulation of TGF‐β
   expression seems to enhance the advancement of infected cells into the
   G2/M phase, linked to disrupted cell cycle control in HBV‐infected
   cells.[105] ^22 Recurrent fusions of the CCNA2 gene and repeated
   rearrangements of the CCNE1 promoter region have been found in a
   specific subgroup of HCC characterized by the activation of CCNA2 or
   CCNE1 gene.[106] ^23 Moreover, CCNA2 has been associated with cancer
   aggressiveness, recurrence, metastasis, and chemoresistance in many
   kinds of cancer. CCNA2's role in tumor formation suggests it might be
   used as a diagnostic indicator and treatment target for HCC and other
   types of cancer.[107] ^16 The precise ways in which HBV disrupts CCNA2
   and other cell cycle genes in HCC patients are not completely known.
   However, our results indicate that the disruption of CCNA2 and other
   cell cycle genes is crucial in the progression of HCC in HBV‐infected
   individuals.

   For instance, CCNB2, another cell cycle‐related gene,[108] ^24 was
   overexpressed according to our analysis data in HBV‐associated HCC
   patients in compare to healthy individuals. CCNB2, a cyclin B family
   member, is crucial for cell proliferation and tumor advancement,
   especially in breast cancer and HCC associated with chronic HBV
   infection.[109] ^24 CCNB2 plays a role in regulating the transition
   from the G2 phase to the M phase by activating CDC2. Inhibiting CCNB2
   results in the arrest of the cell cycle.[110] ^25 , [111]^26 Research
   has shown that CCNB2 is often increased in HBV‐associated HCC and
   breast cancer, as well as in other human cancers such as lung and liver
   cancer, and its dysregulation may result in uncontrolled cell
   proliferation, promoting tumor formation.[112] ^26 Suppressing CCNB2
   has been shown to induce cell death and result in cell cycle arrest in
   the S phase, ultimately causing the malfunction of the G2/M
   checkpoints. Recent research has recognized CCNB2 as a promising
   noninvasive biomarker for breast cancer in peripheral blood mononuclear
   cells.[113] ^27 Therefore, in regards to recent investigations and the
   current study, CCNB2's role in carcinogenesis suggests it might serve
   as a diagnostic marker and therapeutic target for treating HCC and
   other malignancies.

   CDK1, which is necessary for cell division and growth, plays a vital
   role in regulating the cell cycle.[114] ^28 As our findings released,
   CDK1 is a hub gene with increased expression levels in HBV‐associated
   HCC tumors. It is crucial in facilitating the transitions between G2/M,
   G1/S, and G1 phases. CDK1 phosphorylates many substrates related to the
   initiation and completion of mitosis, including BORA, MLL5, GWL, and
   CDCA5.[115] ^28 As recent reports, CDK1 dysregulation is linked to
   carcinogenesis in many types of cancer, such as HCC associated with
   chronic HBV infection. Research has shown that CDK1 is excessively
   expressed in tumor cells, such as HCC.[116] ^29

   Interestingly, Zhu et al. found a new circular RNA named HBV_circ_1,
   generated by HBV, was discovered in HBV‐infected HepG2.2.15 cells and
   HCC tissue associated with HBV. The microarray study of 68 HCCT samples
   revealed a considerably increased abundance of HBV_circ_1 compared to
   paracancerous tissues.[117] ^30 Furthermore, the survival rate of
   patients who tested positive for HBV_circ_1 was notably lower than that
   of patients who tested negative for HBV_circ_1. Transient expression of
   HBV_circ_1 promoted cell proliferation, motility, and invasion while
   suppressing apoptosis in vitro. Moreover, ectopic HBV_circ_1 expression
   led to an increase in tumor growth in live organisms. HBV_circ_1 was
   verified to interact with CDK1 to control cell proliferation. The
   findings indicate that the development of HCC might be facilitated by
   the interaction between HBV_circ_1 and CDK1. The results revealed a
   unique insight into the development and progression of HBV‐related HCC
   and identified a potential target for therapeutic medication
   development.[118] ^30 However, the exact mechanism by which HBV affects
   CDK1 expression and leads to HCC progression is still not fully
   understood and requires further studies. Moreover, it has been
   suggested that CDK1 is a predictive biomarker in HBV‐related HCC, and
   its overexpression is associated with a negative prognosis and the
   advancement of tumors.[119] ^31 Increased levels of CDK1 expression are
   linked to more advanced tumor grades and stages, suggesting its role in
   promoting cancer aggressiveness. Moreover, the expression of CDK1 has
   been associated with the infiltration of immune cells in tumors,
   emphasizing its importance as a prognostic indicator in HBV‐induced
   HCC.[120] ^32

   In addition, inhibiting or targeting CDK1 may improve cancer therapy by
   overcoming immune evasion mechanisms.[121] ^33 Targeting CDK1 has been
   identified as a promising therapeutic approach for treating cancer.
   Several small compounds have been created to specifically block CDK1,
   thereby halting the proliferation of cancer cells. Blocking CDK1 may
   cause tumor cells to stop dividing and undergo a programmed cell death
   pathway, making it a potential target for cancer treatment.[122] ^34
   The significant presence of CDK1 in several types of cancer highlights
   its potential as a target for creating new cancer therapies that may be
   widely applied.[123] ^28

   Since CDK1 can regulate other cell cycle‐related genes such as TOP2A,
   it was expected that TOP2A was upregulated in HBV‐associated HCC
   patients in comparison to the healthy group.[124] ^35 TOP2A, also known
   as DNA topoisomerase II alpha, is an essential enzyme involved in
   several biological activities such as DNA replication, transcription,
   and chromosomal segregation in mitosis.[125] ^36 It is a distinctive
   indicator of cell growth and is involved in the development of
   cancerous tumors. TOP2A resolves topological restrictions during DNA
   replication and transcription by temporarily cutting both DNA strands
   to produce double‐strand breaks (DSBs).[126] ^37 This action
   facilitates the unwinding and disentangling of the DNA strands, a
   process critical for the aforementioned cellular functions.[127] ^37
   Failure to repair these DSBs may lead to cytotoxic damage, genomic
   instability, and cell death. This can result in chromosomal
   translocations and mutations that promote the development of
   cancer.[128] ^38 In addition, high levels of TOP2A have been linked to
   more aggressive tumor behavior and later disease stages in several
   types of cancer such as HCC,[129] ^38 breast cancer,[130] ^36 and
   NSCLC.[131] ^39 Furthermore, upregulation of TOP2A is associated with a
   negative prognosis, increased tumor grades, and stages, suggesting its
   involvement in cancer aggressiveness.[132] ^39 , [133]^40 Notably,
   chemotherapy that targets TOP2 enzymes, such as TOP2A, is often used to
   treat both solid tumors and blood cancers.[134] ^40 However, the
   effectiveness of TOP2 inhibitors as primary chemotherapeutic agents is
   counteracted by the potential for secondary cancers and heart damage,
   underscoring the need for more accurate targeted approaches.[135] ^41 ,
   [136]^42 Research indicates that TOP2A might serve as a novel
   prognostic biomarker and therapeutic target for HCC. However, more
   studies and clinical trials are required to elucidate its regulatory
   processes.[137] ^43

   Currently, there has been little advancement in understanding the
   pathways that cause cancer in HBV infection. Notable events include
   enhanced TERT or TP53 mutation, activation of Wnt, mTOR/PI3K/Akt, and
   Ras/ERK signaling pathways, and exhaustion of CD8+T cells. There is
   increasing evidence that the abnormal composition of the matrisome
   plays a role in how the tumor microenvironment supports the
   development, advancement, and spread of HCC.[138] ^44 , [139]^45 The
   matrisome consists of fundamental ECM components like as collagens,
   glycoproteins, and proteoglycans, as well as ECM‐related proteins such
   as ECM regulators, ECM‐affiliated proteins, and secreted factors.[140]
   ^46 The matrisome of two mice models of HCC has distinct compositions,
   suggesting that ECM modification during HCC initiation is dependent on
   the individual cause.[141] ^47 The matrisome of human intrahepatic
   cholangiocarcinoma has been identified, however, the matrisome of human
   HCC, particularly HBV‐related HCC, has not been discovered yet. As
   mentioned above, DEG genes (CCNA2, CCNB2, CDK1, and TOP2A) are
   increased in ECM structural constituents. Prior research has shown that
   the ECM of the liver undergoes remodeling during HBV infection. The
   hepatitis B e antigen stimulates hepatic stellate cells (HSCs), leading
   to abnormal ECM synthesis. Hepatitis B x protein (HBx) enhances the
   activity of matrix metalloproteinases and promotes the migration of HCC
   cells.[142] ^48 Alterations in the extracellular milieu caused by HBV
   infection stimulate intracellular signaling pathways. The oncogene
   collagen triple helix repeat containing‐1 promotes the advancement of
   HBV‐related HCC by activating hypoxia‐inducible factor 1 alpha and
   vascular endothelial growth factor via the PI3K/Akt/mTOR pathway.[143]
   ^49 Therefore, identification and characterization of the matrisome in
   relation to HBV‐related HCC are urgently required despite existing
   studies.

   The present study has effectively identified key genes and signaling
   pathways that are essential in the development of HBV‐related HCC. The
   characterization presented in this study has significantly enhanced our
   understanding of the underlying processes involved in the development
   and recurrence of HBV‐related HCC. As a result, this research provides
   important insights into potential targets for early diagnostic
   interventions and timely treatment approaches for HBV‐related HCC.
   Nevertheless, the research does demonstrate some limitations. It is
   crucial to validate the validity of the discovered genes and signaling
   pathways by providing evidence of their practical functions in clinical
   samples. The present conclusions are based only on bioinformatics
   analysis, highlighting the need for empirical verification using
   clinical samples. Moreover, it is important to highlight that
   investigations of this kind need rigorous analytical examination and
   careful deliberation. The conducted statistical analysis has
   effectively identified a group of key hub genes that are designated for
   inclusion in databases. However, it is essential to recognize that the
   existing data set is incomplete, necessitating the need to update this
   information to assure the accuracy of future studies. Hence, while this
   analysis has produced noteworthy insights, its findings need
   verification via empirical clinical studies and meticulous data updates
   to foster a strong foundation of scientific knowledge.

   In summary, this study utilized an integrative bioinformatics approach
   to identify critical genes and pathways associated with the progression
   of HBV‐related HCC. The dysregulation of cell cycle control and ECM
   remodeling appear to play major roles in promoting hepatocarcinogenesis
   triggered by chronic HBV infection. Four hub genes, CCNA2, CCNB2, CDK1,
   and TOP2A, were found to be consistently overexpressed in HBV‐positive
   HCC samples and may have utility as diagnostic biomarkers. Further
   experimental validation of these genes as biomarkers in clinical
   samples is warranted. A better understanding of the key mechanisms
   driving HBV‐HCC development will pave the way for biomarker‐based early
   detection and novel therapeutic approaches to improve patient outcomes.

5. CONCLUSION

   In conclusion, this study identified 198 common DEGs that are
   dysregulated in HBV‐associated HCC using bioinformatics analysis.
   Further PPI network analysis highlighted 49 hub genes that may play
   critical regulatory roles. In particular, CCNA2, CCNB2, CDK1, and TOP2A
   were found to be significantly upregulated in HBV‐positive HCC tumor
   samples and may serve as promising diagnostic biomarkers. GO and KEGG
   pathway enrichment analysis also revealed that cell cycle regulation is
   profoundly disrupted, facilitating uncontrolled cell proliferation in
   HBV‐induced liver carcinogenesis. Taken together, these findings
   provide important insights into the key genes and pathways driving
   HBV‐related hepatocarcinogenesis. The hub genes identified, especially
   CCNA2, CCNB2, CDK1, and TOP2A, represent promising biomarker candidates
   and potential therapeutic targets worthy of further investigation for
   improving early HCC detection and treatment outcomes.

AUTHOR CONTRIBUTIONS

   Niloufar Sadat Kalaki: Visualization; validation; writing—original
   draft; investigation; software; resources. Mozhgan Ahmadzadeh:
   Conceptualization; investigation; methodology; writing—review
   and editing. Mohammadreza Saberiyan: Conceptualization; methodology;
   investigation; validation; writing—review and editing; data curation;
   supervision. Mohammad Hadi Karbalaie Niya: Data curation; validation;
   writing—review and editing; project administration; funding
   acquisition. All authors have read and approved the final version of
   the manuscript.

CONFLICT OF INTEREST STATEMENT

   The authors declare no conflict of interest.

ETHICS STATEMENT

   Ethics approved by the Ethical Committee of Iran University of Medical
   Sciences, Tehran, Iran by the code No: IR.IUMS.REC.1402.692.

TRANSPARENCY STATEMENT

   The lead author Mohammadreza Saberiyan, Mohammad Hadi Karbalaie Niya
   affirms that this manuscript is an honest, accurate, and transparent
   account of the study being reported; that no important aspects of the
   study have been omitted; and that any discrepancies from the study as
   planned (and, if relevant, registered) have been explained.

Supporting information

   Supporting information.
   [144]HSR2-7-e2185-s001.docx^ (581.7KB, docx)

ACKNOWLEDGMENTS