|Year : 2016 | Volume
| Issue : 2 | Page : 981-989
Insights into significant pathways and gene interaction networks in peripheral blood mononuclear cells for early diagnosis of hepatocellular carcinoma
Jian Xin Jiang, Chao Yu, Zhi Peng Li, Jie Xiao, Hao Zhang, Mei Yuan Chen, Cheng Yi Sun
Department of Biliary-Hepatic Surgery, Affiliated Hospital of Guiyang Medical College, Guiyang, Guizhou Province, China
|Date of Web Publication||25-Jul-2016|
Cheng Yi Sun
Guiyi Road, Guiyang, Guizhou Province - 550 001
Source of Support: None, Conflict of Interest: None
Background: Early diagnosis of hepatocellular cancer (HCC) significantly helps improve patient survival. However, high specific and sensitive tests for screening patients with early stage of HCC are not yet available. Novel HCC biomarkers based on gene expression profiles of peripheral blood mononuclear cells (PBMCs) might change the situation. Recently, a three gene-based signature for the non-invasive detection of early HCC was reported.
Objective: To compare the differences in global gene expression profiles in PBMCs of healthy individuals and HCC patients, with a specific aim to uncover the significantly altered biological pathways and important hub genes.
Materials and Methods: Two groups of data were extracted from Affymetrix microarray expression dataset GSE49515. One group had 10 PBMCs samples from healthy control individuals, and the other had 10 PBMCs samples from patients with HCC. Gene expression profiles of both groups were analyzed and compared. Furthermore, ribonucleic acid (RNA) levels of seven of the identified differentially expressed genes (DEGs) were further confirmed by quantitative reverse transcription polymerase chain reaction (QRT-PCR).
Results: Significant differences were uncovered in gene expression profiles in PBMCs of healthy individuals and HCC patients. Three hundred and seventy-five up-regulated and 169 down-regulated DEGs were identified. Three hundred and eighty-seven gene ontology (GO) biological processes and 15 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were over-represented by the identified DEGs.
Conclusions: Using identified DEGs, significantly changed biological processes such as nucleic acid metabolic process and KEGG pathways such as cytokine-cytokine receptor interaction in PBMCs of HCC patients were identified. In addition, several important hub genes, for example, CUL4A, and interleukin (IL) 8 were also uncovered.
Keywords: CUL4A, early diagnosis, hepatocellular cancer, interaction networks, IL-8, peripheral blood mononuclear cells, pathways and gene, STAT3
|How to cite this article:|
Jiang JX, Yu C, Li ZP, Xiao J, Zhang H, Chen MY, Sun CY. Insights into significant pathways and gene interaction networks in peripheral blood mononuclear cells for early diagnosis of hepatocellular carcinoma. J Can Res Ther 2016;12:981-9
|How to cite this URL:|
Jiang JX, Yu C, Li ZP, Xiao J, Zhang H, Chen MY, Sun CY. Insights into significant pathways and gene interaction networks in peripheral blood mononuclear cells for early diagnosis of hepatocellular carcinoma. J Can Res Ther [serial online] 2016 [cited 2020 Jul 16];12:981-9. Available from: http://www.cancerjournal.net/text.asp?2016/12/2/981/154081
| > Introduction|| |
Primary liver cancer is the sixth most frequent cancer globally and the third leading cause of cancer death. Hepatocellular carcinoma (HCC), accounting for 75% of all primary liver cancers, is the most common type of liver cancer. Cirrhosis of the liver caused by chronic hepatitis C virus (HCV) and hepatitis B virus (HBV) infection is the major risk factor. Diagnosis of HCC relies on a number of different diagnostic modalities including ultrasound, computed tomography, biopsy, and blood test. Although imaging modalities such as sonography, computed tomography, and magnetic resonance are good tools for detecting patients with large size tumors, their effectiveness are significantly reduced against small size tumors. As a result, effectiveness of imaging modalities depends mainly on tumor size and stages of cancer, with high-grade tumors are easier to be diagnosed but often have a poor prognosis while low-grade tumors may not be noticed for years. To improve the prospect of survival, early diagnosis is critical, thus exploring sensitive ways for HCC detection and diagnosis is urgent to us. However, developing an effective diagnostic test for HCC is not an easy task since the disease is often asymptomatic until it reaches an advanced stage. Tumor markers point out a promising way for early diagnosis of cancer. Currently, α-fetopotein (AFP) together with iconography and pathology detection is routinely used in clinics for early diagnosis of liver cancer. However, the specificity and sensitivity of AFP against HCC are not satisfactory.
Peripheral blood mononuclear cells (PBMC) are immune cells such as lymphocytes and monocytes. They are a important component in host immune system to fight against various abnormal conditions such as infection and cancer. Previous studies have shown that detection of changes of PBMCs might help improve early diagnosis of cancer.,, In a recent study, a blood-based three-gene signature set (CXCR2, CCR2, and EP400) was reported. CXCR2, CCR2, and EP400 were able to identify HCC individually with accuracies of 82.4%, 78.4%, and 65%, respectively. When these three genes were combined, they had a sensitivity of 93% and a specificity of 89% against HCC. In the current study, we focused on differences in the gene expression profiles of PBMCs in healthy individuals and patients, with a specific interest to identify significantly changed biological processes and KEGG pathways in PBMCs of HCC patients in comparison to healthy control subjects.
| > Materials and Methods|| |
Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) was searched and Affymetrix microarray expression dataset GSE49515 was obtained. Two groups of data were suitable for our research. One group consisted of 10 samples of PBMCs from healthy control individuals. The other had 10 samples of PBMCs from patients with HCC. Unprocessed data sets (cel files) were collected for further analysis. Affymetrix Human Genome U133 Plus 2.0 Array was applied in the experiments and the probe annotation files were downloaded accordingly for further research.
Data processing and filtering
Considering the array platform, we applied GCRMA in this research to quantify microarray signal. The normalization process mainly consisted of three steps: Model-based background correction, quantile normalization and summarization. In order to filter out uninformative data such as control probe sets and other internal controls as well as removing genes which were expressed uniformly close to background detection levels, we used the “genefilter” package in R language to handle it. However, the filter did not remove probe sets without Entrez Gene identifiers or have identical Entrez Gene identifiers.
Differentially expressed genes (DEGs) analysis
We performed statistical comparison between normal and HCC samples. The up or down level of gene expression was obtained by comparing HCC samples to normal samples. Limma in R language was applied to identify the differential expression of the comparison. For probes which had identical Entrez Gene identifiers, only the probe occupied the biggest variance was kept for further DEG analysis. Only those genes with absolute log2 (fold change) >1.5 and adjusted P < 0.01 were recognized as statistically differentially expressed between the two sample groups. The adjusted P value was obtained through applying Benjamini and Hochberg's (BH) false discovery rate correction on the original P value, and fold change threshold was selected based on our purpose of focusing on significant DEGs.
We performed hierarchical clustering to classify the analyzed samples based on gene expression profiles. Hierarchical clustering was carried out using DEGs to observe the global gene expression patterns. Besides, the DEGs, which were classified in specific gene ontology (GO) biological process, and KEGG pathway analysis, were further extracted and the expression pattern of those DEGs was characterized, and the heat maps for the DEGs classified in targeted biological processes or KEGG pathways were generated using R package.
GO and KEGG pathway analysis
We utilized R packages—GO.db, KEGG.db, and KEGGREST to detect GO categories and KEGG pathways with significant over-representation in DEGs comparing with the whole genome. The significantly enriched biological processes were identified as P value less than threshold value 0.01. As to KEGG pathway, P value was set to be less than 0.05.
Construction of biological network
We downloaded protein-protein interaction (PPI) data from HPRD, BIOGRID, and PIP databases. Pair interactions, which were included in any of the three databases, were chosen to be included in our curated PPI database. As a result, 561,405 pair interactions were included in our database. Cytoscape was utilized to construct interaction network. Interacted gene pairs existed in our curated PPI database were imported as stored network. After functional enrichment analysis, the DEGs specified in dramatically altered biological processes (GO terms) and KEGG pathways were mapped to corresponding networks respectively to analyze interaction.
Isolation of total ribonucleic acid (RNA) from PBMCs
Peripheral blood samples were first obtained from eight healthy individuals and twenty HCC patients. PBMCs were then isolated by Ficoll-Hipaque density gradient centrifugation (Miltenyi Biotec Inc. Germany) as described previously. Total RNA was extracted using a Qiagen RNAeasy RNA isolation kit (Qiagen, Valencia, CA, USA) and then converted to complementary deoxyribonucleic acid (cDNA) using the superscript II cDNA synthesis kit (Invitrogen, Carlsbad, CA, USA).
| > Results|| |
Differential expression analysis
To determine if PBMCs in the systemic circulation of HCC patients is influenced by the development of HCC, we compared gene expression profiles of PBMCs of normal healthy subjects and HCC patients to identify genes with significantly differential expression levels. Setting the threshold as absolute log2 (fold change) >1.5 and adjusted P < 0.01, 544 DEGs were identified, among which 375 were up-regulated and 169 were down-regulated [Table 1],[Table 2],[Table 3].
|Table 1: Statistical distribution of differentially expressed genes in normal and HCC samples|
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|Table 2: Top 30 up-regulated genes (log2 (fold change) >1.5 and adjusted P value <0.01)|
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|Table 3: Top 30 down-regulated genes (log2 (fold change) < -1.5 and adjusted P value <0.01)|
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Quantitative reverse transcription polymerase chain reaction validation
To further confirm gene expression levels of significantly changed DEGs, we performed quantitative reverse transcription polymerase chain reaction (QRT PCR) to investigate RNA levels of seven DEGs (IL8, CUL4A, CUL1, CXCR2, CCR1, CCR2, CXCL5, and PF4). These DEGs were chosen because they were biological important and involved in a number of most significantly changed biological processes and KEGG pathways such as chemotaxis and cytokine-cytokine receptor interaction. As expected, RNA levels of IL8, CUL4A, and CUL1 were significantly reduced in PMBCs of HCC patients in comparison to PMBCs of healthy individuals [Figure 1]. Meanwhile, as compared to healthy individuals, levels of CXCR2, CCR1, CCR2, CXCL5, and PF4 were significantly increased in PMBCs of HCC patients [Figure 1].
|Figure 1: Ribonucleic acid (RNA) expression level of seven significantly changed differentially expressed genes (DEGs) analyzed by quantitative reverse transcription polymerase chain reaction (QRT-PCR)|
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Construction of biological network
By comparing gene expression profiles of PBMCs of healthy individuals and HCC patients, 544 DEGs were identified, among which 375 were up-regulated and 169 were down-regulated. This suggested that development of HCC might have a direct effect on the gene expression of systemically circulating PBMCs. To further explore these assumed influences, identified DEGs were used to construct biological networks which can provide information on important potential functional genes [Figure 2]. Our results showed that most genes connected with each other and formed a large network which was made of several small sub-networks. In addition, the network revealed a number of hub genes with important biological functions. These hub genes were either involved in significant biological processes or well-known oncogenes and tumor suppressor genes. Among these genes, expression level of CUL1, STAT3, CUL4A, IL8, JUN, and CDC42 were down-regulated, whereas, level of CTTN and DUSP6 were up-regulated. CUL1 and CUL4A are involved in protein degradation and ubiquitination., Protein product of STAT3 gene is a transcription factor, which plays important roles in many cellular processes such as cell growth and apoptosis. IL8 gene encodes a chemokine which has important functions in chemotaxis and phagocytosis. As a well-known proto-oncogene, JUN encodes a protein known as Jun which was the first discovered oncogenic transcription factor. Protein product of CDC42 regulates multiple signaling pathways governing diverse cellular functions including cell cycle progression, cell morphology, migration and endcocytosis. CTTN gene encodes a protein known as Cortactin. Cortactin promotes cell migration and endocytosis. DUSP6 protein is a member of the dual specificity protein phosphatase subfamily and it is involved in cellular proliferation and differentiation. Studies have shown that the enzyme is involved in some types of cancer. Taken together, the results showed that the development of HCC has certainly induced significant changes in the gene expression of PBMCs of HCC patients.
|Figure 2: Heat map of differentially expressed genes (DEGs) (a) and corresponding biological network (b) (a) Heat map of hierarchical clustering of all DEGs (10 normal samples and 10 hepatocellular cancer (HCC) samples). “Red” indicates high relative expression, and “green” indicates low relative expression. (b) Biological network was constructed according to the direct connections among all DEGs|
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GO and KEGG pathway analysis
We next investigated the biological processes. 387 GO biological processes (P < 0.01) and 15 KEGG pathways (P < 0.05) were over-represented by the identified DEGs. Heat maps and biological networks of significantly enriched GO biological processes or KEGG pathways were constructed [Figure 3],[Figure 4],[Figure 5],[Figure 6],[Figure 7]. Top significant enrichment biological processes included nucleic acid metabolic process, cell chemotaxis, response to stress, and cell death [Table 4]. As shown in [Figure 3], for nucleic acid metabolic process, a large biological network involving many genes was identified. For cell chemotaxis, two separate small biological networks were found [Figure 4]. In one of the networks, IL-8 gene directly interacted with CXCR2, CXCL5, PF4, and F2RL1. Meanwhile, CCR1, CCR2, CCR5, and C3AR1 formed a separate small network. For cell death, a large biological network was also identified, in which JUN, CUL1, STAT3, FADD, and TNFRSF1A were important regulatory genes [Figure 5]. Meanwhile, top significant enrichment KEGG pathways were cytokine-cytokine receptor interaction, chemokine signaling pathway, and toll-like receptor signaling pathway [Table 5]. For cytokine-cytokine receptor interaction, three small corresponding biological networks were found [Figure 6]. In the first biological network, IL-8 gene directly interacted with PF4, CXCR5, and CXCL2. In the second network, TNFRSF1A directly connected with TNFSF10 and FASLG. The third network was consisted of CCR1, 2, and 5. For chemokine signaling pathway, two separate corresponding networks were identified. While one of the networks centered on IL-8; CCR1, 2, 5, and STAT3 formed the other network [Figure 7]. Intriguingly, several important genes such as IL8, CCR1, CC2, CCR5, CXCL5, and CXCR2 were repeatedly detected in the most significantly changed biological processes and pathways. These results suggested that development of HCC can significantly affect PBMCs, thus providing distinct transcriptional features of DEGs even during the early stage of HCC.
|Figure 3: Heat map of gene ontology (GO):0090304/nucleic acid metabolic process (a) and corresponding biological network (b). (a) Heat map of hierarchical clustering of all datasets (10 normal samples and 10 hepatocellular cancer (HCC) samples) using differentially expressed genes (DEGs) in “nucleic acid metabolic process.” “Red” indicates high relative expression, and “green” indicates low relative expression. (b) Biological network was constructed according to the direct connections among DEGs|
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|Figure 4: Heat map of gene ontology (GO):0060326/cell chemotaxis (a) and corresponding biological network (b). (a) Heat map of hierarchical clustering of all datasets (10 normal samples and 10 hepatocellular cancer (HCC) samples) using differentially expressed genes (DEGs) in “cell chemotaxis.” “Red” indicates high relative expression, and “green” indicates low relative expression. (b) Biological network was constructed according to the direct connections among DEGs|
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|Figure 5: Heat map of gene ontology (GO):0008219/cell death (a) and corresponding biological network (b). (a) Heat map of hierarchical clustering of all datasets (10 normal samples and 10 hepatocellular cancer (HCC) samples) using differentially expressed genes (DEGs) in “cell death.” “Red” indicates high relative expression, and “green” indicates low relative expression. (b) Biological network was constructed according to the direct connections among DEGs|
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|Figure 6: Heat map of Kyoto Encyclopedia of Genes and Genomes (KEGG):04060/cytokine-cytokine receptor interaction (a) and corresponding biological network (b). (a) Heat map of hierarchical clustering of all data sets (10 normal samples and 10 hepatocellular cancer (HCC) samples) using differentially expressed genes (DEGs) in “cytokine-cytokine receptor interaction.” “Red” indicates high relative expression, and “green” indicates low relative expression. (b) Biological network were constructed according to the direct connections among DEGs|
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|Figure 7: Heat map of Kyoto Encyclopedia of Genes and Genomes (KEGG):04062/chemokine signaling pathway (a) and corresponding biological network (b). (a) Heat map of hierarchical clustering of all data sets (10 normal samples and 10 hepatocellular cancer (HCC) samples) using differentially expressed genes (DEGs) in “chemokine signaling pathway.” “Red” indicates high relative expression, and “green” indicates low relative expression. (b) Biological network were constructed according to the direct connections among DEGs|
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| > Discussion|| |
Early diagnosis of HCC helps improve patient prognosis markedly. Surgical removal of HCC provides the best opportunity for possible cure. Patients who are diagnosed at an early stage can achieve 5-year survival rate of around 70%. Unfortunately, less than 20% of patients meet the criteria for resection at time of diagnosis. For those with advanced HCC, they are only eligible for palliative treatments, thus median survival is less than one year. Current diagnostic modalities are not specific and sensitive enough to screen for patients with early stage HCC. As a result, novel, non-invasive diagnostic modality with higher specificity and sensitivity is in urgent demand. Previously, a number of studies reported that genetic expression profile in PBMCs are significantly changed in patients with different cancers.,,,,,,,, Results from these studies indicated that the alterations in gene expression profile between healthy control subjects and cancer patients were much more significant than the inter-sample variation found in the healthy controls. This suggested that significantly changed gene expression profile of PBMCs from cancer patients have important potential diagnostic and prognostic values in cancer as surrogate transcriptional markers. For example, in a study of advanced renal cell carcinoma, Twine and colleagues reported that an 8-gene classifier set developed from the DEGs of PBMCs could predict the diagnosis of malignancy with 100% accuracy indicating the invaluable potential of PBMCs in cancer diagnosis. Furthermore, the identified gene network of altered gene expression of PBMCs might also provide novel insights into the disregulation of host immune system in cancer patients.
A recent study successfully identified a blood-based three-gene signature for the non-invasive detection of early HCC. By using microarray analysis to profile the PBMCs of HCC patients, a set of three genes including CXCR2, CCR2, and EP400 were uncovered. It was reported that the three genes were able to identify HCC individually with accuracies of 82.4%, 78.4%, and 65%, respectively. While combined, their specificity and sensitivity against HCC was further increased. In the current study, by analyzing the same set of online data, we aimed to explore more functional information about significantly changed genes, hub genes of biological networks, and highly presented biological processes and pathways in PBMCs of HCC patients. By comparing gene expression profile of 10 healthy subjects and 10 HCC patients, 544 DEGs including 375 up-regulated genes and 169 down-regulated genes were uncovered. In the study, we also constructed biological network using identified DEGs. A large biological network was revealed and a number of important hub genes were identified. Among these hub genes, CUL1 gene encodes a protein which plays an important role in protein degradation and protein ubiquitination. It is an essential component of the SKP1-CUL1-F-box protein (SCF) E3 ubiquitin ligase complex. Aberrant regulation of SCF E3 ligases is associated with various human diseases such as cancer. STAT3 gene encodes a transcription factor which mediates the expression of a variety of genes in response to cell stimuli, and thus plays a key role in many important cellular processes such as cell growth and apoptosis. Mutations of the gene have been detected in a spectrum of cancers. Intriguingly, the gene has been reported to act as both oncogene and tumor suppressor gene in different types of cancer. CUL4A gene encodes a scaffold protein which mediates turnover of key regulatory proteins. The gene has also been repeatedly implicated in cancers. IL8 gene encodes a chemokine known as interleukin 8 (IL-8). Important functions of IL-8 include chemotaxis, phagocytosis, and angiogenesis. Studies have shown that IL-8 has tumorigenic properties and it may act as an important regulatory factor in tumor microenvironment., As a well known proto-oncogene, JUN encodes a protein known as Jun which is the first discovered oncogenic transcription factor. The gene was found to play important functions in tumorigenesis and cancer progression., CDC4 gene is a tumor-suppressor gene and CDC4 protein is a substrate recognition component of SCF ubiquitin ligase complex. Its main function involves targeting of cell cycle regulators for proteolysis. Mutations of CDC4 have been found in several types of cancer. CTTN gene encodes a protein known as cortactin which have important functions in cell migration and endocytosis. Studies have shown cancer cells with increased level of cortactin are especially invasive and migratory. DUSP6 protein is a protein phosphatase which inactivates its target kinases by dephosphorylation. DUSP6 gene has been described as either tumor-suppressor gene or oncogene., In general, the functions of identified hub genes mainly include protein degradation and ubiquitination, regulation of cell signaling, regulation of cell cycle, chemotaxis, cell growth and apoptosis, and cell migration. Furthermore, mutations in some of the hub genes have been previously reported in human cancers. Protein degradation and ubiquitination plays an important role in the precise regulation of cell cycle. For accurate regulation of cell-cycle progression, cyclin-dependent kinases are under strict control of ubiquitination and subsequent protein degradation. Cell signaling is a complex system of cellular communication which is crucial for the regulation of multiple cellular activities such as immunity and homeostasis. Disregulation of cell cycle, proliferation and apoptosis have been found in all types of human cancer. The identified biological network and hub genes provide us with more information on the features of commonly influenced biological pathways in PBMCs of HCC patients. In the study, we also investigated actual RNA expression level of seven DEGs using blood samples of healthy individuals and HCC patients. These genes were either hub genes identified in the current study or genes involved in important biological functions such as cell chemotaxis, cytokine-cytokine receptor interaction, and chemokine signaling pathway. It was found that RNA levels of IL8, CUL4A, and CUL1 were significantly reduced, whereas levels of CXCR2, CCR1, CCR2, CXCL5, and PF4 were significantly increased in PMBCs of HCC patients. In addition, using the identified DEGs, 387 GO biological processes and 15 KEGG pathways were highlighted. It was found that top significant biological processes included nucleic acid metabolic process, cell chemotax is, and response to stress. Meanwhile, top significant enrichment KEGG pathways were cytokine-cytokine receptor interaction, chemokine signaling pathway, and toll-like receptor signaling pathway. These results suggested that, as compared to healthy individuals, PBMCs of HCC patients were more activated with increased DNA, RNA synthesis and degradation, cell migration, and cell signaling. Presumably, all these changes in PBMCs of HCC patients might be caused by environmental stress triggered by the local development of liver tumor and host's response against it. Taken together, these results suggested that development of HCC might directly induce significant changes in the gene expression of systemically circulating PBMCs. As a result, the potential of differential gene expression profiling in PBMCs, or of a pre-determined gene classifier set established from it, to be developed for early diagnosis of HCC can thus be quite feasible and reliable. The two most likely mechanisms underlying this differential expression are the immune system's recognition of the cancer and evasion of the immune surveillance by the cancer. There is evidence that differential expression in PBMCs may offer the possibility to detect a neoplastic lesion even before it gains invasive capabilities. This is of great significance since most cancer patients die of metastatic disease and early diagnosis is a critical factor in improving survival.
In conclusion, we have identified the differences in gene expression between PBMCs of healthy control individuals and HCC patients. In the study, many aberrantly regulated genes including several important hub genes were identified. Furthermore, we also uncovered a number of significantly changed biological processes and pathways in the PBMCs of HCC patients. Changes of these biological processes and pathways in HCC patients reflect the influence of the development of HCC on systematically circulating PBMCs and host's reaction towards the disease. As a result, the study provides more evidence for the feasibility of the development of novel PBMCs-based diagnostic markers for the early diagnosis of HCC.
| > Acknowledgments|| |
This work was supported by the National Natural Science Foundation of China (Grant No. 81160311); the work was also supported by Science and Education Foundation for Young Talent of Guizhou Province (Grant No. (2012) 177). The authors are grateful to all study participants.
| > References|| |
Yazici C, Niemeyer DJ, Iannitti DA, Russo MW. Hepatocellular carcinoma and cholangiocarcinoma: An update. Expert Rev Gastroenterol Hepatol 2014;8:63-82.
Gomaa AI, Khan SA, Leen EL, Waked I, Taylor-Robinson SD. Diagnosis of hepatocellular carcinoma. World J Gastroenterol 2009;15:1301-14.
Fowler KJ, Saad NE, Linehan D. Imaging approach to hepatocellular carcinoma, cholangiocarcinoma, and metastatic colorectal cancer. Surg Oncol Clin N
Saar B, Kellner-Weldon F. Radiological diagnosis of hepatocellular carcinoma. Liver Int 2008;28:189-99.
Zhao YJ, Ju Q, Li GC. Tumor markers for hepatocellular carcinoma. Mol Clin Oncol 2013;1:593-8.
Williams MA, Newland AC, Kelsey SM. The potential for monocyte-mediated immunotherapy during infection and malignancy. Part I: Apoptosis induction and cytotoxic mechanisms. Leuk Lymphoma 1999;34:1-23.
Nichita C, Ciarloni L, Monnier-Benoit S, Hosseinian S, Dorta G, Ruegg C. A novel gene expression signature in peripheral blood mononuclear cells for early detection of colorectal cancer. Aliment Pharmacol Ther 2014;39:507-17.
Baine MJ, Chakraborty S, Smith LM, Mallya K, Sasson AR, Brand RE, et al
. Transcriptional profiling of peripheral blood mononuclear cells in pancreatic cancer patients identifies novel genes with potential diagnostic utility. PLoS One 2011;6:e17014.
Sakai Y, Honda M, Fujinaga H, Tatsumi I, Mizukoshi E, Nakamoto Y, et al
. Common transcriptional signature of tumor-infiltrating mononuclear inflammatory cells and peripheral blood mononuclear cells in hepatocellular carcinoma patients. Cancer Res 2008;68:10267-79.
Shi M, Chen MS, Sekar K, Tan CK, Ooi LL, Hui KM. A blood-based three-gene signature for the non-invasive detection of early human hepatocellular carcinoma. Eur J Cancer 2014;50:928-36.
Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM. Systematic determination of genetic network architecture. Nature Genet 1999;22:281-5.
Tateno M, Honda M, Kawamura T, Honda H, Kaneko S. Expression profiling of peripheral-blood mononuclear cells from patients with chronic hepatitis C undergoing interferon therapy. J Infect Dis 2007;195:255-67.
Lisztwan J, Marti A, Sutterluty H, Gstaiger M, Wirbelauer C, Krek W. Association of human CUL-1 and ubiquitin-conjugating enzyme CDC34 with the F-box protein p45(SKP2): Evidence for evolutionary conservation in the subunit composition of the CDC34-SCF pathway. EMBO J 1998;17:368-83.
Alvarez JV, Greulich H, Sellers WR, Meyerson M, Frank DA. Signal transducer and activator of transcription 3 is required for the oncogenic effects of non-small-cell lung cancer-associated mutations of the epidermal growth factor receptor. Cancer Res 2006;66:3162-8.
Akira S, Nishio Y, Inoue M, Wang XJ, Wei S, Matsusaka T, et al
. Molecular cloning of APRF, a novel IFN-stimulated gene factor 3 p91-related transcription factor involved in the gp130-mediated signaling pathway. Cell 1994;77:63-71.
Baggiolini M, Clark-Lewis I. Interleukin-8, a chemotactic and inflammatory cytokine. FEBS Lett 1992;307:97-101.
Vogt PK. Fortuitous convergences: The beginnings of JUN. Nat Rev Cancer 2002;2:465-9.
Tang X, Orlicky S, Lin Z, Willems A, Neculai D, Ceccarelli D, et al
. Suprafacial orientation of the SCFCdc4 dimer accommodates multiple geometries for substrate ubiquitination. Cell 2007;129:1165-76.
Weaver AM, Karginov AV, Kinley AW, Weed SA, Li Y, Parsons JT, et al
. Cortactin promotes and stabilizes Arp2/3-induced actin filament network formation. Curr Biol 2001;11:370-4.
Messina S, Frati L, Leonetti C, Zuchegna C, Di Zazzo E, Calogero A, et al
. Dual-specificity phosphatase DUSP6 has tumor-promoting properties in human glioblastomas. Oncogene 2011;30:3813-20.
Motola-Kuba D, Zamora-Valdes D, Uribe M, Mendez-Sanchez N. Hepatocellular carcinoma. An overview. Ann Hepatol 2006;5:16-24.
Bismuth H, Majno PE, Adam R. Liver transplantation for hepatocellular carcinoma. Semin Liver Dis 1999;19:311-22.
Padhya KT, Marrero JA, Singal AG. Recent advances in the treatment of hepatocellular carcinoma. Curr Opin Gastroenterol 2013;29:285-92.
Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC, Relman DA, et al
. Individuality and variation in gene expression patterns in human blood. Proc Natl Acad Sci U S A 2003;100:1896-901.
Twine NC, Stover JA, Marshall B, Dukart G, Hidalgo M, Stadler W, et al
. Disease-associated expression profiles in peripheral blood mononuclear cells from patients with advanced renal cell carcinoma. Cancer Res 2003;63:6069-75.
Burczynski ME, Twine NC, Dukart G, Marshall B, Hidalgo M, Stadler WM, et al
. Transcriptional profiles in peripheral blood mononuclear cells prognostic of clinical outcomes in patients with advanced renal cell carcinoma. Clin Cancer Res 2005;11:1181-9.
Huang H, Dong X, Kang MX, Xu B, Chen Y, Zhang B, et al
. Novel blood biomarkers of pancreatic cancer-associated diabetes mellitus identified by peripheral blood-based gene expression profiles. Am J Gastroenterol 2010;105:1661-9.
Dijkstra S, Leyten GH, Jannink SA, de Jong H, Mulders PF, van Oort IM, et al
. KLK3, PCA3, and TMPRSS2-ERG expression in the peripheral blood mononuclear cell fraction from castration-resistant prostate cancer patients and response to docetaxel treatment. Prostate 2014;74:1222-30.
Xie CM, Wei W, Sun Y. Role of SKP1-CUL1-F-box-protein (SCF) E3 ubiquitin ligases in skin cancer. J Genet Genomics 2013;40:97-106.
Sharma P, Nag A. CUL4A ubiquitin ligase: A promising drug target for cancer and other human diseases. Open Biol 2014;4:130217.
Brat DJ, Bellail AC, Van Meir EG. The role of interleukin-8 and its receptors in gliomagenesis and tumoral angiogenesis. Neuro Oncol 2005;7:122-33.
Waugh DJ, Wilson C. The interleukin-8 pathway in cancer. Clin Cancer Res 2008;14:6735-41.
Eferl R, Ricci R, Kenner L, Zenz R, David JP, Rath M, et al
. Liver tumor development. c-Jun antagonizes the proapoptotic activity of p53. Cell 2003;112:181-92.
Szabo E, Riffe ME, Steinberg SM, Birrer MJ, Linnoila RI. Altered cJUN expression: An early event in human lung carcinogenesis. Cancer Res 1996;56:305-15.
Calhoun ES, Jones JB, Ashfaq R, Adsay V, Baker SJ, Valentine V, et al
. BRAF and FBXW7 (CDC4, FBW7, AGO, SEL10) mutations in distinct subsets of pancreatic cancer: Potential therapeutic targets. Am J Pathol 2003;163:1255-60.
Weaver AM. Invadopodia: Specialized cell structures for cancer invasion. Clin Exp Metastasis 2006;23:97-105.
Ma J, Yu X, Guo L, Lu SH. DUSP6, a tumor suppressor, is involved in differentiation and apoptosis in esophageal squamous cell carcinoma. Oncol Lett 2013;6:1624-30.
Teixeira LK, Reed SI. Ubiquitin ligases and cell cycle control. Annu Rev Biochem 2013;82:387-414.
Scott JD, Pawson T. Cell signaling in space and time: Where proteins come together and when they're apart. Science 2009;326:1220-4.
Evan GI, Vousden KH. Proliferation, cell cycle and apoptosis in cancer. Nature 2001;411:342-8.
Zhao F, Obermann S, von Wasielewski R, Haile L, Manns MP, Korangy F, et al
. Increase in frequency of myeloid-derived suppressor cells in mice with spontaneous pancreatic carcinoma. Immunology 2009;128:141-9.
Coghlin C, Murray GI. Current and emerging concepts in tumour metastasis. J Pathol 2010;222:1-15.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]