Genomic characterization and immunotherapy for microsatellite instability-high in cholangiocarcinoma.

BACKGROUND: Microsatellite instability-high (MSI-H) is a unique genomic status in many cancers. However, its role in the genomic features and immunotherapy in cholangiocarcinoma (CCA) is unclear. This study aimed to systematically investigate the genomic characterization and immunotherapy efficacy of MSI-H patients with CCA. METHODS: We enrolled 887 patients with CCA in this study. Tumor samples were collected for next-generation sequencing. Differences in genomic alterations between the MSI-H and microsatellite stability (MSS) groups were analyzed. We also investigated the survival of PD-1 inhibitor-based immunotherapy between two groups of 139 patients with advanced CCA. RESULTS: Differential genetic alterations between the MSI-H and MSS groups included mutations in ARID1A, ACVR2A, TGFBR2, KMT2D, RNF43, and PBRM1 which were enriched in MSI-H groups. Patients with an MSI-H status have a significantly higher tumor mutation burden (TMB) (median 41.7 vs. 3.1 muts/Mb, P < 0.001) and more positive programmed death ligand 1 (PD-L1) expression (37.5% vs. 11.9%, P < 0.001) than those with an MSS status. Among patients receiving PD-1 inhibitor-based therapy, those with MSI-H had a longer median overall survival (OS, hazard ratio (HR) = 0.17, P = 0.001) and progression-free survival (PFS, HR = 0.14, P < 0.001) than patients with MSS. Integrating MSI-H and PD-L1 expression status (combined positive score ≥ 5) could distinguish the efficacy of immunotherapy. CONCLUSIONS: MSI-H status was associated with a higher TMB value and more positive PD-L1 expression in CCA tumors. Moreover, in patients with advanced CCA who received PD-1 inhibitor-based immunotherapy, MSI-H and positive PD-L1 expression were associated with improved both OS and PFS. TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov on 07/01/2017 (NCT03892577).

Identification of "regulation of RhoA activity panel" as a prognostic and predictive biomarker for gastric cancer.

RhoA is a member of the RHO family GTPases and is associated with essential functions in gastric cancer. In this study, we identified a gastric cancer biomarker, termed the "regulation of RhoA activity panel" (RRAP). Patients with gastric cancer from The Cancer Genome Atlas database were divided into training (N=160) and validation (N=155) cohorts. A cohort of 109 Chinese gastric cancer patients was utilized as an independent validation. Patients with mutated RRAP showed significantly better overall survival than patients with wild type RRAP. We also analyzed the association between RRAP and the migration capacity, immune-related signatures, and the tumor microenvironment. RRAP-mutant tumors had a significantly lower degree of lymph node metastasis and lower activities of migration-related pathways. These tumors also showed significantly increased immune cell infiltration and cytotoxic activity. Furthermore, two independent patient cohorts who received immune checkpoint blockade therapy were assessed for RRAP mutant status. As expected, for both immunotherapy cohorts, higher response rates to immune checkpoint blockade therapy were observed in patients with RRAP-mutant tumors than in patients with wild type RRAP tumors. Overall, this study indicates that the RRAP gene set is a potential biomarker for gastric cancer prognosis and therapeutic selection.

miR-17-5p and miR-20a-5p suppress postoperative metastasis of hepatocellular carcinoma via blocking HGF/ERBB3-NF-κB positive feedback loop.

Dysregulation of microRNA (miRNA) is a frequent event in hepatocellular carcinoma (HCC), but little is known whether it is a bystander or an actual player on residual HCC metastasis during liver microenvironment remodeling initiated by hepatectomy. Methods: The differently expressed miRNAs and mRNAs were identified from RNA-seq data. Western blot, qRT-PCR, fluorescence in situ hybridization, immunofluorescence and immunohistochemical were used to detect the expression of miRNA and mRNA in cell lines and patient tissues. The biological functions were investigated in vitro and in vivo. Chromatin immunoprecipitation, proximity ligation and luciferase reporter assay were used to explore the specific binding of target genes. The expression of HGF/ERBB3 signaling was detected by Western blot. Results: In this study, HGF induced by hepatectomy was shown to promote metastasis of residual HCC cells. miR-17-5p and miR-20a-5p were confirmed to play inhibitory roles on HCC metastasis. And ERBB3 was found to be the common target of miR-17-5p and miR-20a-5p. HCC cells with lower levels of miR-17-5p and miR-20a-5p or higher level of ERBB3 were often more sensitive to response HGF stimuli and to facilitate metastatic colonization both in vitro and in vivo experimental systems. Furthermore, HGF reinforced ERBB3 expression by NF-κB transcriptional activity in a positive feedback loop. Of particular importance, HCC patients with lower levels of miR-17-5p and miR-20a-5p or higher level of ERBB3 had significantly shorter OS and PFS survivals after surgical resection. Conclusion: miR-17-5p and miR-20a-5p could suppress postoperative metastasis of hepatocellular carcinoma via blocking HGF/ERBB3-NF-κB positive feedback loop and offer a new probable strategy for metastasis prevention after HCC resection.

The analysis of risk factors for diabetic nephropathy progression and the construction of a prognostic database for chronic kidney diseases.

BACKGROUND: Diabetic nephropathy (DN) affects about 40% of diabetes mellitus (DM) patients and is the leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) all over the world, especially in high- and middle-income countries. Most DN has been present for years before it is diagnosed. Currently, the treatment of DN is mainly to prevent or delay disease progression. Although many important molecules have been discovered in hypothesis-driven research over the past two decades, advances in DN management and new drug development have been very limited. Moreover, current animal/cell models could not replicate all the features of human DN, while the development of Epigenetics further demonstrates the complexity of the mechanism of DN progression. To capture the key pathways and molecules that actually affect DN progression from numerous published studies, we collected and analyzed human DN prognostic markers (independent risk factors for DN progression). METHODS: One hundred and fifty-one DN prognostic markers were collected manually by reading 2365 papers published between 01/01/2002 and 12/15/2018. One hundred and fifteen prognostic markers of other four common CKDs were also collected. GO and KEGG enrichment analysis was done using g:Profiler, and a relationship network was built based on the KEGG database. Tissue origin distribution was derived mainly from The Human Protein Atlas (HPA), and a database of these prognostic markers was constructed using PHP Version 5.5.15 and HTML5. RESULTS: Several pathways were significantly enriched corresponding to different end point events. It is shown that the TNF signaling pathway plays a role through the process of DN progression and adipocytokine signaling pathway is uniquely enriched in ESRD. Molecules, such as TNF, IL6, SOD2, etc. are very important for DN progression, among which, it seems that "AGER" plays a pivotal role in the mechanism. A database, dbPKD, was constructed containing all the collected prognostic markers. CONCLUSIONS: This study developed a database for all prognostic markers of five common CKDs, offering some bioinformatics analyses of DN prognostic markers, and providing useful insights towards understanding the fundamental mechanism of human DN progression and for identifying new therapeutic targets.

Whole-exome mutational and transcriptional landscapes of combined hepatocellular cholangiocarcinoma and intrahepatic cholangiocarcinoma reveal molecular diversity.

BACKGROUND: Primary liver cancer (PLC) is the third largest contributor to cancer mortality in the world. PLC is a heterogeneous disease that encompasses several biologically distinct subtypes including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (CHC). CHC is a distinct, albeit rare, subtype of PLC and is comprised of cells with histopathological features of both HCC and ICC. Several studies have focused on the mutation and expression landscapes of HCC and ICC. However, studies of CHC were rare. OBJECTIVE: The aim of the current study was to identify genetic and gene expression alterations in the carcinogenesis and development of CHC and ICC in the Chinese population. Unraveling both similar and differing patterns among these subtypes may help to identify personalized medicine approaches that could improve patient survival. METHODS: Whole genome sequencing (WGS), whole exome sequencing (WES) and RNA-seq were performed on 10 ICC and 10 CHC samples, matched with adjacent non-tumor liver tissue specimens. Comparative analysis was performed using HCC datasets from The Cancer Genome Atlas (TCGA). RESULTS: Mutational and transcriptional landscapes of CHC and ICC were clearly delineated. TP53 and CTNNB1 were identified as exhibiting mutations in CHC. ARID1A, PBRM1, and IDH1 were frequently mutated in ICC. RYR3, FBN2, and KCNN3 are associated with cell migration and metastasis and might be driver genes in CHC. KCNN3 was identified as also exhibiting mutations in ICC. The ECM-receptor interaction pathway associated fibrogenic hepatic progenitor cell differentiation and liver fibrosis may play an important role in carcinogenesis of PLC. Chromatin remodeling and chromosome organization are key processes in carcinogenesis and development in PLC. P53 related pathways showed alterations in CHC and HCC. Inflammation may be a key factor involved in ICC carcinogenesis. CONCLUSION: CHC and ICC are different subtypes of PLC. This study discusses predominantly the molecular genetic details of PLC subtypes and highlights the need for an accurate diagnosis and treatment of specific PLC subtypes to optimize patient management.

Integrative Analysis of Dysfunctional Modules Driven by Genomic Alterations at System Level Across 11 Cancer Types.

AIM AND OBJECTIVE: Integrating multi-omics data to identify driver genes and key biological functions for tumorigenesis remains a major challenge. METHOD: A new computational pipeline was developed to identify the Driver Mutation-Differential Co-Expression (DM-DCE) modules based on dysfunctional networks across 11 TCGA cancers. RESULTS: Functional analyses provided insight into the properties of various cancers, and found common cellular signals / pathways of cancers. Furthermore, the corresponding network analysis identified conservations or interactions across different types of cancers, thus the crosstalk between the key signaling pathways, immunity and cancers was found. Clinical analysis also identified key prognostic / survival patterns. CONCLUSION: Taken together, our study sheds light on both cancer-specific and cross-cancer characteristics systematically.

Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles.

OBJECTIVE: Synergistic drug combinations are promising therapies for cancer treatment. However, effective prediction of synergistic drug combinations is quite challenging as mechanisms of drug synergism are still unclear. Various features such as drug response, and target networks may contribute to prediction of synergistic drug combinations. In this study, we aimed to construct a computational model to predict synergistic drug combinations. METHODS: We designed drug physicochemical features and network features, including drug chemical structure similarity, target distance in protein-protein network and targeted pathway similarity. At the same time, we designed fifteen pharmacogenomics features using drug treated gene expression profiles based on the background of cancer-related biology network. Based on these eighteen features, we built a prediction model for Synergistic Drug combination using Random forest algorithm (SyDRa). RESULTS: Our model achieved a quite good performance with AUC value of 0.89 and Out-of-bag estimate error rate of 0.15 in training dataset. Using the random anti-cancer drug combinations which have transcriptional profile data in the Connectivity Map dataset as the testing dataset, we identified 28 potentially synergistic drug combinations, three out of which had been reported to be effective drug combinations by literatures. CONCLUSIONS: We studied eighteen features for drug combinations and built a computational model using random forest algorithm. The model was evaluated using an independent test dataset. Our model provides an efficient strategy to identify potentially synergistic drug combinations for cancer and may help reduce the search space for high-throughput synergistic drug combinations screening.

GOLM1 Modulates EGFR/RTK Cell-Surface Recycling to Drive Hepatocellular Carcinoma Metastasis.

The mechanism of cancer metastasis remains poorly understood. Using gene profiling of hepatocellular carcinoma (HCC) tissues, we have identified GOLM1 as a leading gene relating to HCC metastasis. GOLM1 expression is correlated with early recurrence, metastasis, and poor survival of HCC patients. Both gain- and loss-of-function studies determine that GOLM1 acts as a key oncogene by promoting HCC growth and metastasis. It selectively interacts with epidermal growth factor receptor (EGFR) and serves as a specific cargo adaptor to assist EGFR/RTK anchoring on the trans-Golgi network (TGN) and recycling back to the plasma membrane, leading to prolonged activation of the downstream kinases. These findings reveal the functional role of GOLM1, a Golgi-related protein, in EGFR/RTK recycling and metastatic progression of HCC.

dbPHCC: a database of prognostic biomarkers for hepatocellular carcinoma that provides online prognostic modeling.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant cancers with a poor prognosis. For decades, more and more biomarkers were found to effect on HCC prognosis, but these studies were scattered and there were no unified identifiers. Therefore, we built the database of prognostic biomarkers and models for hepatocellular carcinoma (dbPHCC). METHODS: dbPHCC focuses on biomarkers which were related to HCC prognosis by traditional experiments rather than high-throughput technology. All of the prognostic biomarkers came from literatures issued during 2002 to 2014 in PubMed and were manually selected. dbPHCC collects comprehensive information of candidate biomarkers and HCC prognosis. RESULTS: dbPHCC mainly contains 567 biomarkers: 323 proteins, 154 genes, and 90 microRNAs. For each biomarker, the reference information, experimental conditions, and prognostic information are shown. Based on two available patient cohort data sets, an exemplified prognostic model was constructed using 15 phosphotransferases in dbPHCC. The web interface does not only provide a full range of browsing and searching, but also provides online analysis tools. dbPHCC is available at http://lifecenter.sgst.cn/dbphcc/ CONCLUSIONS: dbPHCC provides a comprehensive and convenient search and analysis platform for HCC prognosis research. GENERAL SIGNIFICANCE: dbPHCC is the first database to focus on experimentally verified individual biomarkers, which are related to HCC prognosis. Prognostic markers in dbPHCC have the potential to be therapeutic drug targets and may help in designing new treatments to improve survival of HCC patients. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

[Gene regulatory network of hepatocellular carcinoma: a review].

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. HCC gene regulatory network (HCC GRN), whose nodes consist of genes, miRNAs or TFs and whose edges consist of interaction relationships of nodes, is one of the important ways to study molecular mechanism of HCC. Based on various experimental data, types of HCC GRNs could be conducted such as TF-miRNA regulatory network. Integrating the studies of HCC GRN, TF-miRNA transcriptional regulatory network performs better in identifying core genes which play important roles in network disturbances. It is a trend that gene variations and transcriptional regulatory networks should be combined, however the corresponding research is almost blank. This review summarizes the source of HCC data sources, the classification, character, and research program of HCC GRN. Finally, according to present analysis and discussion of progress and research status of HCC GRN, we provide a useful reference for researchers.

Systematic analysis of the associations between adverse drug reactions and pathways.

Adverse drug reactions (ADRs) are responsible for drug candidate failure during clinical trials. It is crucial to investigate biological pathways contributing to ADRs. Here, we applied a large-scale analysis to identify overrepresented ADR-pathway combinations through merging clinical phenotypic data, biological pathway data, and drug-target relations. Evaluation was performed by scientific literature review and defining a pathway-based ADR-ADR similarity measure. The results showed that our method is efficient for finding the associations between ADRs and pathways. To more systematically understand the mechanisms of ADRs, we constructed an ADR-pathway network and an ADR-ADR network. Through network analysis on biology and pharmacology, it was found that frequent ADRs were associated with more pathways than infrequent and rare ADRs. Moreover, environmental information processing pathways contributed most to the observed ADRs. Integrating the system organ class of ADRs, we found that most classes tended to interact with other classes instead of themselves. ADR classes were distributed promiscuously in all the ADR cliques. These results reflected that drug perturbation to a certain pathway can cause changes in multiple organs, rather than in one specific organ. Our work not only provides a global view of the associations between ADRs and pathways, but also is helpful to understand the mechanisms of ADRs.

TMREC: A Database of Transcription Factor and MiRNA Regulatory Cascades in Human Diseases.

Over the past decades, studies have reported that the combinatorial regulation of transcription factors (TFs) and microRNAs (miRNAs) is essential for the appropriate execution of biological events and developmental processes. Dysregulations of these regulators often cause diseases. However, there are no available resources on the regulatory cascades of TFs and miRNAs in the context of human diseases. To fulfill this vacancy, we established the TMREC database in this study. First, we integrated curated transcriptional and post-transcriptional regulations to construct the TF and miRNA regulatory network. Next, we identified all linear paths using the Breadth First Search traversal method. Finally, we used known disease-related genes and miRNAs to measure the strength of association between cascades and diseases. Currently, TMREC consists of 74,248 cascades and 25,194 cascade clusters, involving in 412 TFs, 266 miRNAs and 545 diseases. With the expanding of experimental support regulation data, we will regularly update the database. TMREC aims to help experimental biologists to comprehensively analyse gene expression regulation, to understand the aetiology and to predict novel therapeutic targets. TMREC is freely available at http://bioinfo.hrbmu.edu.cn/TMREC/.

Identifying prognostic features by bottom-up approach and correlating to drug repositioning.

BACKGROUND: Traditionally top-down method was used to identify prognostic features in cancer research. That is to say, differentially expressed genes usually in cancer versus normal were identified to see if they possess survival prediction power. The problem is that prognostic features identified from one set of patient samples can rarely be transferred to other datasets. We apply bottom-up approach in this study: survival correlated or clinical stage correlated genes were selected first and prioritized by their network topology additionally, then a small set of features can be used as a prognostic signature. METHODS: Gene expression profiles of a cohort of 221 hepatocellular carcinoma (HCC) patients were used as a training set, 'bottom-up' approach was applied to discover gene-expression signatures associated with survival in both tumor and adjacent non-tumor tissues, and compared with 'top-down' approach. The results were validated in a second cohort of 82 patients which was used as a testing set. RESULTS: Two sets of gene signatures separately identified in tumor and adjacent non-tumor tissues by bottom-up approach were developed in the training cohort. These two signatures were associated with overall survival times of HCC patients and the robustness of each was validated in the testing set, and each predictive performance was better than gene expression signatures reported previously. Moreover, genes in these two prognosis signature gave some indications for drug-repositioning on HCC. Some approved drugs targeting these markers have the alternative indications on hepatocellular carcinoma. CONCLUSION: Using the bottom-up approach, we have developed two prognostic gene signatures with a limited number of genes that associated with overall survival times of patients with HCC. Furthermore, prognostic markers in these two signatures have the potential to be therapeutic targets.

Identification of HPV integration and gene mutation in HeLa cell line by integrated analysis of RNA-Seq and MS/MS data.

HeLa cell line, which was derived from cervical carcinoma, provides an idea platform to study both the integration of human papillomavirus and the massive mutations occurring on the cancer cell genome. Proteogenomics is a field with the intersection of proteomics and genomics to perform gene annotation and identify gene mutation. In this work, we first identified the SNV/INDEL, structural variation (SV), and virus infection/integration events from RNA-Seq data of HeLa cell line; then, by applying proteogenomics strategy, we were able to detect some of the genomic events with the tandem mass spectrometry (MS/MS) data from the same sample. Furthermore, some of the mutated peptides were experimentally validated using multiple reaction monitoring technology. The integrated analysis of the RNA-Seq and MS/MS data not only renders the discovery of HeLa cell genome variations more credible but also illustrates a practical workflow for protein-coding mutation discovery in cancer-related studies.

Integrated analysis of mutation data from various sources identifies key genes and signaling pathways in hepatocellular carcinoma.

BACKGROUND: Recently, a number of studies have performed genome or exome sequencing of hepatocellular carcinoma (HCC) and identified hundreds or even thousands of mutations in protein-coding genes. However, these studies have only focused on a limited number of candidate genes, and many important mutation resources remain to be explored. PRINCIPAL FINDINGS: In this study, we integrated mutation data obtained from various sources and performed pathway and network analysis. We identified 113 pathways that were significantly mutated in HCC samples and found that the mutated genes included in these pathways contained high percentages of known cancer genes, and damaging genes and also demonstrated high conservation scores, indicating their important roles in liver tumorigenesis. Five classes of pathways that were mutated most frequently included (a) proliferation and apoptosis related pathways, (b) tumor microenvironment related pathways, (c) neural signaling related pathways, (d) metabolic related pathways, and (e) circadian related pathways. Network analysis further revealed that the mutated genes with the highest betweenness coefficients, such as the well-known cancer genes TP53, CTNNB1 and recently identified novel mutated genes GNAL and the ADCY family, may play key roles in these significantly mutated pathways. Finally, we highlight several key genes (e.g., RPS6KA3 and PCLO) and pathways (e.g., axon guidance) in which the mutations were associated with clinical features. CONCLUSIONS: Our workflow illustrates the increased statistical power of integrating multiple studies of the same subject, which can provide biological insights that would otherwise be masked under individual sample sets. This type of bioinformatics approach is consistent with the necessity of making the best use of the ever increasing data provided in valuable databases, such as TCGA, to enhance the speed of deciphering human cancers.

The pan-cancer analysis of gene expression patterns in the context of inflammation.

Although several studies have investigated the essential roles of inflammation in tumor progression, not many have systematically analyzed gene expression patterns across diverse cancers in the context of inflammation. In this study, in order to better understand the inflammatory scenario, we initially constructed the inflammatory timeline (IT) based on two gene expression profiles during inflammatory progression (inflammatory bowel disease and Helicobacter pylori infection). Then, we separately identified the differentially expressed genes (DEGs) from 25 cancer-related microarray data. By comparing the distributions of DEGs in the IT, we identified three novel pan-cancer gene expression patterns. In the first pattern, the up-regulated genes in cancers were over-expressed in the early phase of inflammation, while the down-regulated genes were over-expressed in the late phase of inflammation. The second pattern was the opposite of the first one. The third pattern appeared to be transitional between the first and second patterns. We found that some cancers with different tissue origins have similar gene expression patterns. Finally, we identified two sets of tissue-independent inflammatory signatures that were over-expressed in early and late phases of inflammation, respectively. The dominant biological processes of early inflammatory signatures were cell proliferation, DNA replication, and DNA repair, whereas the late inflammatory signatures were reflective of innate immune response, neutrophil migration, and antigen processing. These inflammatory signatures may be useful to predict gene expression patterns in human cancers. Therefore, the pan-cancer analysis of gene expression patterns in the context of inflammation provides a novel insight into cancers and an unprecedented opportunity to develop new therapies.

Integrative analysis of transcriptional regulatory network and copy number variation in intrahepatic cholangiocarcinoma.

BACKGROUND: Transcriptional regulatory network (TRN) is used to study conditional regulatory relationships between transcriptional factors and genes. However few studies have tried to integrate genomic variation information such as copy number variation (CNV) with TRN to find causal disturbances in a network. Intrahepatic cholangiocarcinoma (ICC) is the second most common hepatic carcinoma with high malignancy and poor prognosis. Research about ICC is relatively limited comparing to hepatocellular carcinoma, and there are no approved gene therapeutic targets yet. METHOD: We first constructed TRN of ICC (ICC-TRN) using forward-and-reverse combined engineering method, and then integrated copy number variation information with ICC-TRN to select CNV-related modules and constructed CNV-ICC-TRN. We also integrated CNV-ICC-TRN with KEGG signaling pathways to investigate how CNV genes disturb signaling pathways. At last, unsupervised clustering method was applied to classify samples into distinct classes. RESULT: We obtained CNV-ICC-TRN containing 33 modules which were enriched in ICC-related signaling pathways. Integrated analysis of the regulatory network and signaling pathways illustrated that CNV might interrupt signaling through locating on either genomic sites of nodes or regulators of nodes in a signaling pathway. In the end, expression profiles of nodes in CNV-ICC-TRN were used to cluster the ICC patients into two robust groups with distinct biological function features. CONCLUSION: Our work represents a primary effort to construct TRN in ICC, also a primary effort to try to identify key transcriptional modules based on their involvement of genetic variations shown by gene copy number variations (CNV). This kind of approach may bring the traditional studies of TRN based only on expression data one step further to genetic disturbance. Such kind of approach can easily be extended to other disease samples with appropriate data.

Application of microRNA and mRNA expression profiling on prognostic biomarker discovery for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most highly malignant and lethal cancers of the world. Its pathogenesis has been reported to be multi-factorial, and the molecular carcinogenesis of HCC can not be attributed to just a few individual genes. Based on the microRNA and mRNA expression profiling of normal liver tissues, pericancerous hepatocellular tissues and hepatocellular carcinoma tissues, we attempted to find prognosis related gene sets for HCC patients. RESULTS: We identified differentially expressed genes (DEG) from three comparisons: Cancer/Normal, Cancer/Pericancerous and Pericancerous/Normal. GSEA (gene set enrichment analysis) were performed. Based on the enriched gene sets of GO terms, pathways and transcription factor targets, it was found that the genome instability and cell proliferation increased while the metabolism and differentiation decreased in HCC tissues. The expression profile of DEGs in each enriched gene set was used to correlate to the postoperative survival time of HCC patients. Nine gene sets were found to prognostic correlation. Furthermore, after substituting DEG-targeting-microRNA for DEG members of each gene set, two gene sets with the microRNA expression profiles were obtained that had prognostic potential. CONCLUSIONS: The malignancy of HCC could be represented by gene sets, and pericancerous liver exhibits important characteristics of liver cancer. The expression level of gene sets not only in HCC but also in the pericancerous liver showed potential for prognosis implying an option for HCC prognosis at an early stage. Additionally, the gene-targeting-microRNA expression profiles also showed prognostic potential, demonstrating that the multi-factorial molecular pathogenesis of HCC is contributed by various genes and microRNAs.