Five-CpG-based prognostic signature for predicting survival in hepatocellular carcinoma patients

Nucleophosmin 1 associating with engulfment and cell motility protein 1 regulates hepatocellular carcinoma cell chemotaxis and metastasis

The chemokine, C-X-C motif chemokine ligand 12 (CXCL12) and its G-protein-coupled receptor (GPCR) and C-X-C chemokine receptor type 4 (CXCR4), are closely associated with promoting hepatocellular carcinoma (HCC) chemotaxis and metastasis. The binding of CXCL12 and CXCR4 depends on the heterotrimeric Gi proteins to regulate actin polymerisation and mobility in HCC. Although the role of GPCR/Gi signalling in carcinogenesis migration has been intensively studied, the detailed mechanism remains largely unknown. In this study, a small interfering RNA technique was used to knock down the Nucleophosmin 1 (NPM1) gene expression. Through the chemotaxis and invasion assays, wound healing, proliferation, filamentous-actin, immunofluorescence, immunohistochemical assays, and co-immunoprecipitation assays, we investigated the specific biological role and underlying mechanisms of the NPM1 in HCC. Additionally, dimethyl fumarate (DMF), a fumaric acid ester, was used to inhibit the HCC cell chemokines and metastasis by regulating ELMO1 and NPM1. Therefore, this study reported that NPM1 gene expression was upregulated in the HCC tissues and cell lines. The NPM1 knockdown significantly inhibited the proliferation, migration, and chemotaxis of the HepG2 cells in vitro. Further mechanistic studies suggested that the NPM1 interacts with ELMO1 and the CXCL12/CXCR4 pathway activates NPM1-dependent regulation of the ELMO1 localisation. Furthermore, the DMF significantly inhibited tumour metastasis induced by the NPM1/ELMO1 signalling pathway, as observed in in vitro cell functional experiments. These data suggested that as a potentially novel therapeutic approach, the simultaneous targeting of NPM1 and ELMO1 could effectively be used to treat HCC.

FYB2 Is a Potential Prognostic Biomarker for Hepatocellular Carcinoma

FYB2 (also known as C1orf168 or ARAP) is an adaptor protein involved in T-cell receptor (TCR)-mediated T-cell activation and adhesion. However, the correlation of FYB2 with prognosis and cancer needs further investigation. In this study, we analyzed the expression levels of FYB2 in hepatocellular carcinoma (LIHC) tumor tissues and correlated it with the pathological stages, survival outcomes, and tumor grades. We found that the expression of FYB2 was significantly downregulated in LIHC. Low FYB2 level leading to weak survival outcomes is linked with advanced tumor grades and elevated pathological stages. Cox regression analysis showed that FYB2 and AJCC-M stages can be used as independent prognostic factors for LIHC. GSEA analysis revealed that FYB2 would be notably correlated with the cellular metabolism-related pathways and particularly involved in the regulation of cancer-related pathways. Single-cell transcriptome analysis revealed that FYB2-positive cells were mainly distributed in hepatocytes, and compared with other cells, the upregulated genes of these cells were mainly enriched in metabolism-related functions. The results of the spatial transcriptome revealed that the expression of FYB2 in the adjacent area was higher than in the tumor area. These results showed that FYB2 is likely to be a new prognostic biomarker in LIHC and would help provide individual treatment decisions for LIHC patients.

Transcriptional activation of S100A2 expression by HIF-1α via binding to the hypomethylated hypoxia response elements in HCC cells

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers. Dysregulation of S100A2 has recently been found in many cancers including HCC. However, its regulatory mechanism in HCC remains poorly understood, especially in hypoxia. In this study, we found that S100A2 is upregulated and correlated with the clinicopathological features of HCC patients. Moreover, the elevated S100A2 showed worse overall survival. Functionally, S100A2 inhibition decreased the proliferation and migration of HepG2 cells. Interestingly, we found that HIF-1α directly binds to hypoxia response elements (HREs) of the S100A2 promoter region. S100A2 expression could be induced in an HIF-1α-dependent manner under hypoxia. Furthermore, S100A2 silencing significantly suppressed HCC cell proliferation and invasion under hypoxia. Mechanistically, pyrosequencing results showed that the hypomethylation status of CpG located in the HRE at the S100A2 promoter was correlated with S100A2 induction. Additionally, HIF-1α- mediated S100A2 activation was associated with TET2-related epigenetic inactivation. TET2 was enriched in the HRE of the S100A2 promoter in HepG2 cells. Finally, S100A2 methylation-related genes and pathways were analyzed. We found that the methylation of S100A2 is correlated with ANXA2, PPP1R15A, and FOS, which include in a hypoxia-related gene set from the GSEA database. Moreover, some EMT-related genes are associated with the methylation of S100A2 in HCC. Conclusively, our study thus uncovered a novel mechanism showing that hypoxia/HIF-1α signaling associated with DNA methylation enhances S100A2 expression in HCC. S100A2 may be useful as a target for facilitating novel diagnostic and therapeutic strategies in liver cancer.

The effect of key DNA methylation in different regions on gene expression in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common cancer with high morbidity and mortality. As we all know, the alteration of DNA methylation has a crucial impact on the occurrence of HCC. However, the mechanism of the effect of DNA methylation in different regions on gene expression is still unclear. Here, by computing and analyzing the distribution of differential methylation in 12 different regions in HCC tissues and adjacent normal tissues, not only the hypermethylation of CpG islands and global hypomethylation were found, but also a stable distribution pattern of differential methylation in HCC was found. Then the correlations between DNA methylations in different regions and gene expressions were calculated, and the diversity of correlations in different regions was determined. The key genes of differential methylation and differential expression related to the survival of HCC patients were obtained by using Cox regression analysis, a four-gene prognostic risk scoring model was constructed, and the prognostic performance was well verified. The regions of the differentially methylated CpG sites corresponding to the four key genes were located and their influences on the expression were analyzed. The results indicate that the promoter, first exon, 5'UTR, sixth exon, N_Shore, and S_Shore hypomethylation promotes the expression of key oncogenes, which together lead to the occurrence of HCC. These results might help to study the role of DNA methylation in HCC and provide potential biomarkers for the diagnosis of HCC.

Integrative omics analysis reveals effective stratification and potential prognosis markers of pan-gastrointestinal cancers

Gastrointestinal (GI) tract cancers are the most common malignant cancers with high mortality rate. Pan-cancer multi-omics data fusion provides a powerful strategy to examine commonalities and differences among various cancer types and benefits for the identification of pan-cancer drug targets. Herein, we conducted an integrative omics analysis on The Cancer Genome Atlas pan-GI samples including six carcinomas and stratified into 9 clusters, i.e. 5 single-type-dominant clusters and 4 mixed clusters, the clustering reveals the molecular features of different subtypes, other than the organ and cell-of-origin classifications. Especially the mixed clusters revealed the homogeneity of pan-GI cancers. We demonstrated that the prognosis differences among pan-GI subtypes based on multi-omics integration are more significant than clustering by single-omics. The potential prognostic markers for pan-GI stratification were identified by proportional hazards model, such as PSCA (for colorectal and stomach cancer) and PPP1CB (for liver and pancreatic cancer), which have prominent prognostic power supported by high concordance index.

•

Pan-cancer multi-omics strategy reveals homogeneity and heterogeneity of pan-GI cancers

•

Identify 9 iclusters with significantly different survival and molecular features

•

Potential prognostic markers have prominent power supported by concordance index

Prognostic Cancer Gene Expression Signatures: Current Status and Challenges

Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.

Immunohistochemical Signature Add Prognostic Value in Patients With Early and Intermediate Hepatocellular Carcinoma Underwent Curative Liver Resection

Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide; however, accurate prognostic tools are still lacking. We aimed to identify immunohistochemistry (IHC)-based signature as a prognostic classifier to predict recurrence and survival in patients with HCC at Barcelona Clinic Liver Cancer (BCLC) early- and immediate-stage. In total, 567 patients who underwent curative liver resection at two independent centers were enrolled. The least absolute shrinkage and selection operator regression model was used to identify significant IHC features, and penalized Cox regression was used to further narrow down the features in the training cohort (n = 201). The candidate IHC features were validated in internal (n = 101) and external validation cohorts (n = 265). Three IHC features, hepatocyte paraffin antigen 1, CD34, and Ki-67, were identified as candidate predictors for recurrence-free survival (RFS), and were used to categorize patients into low- and high-risk recurrence groups in the training cohort (P < 0.001). The discriminative performance of the 3-IHC_based classifier was validated using internal and external cohorts (P < 0.001). Furthermore, we developed a 3-IHC_based nomogram integrating the BCLC stage, microvascular invasion, and 3-IHC_based classifier to predict 2- and 5-year RFS in the training cohort; this nomogram exhibited acceptable area under the curve values for the training, internal validation, and external validation cohorts (2-year: 0.817, 0.787, and 0.810; 5-year: 0.726, 0.662, and 0.715; respectively). The newly developed 3-IHC_based classifier can effectively predict recurrence and survival in patients with early- and intermediate-stage HCC after curative liver resection.

Construction and Validation of Novel Diagnostic and Prognostic DNA Methylation Signatures for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent life-threatening human cancers and the leading cause of cancer-related mortality, with increased global incidence within the last decade. Identification of effective diagnostic and prognostic biomarkers would enable reliable risk stratification and efficient screening of high-risk patients, thereby facilitating clinical decision-making. Herein, we performed a comprehensive, robust DNA methylation analysis based on genome-wide DNA methylation profiling. We constructed a diagnostic signature with five DNA methylation markers, which precisely distinguished HCC patients from normal controls. Cox regression and LASSO analysis were applied to construct a prognostic signature with four DNA methylation markers. A one-to-one correlation analysis was carried out between genes of the whole genome and our prognostic signature. Exploration of the biological function and the role of the underlying significantly correlated genes was conducted. A mixed dataset of 463 HCC patients and 253 normal controls, derived from six independent datasets, was used to valid the diagnostic signature. Results showed a specificity of 96.84% and sensitivity of 96.77%. Class scores for the diagnostic signature were significantly different between normal controls, individuals with liver diseases, and HCC patients. The present signature has the potential to serve as a biomarker to monitor health in normal controls. Additionally, HCC patients were successfully separated into low-risk and high-risk groups by the prognostic signature, with a better prognosis for patients in the low-risk group. Kaplan-Meier and ROC analysis confirmed that the prognostic signature performed well. We found eight of the top ten genes to positively correlate with risk scores of the prognostic signature, and to be involved in cell cycle regulation. This eight-gene panel also served as a prognostic signature. The robust evidence presented in this study therefore demonstrates the effectiveness of the prognostic signature. In summary, we constructed diagnostic and prognostic signatures, which have potential for use in diagnosis, surveillance, and prognostic prediction for HCC patients. Eight genes that were significantly and positively correlated with the prognostic signature were strongly associated with cell cycle processes. Therefore, the prognostic signature can be used as a guide by which to measure responsiveness to cell-cycle-targeting agents.

A systematic review and meta-analysis of higher expression of folate receptor alpha (FOLR1) predicts poor cancer prognosis

Folate receptor alpha (FOLR1), a glycosylphosphatidylinositol-linked protein, is a well characterized folate transporter. However, the prognostic power of FOLR1 in cancer remains controversial. We conducted a meta-analysis to assess the prognostic roles of FOLR1 on different cancers. Twelve studies involving 4471 patients were included in this meta-analysis. The pooled analysis indicated that high FOLR1 significantly predicted poor overall survival (OS) (pooled hazard ratio (HR) = 0.78, 95% confidence interval (CI) = 0.64-0.94, p = 0.009) and the disease-free survival (DFS) (HR = 1.25, 95% CI = 1.07-1.47, p = 0.005). Subgroup analyses based on tumour type found that high FOLR1 level was associated with poor OS in breast cancer (HR = 2.66, 95% CI = 1.54-4.59, p = 0.0005) and endometrial carcinoma (HR = 1.30, 95% CI = 1.05-1.61, p = 0.02). However, FOLR1 has relatively weakly correlation with gender, tumour size and chemotherapy. Additionally, overexpression of FOLR1 was correlated with grade, FIGO stage, vital status and nodule status. The present meta-analysis indicated that the high expression of FOLR1 is associated with the poor survival of cancer patients, which is helpful for the clinical decision-making process.

Increased KIF21B expression is a potential prognostic biomarker in hepatocellular carcinoma

BACKGROUND

The kinesin superfamily protein member KIF21B plays an important role in regulating mitotic progression; however, the function and mechanisms of KIF21B in cancer, particularly in hepatocellular carcinoma (HCC), are unknown.

AIM

To explore the role of KIF21B in hepatocellular carcinoma and its effect on prognosis after hepatectomy.

METHODS

First, data on the differential expression of KIF21B in patients with HCC from The Cancer Genome Atlas database was analyzed. Subsequently, the expression levels of KIF21B in HCC cell lines and hepatocytes were detected by reverse transcription-polymerase chain reaction, and its biological effect on BEL-7404 cells was evaluated by KIF21B knockdown. Immunohistochemical analysis was used to validate the differential expression of KIF21B in HCC tissues and adjacent normal tissues from 186 patients with HCC after hepatectomy. The Kaplan-Meier method was used to assess prognosis significance.

RESULTS

KIF21B expression levels were significantly higher in HCC tissues than in corresponding adjacent normal tissues. The expression levels of KIF21B in four HCC cell lines were higher than that in normal liver cells. Functional experiments showed that KIF21B knockdown remarkably suppressed cell proliferation and induced apoptosis. Moreover, immunohistochemistry results are consistent with The Cancer Genome Atlas analysis, with KIF21B expression levels being increased in HCC tissues compared to adjacent normal tissues. Univariate and multivariate analyses revealed KIF21B as an independent risk factor for overall survival and disease-free survival in patients with HCC after hepatectomy.

CONCLUSION

Taken together, our results provide evidence that KIF21B plays an important role in HCC progression and may be a potential diagnostic and prognostic marker for HCC.

14-CpG-Based Signature Improves the Prognosis Prediction of Hepatocellular Carcinoma Patients

Background

Epigenetic dysregulation via alteration of DNA methylation often occurs during the development and progression of cancer, including hepatocellular carcinoma (HCC). In the past, many patterns of single-gene DNA methylation have been extensively explored in the context of HCC prognosis prediction. However, the combined model of a mixture of CpGs has rarely been evaluated. In the present study, we aimed to develop and validate a CpG-based signature model for HCC patient prognosis.

Methods

Data from methylation profiling of GSE73003, GSE37988, and GSE57958 from the Gene Expression Omnibus (GEO) database and 371 HCC patients from the Cancer Genome Atlas (TCGA) were downloaded. The 371 HCC patients were randomly divided into a development cohort (N = 263) and a validation cohort (N = 108). Two algorithms, least absolute shrinkage and selection operator (LASSO) and robust likelihood-based survival analysis, were used to select the most significant CpGs associated with overall survival (OS) time and were used to develop and validate a methylation-based signature (MSH) for HCC patient prognosis. In addition, the prognostic efficacy of the MSH was compared with that of AJCC TNM classification and other CpG-based MSHs from TCGA. Finally, a nomogram incorporating the MSH and clinicopathologic factors was also developed.

Results

Fourteen differential CpGs associated with OS were identified in HCC patients. The MSH, based on these 14 differential CpGs, could effectively divide HCC patients into two distinct subgroups with high risk or low risk of death (P < 0.0001) in the development cohort (26.35 vs 83.18 months, HR = 3.83, 95% CI: 2.56–5.90, P < 0.0001) and in the validation cohort (40.37 vs 107.03 months, HR = 2.23, 95% CI: 1.22–4.17, P=0.01). Univariate analysis showed that the MSH was significantly associated with OS, and the multivariate analysis also showed that the MSH was an independent prognostic factor for the OS of HCC patients in the two cohorts. In addition, stratified survival analysis indicated that the MSH still exhibited good prognostic value in different subgroups classified by AFP, cirrhosis, Child-Pugh A, tumor histologic grade, and AJCC stage. Moreover, time-dependent ROC analysis showed better performance of the MSH in predicting 3-year and 5-year survival of HCC patients than of AJCC stage and other CpG-based signatures from TCGA. The MSH-based nomogram also performed well in predicting 1-year, 3-year, and 5-year OS (C-index: 0.709).

Conclusion

The 14-CpG-based signature is significantly associated with OS and may be used as a novel prognostic biomarker for HCC patients.

Identification of Aberrantly Methylated Differentially CpG Sites in Hepatocellular Carcinoma and Their Association With Patient Survival

This study aimed to identify aberrantly methylated differentially methylated CpG sites (DMCs) and investigate their prognostic value in hepatocellular carcinoma (HCC). A total of 2,404 DMCs were selected from Gene Expression Omnibus (GEO) and validated by The Cancer Genome Atlas (TCGA). The TCGA cohort was divided into a training cohort and a validating cohort. First, the prognostic model based on six DMCs, including cg08351331, cg02910574, cg09947274, cg17589341, cg24652919, and cg26545968, was constructed based on the least absolute shrinkage and selection operator (LASSO) regression Cox analysis. The area under the curve (AUC) of the DMC-based model was 0.765 in the training cohort and 0.734 in the validating cohort. The accuracy of a model combining the DMC signature and American Joint Committee on Cancer (AJCC) stage, with an AUC of 0.795, was better than that of the DMCs or AJCC stage alone. Second, further analysis revealed that the methylation rate of cg08351331 was negatively associated with the expression of its relative gene, lipopolysaccharide-binding protein (LBP). Besides, the gene expression of LBP was significantly associated with poor overall survival in patients with hepatitis B virus (HBV) infection. Finally, these findings were confirmed by GSE57956 data and our own cohort. In conclusion, we established an accurate DMC-based prognostic model that could be combined with AJCC stage to improve the accuracy of prognostic prediction in HCC. Moreover, our preliminary data indicate that LBP may be a new key factor in HBV-induced HCC initiation through the regulation of its methylation.

Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma

Background

CpG island methylator phenotype (CIMP), a common biological phenomenon characterized by a subset of concurrently methylated genes, can have an influence on the progression of multiple cancers. However, the potential mechanism of CIMP in hepatocarcinogenesis and its clinical relevance remains only partially understood.

Methods: We used a methylation array from the cancer genome atlas (TCGA) to stratify HCC patients into different CIMP subtypes, and evaluated their correlation with clinical characteristics. In addition, mutation, CNV, and transcriptome profiles were also utilized to evaluate the distinctive genomic patterns correlated with CIMP. Finally, a CIMP-associated prognostic model (CPM) was trained and validated using four independent datasets.

Findings

A subgroup of patients was identified as having CIMP-H, which was associated with worse OS and DFS. Gene enrichment analysis indicated that the terms “liver cancer with EPCAM up”, “tumor invasiveness up”, “methyltransferase complex”, and “translational initiation” were enriched in CIMP-H subgroup. Notably, somatic mutation analysis indicated that CIMP-H patients presented with a higher mutation burden of BRD4, DDIAS and NOX1. Moreover, four CPM associated genes could significantly categorize patients into low- and high-risk groups in the training dataset and another 3 independent validation datasets. Finally, a nomogram incorporating a classifier based on four mRNAs, pathological M stage and CIMP status was established, which showed a favorable discriminating ability and might contribute to clinical decision-making for HCC.

Interpretation

Our work highlights the potential clinical application value of CPM in predicting the overall survival of HCC patients and the mechanisms underlying the role of CIMP in hepatocarcinogenesis.

Fund

This work was supported by the State Key Project on Infectious Diseases of China (2018ZX10723204-003), the National Nature Science Foundation of China (Nos. 81874065, 81500565, 81874149, 81572427, and 81401997), the Hepato-Biliary-Pancreatic Malignant Tumor Investigation Fund of Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH11800001-2018356).