HCCDB: A Database of Hepatocellular Carcinoma Expression Atlas

Expression and function of myelin expression factor 2 in hepatocellular carcinoma

INTRODUCTION

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours in the world and has a high mortality rate. However, the pathogenesis of HCC remains unclear. This study aimed to investigate the potential biomarkers of HCC.

METHODS

ONCOMINE, HCCDB and THE HUMAN PROTEIN ATLAS were used to identify myelin expression factor 2 (MYEF2) as a potential biomarker for HCC. The Cancer Genome Atlas database was used to further validate and analyse the value of MYEF2. Kaplan-Meier Plotter was used for the prognostic analysis. The COX regression model and Kaplan-Meier method were used to investigate the clinical value of MYEF2 in the prognosis of HCC by reviewing the survival status of patients. Fluorescent quantitative polymerase chain reaction (qPCR) and immunohistochemistry were used to detect the expressions of the MYEF2 mRNA and protein in HCC tissues and cell lines. qPCR and Western blotting were used to validate the efficiency of MYEF2 knockout and overexpression in HCC cells. The invasion and migration abilities regulated by MYEF2 were detected by performing transwell and wound healing assays.

RESULTS

MYEF2 is significantly upregulated in HCC and is mainly located in the nucleus of HCC cells. MYEF2 expression is significantly associated with the tumour stage, histological grade and TNM stage. High MYEF2 expression is an independent prognostic factor for patients with HCC. Functionally, elevated MYEF2 facilitated cell migration and invasion in vitro. In contrast, decreased MYEF2 inhibited cell migration and invasion.

CONCLUSIONS

MYEF2 may be a novel biomarker with potential diagnosis and prognosis values and as a potential therapeutic target for HCC.

A novel transcription factor-based signature to predict prognosis and therapeutic response of hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive malignancies with increasing incidence worldwide. The oncogenic roles of transcription factors (TFs) were increasingly recognized in various cancers. This study aimed to develop a predicting signature based on TFs for the prognosis and treatment of HCC. Methods: Differentially expressed TFs were screened from data in the TCGA-LIHC and ICGC-LIRI-JP cohorts. Univariate and multivariate Cox regression analyses were applied to establish a TF-based prognostic signature. The receiver operating characteristic (ROC) curve was used to assess the predictive efficacy of the signature. Subsequently, correlations of the risk model with clinical features and treatment response in HCC were also analyzed. The TF target genes underwent Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, followed by protein-protein-interaction (PPI) analysis. Results: A total of 25 differentially expressed TFs were screened, 16 of which were related to the prognosis of HCC in the TCGA-LIHC cohort. A 2-TF risk signature, comprising high mobility group AT-hook protein 1 (HMGA1) and MAF BZIP transcription factor G (MAFG), was constructed and validated to negatively related to the overall survival (OS) of HCC. The ROC curve showed good predictive efficiencies of the risk score regarding 1-year, 2-year and 3-year OS (mostly AUC >0.60). Additionally, the risk score independently predicted OS for HCC patients both in the training cohort of TCGA-LIHC dataset (HR = 2.498, p = 0.007) and in the testing cohort of ICGC-LIRI-JP dataset (HR = 5.411, p < 0.001). The risk score was also positively correlated to progressive characteristics regarding tumor grade, TNM stage and tumor invasion. Patients with a high-risk score were more resistant to transarterial chemoembolization (TACE) treatment and agents of lapatinib and erlotinib, but sensitive to chemotherapeutics. Further enrichment and PPI analyses demonstrated that the 2-TF signature distinguished tumors into 2 clusters with proliferative and metabolic features, with the hub genes belonging to the former cluster. Conclusion: Our study identified a 2-TF prognostic signature that indicated tumor heterogeneity with different clinical features and treatment preference, which help optimal therapeutic strategy and improved survival for HCC patients.

Associating transcriptomics data with inflammatory markers to understand tumour microenvironment in hepatocellular carcinoma

BACKGROUND

Liver cancer is the fourth leading cause of cancer-related death globally which is estimated to reach more than 1 million deaths a year by 2030. Among liver cancer types, hepatocellular carcinoma (HCC) accounts for approximately 90% of the cases and is known to have a tumour promoting inflammation regardless of its underlying aetiology. However, current promising treatment approaches, such as immunotherapy, are partially effective for most of the patients due to the immunosuppressive nature of the tumour microenvironment (TME). Therefore, there is an urgent need to fully understand TME in HCC and discover new immune markers to eliminate resistance to immunotherapy.

METHODS

We analyse three microarray datasets, using unsupervised and supervised methods, in an effort to discover signature genes. First, univariate, and multivariate, feature selection methods, such as the Boruta algorithm, are applied. Subsequently, an optimisation procedure, which utilises random forest algorithm with three dataset pairs combinations, is performed. The resulting optimal gene sets are then combined and further subjected to network analysis and pathway enrichment analysis so as to obtain information related to their biological relevance. The microarray datasets were analysed via the MCP-counter, CIBERSORT, TIMER, EPIC, and quanTIseq deconvolution methods and an estimation of cell type abundances for each dataset sample were identified. The differences in the cell type abundances, between the adjacent and tumour sample groups, were then assessed using a Wilcoxon Rank Sum test (p-value < 0.05).

RESULTS

The optimal gene signature sets, derived from each of the data pairs combination, achieved AUC values ranging from 0.959 to 0.988 in external validation sets using Random Forest model. CLEC1B and PTTG1 genes are retrieved across each optimal set. Among the signature genes, PTTG1, AURKA, and UBE2C genes are found to be involved in the regulation of mitotic sister chromatid separation and anaphase-promoting complex (APC) dependent catabolic process (adjusted p-value < 0.001). Additionally, the application of deconvolution algorithms revealed significant changes in cell type abundances of Regulatory T (Treg) cells, M0 and M1 macrophages, and T CD8+ cells between adjacent and tumour samples.

CONCLUSION

We identified ECM1 gene as a potential immune-related marker acting through immune cell migration and macrophage polarisation. Our results indicate that macrophages, such as M0 macrophage and M1 macrophage cells, undergo significant changes in HCC TME. Moreover, our immune deconvolution approach revealed significant infiltration of Treg cells and M0 macrophages, and a significant decrease in T CD8+ cells and M1 macrophages in tumour samples.

Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is currently one of the most life-threatening diseases worldwide. However, the factors, genes, and processes involved in the mechanisms of HCC initiation, development, and metastasis remain to be identified.Methods: WNT signalling pathways may play important roles in cancer initiation and progression. Thus, it would be informative to construct a WNT signature-based gene model for the prognosis of HCC and the prediction of therapeutic efficacy. We curated genomic profiles for HCC from The Cancer Genome Atlas (TCGA) and divided them into training and internal validation datasets. We also used samples from GSE14520 and HCCDB18 as validation datasets and clustered them by ConsensusClusterPlus analysis. We applied WebGestaltR to the WNT score-associated differentially expressed genes (DEGs) and conducted a signalling pathway enrichment analysis. We assessed the tumour immune microenvironment with ESTIMATE, Microenvironment Cell Populations (MCP)-counter, single-sample gene set enrichment analysis (ssGSEA), and tumour immune dysfunction and exclusion (TIDE).Results: We performed a least absolute shrinkage and selection operator (LASSO) regression analysis to identify the prognosis-related hub genes, identified the risk and protective factor genes associated with HCC, classified them into two clusters, and found that Cluster 2 had a significantly better prognosis than Cluster 1. Moreover, the latter had advanced clinical features compared with the former. Uridine-cytosine kinase 1 (UCK1), myristoylated alanine-rich C-kinase substrate-like protein 1 (MARCKSL1), P-antigen family member 1 (PAGE1), and killer cell lectin-like receptor B1 (KLRB1) were detected and used to construct a simplified prognostic model for HCC. The high risk score subgroup showed a poorer prognosis than the low risk score subgroup, and the model assessed HCC prognosis consistently and effectively.Conclusions: The WNT score-related gene-based model designed and evaluated herein had strong prognostic and predictive ability for HCC and could, therefore, facilitate decision-making in the prognosis and therapeutic efficacy assessment of HCC.

Comparative RNA-Sequencing Analysis Reveals High Complexity and Heterogeneity of Transcriptomic and Immune Profiles in Hepatocellular Carcinoma Tumors of Viral (HBV, HCV) and Non-Viral Etiology

Background and Objectives: Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, is the leading cause of cancer-related mortality. It arises and progresses against fibrotic or cirrhotic backgrounds mainly due to infection with hepatitis viruses B (HBV) or C (HCV) or non-viral causes that lead to chronic inflammation and genomic changes. A better understanding of molecular and immune mechanisms in HCC subtypes is needed. Materials and Methods: To identify transcriptional changes in primary HCC tumors with or without hepatitis viral etiology, we analyzed the transcriptomes of 24 patients by next-generation sequencing. Results: We identified common and unique differentially expressed genes for each etiological tumor group and analyzed the expression of SLC, ATP binding cassette, cytochrome 450, cancer testis, and heat shock protein genes. Metascape functional enrichment analysis showed mainly upregulated cell-cycle pathways in HBV and HCV and upregulated cell response to stress in non-viral infection. GeneWalk analysis identified regulator, hub, and moonlighting genes and highlighted CCNB1, ACTN2, BRCA1, IGF1, CDK1, AURKA, AURKB, and TOP2A in the HCV group and HSF1, HSPA1A, HSP90AA1, HSPB1, HSPA5, PTK2, and AURKB in the group without viral infection as hub genes. Immune infiltrate analysis showed that T cell, cytotoxic, and natural killer cell markers were significantly more highly expressed in HCV than in non-viral tumors. Genes associated with monocyte activation had the highest expression levels in HBV, while high expression of genes involved in primary adaptive immune response and complement receptor activity characterized tumors without viral infection. Conclusions: Our comprehensive study underlines the high degree of complexity of immune profiles in the analyzed groups, which adds to the heterogeneous HCC genomic landscape. The biomarkers identified in each HCC group might serve as therapeutic targets.

Overexpression of SMS in the tumor microenvironment is associated with immunosuppression in hepatocellular carcinoma

Disorders of polyamine metabolism may contribute to the development of hepatocellular carcinoma (HCC), but the precise mechanism remains unknown. This study reports that spermine synthase (SMS), an enzyme involved in polyamine biosynthesis, is overexpressed in HCC and not associated with hepatitis virus infection in HCC patients. The results of analyzing the clinical data of HCC patients showed that SMS level as a categorical dependent variable was related to clinicopathological features of poor prognosis. Furthermore, the Kaplan-Meier survival analysis and ROC curve indicated that increased SMS level is associated with poor survival rate in HCC and may be a potential biomarker to discriminate HCC tissues. However, SMS overexpression limited the therapeutic effect of immune checkpoint blockade (ICB), which seemed to be related to the immunosuppressive effect of the HCC immune microenvironment formed by higher mRNA transcript levels of immune checkpoints and higher infiltration levels of immunosuppressive cells. In samples with high and low SMS expression, functional enrichment analysis of the differentially expressed genes (DEGs) showed that SMS may be linked to the occurrence and development of HCC by affecting a variety of immune-related pathways, such as Intestinal immune network for IgA production, Fc gamma R-mediated phagocytosis, Antigen processing and presentation, Th1 and Th2 cell differentiation. Subsequently, analysis of the co-expression network of SMS in the liver hepatocellular carcinoma (LIHC) cohort revealed that SMS has a broad impact on multiple important immune- and metabolic-related processes in HCC. In summary, SMS is a promising biomarker to differentiate the prognosis, immune characteristics, and holds promise as a potential target for ICB therapy to improve HCC.

Molecular subtyping and IMScore based on immune-related pathways, oncogenic pathways, and DNA damage repair pathways for guiding immunotherapy in hepatocellular carcinoma patients

Background

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. Although immunotherapy provides hope for advanced HCC patients, the outcomes are not satisfactory and vary by individual case. In this study, we sought to establish novel molecular subtypes and a stable model based on tumor-related pathways for guiding the immunotherapy in HCC patients.

Methods

A total of 15 pathways including immune pathways, stromal pathways, oncogenic pathways, and DNA damage repair pathways were used to construct molecular subtypes through consensus clustering. Immune characteristics, gene mutations, and genomic alterations including copy number variations and homologous recombination deficiency (HRD) were analyzed in different clusters. The Tumor Immune Dysfunction and Exclusion (TIDE) framework was used to predict the response to immunotherapy. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were employed to screen prognostic genes for constructing a risk model.

Results

Three clusters/subtypes were constructed including Immune-E, Immune-D and Stromal-E. Immune-D had the worst prognosis and high enrichment of HRD pathways. Immune-E had higher immune infiltration, higher expression of major histocompatibility complex (MHC)-related genes, and higher expression of PD1, PDL1, CTLA4, and LAG3. TP53 alterations frequently occurred in Immune-D. Immune-E had a relatively high response to immunotherapy and was sensitive to chemotherapeutic drugs. Moreover, we constructed an IMScore model that was effective to classify HCC patients into different risk groups, and the IMScore had a better performance than the TIDE score.

Conclusions

This study revealed the complex interaction among the tumor microenvironment (TME), genomic alterations, and tumor-related pathways by exploring the molecular difference of 3 subtypes. The IMScore model has potential to provide guidance for immunotherapy in HCC patients.

Acyl-CoA thioesterase 9 promotes tumour growth and metastasis through reprogramming of fatty acid metabolism in hepatocellular carcinoma

Acyl-CoA thioesterase 9 (ACOT9) is a critical regulator of cellular utilization of fatty acids by catalysing the hydrolysis of acyl-CoA thioesters to non-esterified fatty acid and coenzyme A (CoA). Recently, ACOT9 was reported to participate in the pathogenesis of non-alcoholic liver disease (NAFLD), which arises from aberrant lipid metabolism and serves as a risk factor for hepatocellular carcinoma (HCC). However, the functions of ACOT9 in carcinogenesis and aberrant lipid metabolism in HCC remain unexplored. Here, we found that ACOT9 expression is significantly elevated in HCC at least partially due to the down-regulation of miR-449c-3p. Upregulation of ACOT9 is closely associated with poor prognosis for patients with HCC. Knockdown of ACOT9 expression in HCC cells significantly decreased cell proliferation, colony formation, migration and invasion, mainly through suppression of G1-to-S cell cycle transition and epithelial-to-mesenchymal transition (EMT). By contrast, forced ACOT9 expression promoted HCC growth and metastasis. In addition, we found that ACOT9 reprogrammed lipid metabolism in HCC cells by increasing de novo lipogenesis. Furthermore, we demonstrated that increased lipogenesis was involved in ACOT9-promoted HCC growth and metastasis. Altogether, we demonstrate that ACOT9 plays a critical oncogenic role in the promotion of tumour growth and metastasis by reprogramming lipid metabolism in HCC, indicating ACOT9 as a potential therapeutic target in treatment of HCC.

The Prognostic Significance and Potential Mechanism of Prolyl 3-Hydroxylase 1 in Hepatocellular Carcinoma

Background. Prolyl 3-hydroxylase 1 (P3H1) is essential for human collagen synthesis. Here, we investigated its relevance to multiple cancers, especially hepatocellular carcinoma (LIHC). Methods. We estimated the relationship of P3H1 with 33 cancers using publicly available databases. And immunohistochemistry was utilized to verify the P3H1 expression in liver, gastric, colon, pancreatic, and rectal cancer. Then, we attenuated P3H1 expression in BEL-7402 and HLF cells by lentivirus technology and assessed the effect of P3H1 on cell proliferation, migration, and invasion. Results. Bioinformatic analysis revealed a significantly higher expression of P3H1 in almost all tumors, which was consistent with the immunohistochemical findings in the liver, gastric, colon, pancreatic, and rectal cancers. P3H1 expression was associated with overall survival, progression-free interval, disease-specific survival, and disease-free interval in most cancers, particularly in LIHC. Besides, we also found that P3H1 expression was an independent prognostic factor for LIHC. And knockdown of P3H1 significantly reduced liver cancer cell proliferation, migration, and invasion in liver cancer cells. Interestingly, P3H1 expression levels showed a significant positive connection with Th2 infiltration through multiple immune infiltration algorithms. ICI treatment was less effective in LIHC patients with high P3H1 expression. Finally, we also identified an upstream regulatory mechanism of P3H1 in LIHC, namely, AL355488.1, HCG18, and THUMPD3-AS1/hsa-miR-29c-3p-P3H1 axis. Conclusion. We have systematically described for the first time that P3H1 is closely related to various tumors, particularly in LIHC, and interference with P3H1 may be a therapeutic target for patients with LIHC.

The Prognostic Significance of FKBP1A and Its Related Immune Infiltration in Liver Hepatocellular Carcinoma

Liver hepatocellular carcinoma (LIHC) remains a global health challenge with poor prognosis and high mortality. FKBP1A was first discovered as a receptor for the immunosuppressant drug FK506 in immune cells and is critical for various tumors and cancers. However, the relationships between FKBP1A expression, cellular distribution, tumor immunity, and prognosis in LIHC remain unclear. Here, we investigated the expression level of FKBP1A and its prognostic value in LIHC via multiple datasets including ONCOMINE, TIMER, GEPIA, UALCAN, HCCDB, Kaplan–Meier plotter, LinkedOmics, and STRING. Human liver tissue microarray was employed to analyze the characteristics of FKBP1A protein including the expression level and pathological alteration in cellular distribution. FKBP1A expression was significantly higher in LIHC and correlated with tumor stage, grade and metastasis. The expression level of the FKBP1A protein was also increased in LIHC patients along with its accumulation in endoplasmic reticulum (ER). High FKBP1A expression was correlated with a poor survival rate in LIHC patients. The analysis of gene co-expression and the regulatory pathway network suggested that FKBP1A is mainly involved in protein synthesis, metabolism and the immune-related pathway. FKBP1A expression had a significantly positive association with the infiltration of hematopoietic immune cells including B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells. Moreover, M2 macrophage infiltration was especially associated with a poor survival prognosis in LIHC. Furthermore, FKBP1A expression was significantly positively correlated with the expression of markers of M2 macrophages and immune checkpoint proteins such as PD-L1, CTLA-4, LAG3 and HAVCR2. Our study demonstrated that FKBP1A could be a potential prognostic target involved in tumor immune cell infiltration in LIHC.

Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq

Background

Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.

Methods

From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.

Results

Eleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO-Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.

Conclusion

To precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.

Identification of ACSF gene family as therapeutic targets and immune-associated biomarkers in hepatocellular carcinoma

Acyl-CoA synthetases (ACSs) are responsible for acyl-CoA synthesis from nonpolar hydrophilic fatty acids and play a vital role in many metabolic processes. As a category of ACS isozymes, members of ACS family (AACS, ACSF2-3, AASDH) participate in lipid metabolism; however, their expression patterns, regulatory mechanisms and effects in hepatocellular carcinoma (HCC) are poorly understood. Here, through evaluating the expression profiles of ACSF gene family, we found that upregulated AACS might be more significant and valuable in development and progression of HCC. Consequently, the mRNA expression levels of AACS and ACSF2 was accordantly increased in HCC. Kaplan-Meier plotter revealed that HCC patients with high level of AACS were highly related to a shorter overall survival time and relapse-free survival. Genetic alterations using cBioPortal revealed that the alteration rate of AACS were 5%. We also found that the functions of ACSF gene family were linked to several cancer-associated pathways, including long-term potentiation, phospholipase D signaling pathway and purine metabolism. TIMER database indicated that the AACS and ACSF2 had a strong relationship with the infiltration of six types of immune cells (macrophages, neutrophils, CD8+ T-cells, B-cells, CD4+ T-cells and dendritic cells). Next, Diseasemeth database revealed that the global methylation levels of ACSF2 was higher in HCC patients. In conclusion, this study firstly demonstrated that Acyl-CoA synthesis gene family, in particular, AACS, could be associated with immune microenvironment, thereby influencing the development and prognosis of patients with HCC.

Intra-tumor heterogeneity and prognostic risk signature for hepatocellular carcinoma based on single-cell analysis

Intra-tumor heterogeneity poses a serious challenge in the treatment of cancer, including hepatocellular carcinoma (HCC). Recent developments in single-cell RNA sequencing (scRNA-seq) make it possible to examine the heterogeneity of tumor cells. The Gene Expression Omnibus (GEO) database was retrieved to obtain scRNA-seq data of 13 HCC and 8 para cancer samples, and the cells were clustered using FindNeighbors and FindClusters functions. Cell subsets were defined using the "Enricher" function of the clusterProfiler package. Monocle was used to predict cell developmental trajectory. The LIMMA package included in the R program was utilized to detect differentially expressed genes (DEGs) between HCC and paracancerous tissues. Univariate Cox analysis and Least Absolute and Selection Operator (Lasso) Cox regression analysis were conducted to establish a risk assessment model. Thirteen cell subpopulations were identified from the sequencing data of 64,634 single cells. Four cell subgroups (dendritic cells, hepatocytes, liver bud hepatic cells, and liver progenitor cells) in tumor tissues were highly enriched. Between HCC and para cancer tissues, 3024 DEGs were identified, and 641 were specific markers of four cell subgroups. To develop a prognostic risk model, 9 genes among the 641 genes were selected. In the training and validation sets, the model demonstrated a higher 5-year AUC and independently served as a prognostic marker as confirmed by multivariate and univariate Cox analyses. This study revealed the characteristics of different cell subpopulations of immune cells and tumor cells from the HCC microenvironment. We established a novel nine-gene prognostic model to determine the death risk of HCC patients. The discoveries in this research improved the current knowledge of HCC heterogeneity and may inspire future research.

Transcriptional level evaluation of osteopontin/miRNA-181a axis in hepatocellular carcinoma cell line-secreted extracellular vesicles

Recent evidence suggested the role of secreted extracellular vesicles (EVs) in the intracellular signalling within the liver becoming a promising candidate as biomarker in hepatocellular carcinoma (HCC). Osteopontin (OPN) seems to play a relevant role both for early diagnosis of HCC than on the mechanisms that drive oncogenesis but, to date, information on the expression levels of OPN in EVs secreted by HCC tumor cell line are missing. The study aimed to verify, by transcriptional and proteomic study, the presence of OPN in EVs secreted by tumorigenic (HepG2) and non-tumorigenic hepatocyte cell line (WRL68), and to analyse the expression variations of OPN, its isoforms and miRNA-181a in both these EVs. "In silico analysis" was also performed via the Gene expression Profiling Interactive analysis (GEPIA) and Hepatocellular Carcinoma Database (HCCDB). An up-regulation of OPN in EVs secreted by HepG2 with respect to WRL68 was found in line with the results obtained by the "in silico analysis". The study demonstrates, for the first time, the OPN isoforms and its modulator miRNA-181a expression in EVs secreted by both cell lines, highlighting high levels of OPN isoforms in EVs secreted by HepG2 and identifying OPN as a promising biomarker for HCC diagnosis.

Integrative analyses of prognosis, tumor immunity, and ceRNA network of the ferroptosis-associated gene FANCD2 in hepatocellular carcinoma

Extensive evidence has revealed that ferroptosis plays a vital role in HCC development and progression. Fanconi anemia complementation group D2 (FANCD2) has been reported to serve as a ferroptosis-associated gene and has a close relationship with tumorigenesis and drug resistance. However, the impact of the FANCD2-related immune response and its mechanisms in HCC remains incompletely understood. In the current research, we evaluated the prognostic significance and immune-associated mechanism of FANCD2 based on multiple bioinformatics methods and databases. The results demonstrated that FANCD2 was commonly upregulated in 15/33 tumors, and only the high expression of FANCD2 in HCC was closely correlated with worse clinical outcomes by OS and DFS analyses. Moreover, ncRNAs, including two major types, miRNAs and lncRNAs, were closely involved in mediating FANCD2 upregulation in HCC and were established in a ceRNA network by performing various in silico analyses. The DUXAP8-miR-29c-FANCD2 and LINC00511-miR-29c-FANCD2 axes were identified as the most likely ncRNA-associated upstream regulatory axis of FANCD2 in HCC. Finally, FANCD2 expression was confirmed to be positively related to HCC immune cell infiltration, immune checkpoints, and IPS analysis, and GSEA results also revealed that this ferroptosis-associated gene was primarily involved in cancer-associated pathways in HCC. In conclusion, our investigations indicate that ncRNA-related modulatory overexpression of FANCD2 might act as a promising prognostic and immunotherapeutic target against HCC.

CCDC25 may be a potential diagnostic and prognostic marker of hepatocellular carcinoma: Results from microarray analysis

Background

Hepatocellular carcinoma (HCC) is a tumor with a high recurrence rate, poor prognosis, and rapid progression. Therefore, it is necessary to find a novel biomarker for HCC. Coiled-coil domain containing 25 (CCDC25) has been identified as a target molecule that mediates liver metastasis in colon cancer. However, the molecular mechanisms of CCDC25 in HCC are unknown. This study aimed to explore the role of CCDC25 in HCC.

Methods

The expression of CCDC25 in HCC was identified through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Receiver operating characteristic curve (ROC) curves were drawn to evaluate the diagnostic value of CCDC25 for HCC. The effect of CCDC25 on the prognosis of HCC was analyzed by using the Kaplan–Meier plotter. Co-expressed genes and Gene Set Enrichment Analysis (GSEA) were used to explore the related functions and regulatory signaling pathways of CCDC25. Moreover, we employed the Tumor Immune Estimation Resource (TIMER) database and CIBERSORT algorithm to investigate the relationship between CCDC25 and the tumor immune microenvironment (TME) in HCC. Meanwhile, the effect of CCDC25 on the sensitivity of HCC patients to chemotherapy drugs was evaluated. Finally, we explored the prognostic methylation sites of CCDC25 using the MethSurv database.

Results

CCDC25 expression was low in HCC. Low CCDC25 expression was significantly associated with poor overall survival of HCC and may be comparable to the ability of AFP to diagnose HCC. Dysregulation of glucose metabolism, fatty acid metabolism, amino acid metabolism, ubiquitination modification, and apoptosis inhibition caused by CCDC25 downregulation may be the causes and results of HCC. In addition, CCDC25 was positively correlated with the infiltration level of various adaptive antitumor immune cells. The levels of immune cell infiltration and immune checkpoint expression were lower in the samples with high CCDC25 expression. What is more, we found that downregulated CCDC25 may increase the sensitivity or resistance of HCC patients to multiple drugs, including sorafenib. We also identified a methylation site for CCDC25, which may be responsible for poor prognosis and low CCDC25 expression in HCC patients. Finally, CCDC25 may be associated with HCC ferroptosis.

Conclusions

CCDC25 may be a potential diagnostic and prognostic marker for HCC and is associated with immune infiltration and ferroptosis.

OIT3 serves as a novel biomarker of hepatocellular carcinoma by mediating ferroptosis via regulating the arachidonic acid metabolism

Background

Oncoprotein-Induced Transcript 3 Protein (OIT3) was identified as a liver-specific gene with abnormal expression in hepatocellular carcinoma (HCC). Herein, we aimed to examine the function and specific mechanism of OIT3 in HCC.

Methods

Bioinformatic analyses and tissue microarray via immunohistochemistry were used to validate the expression of OIT3 in HCC. The biofunctions of OIT3 in HCC were determined in vitro and in vivo. The mechanism was confirmed by RNA-Sequence and Western blotting. The uni- and multivariate analyses were used to identify the independent predictors for HCC.

Results

Low expression of OIT3 was observed in HCC and predicted a poor clinical outcome. Ectopic expression of OIT3 could inhibit the proliferation, migration, and invasion abilities of HCC cells. Mechanistically, OIT3 upregulated the expression of ALOX15 and CYP4F3, thus inducing arachidonic acid increase, ROS accumulation, and lipid peroxidation, and eventually causing ferroptosis. OIT3 was validated as a prognostic predictor for HCC patients.

Conclusions

Our findings revealed a novel role of OIT3 in the process of tumorigenesis of HCC. OIT3 inhibited reproliferation, migration, and invasion of HCC cells by triggering ferroptosis, which indicates that OIT3 could serve as a potential biomarker in HCC.

Spindle and Kinetochore-associated Family Genes are Prognostic and Predictive Biomarkers in Hepatocellular Carcinoma

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is one of the most frequent malignant tumors. Spindle and kinetochore-associated (SKA) family genes are essential for the maintenance of the metaphase plate and spindle checkpoint silencing during mitosis. Recent studies have indicated that dysregulation of SKA family genes induces tumorigenesis, tumor progression, and chemoresistance via modulation of cell cycle and DNA replication. However, the differential transcription of SKAs in the context of HCC and its prognostic significance has not been demonstrated.

METHODS

Bioinformatics analyses were performed using TCGA, ONCOMINE, HCCDB, Kaplan-Meier plotter, STRING, GEPIA databases. qRT-PCR, western blot, and functional assays were utilized for in vitro experiments.

RESULTS

We found remarkable upregulation of transcripts of SKA family genes in HCC samples compared with normal liver samples on bioinformatics analyses and in vitro validation. Interaction analysis and enrichment analysis showed that SKA family members were mainly related to microtubule motor activity, mitosis, and cell cycle. Immuno-infiltration analysis showed a correlation of all SKA family genes with various immune cell subsets, especially T helper 2 (Th2) cells. Transcriptional levels of SKA family members were positively associated with histologic grade, T stage, and α-fetoprotein in HCC patients. Receiver operating characteristic curve analysis demonstrated a strong predictive ability of SKA1/2/3 for HCC. Increased expression of these SKAs was associated with unfavorable overall survival, progression-free survival, and disease-specific survival. On Cox proportional hazards regression analyses, SKA1 upregulation and pathological staging were independent predictors of overall survival and disease-specific survival of HCC patients. Finally, clinical tissue microarray validation and in vitro functional assays revealed SKA1 acts an important regulatory role in tumor malignant behavior.

CONCLUSIONS

SKA family members may potentially serve as diagnostic and prognostic markers in the context of HCC. The correlation between SKAs and immune cell infiltration provides a promising research direction for SKA-targeted immunotherapeutics for HCC.

Comprehensive analysis of complement-associated molecular features in hepatocellular carcinoma

The complement cascade plays a "complementing" role in human immunity. However, the potential roles of complement system in impacting molecular and clinical features of hepatocellular carcinoma (HCC) remain unclear. In this study, eleven public datasets are analyzed to compare the complement status between normal and cancerous samples based on 18 classical complement-associated genes. The complement scores are constructed to quantify complement signatures of individual tumors. HCC patients in the The Cancer Genome Atlas (TCGA) cohort are focused to perform systematical analyses between complement status and immune infiltration, miRNA expression, DNA methylation, clinicopathological features, and drug response. The results show that the complement scores in normal tissues are dramatically higher than those of tumor tissues. Tumor samples in the TCGA cohort are classified into complement score-low and score-high groups. Pathway analysis reveals that tumor-promoting pathways are typically inhibited in complement score-high group. This study also shows that tumor-killing immune cells, such as CD8 ⁺ T cells and natural killer cells are abundant and tumor-suppressing miRNAs are upregulated in complement score-high samples. In addition, we identify that complement scores are negatively correlated with certain clinical features, including pathological grade, clinical-stage, and portal vein invasion. Moreover, various molecular features together with complement scores are found to be correlated with response to anti-cancer drugs. This study provides a comprehensive and multidimensional analysis conducive to understanding the role of complement in cancer.

A novel HCC prognosis predictor PDSS1 affects the cell cycle through the STAT3 signaling pathway in HCC

Decaprenyl diphosphate synthase subunit 1 (PDSS1) is closely related to a variety of human diseases, but its expression pattern and biological function in HCC have not been studied to date.

Methods

The expression level of PDSS1 was analyzed using the TCGA and GEO databases. The relationships between PDSS1 and patient clinicopathological characteristics were verified based on TCGA clinical data. Additionally, the co-expressed genes of PDSS1were investigated and Gene Set Enrichment Analysis (GSEA) was conducted using LinkedOmics. Next, the association between PDSS1 and immune infiltration was determined using version 1.34.0 of the GSVA package. EdU assay, colony-formation assay, transwell assay, wound-healing assay, and flow cytometry analysis were used to assess the effect of PDSS1 on the cell phenotype.

Results

PDSS1 was upregulated in HCC compared with adjacent tissues. High PDSS1 in HCC was associated with poor overall survival, disease-specific survival, and progress-free interval. Results suggested that PDSS1 may activate multiple oncogenic pathways in HCC, especially those involved in the cell cycle. The expression of PDSS1 was significantly related to Th2 cells, TFH, T helper cells, NK CD56bright cells, cytotoxic cells, DC, CD8 T cells, and neutrophils. PDSS1 knockdown inhibited cell proliferation, cell cycle, migration and invasion. Furthermore, PDSS1 acted as an oncogene through the STAT3 signaling pathway.

Conclusion

Our study reveals that a high level of PDSS1 is significantly correlated with poor patient prognosis and immune cell infiltration in HCC. PDSS1 may be a novel biomarker and potential therapeutic target for HCC.

Comprehensive Analysis of TRIM Family Genes in Hepatitis Virus B-Related Hepatoma Carcinoma

Background: As significant components of E3 ligases, the tripartite motif (TRIM) proteins participate in various biological processes and facilitate the development of several diseases. Nevertheless, the correlations of TIRMs with hepatitis B virus (HBV)-positive hepatoma carcinoma (HCC) are not well elaborated.

Methods: The expression profile of TRIM genes in HBV-associated HCC and related clinical information were extracted from the Cancer Genome Atla (TCGA) database and the International Cancer Genome Consortium (ICGC) database. Dependent on the ConsensusPathDB and STRING databases, the gene ontology, Reactome pathways, and protein-protein interaction were assessed. Relied on TIMER 2.0 database, the relationship of the TRIMs with immune infiltration was investigated. Using multivariate analysis and Kaplan Meier analysis, the association between TRIM genes and the prognostic value was examined.

Results: A total of 17 TRIM genes, including TRIM16, TRIM17, and TRIM31 with fold change no less than 1.5, were discovered to upregulate in HBV-associated HCC in both TCGA and ICGC cohorts. Relied on gene enrichment analysis, the identified TRIMs were observed to not only be related to the interferon and cytokine signaling but also linked to the adaptive immune system. Particularly, the co-expression patterns of identified TRIMs with other E3 ligase genes and many innate immune genes that are associated with Toll-like receptor signaling, apoptosis, and SUMOylation. Besides, some of identified TRIM expressions were also linked to the infiltration levels of T cells and B cells. Additionally, several TRIM genes were associated with various clinical factors and relevant to the poor survival of HBV-associated HCC.

Conclusion: Our findings could deepen our understanding of TRIMs and their correlations with HBV-associated HCC. Furthermore, some of these TRIMs may be utilized as new prognostic markers of HBV-related HCC prognosis, or act as potential molecular targets for the disease.

Significance of Liver Zonation in Hepatocellular Carcinoma

Liver zonation is fundamental to normal liver function, and numerous studies have investigated the microstructure of normal liver lobules. However, only a few studies have explored the zonation signature in hepatocellular carcinoma (HCC). In this study, we investigated the significance of liver zonation in HCC with the help of single-cell RNA sequencing (scRNA-seq) and multicolor immunofluorescence staining. Liver zonation-related genes were extracted from the literature, and a three-gene model was established for HCC prognosis. The model reliability was validated using bulk RNA and single-cell RNA-level data, and the underlying biological mechanism was revealed by a functional enrichment analysis. The results showed that the signaling pathways of high-risk groups were similar to those of perivenous zones in the normal liver, indicating the possible regulating role of hypoxia in HCC zonation. Furthermore, the co-staining results showed that the low-grade tumors lost their zonation features whereas the high-grade tumors lost the expression of zonation-related genes, which supported the results obtained from the sequencing data.

Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

BACKGROUND

At present, the diagnostic ability of hepatocellular carcinoma (HCC) based on serum alpha-fetoprotein level is limited. Finding markers that can effectively distinguish cancer and non-cancerous tissues is important for improving the diagnostic efficiency of HCC.

RESULTS

In this study, we developed a predictive model for HCC diagnosis using personalized biological pathways combined with a machine learning algorithm based on regularized regression and carry out relevant examinations. In two training sets, the overall cross-study-validated area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve and the Brier score of the diagnostic model were 0.987 [95%confidence interval (CI): 0.979-0.996], 0.981 and 0.091, respectively. Besides, the model showed good transferability in external validation set. In TCGA-LIHC cohort, the AUROC, AURPC and Brier score were 0.992 (95%CI: 0.985-0.998), 0.967 and 0.112, respectively. The diagnostic model has accomplished very impressive performance in distinguishing HCC from non-cancerous liver tissues. Moreover, we further analyzed the extracted biological pathways to explore molecular features and prognostic factors. The risk score generated from a 12-gene signature extracted from the characteristic pathways was correlated with some immune related pathways and served as an independent prognostic factor for HCC.

CONCLUSION

We used personalized biological pathways analysis and machine learning algorithm to construct a highly accurate HCC diagnostic model. The excellent interpretable performance and good transferability of this model enables it with great potential for personalized medicine, which can assist clinicians in diagnosis for HCC patients.

Expression of ALG3 in Hepatocellular Carcinoma and Its Clinical Implication

Background: Recent studies have shown that alpha-1,3-mannosyltransferase (ALG3) promoted tumorigenesis and progression in multiple cancer types. Our study planned to explore the clinical implication and potential function of ALG3 in hepatocellular carcinoma.

Materials and Methods: Data from public databases were used to analyze the ALG3 expression and its impact on the clinical significance of patients with HCC. The ALG3 expression was confirmed by qRT-PCR and Western blot. Immunohistochemistry was used to confirm the ALG3 expression and explore its clinical implication in HCC. KEGG, GO, and GSEA enrichment analyses were utilized to explore the biological pathways related to ALG3 in HCC. TIMER2.0 was applied to assess the association between ALG3 and immune infiltration. CCK8, MTT, and transwell assays were used to investigate the role of ALG3 downregulation in HCC cell lines.

Results: qRT-PCR, WB, and IHC proved ALG3 was highly overexpressed in HCC tissues. The Kaplan–Meier analysis verified the overexpression of ALG3 was related to poor overall survival (p < 0.001). Multivariate cox regression analysis showed that the high ALG3 expression was an independent risk prognostic factor. GSEA and TIMER2.0 predicted that ALG3 participates in cell differentiation and cycle and correlates with immune cell infiltration. Transwell assay results showed that ALG3 silencing also impaired the invasion ability of HCC cells.

Conclusion: ALG3 was overexpressed and considered a potential indicator of survival in HCC, and our findings provided a novel therapeutic target for HCC.

Actin-related protein 2/3 complex subunit 2-enriched extracellular vesicles drive liver cancer metastasis

BACKGROUND

Extracellular vesicles (EVs) play pivotal roles in tumor growth, cancer metastasis and angiogenesis. Here, we aimed to identify proteins that contribute to the functionality of EVs derived from metastatic hepatocellular carcinoma (HCC) cells.

METHODS

Proteins of EVs derived from metastatic HCC cells and normal liver cells were analyzed by mass spectrometry. Proteomic profiling identified actin-related protein 2/3 complex subunit 2 (ARPC2) to be highly expressed in EVs of metastatic HCC cells. The expression of ARPC2 in EVs and HCC tissues was examined using immunoblotting and TCGA database, respectively. The functional roles of EV-ARPC2 were investigated by knockout approach and various in vitro and in vivo assays.

RESULTS

ARPC2 was highly expressed in EVs of metastatic cells but barely detected in non-metastatic HCC cells and normal liver cells. Immunogold labeling showed the presence of APRC2 on the surface of EVs. Analysis of TCGA database of liver cancer revealed ARPC2 overexpression was correlated with poor prognosis of patients. ARPC2 was knockout in metastatic HCC cells. EVs derived from knockout cells displayed compromised activity in enhancing cell growth, motility and metastasis compared to EVs of control cells. Pimozide, an inhibitor of APRC2, also inhibited the promoting effect of EVs of metastatic cells in lung colonization of tumor cells in mice.

CONCLUSION

This study reveals previously unreported expression and function of ARPC2 in EVs. EVs with highly expressed ARPC2 enhance cancer cell growth and metastasis. ARPC2 may provide a prospective target for the novel treatment of HCC patients.

Analysis of multiple databases identifies crucial genes correlated with prognosis of hepatocellular carcinoma

Despite advancements made in the therapeutic strategies on hepatocellular carcinoma (HCC), the survival rate of HCC patient is not satisfactory enough. Therefore, there is an urgent need for the valuable prognostic biomarkers in HCC therapy. In this study, we aimed to screen hub genes correlated with prognosis of HCC via multiple databases. 117 HCC-related genes were obtained from the intersection of the four databases. We subsequently identify 10 hub genes (JUN, IL10, CD34, MTOR, PTGS2, PTPRC, SELE, CSF1, APOB, MUC1) from PPI network by Cytoscape software analysis. Significant differential expression of hub genes between HCC tissues and adjacent tissues were observed in UALCAN, HCCDB and HPA databases. These hub genes were significantly associated with immune cell infiltrations and immune checkpoints. The hub genes were correlated with clinical parameters and survival probability of HCC patients. 147 potential targeted therapeutic drugs for HCC were identified through the DGIdb database. These hub genes could be used as novel prognostic biomarkers for HCC therapy.

RAB42 is a Potential Biomarker that Correlates With Immune Infiltration in Hepatocellular Carcinoma

Backgrounds: Hepatocellular carcinoma (HCC) is a malignant cancer with high mortality. Previous studies have reported that RAB42 is associated with prognosis and progression in glioma. However, the role of RAB42 in HCC is still unknown. Therefore, we aimed to elucidate the value of RAB42 in the predicting prognosis of HCC, and its relationship with immune cells infiltration.

Methods: UALCAN, HCCDB, and MethSurv databases were used to examine the expression and methylation levels of RAB42 in HCC and normal samples. cBioPortal and MethSurv were used to identify genetic alterations and DNA methylation of RAB42, and their effect on prognosis. The correlations between RAB42 and the immune cells and cancer-associated fibroblasts infiltration were analyzed by TIMER, TISIDB, and GEPIA database. The LinkedOmics database was used to analyze the enriched pathways associated with genes co-expressed with RAB42. EdU assay was used to evaluate the proliferation ability of liver cancer cells, and transwell assay was used to detect the invasion and migration ability of liver cancer cells.

Results: The expression levels of RAB42 were increased in HCC tissues than that in normal tissues. Highly expressed RAB42 was significantly correlated with several clinical parameters of HCC patients. Moreover, increased RAB42 expression clearly predicted poor prognosis in HCC. Furthermore, multivariate Cox regression analysis showed that RAB42 was an independent prognostic factor in HCC. The RAB42 genetic alteration rate was 5%. RAB42 DNA methylation in HCC tissues was lower than that in normal tissues. Among the 7 DNA methylation CpG sites, two were related to the prognosis of HCC. The results of gene set enrichment analysis (GSEA) showed that RAB42 was associated with various immune cells and cancer-associated fibroblasts infiltration in HCC. Meanwhile, we found RAB42 methylation was strongly correlated with immune infiltration levels, immunomodulators, and chemokines. Experiments in vitro indicated that knockdown of RAB42 inhibited the proliferation, invasion, and migration of liver cancer cells.

Conclusions: Our study highlights the clinical importance of RAB42 in HCC and explores the effect of RAB42 on immune infiltration in the tumor microenvironment, and RAB42 may act as a pro-oncogene that promotes HCC progression.

A nomogram based on a three pyroptosis gene model and clinical parameters for predicting prognosis of hepatocellular carcinoma

Globally, hepatocellular carcinoma (HCC) has a dismal prognosis and studies have shown that accurate prognostic risk assessment can have clinically significant benefits for patients with HCC patients. After successively performing univariate Cox regression, Lasso regression, and stepwise multivariate Cox regression analysis, three pyroptosis gene (GPX4, NLRP1, and NLRP6) were selected to construct and validate the prognostic model of HCC based on public data. The expression pattern and prognostic implication of GPX4 in HCC was validated by immunohistochemistry staining in HCC specimens collected from Affiliated Hospital of Jining Medical University. A nomogram combined model and clinical characteristics was plotted after the prognostic predictive value of model was validated with receiver operating characteristic curves and Kaplan-Meier survival analysis. Our results indicate that assessing pyroptosis gene expression may be useful to predict the prognosis of HCC patients by enhancing antitumor immunity.

Data mining of key genes expression in hepatocellular carcinoma: novel potential biomarkers of diagnosis prognosis or progression

Hepatocellular carcinoma (HCC) is one of the main cancer-related causes of death worldwide. The study aimed to perform a data mining analysis of the expression and regulatory role of key genes in HCC to reveal novel potential biomarkers of diagnosis prognosis, or progression since their availability is still almost lacking. Starting from data of our cohort of patients (HCV-positive HCC pts undergoing liver transplantation (LR, n = 10) and donors (LD, n = 14), deeply analyzed previously, in which apelin, osteopontin, osteoprotegerin, NOTCH-1, CASP-3, Bcl-2, BAX, PTX3, and NPTX2 were analyzed, we applied statistical analysis and in-silico tools (Gene Expression Profiling Interactive Analysis, HCCDB database and GeneMania, UALCAN) to screen and identify the key genes. Firstly, we performed a stepwise regression analysis using our mRNA-datasets which revealed that higher expression levels of apelin and osteopontin were positively associated with the HCC and identified that the most consistently differentially expressed gene across multiple HCC expression datasets was only OPN. This comprehensive strategy of data mining evidenced that OPN might have a potential function as an important tumor marker-driven oncogenesis being associated with poor prognosis of HCC patients.

E2F1-mediated GINS2 transcriptional activation promotes tumor progression through PI3K/AKT/mTOR pathway in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has high morbidity and mortality rates. It is therefore imperative to study the underlying mechanism of HCC to identify potential prognostic biomarkers and therapeutic targets. Recently, GINS2 has been identified to be a cancer-promoting gene in different cancer types. Nevertheless, the exact mechanism of GINS2 in HCC remains to be elucidated. To systematically explore the significance of GINS2, we first assessed the relative expression of GINS2 in pan-cancers based on data obtained from the HCCDB, TIMER, and TCGA databases. Then, we explored the clinical significance of GINS2 in HCC through Kaplan-Meier method as well as univariate and multivariate cox regression analysis. Additionally, functional enrichment analysis of GINS2 was done through GO, KEGG, PPI network, and immune cell infiltration analyses. Functional experiments were also conducted to investigate the biological significance of GINS2 in HCC cell lines. Our research revealed that GINS2 is involved in HCC progression and highlighted its potential value as a crucial diagnostic and therapeutic target for HCC.

The CpG Island-Binding Protein SAMD1 Contributes to an Unfavorable Gene Signature in HepG2 Hepatocellular Carcinoma Cells

The unmethylated CpG island-binding protein SAMD1 is upregulated in many human cancer types, but its cancer-related role has not yet been investigated. Here, we used the hepatocellular carcinoma cell line HepG2 as a cancer model and investigated the cellular and transcriptional roles of SAMD1 using ChIP-Seq and RNA-Seq. SAMD1 targets several thousand gene promoters, where it acts predominantly as a transcriptional repressor. HepG2 cells with SAMD1 deletion showed slightly reduced proliferation, but strongly impaired clonogenicity. This phenotype was accompanied by the decreased expression of pro-proliferative genes, including MYC target genes. Consistently, we observed a decrease in the active H3K4me2 histone mark at most promoters, irrespective of SAMD1 binding. Conversely, we noticed an increase in interferon response pathways and a gain of H3K4me2 at a subset of enhancers that were enriched for IFN-stimulated response elements (ISREs). We identified key transcription factor genes, such as IRF1, STAT2, and FOSL2, that were directly repressed by SAMD1. Moreover, SAMD1 deletion also led to the derepression of the PI3K-inhibitor PIK3IP1, contributing to diminished mTOR signaling and ribosome biogenesis pathways. Our work suggests that SAMD1 is involved in establishing a pro-proliferative setting in hepatocellular carcinoma cells. Inhibiting SAMD1's function in liver cancer cells may therefore lead to a more favorable gene signature.

Tubulin Alpha 1b Is Associated with the Immune Cell Infiltration and the Response of HCC Patients to Immunotherapy

Tubulin alpha 1b (TUBA1B) is an important microtubule isoform that is involved in the formation of the cytoskeleton. The objective of our study was to explore the potential of TUBA1B in predicting the prognosis of HCC and patients’ response to immunotherapy. Raw data was extracted from TCGA and GEO databases, and then HCCDB, TIMER, HPA, and GEPIA websites, as well as R software, were used to perform bioinformatics analysis to investigate the potential of TUBA1B as a prognostic and immunotherapeutic marker for hepatocellular carcinoma (HCC). We found that both TUBA1B mRNA and protein were highly expressed in HCC. TUBA1B was proved to be an independent prognostic predictor of HCC. Additionally, TUBA1B expression was associated with the infiltration of several immune cells in HCC. Moreover, TUBA1B was coexpressed with immune-related genes and immune checkpoints. Patients expressing high TUBA1B responded better to immune checkpoint inhibitor (ICI) therapy. GO and KEGG analyses revealed that TUBA1B may be involved in the processes of cell cycle, spliceosome, and DNA replication. In conclusion, TUBA1B is expected to be a prognostic and immunotherapeutic marker for HCC.

ASF1B is a Promising Prognostic Biomarker and Correlates With Immunotherapy Efficacy in Hepatocellular Carcinoma

Background: Anti-silencing function 1B (ASF1B), a histone H3-H4 chaperone, is crucial for S-phase progression and cell proliferation. Recent studies have shown that ASF1B may be used as a new proliferation marker for cancer prognosis. However, the prognostic value and effect of ASF1B on tumor cells and the immune microenvironment in hepatocellular carcinoma (HCC) remain unclear.

Methods: We analyzed the expression of ASF1B and its prognostic value using The Cancer Genome Atlas (TCGA) database (as a training set) and other databases, and we validated the findings by immunohistochemistry in our clinical database, containing 141 HCC patients (as a validation set). Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to probe the tumor-associated biological processes of ASF1B in HCC. The interrelationships between ASF1B expression and tumor immunological characteristics were analyzed by multiple databases. The Imvigor210 cohort was retrieved to assess the ability of ASF1B to predict immunotherapy efficacy.

Results: ASF1B was highly expressed in tumor tissue compared to paracancerous tissue. High ASF1B expression was associated with worse overall survival (OS) and progression-free survival (PFS) in the training set (p = 0.005, p < 0.001) and validation set (p < 0.001, p < 0.001). Multivariate analysis revealed that ASF1B was an independent prognostic factor associated with OS and PFS. GSEA and GSVA suggested that ASF1B was involved in tumor-associated biological processes, including the cell cycle, DNA replication, base excision repair, mismatch repair, RNA degradation, ubiquitin-mediated proteolysis, and nucleotide excision repair. Further analysis revealed that the levels of ASF1B were positively correlated with the immune cells infiltration of B cells, CD8⁺ T cells, CD4⁺ T cells, neutrophils, and dendritic cells. However, ASF1B was positively correlated with Treg cell infiltration and inhibitory immune checkpoints in exhausted T cells. Patients who received anti-PD-L1 immunotherapy with high ASF1B expression had a higher objective response.

Conclusion: The ASF1B level is an independent prognostic factor and may serve as a potential immunotherapeutic target.

The clinical relevance of epithelial-mesenchymal transition and its correlations with tumorigenic immune infiltrates in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a cancer with extremely high mortality. Epithelial-mesenchymal transition (EMT) may play an important role in the occurrence, invasion, and prognosis of HCC; however, its relationship with immunity in HCC has not yet been studied. Therefore, we investigated the diagnostic and prognostic values of EMT and explored its potential connections with tumorigenic immune infiltrates in HCC. We first proposed a quantitative metric of EMT activity, the EMT score. After applying this metric to 20 datasets from the Integrative Molecular Database of Hepatocellular Carcinoma, The Cancer Genome Atlas, and the Gene Expression Omnibus, we explored the ability of the EMT score to stratify across sample types. We then applied the EMT score for survival analysis and to differentiate patients with/without vascular invasion to test its prognostic value. We also collected and calculated data on the abundance of immune cells and immune cell markers in HCC and investigated their correlations with EMT scores. Finally, we synthesized and analyzed 20 datasets and constructed an EMT-gene-immune linkage network. The results showed higher EMT scores in HCC samples than in cirrhotic and normal livers. The cases with higher EMT scores also showed poorer performance in terms of prognostic factors such as vascular invasion and overall survival time. Our research demonstrated a broad correlation between EMT and the tumor immune microenvironment, and we uncovered multiple potential linkers associated with both EMT and immunity. Studying EMT has clinical relevance and high diagnostic and prognostic value for HCC.

Construction and Validation of Two Hepatocellular Carcinoma-Progression Prognostic Scores Based on Gene Set Variation Analysis

Background: Liver hepatocellular carcinoma (LIHC) remains a global health challenge with a low early diagnosis rate and high mortality. Therefore, finding new biomarkers for diagnosis and prognosis is still one of the current research priorities.

Methods: Based on the variation of gene expression patterns in different stages, the LIHC-development genes (LDGs) were identified by differential expression analysis. Then, prognosis-related LDGs were screened out to construct the LIHC-unfavorable gene set (LUGs) and LIHC-favorable gene set (LFGs). Gene set variation analysis (GSVA) was conducted to build prognostic scoring models based on the LUGs and LFGs. ROC curve analysis and univariate and multivariate Cox regression analysis were carried out to verify the diagnostic and prognostic utility of the two GSVA scores in two independent datasets. Additionally, the key LCGs were identified by the intersection analysis of the PPI network and univariate Cox regression and further evaluated their performance in expression level and prognosis prediction. Single-sample GSEA (ssGSEA) was performed to understand the correlation between the two GSVA enrichment scores and immune activity.

Result: With the development of LIHC, 83 LDGs were gradually upregulated and 247 LDGs were gradually downregulated. Combining with LIHC survival analysis, 31 LUGs and 32 LFGs were identified and used to establish the LIHC-unfavorable GSVA score (LUG score) and LIHC-favorable GSVA score (LFG score). ROC curve analysis and univariate/multivariate Cox regression analysis suggested the LUG score and LFG score could be great indicators for the early diagnosis and prognosis prediction. Four genes (ESR1, EHHADH, CYP3A4, and ACADL) were considered as the key LCGs and closely related to good prognosis. The frequency of TP53 mutation and copy number variation (CNV) were high in some LCGs. Low-LFG score patients have active metabolic activity and a more robust immune response. The high-LFG score patients characterized immune activation with the higher infiltration abundance of type I T helper cells, DC, eosinophils, and neutrophils, while the high-LUG score patients characterized immunosuppression with the higher infiltration abundance of type II T helper cells, TRegs, and iDC. The high- and low-LFG score groups differed significantly in immunotherapy response scores, immune checkpoints expression, and IC50 values of common drugs.

Conclusion: Overall, the LIHC-progression characteristic genes can be great diagnostic and prognostic signatures and the two GSVA score systems may become promising indices for guiding the tumor treatment of LIHC patients.

Integrated analysis of the prognostic and oncogenic roles of OPN3 in human cancers

Background

Emerging cell- or tissue-based evidence has demonstrated that opsin 3 (OPN3) mediates a variety of pathological processes affecting tumorigenesis, clinical prognosis, and treatment resistance in some cancers. However, a comprehensive analysis of OPN3 across human cancers is unavailable. Therefore, a pancancer analysis of OPN3 expression was performed and its potential oncogenic roles were explored.

Methods

The expression and characterization of OPN3 were evaluated among 33 tumour types using The Cancer Genome Atlas (TCGA) dataset. Additionally, the OPN3 RNA level and overall survival (OS) in relation to its expression level in 33 cancer types were estimated. Based on the analysis above, 347 samples from 5 types of tumours were collected and detected for the protein expression of OPN3 by immunohistochemical assay. Furthermore, the biological role of OPN3 in cancers was evaluated via gene set enrichment analysis (GSEA).

Results

The OPN3 expression level was heterogeneous across cancers, yet a remarkable difference existed between OPN3 expression and patient overall survival among the 7 types of these 33 cancers. Consistently, a high immunohistochemical score of OPN3 was significantly associated with a poor prognosis among patients with 5 types of tumours. Additionally, OPN3 expression was involved in cancer-associated fibroblast infiltration in 5 types of tumours, and promoter hypomethylation of OPN3 was observed in 3 tumour types. Additionally, OPN3 protein phosphorylation sites of Tyr140 and Ser380 were identified via posttranscriptional modification analysis, suggesting the potential function of Tyr140 and Ser380 phosphorylation in tumorigenesis. Furthermore, the enrichment analysis was mainly concentrated in C7orf70, C7orf25 and the “ribosome” pathway by GSEA in 5 types of cancers, indicating that OPN3 might affect tumorigenesis and progression by regulating gene expression and ribosome biogenesis.

Conclusions

High expression of OPN3 was significantly associated with a poor clinical prognosis in five types of cancers. Its molecular function was closely associated with the ribosomal pathway.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09219-7.

CPNE1 is a potential prognostic biomarker, associated with immune infiltrates and promotes progression of hepatocellular carcinoma

BACKGROUND

Copine1 (CPNE1), the first discovered CPNE1 family member, participates in the process of carcinogenesis and development of diverse tumors. Our study aimed to investigate the expression and prognostic value of CPNE1 gene in hepatocellular carcinoma (HCC), to explore its functional network in HCC and its effects on biological behaviors.

METHODS

HCCDB, CCLE, HPA and LinkedOmics online databases were used to explore the expression of CPNE1 gene and analyze the co-expression network of CPNE1 in hepatocellular carcinoma. Gene set enrichment analysis (GSEA) was used for GO functional annotation, KEGG pathway enrichment analysis and regulators of CPNE1 networks in LIHC. HepG2 and MHCC-97H cells were selected to construct CPNE1 knockdown cell lines by transfection with siRNA, and Hep3B cell was selected to construct CPNE1 overexpression cell line by transfection with plasmid. The effect of CPNE1 on the proliferation of hepatocellular carcinoma cells was examined by CCK8 assay and clone formation assay; the effect of CPNE1 on the migration ability of hepatocellular carcinoma cells was assessed by cell scratch assay and Transwell cell migration assay; finally, the expression of related signaling pathway proteins was examined by Western Blot. The correlation of CPNE1 expression with immune infiltration and immune checkpoint molecules in HCC tissues was analyzed using TIMER online database and GSEA.

RESULTS

CPNE1 was highly expressed in HCC tissues and significantly correlated with sex, age, cancer stage and tumor grade. Overall survival (OS) was significantly lower in patients with high CPNE1 expression than in patients with low CPNE1 expression, and CPNE1 could be used as an independent prognostic indicator for HCC. Knockdown of CPNE1 gene inhibited the AKT/P53 pathway, resulting in decreased proliferation, migration and invasion of HCC cells. Overexpression of CPNE1 gene showed the opposite results. The level of CPNE1 expression in HCC was significantly and positively correlated with the level of infiltration of B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells (P < 0.001). GSEA results also showed that CPNE1 of LIHC was involved in some immune response regulating signaling pathways.

CONCLUSIONS

Our study firstly found the expression of CPNE1 was significantly higher in LIHC tissues than in normal liver tissues, and high CPNE1 expression was associated with poor prognosis. In addition, we identified the possible mechanism by which CPNE1 functioned in LIHC. CPNE1 influenced AKT/P53 pathway activation and LIHC cell proliferation and migration. There was a significant correlation between CPNE1 expression and tumor immune infiltration in LIHC.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

JEBIN: analyzing gene co-expressions across multiple datasets by joint network embedding

The inference of gene co-expression associations is one of the fundamental tasks for large-scale transcriptomic data analysis. Due to the high dimensionality and high noises in transcriptomic data, it is difficult to infer stable gene co-expression associations from single dataset. Meta-analysis of multisource data can effectively tackle this problem. We proposed Joint Embedding of multiple BIpartite Networks (JEBIN) to learn the low-dimensional consensus representation for genes by integrating multiple expression datasets. JEBIN infers gene co-expression associations in a nonlinear and global similarity manner and can integrate datasets with different distributions in linear time complexity with the gene and total sample size. The effectiveness and scalability of JEBIN were verified by simulation experiments, and its superiority over the commonly used integration methods was proved by three indexes on real biological datasets. Then, JEBIN was applied to study the gene co-expression patterns of hepatocellular carcinoma (HCC) based on multiple expression datasets of HCC and adjacent normal tissues, and further on latest HCC single-cell RNA-seq data. Results show that gene co-expressions are highly different between bulk and single-cell datasets. Finally, many differentially co-expressed ligand-receptor pairs were discovered by comparing HCC with adjacent normal data, providing candidate HCC targets for abnormal cell-cell communications.

SLC39A1 Overexpression is Associated with Immune Infiltration in Hepatocellular Carcinoma and Promotes Its Malignant Progression

Background

Solute carrier family 39 member 1 (SLC39A1) has been identified as a zinc ion transport protein that possesses oncogenic properties in various types of cancers. However, its potential function in hepatocellular carcinoma (HCC) remains unknown. This study aimed to investigate the expression profile and potential mechanisms of SLC39A1 in HCC.

Methods

SLC39A1 expression was analyzed using multiple databases. The clinical significance and associated biological pathways of SLC39A1 were investigated using bioinformatics analysis. Potential correlations between SLC39A1 expression and tumor immunity in HCC were also evaluated using single-sample gene set enrichment analysis (GSEA). Sixty paired HCC samples were used to verify the expression pattern of SLC39A1. In vitro studies were performed to investigate the oncogenic effects of SLC39A1 in HCC. Western blot analysis was conducted to further investigate the possible involved signaling pathways.

Results

The overexpression of SLC39A1 in HCC was determined by bioinformatics analysis and was confirmed in tissues from our center. SLC39A1 overexpression was also significantly correlated with worse prognosis, advanced TNM stage, and histological grade. GSEA analysis demonstrated that SLC39A1 overexpression was involved in various tumor-related pathways, such as the cell cycle and Wnt signaling pathway. SLC39A1 knockdown repressed the proliferation, invasion, and migration abilities of HCC cells. Furthermore, SLC39A1 knockdown decreased the expression of the tumor progression-related proteins (eg, cyclin D1 and MMP2) and Wnt signaling pathway-related proteins (eg, Wnt3A and β-catenin). In addition, SLC39A1 overexpression may be associated with impaired tumor immunity in HCC, as evidenced by the increased infiltration of Th2 cells and reduced infiltration of cytotoxic cells.

Conclusion

These findings preliminarily suggested the crucial effect of SLC39A1 overexpression on HCC tumor progression and immunosuppression, suggesting its potential as a novel prognostic and therapeutic target in HCC.

Insights into the Association Between QSER1 and M2 Macrophages and Remarkable Malignancy Characteristics in Hepatocellular Carcinoma

Purpose

Glutamine and serine rich 1 (QSER1), as a DNA methylation modulator, play a crucial role in transforming tumor cells. Previous studies have shown that QSER1 plays a role in regulating the progression of various malignancies and that QSER1 dysfunction is connected with precancerous lesions of hepatocellular carcinoma (HCC) as well as HCC prognosis. However, little is known about the detailed contribution of QSER1 in HCC.

Patients and Methods

Various statistical methods such as Kaplan–Meier method, AUC analysis, GSEA, and immune-infiltration analysis were used to evaluate the relationship between QSER1 expression and clinical features, prognostic factors, and potential functional mechanisms of QSER1.

Results

QSER1 expression was negatively correlated with clinicopathological features (clinical stage, pathological grade, TP53 mutation, lymph node metastasis) and clinical outcome (overall survival versus recurrence). Functional enrichment analysis further suggested that QSER1 is involved in multiple pathways related to DNA replication and tumor immunity. TIMER analysis indicated that high QSER1 expression was significantly associated with higher macrophage infiltration and poorer macrophage-related outcomes. In particular, QSER1 was significantly more associated with M2 macrophages than M1 macrophages.

Conclusion

Overall, elevated QSER1 is a potential prognostic marker for HCC and is associated with immune infiltration in HCC.

High expression of PDZ-binding kinase is correlated with poor prognosis and immune infiltrates in hepatocellular carcinoma

Background

PDZ-binding kinase (PBK) encodes a serine/threonine protein kinase related to the dual specific mitogen-activated protein kinase kinase (MAPKK) family. There is evidence that overexpression of this gene is associated with tumorigenesis. However, the role of PBK in hepatocellular carcinoma (HCC) remains unclear. Therefore, we evaluated the prognostic role of PBK and its correlation with immune infiltrates in hepatocellular carcinoma.

Methods

The expression of PBK in pan-cancers was studied by Onconmine and TIMER. The expression of PBK in HCC patients and its relationship with clinicopathological characteristics were analyzed using The Gene Expression Profiling Interactive Analysis (GEPIA), The human protein atlas database (HPA), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) databases. Receiver operating characteristic (ROC) curve was used to determine the diagnostic value of PBK in HCC patients. The relationship between PBK and prognosis of HCC was performed by GEPIA and Kaplan Meier plotter web tool. The correlations between the clinical characteristics and overall survival were analyzed by Univariate Cox regression and Multivariate Cox hazards regression to identify possible prognostic factors for HCC patients. LinkedOmics was applied to investigate co-expression associated with PBK and to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The network map of PBK and related genes is constructed by GeneMANIA. Finally, TIMER and TISIDB were used to analyze the correlations between PBK and tumor-infiltrating immune cells.

Results

Multiple database analysis shows that PBK was highly expressed in many types of tumors, including hepatocellular carcinoma, and was significantly related to tumor stage (P=0.0089), age (P=0.0131), and race (P=0.0024) of HCC patients. The receiver operating characteristic (ROC) curve analysis showed that PBK had high diagnostic potential to HCC in GSE76427 (AUC=0.8799), GSE121248 (AUC=0.9224), GSE62232 (AUC=0.9975), and GSE84402 (AUC=0.9541). Multivariate Cox hazards regression showed that high expression of PBK may be an independent risk factor for overall survival in HCC patients (HR = 1.566, 95% CI=1.062–2.311, P= 0.024). The Protein–protein interaction network showed that PBK significantly interacted with LRRC47, ARAF, LGALS9B, TTK, DLG1, and other essential genes. Furthermore, enrichment analysis showed that PBK and co-expressed genes participated in many biological processes, cell composition, molecular functions, and pathways in HCC. Finally, the immune infiltration analysis by TIMER and TISIDB indicated that a significant tightly correlation between PBK and macrophages, neutrophils, as well as chemokines and receptors.

Conclusions

High expression of PBK is significantly correlated with poor survival and immune infiltrates in hepatocellular carcinoma. Our study suggests that PBK can be used as a biomarker of poor prognosis and potential immune therapy target in hepatocellular carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02479-w.

Prognostic Value and Immunological Role of KIFC1 in Hepatocellular Carcinoma

As one of the members of the kinesin family, the role and potential mechanism of kinesin family member C1 (KIFC1) in the development of liver hepatocellular carcinoma (LIHC), especially in the immune infiltration, have not been fully elucidated. In this study, multiple databases and immunohistochemistry were employed to analyze the role and molecular mechanism including the immune infiltration of KIFC1 in LIHC. Generally, KIFC1 mRNA expression was overexpressed in LIHC tissues than normal tissues, and its protein was also highly expressed in the LIHC. KIFC1 mRNA expression was correlated with tumor grade and TNM staging, which was negatively correlated with overall survival and disease-free survival. Moreover, univariable and multivariate Cox analysis revealed that upregulated KIFC1 mRNA is an independent prognostic factor for LIHC. The KIFC1 promoter methylation level was negatively associated with KIFC1 mRNA expression and advanced stages and grade in LIHC. The different methylation sites of KIFC1 had a different effect on the prognosis of LIHC. Specifically, the KIFC1 mRNA expression level showed intense correlation with tumor immunity, such as tumor-infiltrating immune cells and immune scores as well as multiple immune-related genes. Moreover, KIFC1 co-expressed with some immune checkpoints and related to the responses to immune checkpoint blockade (ICB) and chemotherapies. Significant GO analysis showed that genes correlated with KIFC1 served as catalytic activity, acting on DNA, tubulin binding, histone binding, ATPase activity, and protein serine/threonine kinase activity. KEGG pathway analysis showed that these genes related to KIFC1 are mainly enriched in signal pathways such as cell cycle, spliceosome, pyrimidine metabolism, and RNA transport. Conclusively, KIFC1 was upregulated and displayed a prognostic value in LIHC. Moreover, KIFC1 may be involved in the LIHC progression partially through immune evasion and serve as a predictor of ICB therapies and chemotherapies.

STK25 enhances hepatocellular carcinoma progression through the STRN/AMPK/ACC1 pathway

Background

Serine/threonine protein kinase 25 (STK25) plays an important role in regulating glucose and insulin homeostasis and in ectopic lipid accumulation. It directly affects the progression and prognosis of nonalcoholic fatty liver disease (NAFLD). However, the effects of STK25 on lipid metabolism in hepatocellular carcinoma (HCC) remain unexplored. The aim of this study was to investigate the role of STK25 in HCC and to elucidate the underlying mechanisms.

Methods

Immunohistochemistry was used to measure the expression of STK25 in hepatic tissues of HCC patients, and public datasets were used as supplementary material for predicting the expression of STK25 and the prognosis of patients with HCC. The interaction between STK25 and striatin (STRN) was determined by the STRING database, immunohistochemistry and western blot analyses. The involved signaling pathway was detected by the KEGG database and western blot. Moreover, the biological behaviors of the HCC cells were detected by wound healing assays, Transwell invasion assays and oil red O staining. Finally, it was verified again by xenograft model.

Results

STK25 is highly expressed in HCC patients and is associated with poor prognosis. STK25 knockdown inhibited the HCC cell invasion and proliferation, promotes apoptosis. Consistently, STK25 knockdown inhibited tumor growth in xenograft mouse model. Besides, STK25 deficiency decreased lipid synthesis, energy reserve, epithelial-mesenchymal transition (EMT) by down-regulating lipid metabolism signaling pathway. STRN could reverse the change of lipid metabolism.

Conclusions

Our results demonstrated that STK25 interacted with STRN to regulates the energy reserve and EMT via lipid metabolism reprogramming. Accordingly, high expression of STK25 may be associated with HCC patients and poor prognosis, which implicates STK25 could be a potential target for lipid metabolism in cancer therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02421-w.

Integrating the Epigenome and Transcriptome of Hepatocellular Carcinoma to Identify Systematic Enhancer Aberrations and Establish an Aberrant Enhancer-Related Prognostic Signature

Recently, emerging evidence has indicated that aberrant enhancers, especially super-enhancers, play pivotal roles in the transcriptional reprogramming of multiple cancers, including hepatocellular carcinoma (HCC). In this study, we performed integrative analyses of ChIP-seq, RNA-seq, and whole-genome bisulfite sequencing (WGBS) data to identify intergenic differentially expressed enhancers (DEEs) and genic differentially methylated enhancers (DMEs), along with their associated differentially expressed genes (DEE/DME-DEGs), both of which were also identified in independent cohorts and further confirmed by HiC data. Functional enrichment and prognostic model construction were conducted to explore the functions and clinical significance of the identified enhancer aberrations. We identified a total of 2,051 aberrant enhancer-associated DEGs (AE-DEGs), which were highly concurrent in multiple HCC datasets. The enrichment results indicated the significant overrepresentations of crucial biological processes and pathways implicated in cancer among these AE-DEGs. A six AE-DEG-based prognostic signature, whose ability to predict the overall survival of HCC was superior to that of both clinical phenotypes and previously published similar prognostic signatures, was established and validated in TCGA-LIHC and ICGC-LIRI cohorts, respectively. In summary, our integrative analysis depicted a landscape of aberrant enhancers and associated transcriptional dysregulation in HCC and established an aberrant enhancer-derived prognostic signature with excellent predictive accuracy, which might be beneficial for the future development of epigenetic therapy for HCC.