HCCDB: A Database of Hepatocellular Carcinoma Expression Atlas

Integrated Bioinformatic Analysis Reveals the Oncogenic, Survival, and Prognostic Characteristics of TPX2 in Hepatocellular Carcinoma

Targeting protein for Xenopus kinesin-like protein 2 (TPX2), a well-known mitotic protein, has been linked to carcinogenesis in several cancers. This study investigated the role of TPX2 in hepatocellular carcinoma (HCC) from various aspects using bioinformatic analyses. TPX2 expression and its prognostic value in pan-cancers were analyzed using SangerBox. TPX2 expression and its association with prognosis, immune infiltration, tumor mutations, and signaling pathways in HCC were analyzed using UALCAN, BoxKaplan-Meier Plotter, GEPIA, Human Protein Atlas, TIMER 2.0, and SangerBox. Genes co-expressed with TPX2 in HCC were analyzed using the HCCDB database, followed by functional enrichment using SangerBox. Clinical predictive models were established based on TPX2 and its co-expressed genes using the ACLBI database. TPX2 expression significantly increased in pan-cancers and was associated with survival in nearly half of the cancer types. High TPX2 expression has been linked to poor survival outcomes in patients with HCC. TPX2 expression was positively correlated with abundant infiltration of immune cells (including B cells, CD4 + /CD8 + T cells, macrophages, neutrophils, and dendritic cells), TP53 mutation, and carcinogenesis-related pathways, such as the PI3K/AKT/mTOR pathway, cellular response to hypoxia, and tumor proliferation signature. Nineteen genes were found to be co-expressed with TPX2 in HCC, and these genes showed close positive correlations and were mainly implicated in cell cycle-related functions. A prognostic model established using TPX2 and its expressed genes could stratify HCC patients into high- and low-risk groups, with a significantly shorter survival time in high-risk groups. The prognostic model performed well in predicting 1-, 3-, and 5-year survival of patients with HCC, with areas under the curve of 0.801, 0.725, and 0.711, respectively. TPX2 functions as an oncogene in HCC, and its high expression is detrimental to the survival of patients with HCC. Thus, TPX2 may be a prognostic biomarker and potential therapeutic target for HCC.

The prognostic significance and potential mechanism of DBF4 zinc finger in hepatocellular carcinoma

DBF4 zinc finger (DBF4) is a critical component involved in DNA replication and cell proliferation. It acts as a positive regulator of the cell division cycle 7 kinase. In this study, our investigation encompassed the impact of DBF4 on hepatocellular carcinoma (HCC) progression and delved into the potential mechanisms. We utilized open-access databases like TCGA and GEO to analyze the association between DBF4 and 33 different tumor types. We also conducted immunohistochemistry experiments to validate the expression of DBF4 in HCC, STAD, COAD, READ, PAAD, and LGG. Furthermore, we employed lentiviral transduction to knockdown DBF4 in HLF and SMMC cells, as well as to overexpress DBF4 in Huh7 cells. Subsequently, we evaluated the impact of DBF4 on proliferation, migration, and invasion of hepatocellular carcinoma cells. RNA sequencing and KEGG pathway enrichment analysis were also conducted to identify potential pathways, which were further validated through WB experiments. Finally, pathway inhibitor was utilized in rescue experiments to confirm whether DBF4 exerts its effects on tumor cells via the implicated pathway. Our findings revealed that DBF4 exhibited significant expression levels in nearly all examined tumors, which were further substantiated by the results of immunohistochemistry analysis. High DBF4 expression was correlated with poor overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI), disease-free interval (DFI), relapse-free interval (RFI) in majority of tumor types, particularly in patients with HCC. In vitro experiments demonstrated that inhibition of DBF4 impaired the proliferative, migratory, and invasive abilities of HCC cells, whereas overexpression of DBF4 promoted these phenotypes. Sequencing results indicated that DBF4 may induce these changes through the ERBB signaling pathway. Further experimental validation revealed that DBF4 activates the ERBB signaling pathway, leading to alterations in the JNK/STAT, MAPK, and PI3K/AKT signaling pathways, thereby impacting the proliferative, migratory, and invasive abilities of tumor cells. Lastly, treatment of Huh7 cells overexpressing DBF4 with the ERBB2 inhibitor dacomitinib demonstrated the ability of ERBB2 inhibition to reverse the promoting effect of DBF4 overexpression on the proliferative, migratory, and invasive abilities of HCC cells. DBF4 plays a pivotal oncogenic role in HCC by promoting the ERBB signaling pathway and activating its downstream PI3K/AKT, JNK/STAT3, and MAPK signaling pathways. DBF4 may serve as a prognostic biomarker for patients with HCC.

ViroISDC: a method for calling integration sites of hepatitis B virus based on feature encoding

BACKGROUND

Hepatitis B virus (HBV) integrates into human chromosomes and can lead to genomic instability and hepatocarcinogenesis. Current tools for HBV integration site detection lack accuracy and stability.

RESULTS

This study proposes a deep learning-based method, named ViroISDC, for detecting integration sites. ViroISDC generates corresponding grammar rules and encodes the characteristics of the language data to predict integration sites accurately. Compared with Lumpy, Pindel, Seeksv, and SurVirus, ViroISDC exhibits better overall performance and is less sensitive to sequencing depth and integration sequence length, displaying good reliability, stability, and generality. Further downstream analysis of integrated sites detected by ViroISDC reveals the integration patterns and features of HBV. It is observed that HBV integration exhibits specific chromosomal preferences and tends to integrate into cancerous tissue. Moreover, HBV integration frequency was higher in males than females, and high-frequency integration sites were more likely to be present on hepatocarcinogenesis- and anti-cancer-related genes, validating the reliability of the ViroISDC.

CONCLUSIONS

ViroISDC pipeline exhibits superior precision, stability, and reliability across various datasets when compared to similar software. It is invaluable in exploring HBV infection in the human body, holding significant implications for the diagnosis, treatment, and prognosis assessment of HCC.

Unveiling NUSAP1 as a common gene signature linking chronic HBV infection and HBV-related HCC

BACKGROUND

Hepatitis B virus (HBV) is a significant contributor to the development of hepatocellular carcinoma (HCC). Chronic HBV infection (CHB) facilitates disease progression through various mechanisms. However, the specific factor responsible for the progression of HBV infection to HCC remains unresolved. This study aims to identify the hub gene linking CHB and HBV-related HCC through bioinformatic analysis and experimental verification.

METHODS

Differentially expressed genes (DEGs) were identified in datasets encompassing CHB and HBV-HCC patients from the GEO database. Enriched pathways were derived from GO and KEGG analysis. Hub genes were screened by protein-protein interaction (PPI) analysis and different modules in Cytoscape software. The significance of the selected hub gene in prognosis was further assessed in validated datasets. The effects of hub genes on cell growth and apoptosis were further determined in functional experiments.

RESULTS

The study revealed upregulation of NUSAP1 in CHBs and HBV-HCCs. High expression of NUSAP1 served as an independent predictor for poor prognosis of liver cancers. Functional experiments demonstrated that NUSAP1 promotes cell growth, influences cell cycle process, and protects cells from apoptosis in HepG2.2.15 cells.

CONCLUSION

NUSAP1 serves as a poor prognostic indicator for liver cancers, and potentially plays a crucial role in HBV-HCC progression by promoting proliferation and inhibiting apoptosis.

Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma

The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC.

Withaferin A suppressed hepatocellular carcinoma progression through inducing IGF2BP3/FOXO1/JAK2/STAT3 pathway-mediated ROS production

OBJECTIVE

This study aimed to investigate the underlying molecular mechanisms of Withaferin A (WA) in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

The gene and protein expression were analyzed using RT-qPCR and western blot, respectively. The proliferation of HCC cells was evaluated by CCK-8 assays. The migrative ability of HCC cells was measured by transwell assays.

RESULTS

We revealed that WA suppressed the proliferation and migration of HCC cells and inhibited IGF2BP3 (insulin like growth factor 2 mRNA binding protein 3) expression. IGF2BP3 abundance reversed the reactive oxygen species (ROS) accumulation and suppression of HCC cell proliferation and migration induced by WA. Besides, IGF2BP3 suppressed ROS production to promote the growth and migration of HCC cells. Furthermore, we found that IGF2BP3 exerted its tumor-promotive and ROS-suppressive effect on HCC cells by regulating the expression of FOXO1 (forkhead box O1). In addition, IGF2BP3-stimulated activation of JAK2 (Janus kinase 2)/STAT3 (signal transducer and activator of transcription 3) phosphorylation effectively decreased the transcription of FOXO1. FOXO1 abundance decreased the phosphorylation of JAK2 and STAT3 by increasing ROS level, forming a feedback loop for the inhibition of JAK2/STAT3 signaling activated by IGF2BP3.

CONCLUSIONS

WA-induced ROS inhibited HCC cell growth and migration through the inhibition of IGF2BP3 to deactivate JAK2/STAT3 signaling, resulting in increased FOXO1 expression to further stimulate ROS production and inhibit JAK2/STAT3 signaling.

A prognostic exosome-related long non-coding RNAs risk model related to the immune microenvironment and therapeutic responses for patients with liver hepatocellular carcinoma

Background

Liver hepatocellular carcinoma (LIHC) is the third largest cause of cancer mortality. Exosomes are vital regulators in the development of cancer. However, the mechanisms regarding the association of exosome-related long non-coding RNAs (lncRNAs) in LIHC are not clear.

Methods

LIHC RNA sequences and exosome-associated genes were collected according to The Cancer Genome Atlas (TCGA), Hepatocellular Carcinoma Cell DataBase (HCCDB) and ExoBCD databases, and exosome-related lncRNAs with prognostic differential expression were screened as candidate lncRNAs using Spearman's method and univariate Cox regression analysis. Candidate lncRNAs were then used to construct a prognostic model and mRNA-lncRNA co-expression network. Differentially expressed genes (DEGs) in low- and high-risk groups were identified and enrichment analysis was performed for up- and down-regulated DEGs, respectively. The expression of immune checkpoint-related genes, immune escape potential and microsatellite instability among different risk groups were further analyzed. Quantitative real-time polymerase chain reaction (qRT-PCR) and transwell assay were applied for detecting gene expression levels and invasion and migration ability.

Results

Based on 17 prognostical exosome-associated lncRNAs, four hub lncRNAs (BACE1_AS, DSTNP2, PLGLA, and SNHG3) were selected for constructing a prognostic model, which was demonstrated to be an independent prognostic variable for LIHC. High risk score was indicative of poorer overall survival, lower anti-tumor immune cells, higher genomic instability, higher immune escape potential, and less benefit for immunotherapy. The qRT-PCR test verified the expression level of the lncRNAs in LIHC cells, and the inhibitory effect of BACE1_AS on immune checkpoint genes levels. BACE1_AS silence also depressed the ability of migration and invasion of LIHC cells.

Conclusion

The Risk model constructed by exosome-associated lncRNAs could well predict immunotherapy response and prognostic outcomes for LIHC patients. We comprehensively reveal the clinical features of prognostical exosome-related lncRNAs and their potential ability to predict immunotherapeutic response of patients with LIHC and their prognosis.

Establishment and Validation of a Four-stress Granule-related Gene Signature in Hepatocellular Carcinoma

Background and Aims

Stress granules (SGs) as membrane-less cytoplasmic foci formed in response to unfavorable external stimuli could promote cancer cells to adapt to hostile environments. Hepatocellular carcinoma (HCC) is prone to be highly aggressive once diagnosed, which markedly reduces patient survival time. Therefore, it is crucial to develop valid diagnostic markers to prognosticate HCC patient prognosis, which promotes individualized precision therapeutics in HCC. Considering the pro-tumorigenic activity of SGs, it is of great potential value to construct a prognostic tool for HCC based on the expression profiles of SG-related genes (SGGs).

Methods

Bioinformatic analysis was employed to establish an SGG-based prognostic signature. Western blotting and real-time polymerase chain reaction assays were used to assess the expression patterns of the related SGGs. Loss-of-function experiments were performed to analyze the effect of the SGGs on SG formation and cell survival.

Results

A four-SGG signature (KPNA2, MEX3A, WDR62, and SFN) targeting HCC was established and validated to exhibit a robust performance in predicting HCC prognosis. Consistently, all four genes were further found to be highly expressed in human HCC tissues. More important, we demonstrated that individually knocking down the four SGGs significantly reduced HCC cell proliferation and metastasis by compromising the SG formation process.

Conclusions

We developed an SGG-based predictive signature that can be used as an independent prognostic tool for HCC. The strong predictive power of this signature was further elucidated by the carcinogenic activity of KPNA2, MEX3A, WDR62, and SFN in HCC cells by regulating SG formation.

Prognostic and immune predictive roles of a novel tricarboxylic acid cycle-based model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer. Since the tricarboxylic acid cycle is widely involved in tumor metabolic reprogramming and cuproptosis, investigating related genes may help to identify prognostic signature of patients with HCC. Data on patients with HCC were sourced from public datasets, and were divided into train, test, and single-cell cohorts. A variety of machine learning algorithms were used to identify different molecular subtypes and determine the prognostic risk model. Our findings revealed that the risk score (TRscore), based on the genes OGDHL, CFHR4, and SPP1, showed excellent predictive performance in different datasets. Pathways related to cell cycle and immune inflammation were enriched in the high-risk group, whereas metabolism-related pathways were significantly enriched in the low-risk group. The high-risk group was associated with a greater number of mutations of detrimental biological behavior and higher levels of immune infiltration, immune checkpoint expression, and anti-cancer immunotherapy response. Low-risk patients demonstrated greater sensitivity to erlotinib and phenformin. SPP1 was mainly involved in the interaction among tumor-associated macrophages, T cells, and malignant cells via SPP1-CD44 and SPP1-(ITGA5 + ITGB1) ligand-receptor pairs. In summary, our study established a prognostic model, which may contribute to individualized treatment and clinical management of patients with HCC.

A Comprehensive Review on the Importance of MiRNA-206 in the Animal Model and Human Diseases

MicroRNA-206 (miR-206) is a microRNA that is involved in many human diseases, such as myasthenia gravis, osteoarthritis, depression, cancers, etc. Both inhibition effects and progression roles of miR-206 have been reported for the past few years. High expression of miR-206 was observed in patients with osteoarthritis, gastric cancer and epithelial ovarian cancer compared to normal people. The study also showed that miR-206 promotes cancer progression in breast cancer patients and avascular necrosis of the femoral head. Meanwhile, several studies have shown that expression levels of miR-206 were down-regulated in laryngeal carcinoma cell multiplication, as well as in hepatocellular carcinoma, non-small lung cancer and infantile hemangioma. Moreover, miR-206 was up-regulated in the mild stage of amyotrophic lateral sclerosis patients and then down-regulated in the moderate and severe stages, indicating that miR-206 has the double effects of starting and aggravating the disease. In neuropsychiatric disorders, such as depression, miR-206 also plays an important role in the progression of the disease; the level of miR-206 is most highly expressed in the brains of patients with depression. In the current review, we summarize the role of miR-206 in various diseases, and miR-206 may be developed as a new biomarker for diagnosing diseases in the near future.

Comprehensive scRNA-seq Analysis and Identification of CD8_+T Cell Related Gene Markers for Predicting Prognosis and Drug Resistance of Hepatocellular Carcinoma

BACKGROUND

Tumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis.

METHODS

Single-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy.

RESULTS

We defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility.

CONCLUSION

To predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.

Identification and validation of candidate clinical signatures of apolipoprotein L isoforms in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a lethal malignancy worldwide with an increasing number of new cases each year. Apolipoprotein (APOL) isoforms have been explored for their associations with HCC.The GSE14520 cohort was used for training data; The Cancer Genome Atlas (TCGA) database was used for validated data. Diagnostic, prognostic significance and mechanisms were explored using these cohorts. Risk score models and nomograms were constructed using prognosis-related isoforms and clinical factors for survival prediction. Oncomine and HCCDB databases were further used for validation of diagnostic, prognostic significance. APOL1, 3, and 6 were differentially expressed in two cohorts (all P ≤ 0.05). APOL1 and APOL6 had diagnostic capacity whereas APOL3 and APOL6 had prognostic capacity in two cohorts (areas under curves [AUCs] > 0.7, P ≤ 0.05). Mechanism studies demonstrated that APOL3 and APOL6 might be involved in humoral chemokine signaling pathways (all P ≤ 0.05). Risk score models and nomograms were constructed and validated for survival prediction of HCC. Moreover, diagnostic values of APOL1 and weak APOL6 were validated in Oncomine database (AUC > 0.700, 0.694); prognostic values of APOL3 and APOL6 were validated in HCCDB database (all P < 0.05). Differentially expressed APOL1 and APOL6 might be diagnostic biomarkers; APOL3 and APOL6 might be prognostic biomarkers of RFS and OS for HCC via chemokine signaling pathways.

Identification of 13 Novel Loci in a Genome-Wide Association Study on Taiwanese with Hepatocellular Carcinoma

Liver cancer is caused by complex interactions among genetic factors, viral infection, alcohol abuse, and metabolic diseases. We conducted a genome-wide association study and polygenic risk score (PRS) model in Taiwan, employing a nonspecific etiology approach, to identify genetic risk factors for hepatocellular carcinoma (HCC). Our analysis of 2836 HCC cases and 134,549 controls revealed 13 novel associated loci such as the FAM66C gene, noncoding genes, liver-fibrosis-related genes, metabolism-related genes, and HCC-related pathway genes. We incorporated the results from the UK Biobank and Japanese database into our study for meta-analysis to validate our findings. We also identified specific subtypes of the major histocompatibility complex that influence both viral infection and HCC progression. Using this data, we developed a PRS to predict HCC risk in the general population, patients with HCC, and HCC-affected families. The PRS demonstrated higher risk scores in families with multiple HCCs and other cancer cases. This study presents a novel approach to HCC risk analysis, identifies seven new genes associated with HCC development, and introduces a reproducible PRS model for risk assessment.

Role of organic cation transporter 3 (OCT3) in the response of hepatocellular carcinoma to tyrosine kinase inhibitors

Impaired function of organic cation transporter 1 (OCT1) in hepatocellular carcinoma (HCC) has been associated with unsatisfactory response to sorafenib. However, some patients lacking OCT1 at the plasma membrane (PM) of HCC cells still respond to sorafenib, suggesting that another transporter may contribute to take up this drug. The aim of this study was to investigate whether OCT3 could contribute to the uptake of sorafenib and other tyrosine kinase inhibitors (TKIs) and whether OCT3 determination can predict HCC response to sorafenib. Cells overexpressing OCT3 were used to determine the ability of this carrier to transport sorafenib. Immunostaining of OCT3 was performed in HCC samples obtained in the TRANSFER study. Considering the intensity of staining and the number of OCT3-positive cells, tumors were classified as having absent, weak, moderate, or strong OCT3 expression and were also categorized according to the presence or absence of PM staining. Functional in vitro studies revealed that OCT3 is also able to mediate sorafenib uptake. Other TKIs, such as regorafenib, lenvatinib, and cabozantinib can also interact with this transporter. In silico studies suggested that the expression of OCT3 is better preserved in HCC than that of OCT1. In HCC samples, OCT3 was expressed at the PM of cancer cells, and its presence, detected in 26% of tumors, was associated with better outcomes in patients treated with sorafenib. In conclusion, analysis by immunohistochemistry of OCT3 in the PM of tumor cells may help to predict the response of HCC patients to sorafenib and potentially to other TKIs.

DLAT is a promising prognostic marker and therapeutic target for hepatocellular carcinoma: a comprehensive study based on public databases

Cuproptosis is a new mechanism of cell death that differs from previously identified regulatory cell death mechanisms. Cuproptosis induction holds promise as a new tumour treatment. Therefore, we investigated the value of cuproptosis-related genes in the management of hepatocellular carcinoma (HCC). The cuproptosis-related gene Dihydrolipoamide S-Acetyltransferase (DLAT) were significantly upregulated in liver cancer tissues. High levels of DLAT were an independent prognostic factor for shorter overallsurvival (OS) time. DLAT and its related genes were mainly involved in cell metabolism, tumor progression and immune regulation. DLAT was significantly associated with the level of immune cell infiltration and immune checkpoints in HCC. HCC with high DLAT expression was predicted to be more sensitive to sorafenib treatment. The risk prognostic signature established based on DLAT and its related genes had a good prognostic value. The cuproptosis-related gene DLAT is a promising independent prognostic marker and therapeutic target in HCC. The new prognostic signature can effectively predict the prognosis of HCC patients.

Expression of CKS2 in Hepatocellular Carcinoma: Correlation with Survival Outcomes and Immune Microenvironment

Purpose

Cyclin-dependent kinase regulatory subunit 2 (CKS2) has an important function in regulating cancer progression and cell cycle. This research aims to ascertain how CKS2 plays its part through multi-omics analyses, to reveal its relationship with the immune microenvironment in hepatocellular carcinoma (HCC).

Material and Methods

Multiple databases were used to determine the transcriptional data of CKS2, epigenetic changes, and effects thereof upon the prognosis of HCC patients. The biological functions of CKS2 in HCC were expounded by functional enrichment analysis. TIMER, GSEA, TIP, and online single-cell sequencing databases were adopted for revealing correlations of CKS2 expression with infiltration of immune cells, immunomodulators, immunity cycle, and immune markers in the immune microenvironment of HCC. In addition, qRT-PCR and Western blot were used to validate gene expression in tissues from HCC patients.

Results

Open database analysis confirmed that CKS2 is highly expressed in HCC and that it is related to poor prognosis in HCC patients. Aberrant methylation levels of the two methylation sites of CKS2 in HCC contributed to its high expression and were correlated significantly with survival. The CKS2 expression was positively correlated with most immunomodulators and infiltration levels for B and CD8+T cells, dendritic cells, and macrophages, especially exhausted CD8+T cells. Besides, the CKS2 expression was also found to have significant correlations with immunity cycle steps and diverse immune markers in HCC. The high CKS2 expression was confirmed in HCC at both mRNA and protein levels, showing a significant increase compared to normal tissue.

Conclusion

CKS2 is a potential prognostic biomarker of HCC and can promote the progression of HCC via its influences on the immune environment. Additionally, a positive correlation between CKS2 and immune markers was observed, highlighting its potential as an immunotherapeutic target.

c-Rel-dependent Chk2 signaling regulates the DNA damage response limiting hepatocarcinogenesis

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death. The NF-κB transcription factor family subunit c-Rel is typically protumorigenic; however, it has recently been reported as a tumor suppressor. Here, we investigated the role of c-Rel in HCC.

APPROACH AND RESULTS

Histological and transcriptional studies confirmed expression of c-Rel in human patients with HCC, but low c-Rel expression correlated with increased tumor cell proliferation and mutational burden and was associated with advanced disease. In vivo , global ( Rel-/- ) and epithelial specific ( RelAlb ) c-Rel knockout mice develop more tumors, with a higher proliferative rate and increased DNA damage, than wild-type (WT) controls 30 weeks after N-diethylnitrosamine injury. However, tumor burden was comparable when c-Rel was deleted in hepatocytes once tumors were established, suggesting c-Rel signaling is important for preventing HCC initiation after genotoxic injury, rather than for HCC progression. In vitro , Rel-/- hepatocytes were more susceptible to genotoxic injury than WT controls. ATM-CHK2 DNA damage response pathway proteins were suppressed in Rel-/- hepatocytes following genotoxic injury, suggesting that c-Rel is required for effective DNA repair. To determine if c-Rel inhibition sensitizes cancer cells to chemotherapy, by preventing repair of chemotherapy-induced DNA damage, thus increasing tumor cell death, we administered single or combination doxorubicin and IT-603 (c-Rel inhibitor) therapy in an orthotopic HCC model. Indeed, combination therapy was more efficacious than doxorubicin alone.

CONCLUSION

Hepatocyte c-Rel signaling limits genotoxic injury and subsequent HCC burden. Inhibiting c-Rel as an adjuvant therapy increased the effectiveness of DNA damaging agents and reduced HCC growth.

Molecular subtypes and scoring tools related to Foxo signaling pathway for assessing hepatocellular carcinoma prognosis and treatment responsiveness

Background: Transcription factors in Foxo signaling pathway influence hepatocellular carcinoma metastasis through epithelial mesenchymal transition-related pathways. Prognostic factors in the Foxo signaling pathway are feasible for HCC prognosis and therapeutic management. Methods: Based on the differentially expressed genes and Foxo signaling pathway genes in HCC, the ConsensusClusterPlus package was conducted to identify Foxo signaling pathway-related molecular subtypes in HCC. Based on the DEGs in the FMSs, the optimal prognostic factors in HCC were screened by cox and least absolute shrinkage and selection operator (LASSO) cox analysis to form the Foxo prognosis score (FPS). The prognostic predictive effectiveness of FPS was assessed by Kaplan Meier (K-M) analysis and Receiver Operating Characteristic (ROC) analysis. Additionally, tumor microenvironment (TME) score, tumor mutation burden (TMB) and treatment sensitivity differences in FMSs and FPS groups were also evaluated. Results: There were low, medium and high Foxo signaling pathway activity molecular subtypes in HCC named FMS 1, FMS 2 and FMS 3, respectively. FMS 1 with lowest Foxo signaling pathway activity presented an excellent survival advantage, while FMS 3 with highest Foxo signaling pathway activity exhibited an inhibitory TME status. According to FPS grouping, low FPS exhibited favorable survival, low TMB and anti-tumor activity. Patients in the low FPS group were mostly in the early stage of cancer. Moreover, we found that patients with high and low FPS exhibited different sensitivity to chemotherapy, and patients with low FPS were more sensitive to immunotherapy. Conclusion: We revealed a novel molecular subtype and prognostic tool based on Foxo signaling pathway signature, which could potentially provide a direction for accurate and effective assessment of potential personalized treatment options and prognostic management for HCC patients.

An Updated Overview of Existing Cancer Databases and Identified Needs

Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lipidomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.

IHGA: An interactive web server for large-scale and comprehensive discovery of genes of interest in hepatocellular carcinoma

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer-related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)-assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.

Relevance of the organic anion transporting polypeptide 1B3 (OATP1B3) in the personalized pharmacological treatment of hepatocellular carcinoma

Although pharmacological treatment is the best option for most patients with advanced hepatocellular carcinoma (HCC), its success is very limited, partly due to reduced uptake and enhanced efflux of antitumor drugs. Here we have explored the usefulness of vectorizing drugs towards the organic anion transporting polypeptide 1B3 (OATP1B3) to enhance their efficacy against HCC cells. In silico studies (RNA-Seq data, 11 cohorts) and immunohistochemistry analyses revealed a marked interindividual variability, together with general downregulation but still expression of OATP1B3 in the plasma membrane of HCC cells. The measurement of mRNA variants in 20 HCC samples showed the almost absence of the cancer-type variant (Ct-OATP1B3) together with marked predominance of the liver-type variant (Lt-OATP1B3). In Lt-OATP1B3-expressing cells, the screening of 37 chemotherapeutical drugs and 17 tyrosine kinase receptors inhibitors (TKIs) revealed that 10 classical anticancer drugs and 12 TKIs were able to inhibit Lt-OATP1B3-mediated transport. Lt-OATP1B3-expressing cells were more sensitive than Mock parental cells (transduced with empty lentiviral vectors) to some Lt-OATP1B3 substrates (paclitaxel and the bile acid-cisplatin derivative Bamet-UD2), but not to cisplatin, which is not transported by Lt-OATP1B3. This enhanced response was abolished by competition with taurocholic acid, a known Lt-OATP1B3 substrate. Tumors subcutaneously generated in immunodeficient mice by Lt-OATP1B3-expressing HCC cells were more sensitive to Bamet-UD2 than those derived from Mock cells. In conclusion, Lt-OATP1B3 expression should be screened before deciding the use of anticancer drugs substrates of this carrier in the personalized treatment of HCC. Moreover, Lt-OATP1B3-mediated uptake must be considered when designing novel anti-HCC targeted drugs.

ZNF765 is a prognostic biomarker of hepatocellular carcinoma associated with cell cycle, immune infiltration, m6A modification, and drug susceptibility

Hepatocellular carcinoma (HCC) is an ongoing challenge worldwide. Zinc finger protein 765 (ZNF765) is an important zinc finger protein that is related to the permeability of the blood-tumor barrier. However, the role of ZNF765 in HCC is unclear. This study evaluated the expression of ZNF765 in hepatocellular carcinoma and the impact of its expression on patient prognosis based on The Cancer Genome Atlas (TCGA). Immunohistochemical assays (IHC) were used to examine protein expression. Besides, a colony formation assay was used to examine cell viability. We also explored the relationship between ZNF765 and chemokines in the HCCLM3 cells by qRT-PCR. Moreover, we examined the effect of ZNF765 on cell resistance by measurement of the maximum half-inhibitory concentration. Our research revealed that ZNF765 expression in HCC samples was higher than that in normal samples, whose upregulation was not conducive to the prognosis. The results of GO, KEGG, and GSEA showed that ZNF765 was associated with the cell cycle and immune infiltration. Furthermore, we confirmed that the expression of ZNF765 had a strong connection with the infiltration level of various immune cells, such as B cells, CD4+ T cells, macrophages, and neutrophils. In addition, we found that ZNF765 was associated with m6A modification, which may affect the progression of HCC. Finally, drug sensitivity testing found that patients with HCC were sensitive to 20 drugs when they expressed high levels of ZNF765. In conclusion, ZNF765 may be a prognostic biomarker related to cell cycle, immune infiltration, m6A modification, and drug sensitivity for hepatocellular carcinoma.

A novel prognostic model for hepatocellular carcinoma based on pyruvate metabolism-related genes

Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer, accounting for over 90% of cases. As pyruvate metabolic pathways are often dysregulated in cancer cells, investigating pyruvate metabolism-related genes may help identify prognostic gene signature and develop potential strategies for the management of patients with HCC. The mRNA expression profile, gene mutation data, and clinical information of HCC were obtained from open-source databases. A list of pyruvate metabolism-related genes was downloaded from the MSigDB dataset. Our findings revealed that certain pyruvate metabolism-related genes had copy number variations and single nucleotide variations in patients with liver cancer. Based on pyruvate metabolism-related genes, we stratified patients with HCC into three subtypes with different prognoses, clinical features, mutation profiles, functional annotation, and immune infiltration status. Next, we identified 13 key pyruvate metabolism-related genes significantly correlated with the prognosis of HCC using six machine learning algorithms and constructed a risk model. We also observed that the risk score was positively associated with a worse prognosis and increased immune infiltration. In summary, our study established a prognostic risk model for HCC based on pyruvate metabolism-related genes, which may contribute to the identification of potential prognostic targets and the development of new clinical management strategies for HCC.

Identification of a novel signature based on macrophage-related marker genes to predict prognosis and immunotherapeutic effects in hepatocellular carcinoma

Background

Tumor-related macrophages (TAMs) have emerged as an essential part of the immune regulatory network in hepatocellular carcinoma (HCC). Constructing a TAM-related signature is significant for evaluating prognosis and immunotherapeutic response of HCC patients.

Methods

Informative single-cell RNA sequencing (scRNA-seq) dataset was obtained from the Gene Expression Omnibus (GEO) database, and diverse cell subpopulations were identified by clustering dimension reduction. Moreover, we determined molecular subtypes with the best clustering efficacy by calculating the cumulative distribution function (CDF). The ESTIMATE method, CIBERSORT (cell-type identification by estimating relative subsets of RNA transcripts) algorithm and publicly available tumor immune dysfunction and exclusion (TIDE) tools were used to characterize the immune landscape and tumor immune escape status. A TAM-related gene risk model was constructed through Cox regression and verified in multiple datasets and dimensions. We also performed functional enrichment analysis to detect potential signaling pathways related to TAM marker genes.

Results

In total, 10 subpopulations and 165 TAM-related marker genes were obtained from the scRNA-seq dataset (GSE149614). After clustering 3 molecular subtypes based on TAM-related marker genes, we found significantly different prognostic survival and immune signatures among the three subtypes. Subsequently, a 9-gene predictive signature (TPP1, FTL, CXCL8, CD68, ATP6V1F, CSTB, YBX1, LGALS3, and APLP2) was identified as an independent prognostic factor for HCC patients. Those patients with high RiskScore had a lower survival rate and benefited less from immunotherapy than those with low RiskScore. Moreover, more samples of the Cluster C subtype were enriched in the high-risk group, with higher tumor immune escape incidence.

Conclusions

We constructed a TAM-related signature with excellent efficacy for predicting prognostic survival and immunotherapeutic responses in HCC patients.

Prognostic impact of MICALL1 and associates with immune infiltration in liver hepatocellular carcinoma patients

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is one of the most malignancy over the world. Previous studies have proven that Molecules Interacting with CasL-Like 1 (MICALL1) participated in cellular trafficking cascades, while there has no study to explore the function and carcinogenic mechanism MICALL1 in LIHC.

METHODS

We aimed to investigate the relationship between MICALL1 mRNA expression and LIHC using TCGA database. The expression of MICALL1 protein in clinic samples were examined by UALCAN database. Kaplan-Meier method was used for survival analysis. Logistic regression and Cox regression were performed to evaluate the prognostic significance of MICALL1. The MICALL1-binding protein were built by the STRING tool. Enrichment analysis by GO, KEGG and GSEA was used to explore possible function of MICALL1. The ssGSEA method was used to investigate the association between MICALL1 expression and the immune infiltration level in LIHC.

RESULTS

The expression and prognostic value of different MICAL family members in LIHC were evaluated. The expression of MICALL1 was significantly increased at both the transcript and protein levels in LIHC tissues. Further, the LIHC patients with high MICALL1 levels showed a worse OS, DSS and PFI. Some clinicopathologic features were identified to be related to MICALL1 expression in LIHC included clinical T stage, pathologic stage, histologic grade and AFP concentration. Univariate and multivariate survival analysis showed that MICALL1 was an independent prognostic marker for OS and DSS. Further enrichment analysis revealed that the K-RAS, TNFα/NF-κB and inflammatory response were significantly enriched in the high MICALL1 expression group. Immune infiltration analysis showed that high MICALL1 expression was correlated with infiltration level of macrophage cells, Th2 cells and some other immune cell types, including TFH.

CONCLUSIONS

MICALL1 expression was significantly associated with immune cell infiltration and may regarded as a promising prognostic biomarker for LIHC patients.

Establishment and Validation of a Novel Risk Score for Hepatocellular Carcinoma Based on Bile Acid and Bile Salt Metabolism-Related Genes

Liver cancer is a public disease burden with an increasing incidence rate globally. Bile acid and bile salt's metabolic pathways participate in liver tumorigenesis and regulate the tumor microenvironment. However, there still remains a lack of systematic analysis of the genes related to bile acid and bile salt metabolic pathways in hepatocellular carcinoma (HCC). The mRNA expression data and clinical follow-up information of patients with HCC were obtained from public databases, including The Cancer Genome Atlas, Hepatocellular Carcinoma Database, Gene Expression Omnibus, and IMvigor210. The bile acid and bile salt metabolism-related genes were extracted from Molecular Signatures Database. Univariate Cox and logistic least absolute shrinkage and selection operator regression analyses were conducted to establish the risk model. Single sample gene set enrichment analysis, Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data, and Tumor Immune Dysfunction and Exclusion were adopted to analyze immune status. The efficiency of the risk model was tested using a decision tree and a nomogram. We determined two molecular subtypes based on bile acid and bile salt metabolism-related genes, with the prognosis of the S1 subtype being markedly superior to the S2 subtype. Next, we established a risk model based on the differentially expressed genes between the two molecular subtypes. The high-risk and low-risk groups showed significant differences in the biological pathways, immune score, immunotherapy response, and drug susceptibility. Our results demonstrated the good predictive performance of the risk model in immunotherapy datasets and established that it could be an essential factor affecting the prognosis of HCC. In conclusion, we identified two molecular subtypes based on bile acid and bile salt metabolism-related genes. The risk model established in our study could effectively predict the prognosis of patients with HCC and their immunotherapeutic response, which may contribute to targeted immunotherapy in HCC.

Identification of afatinib-associated ADH1B and potential small-molecule drugs targeting ADH1B for hepatocellular carcinoma

Background: Afatinib is an irreversible epidermal growth factor receptor tyrosine kinase inhibitor, and it plays a role in hepatocellular carcinoma (LIHC). This study aimed to screen a key gene associated with afatinib and identify its potential candidate drugs. Methods: We screened afatinib-associated differential expressed genes based on transcriptomic data of LIHC patients from The Cancer Genome Atlas, Gene Expression Omnibus, and the Hepatocellular Carcinoma Database (HCCDB). By using the Genomics of Drug Sensitivity in Cancer 2 database, we determined candidate genes using analysis of the correlation between differential genes and half-maximal inhibitory concentration. Survival analysis of candidate genes was performed in the TCGA dataset and validated in HCCDB18 and GSE14520 datasets. Immune characteristic analysis identified a key gene, and we found potential candidate drugs using CellMiner. We also evaluated the correlation between the expression of ADH1B and its methylation level. Furthermore, Western blot analysis was performed to validate the expression of ADH1B in normal hepatocytes LO2 and LIHC cell line HepG2. Results: We screened eight potential candidate genes (ASPM, CDK4, PTMA, TAT, ADH1B, ANXA10, OGDHL, and PON1) associated with afatinib. Patients with higher ASPM, CDK4, PTMA, and TAT exhibited poor prognosis, while those with lower ADH1B, ANXA10, OGDHL, and PON1 had unfavorable prognosis. Next, ADH1B was identified as a key gene negatively correlated with the immune score. The expression of ADH1B was distinctly downregulated in tumor tissues of pan-cancer. The expression of ADH1B was negatively correlated with ADH1B methylation. Small-molecule drugs panobinostat, oxaliplatin, ixabepilone, and seliciclib were significantly associated with ADH1B. The protein level of ADH1B was significantly downregulated in HepG2 cells compared with LO2 cells. Conclusion: Our study provides ADH1B as a key afatinib-related gene, which is associated with the immune microenvironment and can be used to predict the prognosis of LIHC. It is also a potential target of candidate drugs, sharing a promising approach to the development of novel drugs for the treatment of LIHC.

Ubiquitin-proteasome system-based signature to predict the prognosis and drug sensitivity of hepatocellular carcinoma

Background: Ubiquitin-proteasome system (UPS) is implicated in cancer occurrence and progression. Targeting UPS is emerging as a promising therapeutic target for cancer treatment. Nevertheless, the clinical significance of UPS in hepatocellular carcinoma (HCC) has not been entirely elucidated. Methods: Differentially expressed UPS genes (DEUPS) were screened from LIHC-TCGA datasets. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate regression analysis were conducted to establish a UPS-based prognostic risk model. The robustness of the risk model was further validated in HCCDB18, GSE14520, and GSE76427 cohorts. Subsequently, immune features, clinicopathologic characteristics, enrichment pathways, and anti-tumor drug sensitivity of the model were further evaluated. Moreover, a nomogram was established to improve the predictive ability of the risk model. Results: Seven UPS-based signatures (ATG10, FBXL7, IPP, MEX3A, SOCS2, TRIM54, and PSMD9) were developed for the prognostic risk model. Individuals with HCC with high-risk scores presented a more dismal prognosis than those with low-risk scores. Moreover, larger tumor size, advanced TNM stage, and tumor grade were observed in the high-risk group. Additionally, cell cycle, ubiquitin-mediated proteolysis, and DNA repair pathways were intimately linked to the risk score. In addition, obvious immune cell infiltration and sensitive drug response were identified in low-risk patients. Furthermore, both nomogram and risk score showed a significant prognosis-predictive ability. Conclusion: Overall, we established a novel UPS-based prognostic risk model in HCC. Our results will facilitate a deep understanding of the functional role of UPS-based signature in HCC and provide a reliable prediction of clinical outcomes and anti-tumor drug responses for patients with HCC.

Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.

Pan-Cancer Analysis of the COVID-19 Causal Gene SLC6A20

Genome-wide association studies demonstrated that the chromosome 3p31.21 locus was linked to the severity of COVID-19 disease. The SLC6A20 gene was reported to be one of the critical causal genes regulated by this locus. Various studies focused on demonstrating the severity of COVID-19 in cancer patients and reported that elevated SARS-CoV-2-associated gene expression might contribute to increased susceptibility for COVID-19 in cancer patients. Given that pan-cancer association for the COVID-19 causal gene SLC6A20 is lacking, we aimed to perform systematic profiling of SLC6A20 in different malignancies. Human Protein Atlas, UALCAN, and Hepatocellular Carcinoma (HCCDB) databases were used to assess SLC6A20 gene expression changes in The Cancer Genome Atlas samples with respect to their normal counterparts. GEPIA and TIMER2.0 databases were used to determine the correlation between SLC6A20 and COVID-19-associated genes. Different databases were used for identification of the correlation of SCL6A20 with infiltrating immune cells. The canSAR database was utilized to determine the association of SCL6A20 with immune profiling in different malignancies. The STRING database was utilized to determine the protein network interacting with SLC6A20. Here, we showed SLC6A20 mRNA expression in pan-cancer samples and their normal counterparts. Increased SCL6A20 expression was associated with tumor grade, and there was a positive correlation with SARS-CoV-2-associated genes. Furthermore, SLC6A20 expression was positively correlated with infiltrating neutrophils and immune-related signatures. Lastly, SLC6A20 expression was found to be associated with the angiotensin converting enzyme 2 homologue, TMEM27, suggesting a potential link between SLC6A20 and COVID-19. Taken together, these results suggest that elevated SLC6A20 levels might be partly responsible for increased susceptibility of cancer patients to COVID-19 disease. Therapeutic intervention strategies against SLC6A20 in cancer patients, alongside other treatment modalities, might offer a benefit in delaying COVID-19 disease.

Comprehensive analysis reveals TSEN54 as a robust prognosis biomarker and promising immune-related therapeutic target for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma represents the most common primary malignancy of all liver cancer types and its prognosis is usually unsatisfactory. TSEN54 encodes a protein constituting a subunit of the tRNA splicing endonuclease heterotetramer. Previous researches concentrated on the contribution of TSEN54 in pontocerebellar hypoplasia, but no studies have yet reported its role in HCC.

METHODS

TIMER, HCCDB, GEPIA, HPA, UALCAN, MEXPRESS, SMART, TargetScan, RNAinter, miRNet, starBase, Kaplan-Meier Plotter, cBioPortal, LinkedOmics, GSEA, TISCH, TISIDB, GeneMANIA, PDB, GSCALite were applied in this research.

RESULTS

We identified the upregulation of TSEN54 expression in HCC and related it to multiple clinicopathological features. Hypomethylation of TSEN54 was closely associated with its high expression. HCC sufferers who held high TSEN54 expression typically had shorter survival expectations. Enrichment analysis showed the involvement of TSEN54 in the cell cycle and metabolic processes. Afterward, we observed that TSEN54 expression level had a positive relationship to the infiltration level of multiple immune cells and the expression of several chemokines. We additionally identified that TSEN54 was related to the expression level of several immune checkpoints and TSEN54 was linked to several m6A-related regulators.

CONCLUSIONS

TSEN54 is a prognostic marker of HCC. TSEN54 could become a prospective candidate for HCC diagnosis and therapy.

Biological function analysis of ARHGAP39 as an independent prognostic biomarker in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, with a high morbidity and low survival rate. Rho GTPase activating protein 39 (ARHGAP39) is a crucial activating protein of Rho GTPases, a novel target in cancer therapy, and it was identified as a hub gene for gastric cancer. However, the expression and role of ARHGAP39 in hepatocellular carcinoma remain unclear. Accordingly, the cancer genome atlas (TCGA) data were used to analyze the expression and clinical value of ARHGAP39 in hepatocellular carcinoma. Further, the LinkedOmics tool suggested functional enrichment pathways for ARHGAP39. To investigate in depth the possible role of ARHGAP39 on immune infiltration, we analyzed the relationship between ARHGAP39 and chemokines in HCCLM3 cells. Finally, the GSCA website was used to explore drug resistance in patients with high ARHGAP39 expression. Studies have shown that ARHGAP39 is highly expressed in hepatocellular carcinoma and relevant to clinicopathological features. In addition, the overexpression of ARHGAP39 leads to a poor prognosis. Besides, co-expressed genes and enrichment analysis showed a correlation with the cell cycle. Notably, ARHGAP39 may worsen the survival of hepatocellular carcinoma patients by increasing the level of immune infiltration through chemokines. Moreover, N6-methyladenosine (m6A) modification-related factors and drug sensitivity were also found to be associated with ARHGAP39. In brief, ARHGAP39 is a promising prognostic factor for hepatocellular carcinoma patients that is closely related to cell cycle, immune infiltration, m6A modification, and drug resistance.

FARSB serves as a novel hypomethylated and immune cell infiltration related prognostic biomarker in hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is a prevalent tumor with high morbidity, and an unfavourable prognosis. FARSB is an aminoacyl tRNA synthase, and plays a key role in protein synthesis in cells. Furthermore, previous reports have indicated that FARSB is overexpressed in gastric tumor tissues and is associated with a poor prognosis and tumorigenesis. However, the function of FARSB in HCC has not been studied.

RESULTS

The results showed that FARSB mRNA and protein levels were upregulated in HCC and were closely related to many clinicopathological characteristics. Besides, according to multivariate Cox analysis, high FARSB expression was linked with a shorter survival time in HCC and may be an independent prognostic factor. In addition, the FARSB promoter methylation level was negatively associated with the expression of FARSB. Furthermore, enrichment analysis showed that FARSB was related to the cell cycle. And TIMER analysis revealed that the FARSB expression was closely linked to tumor purity and immune cell infiltration. The TCGA and ICGC data analysis suggested that FARSB expression is greatly related to m6A modifier related genes. Potential FARSB-related ceRNA regulatory networks were also constructed. What's more, based on the FARSB-protein interaction network, molecular docking models of FARSB and RPLP1 were constructed. Finally, drug susceptibility testing revealed that FARSB was susceptible to 38 different drugs or small molecules.

CONCLUSIONS

FARSB can serve as a prognostic biomarker for HCC and provide clues about immune infiltration, and m6A modification.

Epithelial-mesenchymal transition-related gene prognostic index and phenotyping clusters for hepatocellular carcinoma patients

Epithelial-mesenchymal transition (EMT) contributes to high tumor heterogeneity and the immunosuppressive environment of the HCC tumor microenvironment (TME). Here, we developed EMT-related genes phenotyping clusters and systematically evaluated their impact on HCC prognosis, the TME, and drug efficacy prediction. We identified HCC specific EMT-related genes using weighted gene co-expression network analysis (WGCNA). An EMT-related genes prognostic index (EMT-RGPI) capable of effectively predicting HCC prognosis was then constructed. Consensus clustering of 12 HCC specific EMT-related hub genes uncovered two molecular clusters C1 and C2. Cluster C2 preferentially associated with unfavorable prognosis, higher stemness index (mRNAsi) value, elevated immune checkpoint expression, and immune cell infiltration. The TGF-β signaling, EMT, glycolysis, Wnt β-catenin signaling, and angiogenesis were markedly enriched in cluster C2. Moreover, cluster C2 exhibited higher TP53 and RB1 mutation rates. The TME subtypes and tumor immune dysfunction and exclusion (TIDE) score showed that cluster C1 patients responded well to immune checkpoint inhibitors (ICIs). Half-maximal inhibitory concentration (IC50) revealed that cluster C2 patients were more sensitive to chemotherapeutic and antiangiogenic agents. These findings may guide risk stratification and precision therapy for HCC patients.

ncRNAs-mediated overexpression of STIL predict unfavorable prognosis and correlated with the efficacy of immunotherapy of hepatocellular carcinoma

BACKGROUND

STIL centriolar assembly protein (STIL) is a cytoplasmic protein implicated in cellular growth and proliferation as well as chromosomal stability, which abnormal condition affected tumor immunity and tumor progression. However, the role of STIL in the biological mechanism of hepatocellular carcinoma (HCC) remains unclear.

METHODS

Comprehensive bioinformatic approaches, in vitro functional assays, and validation were conducted to elucidate the oncogenic value of STIL in HCC.

RESULTS

In the present study, we found that STIL may serve as an independent prognostic indicator and a potential oncogene in HCC. Gene set enrichment analysis (GSEA), and Gene set variation analysis (GSVA) showed that upregulated expression of STIL was positively associated with pathways enriched in the cell cycle and DNA damage response. Subsequently, we identified several non-coding RNAs (ncRNAs) accounting for the upregulation of STIL expression using a combination of in silico bioinformatics approaches (including expression analysis, correlation analysis, and survival analysis). Finally, CCNT2-AS1/SNHG1-has-miR-204-5p-STIL axis was screened out as the most potential upstream ncRNA-related pathway of STIL in HCC. Moreover, STIL expression is highly associated with the infiltration of immune cells, the expression of immune checkpoints, as well as the survival benefit of immunotherapy/chemotherapy.

CONCLUSIONS

Our study discloses that ncRNAs-mediated overexpression of STIL independently predicted poor prognosis and correlated with the efficacy of PD-1-targeted immunotherapy in HCC.

ESCCdb: A Comprehensive Database and Key Regulator Exploring Platform Based on Cross Dataset Comparisons for Esophageal Squamous Cell Carcinoma

Esophageal cancer is the seventh most prevalent and the sixth most lethal cancer. Esophageal squamous cell carcinoma (ESCC) is one of the major esophageal cancer subtypes that accounts for 87 % of the total cases. However, its molecular mechanism remains unclear. Here, we present an integrated database for ESCC called ESCCdb, which includes a total of 56 datasets and published studies from the GEO, Xena or SRA databases and related publications. It helps users to explore a particular gene with multiple graphical and interactive views with one click. The results comprise expression changes across 20 datasets, copy number alterations in 11 datasets, somatic mutations from 12 papers, related drugs derived from DGIdb, related pathways, and gene correlations. ESCCdb enables directly cross-dataset comparison of a gene's mutations, expressions and copy number changes in multiple datasets. This allows users to easily assess the alterations in ESCC. Furthermore, survival analysis, drug-gene relationships, and results from whole-genome CRISPR/Cas9 screening can help users determine the clinical relevance, derive functional inferences, and identify potential drugs. Notably, ESCCdb also enables the exploration of the correlation structure and identification of potential key regulators for a process. Finally, we identified 789 consistently differential expressed genes; we summarized recurrently mutated genes and genes affected by significant copy number alterations. These genes may be stable biomarkers or important players during ESCC development. ESCCdb fills the gap between massive omics data and users' needs for integrated analysis and can promote basic and clinical ESCC research. The database is freely accessible at http://cailab.labshare.cn/ESCCdb.

A comprehensive computational analysis to explore the importance of SIGLECs in HCC biology

BACKGROUND

Hepatocellular carcinoma (HCC) is an aggressive, malignant cancer with a complex pathogenesis. However, effective therapeutic targets and prognostic biomarkers are limited. Sorafenib provides delaying cancer progression and survival improvement in advanced HCC. But despite 10 years of research on the clinical application of sorafenib, predictive markers for its therapeutic effect are lacking.

METHODS

The clinical significance and molecular functions of SIGLEC family members were assessed by a comprehensive bioinformatic analysis. The datasets included in this study (ICGC-LIRI-JP, GSE22058 and GSE14520) are mainly based on patients with HBV infections or HBV-related liver cirrhosis. The TCGA, GEO, and HCCDB databases were used to explore the expression of SIGLEC family genes in HCC. The Kaplan-Meier Plotter database was used to evaluate relationships between the expression levels of SIGLEC family genes and prognosis. Associations between differentially expressed genes in the SIGLEC family and tumour-associated immune cells were evaluated using TIMER.

RESULTS

The mRNA levels of most SIGLEC family genes were significantly lower in HCC than in normal tissues. Low protein and mRNA expression levels of SIGLECs were strongly correlated with tumour grade and clinical cancer stage in patients with HCC. Tumour-related SIGLEC family genes were associated with tumour immune infiltrating cells. High SIGLEC expression was significantly related to a better prognosis in patients with advanced HCC treated with sorafenib.

CONCLUSIONS

SIGLEC family genes have potential prognostic value in HCC and may contribute to the regulation of cancer progression and immune cell infiltration. More importantly, our results revealed that SIGLEC family gene expression may be used as a prognostic marker for HCC patients treated with sorafenib.

Identification of a prognostic cuproptosis-related signature in hepatocellular carcinoma

BACKGROUND

Cuproptosis is a new type of copper-induced cell death that is characterized by the aggregation of lipoylated tricarboxylic acid (TCA) cycle proteins. However, its role in hepatocellular carcinoma (HCC) remains unclear. The goal of this research is to develop a cuproptosis-related signature predicting the prognosis of HCC.

METHODS

The cuproptosis-related genes were defined using Pearson correlation coefficients. LASSO-Cox regression analysis was used to evaluate the prognostic values of cuproptosis-related genes to construct a cuproptosis-related prognostic model. The immune microenvironment analysis was performed by "ssGSEA" to reveal the associated immune cell infiltration patterns with the cuproptosis-related genes signature. The expression levels of one of the prognostic genes PDXK were then verified in HCC samples by Western Blot and immunohistochemistry. The potential roles of target genes in cuproptosis were further explored during in-vitro experiments.

RESULTS

A total of 136 cuproptosis-related genes were discovered using Pearson correlation analysis in HCC. A cuproptosis-related signature that included 5 cuproptosis-related genes (PDXK, HPN, SLC25A28, RNFT1, CLEC3B) was established in the TCGA-LIHC training cohort. TCGA validation cohort and another two external validation cohorts confirmed the robustness of the signature's predictive value. Moreover, a nomogram using the risk score was created to best predict the survival of HCC patients. The immune microenvironment analysis revealed distinct immune infiltrations patterns between different risk groups based on the signature model. Furthermore, the upregulation of PDXK was confirmed in HCC tumor tissues in 30 clinical HCC specimens. The knockdown of PDXK reduced the proliferation, migration and invasion of HCC cells. Besides, the expression of PDXK was upregulated after the induction of cuproptosis by elesclomol-CuCL₂, which could be suppressed when pretreated with a copper ion chelator. And PDXK deficiency increased the sensitivity of HCC cells to cuproptosis inducer.

CONCLUSION

Our study identified a new cuproptosis-related gene signature that could predict the prognosis of HCC patient. Besides, the upregulated PDXK could promote the proliferation and metastasis of HCC. And PDXK deficiency facilities cuproptosis in HCC. Therefore, these fundings highlighted that PDXK might serve as a potential diagnostic and therapeutic target for HCC.

High Level of Ubiquitin Conjugate Enzyme E2O Indicates Poor Prognosis of Patients with Hepatocellular Carcinoma

OBJECTIVE

Ubiquitin conjugate enzyme E2O (UBE2O) is a ubiquitin-conjugating enzyme that has been reported to be involved in tumorigenesis. This study investigated the role of UBE2O in hepatocellular carcinoma (HCC).

METHODS

The expression of UBE2O was detected using qRT-PCR, Western blotting, and immunohistochemical staining. Cell proliferation and Transwell assays were used to detect proliferation, migration, and invasion of HCC cells, respectively. Bioinformatic analysis was performed to analyze the relationship between UBE2O and the clinical features, prognosis, and immune cell infiltration of HCC.

RESULTS

UBE2O was significantly over-expressed in HCC tissues. High expression of UBE2O was associated with poor tumor grade and poor prognosis. Functional experiments showed that down-regulation of UBE2O inhibited HCC cell proliferation, migration, and invasion. Co-expression gene analysis and gene set enrichment analysis showed that UBE2O was associated with protein hydrolysis, cell cycle, and cancer-related pathways in HCC. The results of immune analysis revealed that the expression of UBE2O was positively correlated with the immune infiltration and expression of immune-related chemokines of HCC.

CONCLUSIONS

UBE2O is significantly correlated with the prognosis of HCC and may be a valuable prognostic biomarker for HCC.

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.

High Expression of Ten Eleven Translocation 1 Is Associated with Poor Prognosis in Hepatocellular Carcinoma

Background

DNA methylation patterns have been found to be distinct between tumor and normal patients. However, the effect of DNA demethylation enzymes, ten eleven translocation (TET) proteins, has not been comprehensively characterized in liver cancer. In this research, we sought to unravel the linkage of TET proteins with prognosis, immune characteristics and biological pathways in hepatocellular carcinoma (HCC).

Materials and Methods

Four independent datasets with gene expression data and clinical data of HCC samples were downloaded from public databases. CIBERSORT, single sample Gene Set Enrichment Analysis (ssGSEA), MCP-counter, and TIMER were implemented to evaluate immune cell infiltration. limma was employed to screen differentially expressed genes (DEGs) between two groups. The demethylation-related risk model was established by using univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO), and stepwise Akaike information criterion (stepAIC).

Results

TET1 was significantly higher expressed in tumor samples than that in normal samples. HCC patients with advanced stages (III+IV) and grades (G3+G4) had higher TET1 expression compared to early stages (I+II) and grades (G1+G2). HCC samples with high TET1 expression had worse prognosis than that with low expression. High and low TET1 expression groups had distinct immune cell infiltration and response to immunotherapy and chemotherapy. We identified 90 DEGs related to DNA demethylation in high vs. low TET1 expression groups. Furthermore, we established a risk model based on 90 DEGs containing seven key prognostic genes (SERPINH1, CDC20, HACD2, SPHK1, UGT2B15, SLC1A5, and CYP2C9) with effectiveness and robustness in predicting HCC prognosis.

Conclusions

Our study suggested TET1 as a potential indicator in HCC progression. TET1 was closely involved in immune infiltration and activation of oncogenic pathways. The DNA demethylation-related risk model was potential to be applied for predicting HCC prognosis in clinics.

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.