A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients

Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes

Motivation

Molecular subtyping by integrative modeling of multi-omics and clinical data can help the identification of robust and clinically actionable disease subgroups; an essential step in developing precision medicine approaches.

Results

We developed a novel outcome-guided molecular subgrouping framework, called Deep Multi-Omics Integrative Subtyping by Maximizing Correlation (DeepMOIS-MC), for integrative learning from multi-omics data by maximizing correlation between all input -omics views. DeepMOIS-MC consists of two parts: clustering and classification. In the clustering part, the preprocessed high-dimensional multi-omics views are input into two-layer fully connected neural networks. The outputs of individual networks are subjected to Generalized Canonical Correlation Analysis loss to learn the shared representation. Next, the learned representation is filtered by a regression model to select features that are related to a covariate clinical variable, for example, a survival/outcome. The filtered features are used for clustering to determine the optimal cluster assignments. In the classification stage, the original feature matrix of one of the -omics view is scaled and discretized based on equal frequency binning, and then subjected to feature selection using RandomForest. Using these selected features, classification models (for example, XGBoost model) are built to predict the molecular subgroups that were identified at clustering stage. We applied DeepMOIS-MC on lung and liver cancers, using TCGA datasets. In comparative analysis, we found that DeepMOIS-MC outperformed traditional approaches in patient stratification. Finally, we validated the robustness and generalizability of the classification models on independent datasets. We anticipate that the DeepMOIS-MC can be adopted to many multi-omics integrative analyses tasks.

Availability and implementation

Source codes for PyTorch implementation of DGCCA and other DeepMOIS-MC modules are available at GitHub (https://github.com/duttaprat/DeepMOIS-MC).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Tumor biology and immune infiltration define primary liver cancer subsets linked to overall survival after immunotherapy

Primary liver cancer is a rising cause of cancer deaths in the US. Although immunotherapy with immune checkpoint inhibitors induces a potent response in a subset of patients, response rates vary among individuals. Predicting which patients will respond to immune checkpoint inhibitors is of great interest in the field. In a retrospective arm of the National Cancer Institute Cancers of the Liver: Accelerating Research of Immunotherapy by a Transdisciplinary Network (NCI-CLARITY) study, we use archived formalin-fixed, paraffin-embedded samples to profile the transcriptome and genomic alterations among 86 hepatocellular carcinoma and cholangiocarcinoma patients prior to and following immune checkpoint inhibitor treatment. Using supervised and unsupervised approaches, we identify stable molecular subtypes linked to overall survival and distinguished by two axes of aggressive tumor biology and microenvironmental features. Moreover, molecular responses to immune checkpoint inhibitor treatment differ between subtypes. Thus, patients with heterogeneous liver cancer may be stratified by molecular status indicative of treatment response to immune checkpoint inhibitors.

PANoptosis-based molecular subtyping and HPAN-index predicts therapeutic response and survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a highly prevalent and fatal cancer. The role of PANoptosis, a novel form of programmed cell death, in HCC is yet to be fully understood. This study focuses on identifying and analyzing PANoptosis-associated differentially expressed genes in HCC (HPAN_DEGs), aiming to enhance our understanding of HCC pathogenesis and potential treatment strategies.

Methods

We analyzed HCC differentially expressed genes from TCGA and IGCG databases and mapped them to the PANoptosis gene set, identifying 69 HPAN_DEGs. These genes underwent enrichment analyses, and consensus clustering analysis was used to determine three distinct HCC subgroups based on their expression profiles. The immune characteristics and mutation landscape of these subgroups were evaluated, and drug sensitivity was predicted using the HPAN-index and relevant databases.

Results

The HPAN_DEGs were mainly enriched in pathways associated with the cell cycle, DNA damage, Drug metabolism, Cytokines, and Immune receptors. We identified three HCC subtypes (Cluster_1, SFN+PDK4-; Cluster_2, SFN-PDK4+; Cluster_3, SFN/PDK4 intermediate expression) based on the expression profiles of the 69 HPAN_DEGs. These subtypes exhibited distinct clinical outcomes, immune characteristics, and mutation landscapes. The HPAN-index, generated by machine learning using the expression levels of 69 HPAN_DEGs, was identified as an independent prognostic factor for HCC. Moreover, the high HPAN-index group exhibited a high response to immunotherapy, while the low HPAN-index group showed sensitivity to small molecule targeted drugs. Notably, we observed that the YWHAB gene plays a significant role in Sorafenib resistance.

Conclusion

This study identified 69 HPAN_DEGs crucial to tumor growth, immune infiltration, and drug resistance in HCC. Additionally, we discovered three distinct HCC subtypes and constructed an HPAN-index to predict immunotherapeutic response and drug sensitivity. Our findings underscore the role of YWHAB in Sorafenib resistance, presenting valuable insights for personalized therapeutic strategy development in HCC.

A transcriptomic intratumour heterogeneity-free signature overcomes sampling bias in prognostic risk classification for hepatocellular carcinoma

Background & Aims

Intratumour heterogeneity (ITH) fosters the vulnerability of RNA expression-based biomarkers derived from a single biopsy to tumour sampling bias, and is regarded as an unaddressed confounding factor for patient precision stratification using molecular biomarkers. This study aimed to identify an ITH-free predictive biomarker in hepatocellular carcinoma (HCC).

Methods

We interrogated the confounding effect of ITH on performance of molecular biomarkers and quantified transcriptomic heterogeneity utilising three multiregional HCC transcriptome datasets involving 142 tumoural regions from 30 patients. A de novo strategy based on the heterogeneity metrics was devised to develop a surveillant biomarker (a utility gadget using RNA; AUGUR) using three datasets involving 715 liver samples from 509 patients with HCC. The performance of AUGUR was assessed in seven cross-platform HCC cohorts that encompassed 1,206 patients.

Results

An average discordance rate of 39.9% at the level of individual patients was observed applying 13 published prognostic signatures to classify tumour regions. We partitioned genes into four heterogeneity quadrants, from which we developed and validated a reproducible robust ITH-free expression signature AUGUR that showed significant positive associations with adverse features of HCC. High AUGUR risk increased the risk of disease progression and mortality independent of established clinicopathological indices, which maintained concordance across seven cohorts. Moreover, AUGUR compared favourably to the discriminative ability, prognostic accuracy, and patient risk concordant rates of 13 published signatures. Finally, a well-calibrated predictive nomogram integrating AUGUR and tumour-node-metastasis (TNM) stage was established, which generated a numerical probability of mortality.

Conclusions

We constructed and validated an ITH-free AUGUR and nomogram that overcame sampling bias and provided reliable prognostic information for patients with HCC.

Impact and Implications

Intratumour heterogeneity (ITH) is prevalent in hepatocellular carcinoma (HCC), and is regarded as an unaddressed confounding factor for biomarker design and application. We examined the confounding effect of transcriptomic ITH in patient risk classification, and found existing molecular biomarkers of HCC were vulnerable to tumour sampling bias. We then developed an ITH-free expression biomarker (a utility gadget using RNA; AUGUR) that overcame clinical sampling bias and maintained prognostic reproducibility and generalisability across multiple HCC patient cohorts from different commercial platforms. Furthermore, we established and validated a well-calibrated nomogram based on AUGUR and tumour-node-metastasis (TNM) stage that provided an individualised prognostic information for patients with HCC.

A lipid metabolism-based prognostic risk model for HBV-related hepatocellular carcinoma

BACKGROUND

Up to 85% of hepatocellular carcinoma (HCC) cases in China can be attributed to infection of hepatitis B virus (HBV). Lipid metabolism performs important function in hepatocarcinogenesis of HBV-related liver carcinoma. However, limited studies have explored the prognostic role of lipid metabolism in HBV-related HCC. This study established a prognostic model to stratify HBV-related HCC based on lipid metabolisms.

METHODS

Based on The Cancer Genome Atlas HBV-related HCC samples, this study selected prognosis-related lipid metabolism genes and established a prognosis risk model by performing uni- and multi-variate Cox regression methods. The final markers used to establish the model were selected through the least absolute shrinkage and selection operator method. Analysis of functional enrichment, immune landscape, and genomic alteration was utilized to investigate the inner molecular mechanism involved in prognosis.

RESULTS

The risk model independently stratified HBV-infected patients with liver cancer into two risk groups. The low-risk groups harbored longer survival times (with P < 0.05, log-rank test). TP53, LRP1B, TTN, and DNAH8 mutations and high genomic instability occurred in high-risk groups. Low-risk groups harbored higher CD8 T cell infiltration and BTLA expression. Lipid-metabolism (including "Fatty acid metabolism") and immune pathways were significantly enriched (P < 0.05) in the low-risk groups.

CONCLUSIONS

This study established a robust model to stratify HBV-related HCC effectively. Analysis results decode in part the heterogeneity of HBV-related liver cancer and highlight perturbation of lipid metabolism in HBV-related HCC. This study's findings could facilitate patients' clinical classification and give hints for treatment selection.

Hepatitis B virus X protein promotes MAN1B1 expression by enhancing stability of GRP78 via TRIM25 to facilitate hepatocarcinogenesis

BACKGROUND

GRP78 has been implicated in hepatocarcinogenesis. However, the clinical relevance, biological functions and related regulatory mechanisms of GRP78 in hepatitis B virus (HBV)-associated hepatoma carcinoma (HCC) remain elusive.

METHODS

The association between GRP78 expression and HBV-related HCC was investigated. The effects of HBV X protein (HBX) on GRP78 and MAN1B1 expression, biological functions of GRP78 and MAN1B1 in HBX-mediated HCC cells and mechanisms related to TRIM25 on GRP78 upregulation to induce MAN1B1 expression in HBX-related HCC cells were examined.

RESULTS

GRP78 expression was correlated with poor prognosis in HBV-positive HCC. HBX increased MAN1B1 protein expression depending on GRP78, and HBX enhanced the levels of MAN1B1 to promote proliferation, migration and PI3-K/mTOR signalling pathway activation in HCC cells. GRP78 activates Smad4 via its interaction with Smad4 to increase MAN1B1 expression in HBX-expressing HCC cells. TRIM25 enhanced the stability of GRP78 by inhibiting its ubiquitination. HBX binds to GRP78 and TRIM25 and accelerates their interaction of GRP78 and TRIM25, leading to an increase in GRP78 expression.

CONCLUSIONS

HBX enhances the stability of GRP78 through TRIM25 to increase the expression of MAN1B1 to facilitate tumorigenesis, and we provide new insights into the molecular mechanisms underlying HBV-induced malignancy.

Anterior gradient 2 induces resistance to sorafenib via endoplasmic reticulum stress regulation in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) accounts for almost 80% of all liver cancer cases and is the sixth most common cancer and the second most common cause of cancer-related death worldwide. The survival rate of sorafenib-treated advanced HCC patients is still unsatisfactory. Unfortunately, no useful biomarkers have been verified to predict sorafenib efficacy in HCC.

RESULTS

We assessed a sorafenib resistance-related microarray dataset and found that anterior gradient 2 (AGR2) is highly associated with overall and recurrence-free survival and with several clinical parameters in HCC. However, the mechanisms underlying the role of AGR2 in sorafenib resistance and HCC progression remain unknown. We found that sorafenib induces AGR2 secretion via posttranslational modification and that AGR2 plays a critical role in sorafenib-regulated cell viability and endoplasmic reticulum (ER) stress and induces apoptosis in sorafenib-sensitive cells. In sorafenib-sensitive cells, sorafenib downregulates intracellular AGR2 and conversely induces AGR2 secretion, which suppresses its regulation of ER stress and cell survival. In contrast, AGR2 is highly intracellularly expressed in sorafenib-resistant cells, which supports ER homeostasis and cell survival. We suggest that AGR2 regulates ER stress to influence HCC progression and sorafenib resistance.

CONCLUSIONS

This is the first study to report that AGR2 can modulate ER homeostasis via the IRE1α-XBP1 cascade to regulate HCC progression and sorafenib resistance. Elucidation of the predictive value of AGR2 and its molecular and cellular mechanisms in sorafenib resistance could provide additional options for HCC treatment.

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.

Classification and survival prediction in early-stage cirrhosis by gene expression profiling

Liver cirrhosis has been increasingly diagnosed at an early stage owing to the non-invasive diagnostic techniques. However, it is difficult to identify patients at high risk of disease progression. Screening cirrhotic patients with poor prognosis who are most in need of surveillance is still challenging. Gene expression data GSE15654 and GSE14520 were downloaded for performing unsupervised clustering analysis. The prognostic differences between the different clusters were explored by Cox regression. Integrative analysis of gene expression signature, immune cell enrichments and clinical characterization was performed for different clusters. Two distinctive subclasses were identified in HCV-related GSE15654, and Kaplan-Meier analysis indicated that subtype 2 had lower survival rates than subtype 1 (p = 0.0399). Further analysis revealed subtype 2 had a higher density of follicular T helper cells, resting natural killer cells and M0, M2 macrophages while subtype 1 with a higher fraction of naive B cells, memory B cells, resting memory CD 4 T cells, activated natural killer cells and monocytes. 226 differentially expressed genes were identified between the two subtypes, and Reactome analysis showed the mainly enriched pathways were biological oxidations and fatty acid metabolism. Five hub genes (AKT1, RPS16, CDC42, CCND1 and PCBP2) and three significant modules were extracted from the PPI network. The results were validated in HBV-related GSE14520 cohort. We identified two subtypes of patients with different prognosis for hepatitis C-related early-stage liver cirrhosis. Bioinformatics analysis of the gene expression and immune cell profile may provide fresh insight into understanding the prognosis difference.

Genome stability‑related lncRNA ZFPM2‑AS1 promotes tumor progression via miR‑3065‑5p/XRCC4 in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) have a certain link to genomic stability (GS). However, the regulatory relationship of lncRNAs and GS has not been thoroughly investigated in hepatocellular carcinoma (HCC). In the present study, samples were retrieved from The Cancer Genome Atlas with somatic mutations and lncRNA expression data. Cox regression analysis was used to identify independent prognostic factors. The RNA levels were determined by reverse transcription‑quantitative PCR and protein levels were detected by western blot analysis. Cell Counting Kit‑8 and colony‑formation assays were used to assess cell viability. Cell migration was measured by wound‑healing and Transwell assays. Cell apoptosis and cell‑cycle progression were evaluated by flow cytometry. GS was detected by alkaline comet and chromosomal aberration assays. A xenograft model and lung metastasis model were used to assess the role of zinc finger protein, FOG family member 2 antisense 1 (ZFPM2‑AS1) in tumor growth in vivo. The molecular mechanisms underlying the biological functions of ZFPM2‑AS1 were investigated through bioinformatics prediction, RNA pull‑down and luciferase reporter assays. A total of 85 genomic instability‑related lncRNAs were identified and a prognostic model was developed. The prognostic model exhibited good predictive power (area under the receiver operating characteristic curve, 0.786). ZFPM2‑AS1 was significantly upregulated in tumor tissues (P<0.001) and it promoted DNA damage repair (P<0.01) and tumor progression in vitro and in vivo. Luciferase reporter assays demonstrated that miR‑3065‑5p was able to bind directly with ZFPM2‑AS1 and X‑ray repair cross complementing 4 (XRCC4). ZFPM2‑AS1 upregulated XRCC4 expression by acting as a sponge (P<0.001). In the present study, a prognostic model for HCC was developed and validated, and one lncRNA of its components was experimentally investigated. ZFPM2‑AS1 regulates XRCC4 by sponging miR‑3065‑5p to promote GS and HCC progression.

Clinical significance of cylindromatosis expression in primary hepatocellular carcinoma

BACKGROUND AND STUDY AIM

There is currently a lack of sensitive biomarkers for the diagnosis of hepatocellular carcinoma (HCC). Low expression of cylindromatosis (CYLD), a tumor suppressor gene that encodes a deubiquitinase, is associated with the development of HCC. The present study, therefore, aimed to determine the clinical utility of measuring CYLD expression in the early diagnosis of HCC.

PATIENTS AND METHODS

The present study comprised 257 patients from the Affiliated Hospital of Qingdao University including 90 patients with HCC, 41 patients with liver cirrhosis (LC), 46 patients with hepatitis B (HB), and 80 healthy controls. qPCR was used to measure the amounts of CYLD mRNA in stored blood samples. The sensitivity and specificity of CYLD mRNA in diagnosing HCC was analyzed using receiver operator characteristic (ROC) curves. We also obtained HCC data from the Oncomine database to further verify our results.

RESULTS

The relative levels of CYLD mRNA in peripheral blood from patients with HCC (median, 0.060; interquartile range [IQR], 0.019-0.260) was significantly lower than in blood from patients with LC (median, 3.732; IQR, 0.648-14.573), HB (median, 0.419; IQR, 0.255-1.809) and healthy controls (median, 1.262; IQR, 0.279-3.537; P < 0.05). CYLD mRNA levels in peripheral blood were significantly higher in patients with LC compared to healthy controls and patients with HB. Oncomine data demonstrated that CYLD mRNA expression levels in HCC tissues were significantly lower than in normal liver tissues. ROC analysis demonstrated that the combined use of peripheral blood levels of CYLD and AFP had the greatest diagnostic accuracy for HCC (area under the curve (AUC), 0.897; 95 % confidence interval [CI], 0.853-0.942). CYLD had utility as a supplementary marker to AFP for diagnosing HCC.

CONCLUSION

Circulating levels of CYLD mRNA are significantly decreased in patients with HCC, indicating CYLD may have utility as a biomarker of HCC. Combined measurement of CYLD mRNA and AFP protein had the greatest diagnostic accuracy.

Cisplatin or Doxorubicin Reduces Cell Viability via the PTPIVA3-JAK2-STAT3 Cascade in Hepatocellular Carcinoma

Introduction

Hepatocellular carcinoma (HCC) accounts for 80% of all liver cancers and is the 2nd leading cause of cancer-related death in Taiwan. Various factors, including rapid cell growth, a high recurrence rate and drug resistance, make HCC difficult to cure. Moreover, the survival rate of advanced HCC patients treated with systemic chemotherapy remains unsatisfactory. Hence, the identification of novel molecular targets and the underlying mechanisms of chemoresistance in HCC and the development more effective therapeutic regimens are desperately needed.

Methods

An MTT assay was used to determine the cell viability after cisplatin or doxorubicin treatment. Western blotting, qRT‒PCR and immunohistochemistry were utilized to examine the protein tyrosine phosphatase IVA3 (PTP4A3) level and associated signaling pathways. ELISA was utilized to analyze the levels of the inflammatory cytokine IL-6 influenced by cisplatin, doxorubicin and PTP4A3 silencing.

Results

In this study, we found that PTP4A3 in the cisplatin/doxorubicin-resistant microarray was closely associated with the overall and recurrence-free survival rates of HCC patients. Cisplatin or doxorubicin significantly reduced cell viability and decreased PTP4A3 expression in hepatoma cells. IL-6 secretion increased with cisplatin or doxorubicin treatment and after PTP4A3 silencing. Furthermore, PTP4A3 was highly expressed in tumor tissues versus adjacent normal tissues from HCC patients. In addition, we evaluated the IL-6-associated signaling pathway involving STAT3 and JAK2, and the levels of p-STAT3, p-JAK2, STAT3 and JAK2 were obviously reduced with cisplatin or doxorubicin treatment in HCC cells using Western blotting and were also decreased after silencing PTP4A3. Collectively, we suggest that cisplatin or doxorubicin decreases HCC cell viability via downregulation of PTP4A3 expression through the IL-6R-JAK2-STAT3 cascade.

Discussion

Therefore, emerging evidence provides a deep understanding of the roles of PTP4A3 in HCC cisplatin/doxorubicin chemoresistance, which can be applied to develop early diagnosis strategies and reveal prognostic factors to establish novel targeted therapeutics to specifically treat HCC.

Association between HMGA1 and immunosuppression in hepatocellular carcinoma: A comprehensive bioinformatics analysis

The high mobility group A1 (HMGA1) gene is overexpressed in malignant tumors, and its expression level correlates with the progression and metastasis of tumors. However, the specific role of HMGA1 in hepatocellular carcinoma (HCC) and relevant influencing approaches in tumor immunity remain unclear. In this study, the expression and clinical significance of HMGA1 in HCC immunity were analyzed. The expression levels of HMGA1 mRNA and protein in HCC tissue and normal liver tissue were analyzed based on the cancer genome atlas, the gene expression omnibus and the Human Protein Atlas databases. The correlation between HMGA1 and clinicopathological factors was analyzed, and survival was estimated based on the expression of HMGA1. Gene set cancer analysis and the TISIDB database were used to identify tumor-infiltrating immune cells and immune inhibitors. Gene set enrichment analysis was performed to determine the involved signaling pathway. The HMGA1 genetic alterations were identified with the cBioPortal for Cancer Genomics. The expression of HMGA1 mRNA and protein was significantly higher in HCC tissue and negatively correlated with survival. Neutrophils, Th17 cells, several immune inhibitors, and signaling pathways were positively correlated with the expression of HMGA1. Amplification was the main type of genetic alteration in HMGA1. These findings demonstrate that HMGA1 can be a therapeutic target and a potential biomarker to predict the prognosis of patients with HCC. HMGA1 may affect the progression of HCC by suppressing the immune function of these patients.

Transcriptomic Analysis of Hepatitis B Infected Liver for Prediction of Hepatocellular Carcinoma

Hepatocellular cancer (HCC) is a leading cause of cancer-related mortality worldwide, and chronic hepatitis B virus infection (CHB) has been a major risk factor for HCC development. The pathogenesis of HBV-related HCC has been a major focus revealing the interplay of a multitude of intracellular signaling pathways, yet the precise mechanisms and their implementations to clinical practice remain to be elucidated. This study utilizes publicly available transcriptomic data from the livers of CHB patients in order to identify a population with a higher risk of malignant transformation. We report the identification of a novel list of genes (PCM1) which can generate clear transcriptomic sub-groups among HBV-infected livers. PCM1 includes genes related to cell cycle activity and liver cancer development. In addition, markers of inflammation, M1 macrophages and gamma delta T cell infiltration are present within the signature. Genes within PCM1 are also able to differentiate HCC from normal liver, and some genes within the signature are associated with poor prognosis of HCC at the mRNA level. The analysis of the immunohistochemical stainings validated that proteins coded by a group of PCM1 genes were overexpressed in liver cancer, while minimal or no expression was detected in normal liver. Altogether, our findings suggest that PCM1 can be developed into a clinically applicable method to identify CHB patients with a higher risk of HCC development.

HLTF promotes hepatocellular carcinoma progression by enhancing SRSF1 stability and activating ERK/MAPK pathway

Helicase-like transcription factor (HLTF) has been found to be involved in the progression of several tumors, but the role of HLTF in hepatocellular carcinoma (HCC) progression has not been studied. Here, our study explored the underlying mechanism of HLTF in HCC progression for the first time. Database analysis and clinical sample examination indicated that HLTF was upregulated in HCC tissues and was related to poor clinicopathological features in patients. Upregulation of HLTF accelerated the growth and metastasis of HCC cells both in vitro and in vivo. Bioinformatics analysis and subsequent experiments revealed that ERK/MAPK signaling pathway activation was vital to HLTF-mediated proliferation and metastasis in HCC cells. Moreover, HLTF was demonstrated to interact with SRSF1 and contribute to its protein stability to activate the ERK/MAPK signaling pathway and enhance HCC growth and metastasis. In addition, miR-511-5p was expressed at a low level in HCC tissues, was negatively correlated HLTF, and regulated HLTF expression. Our study shows that HLTF plays an oncogenic role in HCC progression and provides a novel biomarker and therapeutic target for the diagnosis and treatment of HCC.

Machine learning integrations for development of a T-cell-tolerance derived signature to improve the clinical outcomes and precision treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is characterized by high rates of recurrence and metastasis and poor prognosis. A recently discovered concept of T cell tolerance (TCT) has become an entirely new target of cancer immunotherapy. Unfortunately, the effect of TCT on the outcomes of HCC has not been explored. In this study, 7 public datasets and one external clinical cohort, including 1716 HCC patients were explored. Through WGCNA analysis and differential analysis, we explored the key TCT-related modulates. A total of 95 machine learning integrations across all validation cohorts were compared and the optimal method with the highest average C-index value was selected to construct the TCT derived signature (TCTS). In all independent clinical cohorts, TCTS showed accurate prediction of the prognosis, and was significantly correlated with clinical indicators and molecular features. Compared with 77 published gene signatures, the TCTS exhibited superior predictive performance. In the external clinical cohort, a novel nomogram (comprising TNM stage, Hepatitis B, Vascular invasion, Perineural invasion, AFP and TCTS) was constructed to test the clinical performance of TCTS. The results showed that the high TCTS scoring group showed dismal prognosis, improved sensitivity to oxaliplatin and good response to anti-PD-1/PD-L1 immunotherapy. Moreover, the low TCTS score group had few genomic alterations, low immune activation and low PD-1/PD-L1 expression levels. In conclusion, TCTS is an ideal biomarker for predicting the clinical outcomes and improving precision treatment of HCC.

Improved prognostic prediction model for liver cancer based on biomarker data screened by combined methods

Liver cancer is a common cause of death from cancer in the population, with the 4th highest mortality rate from cancer worldwide. The high recurrence rate of hepatocellular carcinoma after surgery is an important cause of high mortality among patients. In this paper, based on eight scheduled core markers of liver cancer, an improved feature screening algorithm was proposed based on the analysis of the basic principles of the random forest algorithm, and the system was finally applied to liver cancer prognosis prediction to improve the prediction of biomarkers for liver cancer recurrence, and the impact of different algorithmic strategies on the prediction accuracy was compared and analyzed. The results showed that the improved feature screening algorithm was able to reduce the feature set by about 50% while ensuring that the prediction accuracy was reduced within 2%.

PVRIG Expression Is an Independent Prognostic Factor and a New Potential Target for Immunotherapy in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a frequent and deadly cancer in need of new treatments. Immunotherapy has shown promising results in several solid tumors. The TIGIT/DNAM-1 axis gathers targets for new immune checkpoint inhibitors (ICIs). Here, we aimed at highlighting the potential of this axis as a new therapeutic option for HCC. For this, we built a large transcriptomic database of 683 HCC samples, clinically annotated, and 319 normal liver tissues. We interrogated this database for the transcriptomic expression of each member of the TIGIT/DNAM-1 axis and tested their prognostic value for survival. We then focused on the most discriminant one for these criteria, i.e., PVRIG, and analyzed the clinical characteristics, the disease-free and overall survivals, and biological pathways associated with PVRIG High tumors. Among all members of the TIGIT/DNAM-1 axis, PVRIG expression was higher in tumors than in normal liver, was heterogeneous across tumors, and was the only member with independent prognostic value for better survival. PVRIG High tumors were characterized by a higher lymphocytic infiltrate and enriched for signatures associated with tertiary lymphoid structures and better anti-tumor immune response. These results suggest that patients with PVRIG High tumors might be good candidates for immune therapy involving ICIs, notably ICIs targeting the TIGIT/DNAM-1 axis. Further functional and clinical validation is urgently required.

Comprehensive Analysis of the Sorafenib-Associated Druggable Targets on Differential Gene Expression and ceRNA Network in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant pathological type of liver cancer. Several therapeutic treatments, including sorafenib and regorafenib, have only modestly improved survival in patients with HCC. The aim of this study was to investigate the expression profiles and the regulation of competitive endogenous RNAs (ceRNAs) of the sorafenib-related target genes in HCC. Based on clinical information and expression profiles of HCC clinical samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, shared differentially expressed genes (DEGs) were analyzed and identified. Sorafenib-associated DEGs (SADs) were obtained by intersecting the DEGs with the sorafenib target genes from SuperTarget database. The expression patterns of SADs were verified in the Oncomine database. The biological functions of the SADs were annotated by gene set enrichment analysis (GSEA). In addition, a ceRNA network associated with SADs was constructed. Long non-coding RNAs (lncRNAs) in network that were significantly associated with overall survival were identified as prognosis of patients by Cox regression analysis. Finally, the expression levels of prognostic genes in HCC tissues and cell lines were verified using qRT-PCR. Gene expression differential analysis yielded a total of 146 common DEGs were obtained, including 21 upregulated and 125 downregulated DEGs. Among them, ten SADs were detected to be differentially expressed between tumor and normal tissues, including AXL, CYP2C19, CYP2C8, CYP2C9, CYP3A4, FGFR2, GMNN, PDGFRA, and TTK. GSEA analysis grouped them into three categories by function. The first category (CYP2C19, CYP2C8, CYP2C9 and CYP3A4) and second category (GMNN, TTK and EGER2) had the opposite roles in the enriched terms and pathways, while the third class (AXL and PDGFRA) has enrichment terms and pathways that intersect with those of the first and second categories. A ceRNA network associated with SADs was also constructed including 49 lncRNAs, 14 miRNAs, and 8 mRNAs. Three of these lncRNAs, SNHG7, GAS5 and HCP5, were found upregulated in HCC tissues and to be independent predictors in HCC patients. Significant correlations were found in expression between the prognostic lncRNAs and SADs. Ten SADs were systematically identified using expression data from HCC and normal tissues from TCGA and GEO datasets. GSEA analysis provided us with insight into the function of SADs. In the future, we will continue to explore the mechanisms of coordinated regulation of SADs-related prognostic lncRNAs and SADs at the ceRNA axis level and their potential functions in the development of HCC.

Proteomics profiling of nontumor liver tissues identifies prognostic biomarkers in hepatitis B-related hepatocellular carcinoma

Hepatocellular carcinoma (HCC) often occurs following chronic hepatitis B virus (HBV) infection, leading to high recurrence and a low 5-year survival rate. We developed an overall survival (OS) prediction model based on protein expression profiles in HBV-infected nontumor liver tissues. We aimed to demonstrate the feasibility of using protein expression profiles in nontumor liver tissues for survival prediction. A univariate Cox and differential expression analysis were performed to identify candidate prognostic factors. A multivariate Cox analysis was performed to develop the liver gene prognostic index (LGPI). The survival differences between the different risk groups in the training and validation cohorts were also estimated. A total of 363 patients, 159 in the training cohort, and 204 in the validation cohort were included. Of the 6478 proteins extracted from nontumor liver tissues, we identified 1275 proteins altered between HCC and nontumor liver tissues. A total of 1090 out of 6478 proteins were significantly related to OS. The prognostic values of the proteins in nontumor tissues were mostly positively related to those in the tumor tissues. Protective proteins were mainly enriched in the metabolism-related pathways. From the differentially expressed proteins, the top 10 most significant prognosis-related proteins were submitted for LGPI construction. In the training and validation cohorts, this LGPI showed a great ability for distinguishing patients' OS risk stratifications. After adjusting for clinicopathological features, the LGPI was an independent prognostic factor in the training and validation cohorts. We demonstrated the prognostic value of protein expression profiling in nontumor liver tissues. The proposed LGPI was a promising predictive model for estimating OS in HBV-related HCC.

PUS1 May Be a Potential Prognostic Biomarker and Therapeutic Target for Hepatocellular Carcinoma

Objective

The mechanisms of pseudouridine synthase (PUS) are not definite in hepatocellular carcinoma (HCC), the objective of this study is to investigate the effect of PUS genes in HCC.

Materials and Methods

Differentially expressed and prognostic gene of PUS enzymes was identified based on The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. For the identified gene, pseudouridine synthase 1 (PUS1), was used for further research. The clinicopathological feature of PUS1 was analyzed by Student's t-test. Prognostic significance was explored by Kaplan-Meier (KM) analysis and Cox proportional hazards regression model. Receiver operating characteristic (ROC) curve was applied to appraise diagnostic and prognostic value. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and Gene Set Enrichment Analysis (GSEA) were implemented to explore mechanism of PUS1. A Guangxi cohort was applied to verify differential expression. In vitro cell experiments were implemented to investigate the influence for proliferation, reactive oxygen species (ROS) level, migration, and invasion of HCC cells after a knockdown of PUS1.

Results

PUS1 was significantly overexpressed in HCC tissues, and patients with high PUS1 were related to unpromising clinicopathological features. Survival analysis revealed high PUS1 expression was associated with a poor overall survival (OS) and 1 year-recurrence free survival (RFS), was an independent risk factor. Meanwhile, ROC curve showed that PUS1 had a diagnostic and prognostic significance to HCC. Functional enrichment analysis implied that PUS1 may be involved in metabolic pathways, mitochondrial function, non-alcoholic fatty liver disease (NAFLD), and some important carcinogenic pathways. Cell assays revealed that knockdown of PUS1 significantly constrained the migration, proliferation, invasion and improved the ROS level of HCC cells.

Conclusion

PUS1 may be a prognostic biomarker and a underlying treatment target for HCC.

Inhibition of Annexin A10 Contributes to ZNF281 Mediated Aggressiveness of Hepatocellular Carcinoma

Objective

To investigate the involvement and transcriptional targets of zinc finger protein 281 (ZNF281) in the progression of hepatocellular carcinoma (HCC).

Methods

The expression of ZNF281 in HCC was detected in tissue microarray and cell lines. The role of ZNF281 in aggressiveness of HCC was examined using wound healing, matrigel transwell, pulmonary metastasis model and assays for expression of EMT markers. RNA-seq was used to find potential target gene of ZNF281. Chromatin immunoprecipitation (ChIP) assay and co-immunoprecipitation (Co-IP) were employed to uncover the mechanism of the transcriptional regulation of ZNF281 on the target gene.

Results

ZNF281 was increased in tumor tissues and positively correlated with vascular invasion in HCC. Knockdown of ZNF281 suppressed the migration and invasion with significant alteration of EMT marker expression in HLE and Huh7 HCC cell lines. RNA-seq screening showed that the tumor suppressor gene Annexin A10 (ANXA10) was a most up-regulated gene in response to ZNF281 depletion and responsible for the attenuation of aggressiveness. Mechanistically, ZNF281 interacted with the ANXA10 promoter region harboring ZNF281 recognition sites, and recruited components of nucleosome remodeling and deacetylation (NuRD) complex. By knocking down such components like HDAC1 or MTA1, ANXA10 was released from transcriptional repression by ZNF281/NuRD, and in turn reversed the EMT, invasion and metastasis driven by ZNF281.

Conclusion

ZNF281 drives invasion and metastasis of HCC partially through transcriptional repression of tumor suppressor gene ANXA10 by recruiting NuRD complex.

Clinical Significance of Glycolytic Metabolic Activity in Hepatocellular Carcinoma

High metabolic activity is a hallmark of cancers, including hepatocellular carcinoma (HCC). However, the molecular features of HCC with high metabolic activity contributing to clinical outcomes and the therapeutic implications of these characteristics are poorly understood. We aimed to define the features of HCC with high metabolic activity and uncover its association with response to current therapies. By integrating gene expression data from mouse liver tissues and tumor tissues from HCC patients (n = 1038), we uncovered three metabolically distinct HCC subtypes that differ in clinical outcomes and underlying molecular biology. The high metabolic subtype is characterized by poor survival, the strongest stem cell signature, high genomic instability, activation of EPCAM and SALL4, and low potential for benefitting from immunotherapy. Interestingly, immune cell analysis showed that regulatory T cells (Tregs) are highly enriched in high metabolic HCC tumors, suggesting that high metabolic activity of cancer cells may trigger activation or infiltration of Tregs, leading to cancer cells' evasion of anti-cancer immune cells. In summary, we identified clinically and metabolically distinct subtypes of HCC, potential biomarkers associated with these subtypes, and a potential mechanism of metabolism-mediated immune evasion by HCC cells.

Deciphering Tumour Microenvironment of Liver Cancer through Deconvolution of Bulk RNA-Seq Data with Single-Cell Atlas

Liver cancers give rise to a heavy burden on healthcare worldwide. Understanding the tumour microenvironment (TME) underpins the development of precision therapy. Single-cell RNA sequencing (scRNA-seq) technology has generated high-quality cell atlases of the TME, but its wider application faces enormous costs for various clinical circumstances. Fortunately, a variety of deconvolution algorithms can instead repurpose bulk RNA-seq data, alleviating the need for generating scRNA-seq datasets. In this study, we reviewed major public omics databases for relevance in this study and utilised eight RNA-seqs and one microarray dataset from clinical studies. To decipher the TME of liver cancer, we estimated the fractions of liver cell components by deconvoluting the samples with Cibersortx using three reference scRNA-seq atlases. We also confirmed that Cibersortx can accurately deconvolute cell types/subtypes of interest. Compared with non-tumorous liver, liver cancers showed multiple decreased cell types forming normal liver microarchitecture, as well as elevated cell types involved in fibrogenesis, abnormal angiogenesis, and disturbed immune responses. Survival analysis shows that the fractions of five cell types/subtypes significantly correlated with patient outcomes, indicating potential therapeutic targets. Therefore, deconvolution of bulk RNA-seq data with scRNA-seq atlas references can be a useful tool to help understand the TME.

PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery

Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.

Dopamine receptor D3 is related to prognosis in human hepatocellular carcinoma and inhibits tumor growth

Background

Dopamine receptors have been reported to play important roles in cancer progression. However, the role of dopamine receptor D3 (DRD3) in hepatocellular carcinoma (HCC) remains unclear.

Methods

The expression of DRD3 was detected by immunohistochemistry and real-time qPCR. The prognostic value of DRD3 in patients was investigated by analyzing selected databases, including cBioPortal and Kaplan–Meier plotter. Cell growth was tested by CCK8 assay, and Transwell assays were performed to assess cancer cell migration and invasion. The cAMP/ERK/CREB signaling pathway was evaluated by Western blot analysis and ELISA. An HCC xenograft model was established for in vivo experiments.

Results

DRD3 mRNA expression was significantly higher in nontumor tissues than in tumor tissues. Lower protein expression of DRD3 was related to poor recurrence-free survival (RFS) and overall survival (OS). Kaplan–Meier plotter analysis showed that higher expression of DRD3 mRNA was associated with better OS, RFS, disease-specific survival (DSS), and progression-free survival (PFS). cBioPortal analysis revealed that the alteration group, which harbored genetic mutations in DRD3, exhibited poor OS, RFS, DSS and PFS. According to CCK8 and Transwell assays, stable DRD3 overexpression cell line (ex-DRD3-SK-HEP-1) showed weaker proliferation, migration and invasion behaviors. PD128907, a DRD3 agonist, suppressed proliferation, migration and invasion in HCC cell lines, while U99194, a DRD3 antagonist, enhanced proliferation, migration and invasion in HCC cell lines. Western blot analysis and ELISA revealed that stable DRD3 knock-down cell line (sh-DRD3-PLC/PRF/5) and U99194 both increased the protein levels of cAMP, p-ERK and p-CREB; on the other hand, ex-DRD3-SK-HEP-1 and PD128907 decreased the protein levels of cAMP, p-ERK and p-CREB. SCH772984, an ERK antagonist, abolished the effect of U99194 on the malignant biological behaviors of HCC cells. In vivo, PD128907 suppressed tumor growth, and U99194 enhanced tumor growth.

Conclusion

Our results suggest that down-regulation of DRD3 is strongly involved in the progression of HCC, and DRD3 might be consider as an independent prognostic factor for HCC. Furthermore, DRD3 agonists may be a promising strategy for HCC therapy.

Development and validation of a microenvironment-related prognostic model for hepatocellular carcinoma patients based on histone deacetylase family

BACKGROUND

Histone deacetylase (HDAC) family can remove acetyl groups from histone lysine residues, and their high expression is closely related to the poor prognosis of hepatocellular carcinoma (HCC) patients. Recently, it has been reported to play an immunosuppressive role in the microenvironment, but little is known about the mechanism.

METHODS

Through machine learning, we trained and verified the prognostic model composed of HDACs. CIBERSORT was used to calculate the percentage of immune cells in the microenvironment. Based on co-expression network, potential targets of HDACs were screened. After that, qRT-PCR was employed to evaluate the expression of downstream genes of HDACs, while HPLC-CAD analysis was applied to detect the concentration of arachidonic acid (AA). Finally, Flow cytometry, WB and IHC experiments were used to detect CD86 expression in RAW246.7.

RESULTS

We constructed a great prognostic model composed of HDAC1 and HDAC11 that was significantly associated with overall survival. These HDACs were related to the abundance of macrophages, which might be attributed to their regulation of fatty-acid-metabolism related genes. In vitro experiments, the mRNA expression of ACSM2A, ADH1B, CYP2C8, CYP4F2 and SLC27A5 in HCC-LM3 was significantly down-regulated, and specific inhibitors of HDAC1 and HDAC11 significantly promoted the expression of these genes. HDAC inhibitors can promote the metabolism of AA, which may relieve the effect of AA on the polarization of M1 macrophages.

CONCLUSIONS

Our study revealed the blocking effect of HDAC1 and HDAC11 on the polarization of macrophages M1 in the microenvironment by inhibiting fatty acid metabolism.

Consensus subtypes of hepatocellular carcinoma associated with clinical outcomes and genomic phenotypes

BACKGROUND AND AIMS

Although many studies revealed transcriptomic subtypes of HCC, concordance of the subtypes are not fully examined. We aim to examine a consensus of transcriptomic subtypes and correlate them with clinical outcomes.

APPROACH AND RESULTS

By integrating 16 previously established genomic signatures for HCC subtypes, we identified five clinically and molecularly distinct consensus subtypes. STM (STeM) is characterized by high stem cell features, vascular invasion, and poor prognosis. CIN (Chromosomal INstability) has moderate stem cell features, but high genomic instability and low immune activity. IMH (IMmune High) is characterized by high immune activity. BCM (Beta-Catenin with high Male predominance) is characterized by prominent β-catenin activation, low miRNA expression, hypomethylation, and high sensitivity to sorafenib. DLP (Differentiated and Low Proliferation) is differentiated with high hepatocyte nuclear factor 4A activity. We also developed and validated a robust predictor of consensus subtype with 100 genes and demonstrated that five subtypes were well conserved in patient-derived xenograft models and cell lines. By analyzing serum proteomic data from the same patients, we further identified potential serum biomarkers that can stratify patients into subtypes.

CONCLUSIONS

Five HCC subtypes are correlated with genomic phenotypes and clinical outcomes and highly conserved in preclinical models, providing a framework for selecting the most appropriate models for preclinical studies.

Dysregulation and prometastatic function of glycosyltransferase C1GALT1 modulated by cHP1BP3/ miR-1-3p axis in bladder cancer

Background

Abnormal glycosylation in a variety of cancer types is involved in tumor progression and chemoresistance. Glycosyltransferase C1GALT1, the key enzyme in conversion of Tn antigen to T antigen, is involved in both physiological and pathological conditions. However, the mechanisms of C1GALT1 in enhancing oncogenic phenotypes and its regulatory effects via non-coding RNA are unclear.

Methods

Abnormal expression of C1GALT1 and its products T antigen in human bladder cancer (BLCA) were evaluated with BLCA tissue, plasma samples and cell lines. Effects of C1GALT1 on migratory ability and proliferation were assessed in YTS-1 cells by transwell, CCK8 and colony formation assay in vitro and by mouse subcutaneous xenograft and trans-splenic metastasis models in vivo. Dysregulated circular RNAs (circRNAs) and microRNAs (miRNAs) were profiled in 3 pairs of bladder cancer tissues by RNA-seq. Effects of miR-1-3p and cHP1BP3 (circRNA derived from HP1BP3) on modulating C1GALT1 expression were investigated by target prediction program, correlation analysis and luciferase reporter assay. Functional roles of miR-1-3p and cHP1BP3 on migratory ability and proliferation in BLCA were also investigated by in vitro and in vivo experiments. Additionally, glycoproteomic analysis was employed to identify the target glycoproteins of C1GALT1.

Results

In this study, we demonstrated upregulation of C1GALT1 and its product T antigen in BLCA. C1GALT1 silencing suppressed migratory ability and proliferation of BLCA YTS-1 cells in vitro and in vivo. Subsets of circRNAs and miRNAs were dysregulated in BLCA tissues. miR-1-3p, which is reduced in BLCA tissues, inhibited transcription of C1GALT1 by binding directly to its 3′-untranslated region (3′-UTR). miR-1-3p overexpression resulted in decreased migratory ability and proliferation of YTS-1 cells. cHP1BP3 was upregulated in BLCA tissues, and served as an miR-1-3p “sponge”. cHP1BP3 was shown to modulate migratory ability, proliferation, and colony formation of YTS-1 cells, and displayed tumor-suppressing activity in BLCA. Target glycoproteins of C1GALT1, including integrins and MUC16, were identified.

Conclusions

This study reveals the pro-metastatic and proliferative function of upregulated glycosyltransferase C1GLAT1, and provides preliminary data on mechanisms underlying dysregulation of C1GALT1 via miR-1-3p / cHP1BP3 axis in BLCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02438-7.

Highly expressed FAM189B predicts poor prognosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with persistently high morbidity and mortality. However, the expression, prognostic and clinical significance of FAM189 family genes in HCC remain largely unknown. In this study, the expression levels of FAM189 family genes in HCC were analyzed through TCGA-LIHC and ICGC-LIRI-JP cohorts, and further validated in multiple independent GEO datasets. It was found that the expression of FAM189B was significantly upregulated in HCC tumor tissues, while the expression of FAM189A1 and FAM189A2 was not significantly changed between tumor and adjacent tissues. Further analysis revealed that upregulated copy number variation contributed to increased expression of FAM189B in HCC. Survival analysis showed that highly expressed FAM189B was significantly correlated with unfavorable prognosis, including overall survival, disease-specific survival, and progression-free interval. Univariate and multivariate Cox regression analysis showed that FAM189B was a potential novel prognosis factor for HCC patients. In addition, the association between FAM189B expression and clinical and molecular characteristics was analyzed. High expression of FAM189B was associated with high AFP level, high predicted risk metastasis signature, and TP53 mutation, while there was no significant association between FAM189B expression and cancer stage or tumor grade of HCC. Gene set enrichment analysis revealed that highly expressed FAM189B was closely related with signal pathways and biological processes associated with cell proliferation and cell cycle in HCC. In conclusion, this study suggested that FAM189B was highly expressed in HCC and highly expressed FAM189B may serve as an effective prognostic indicator and a potential therapeutic target for HCC patients.

Comprehensive analysis of the prognostic value and functions of prefoldins in hepatocellular carcinoma

Prefoldins (PFDNs), a group of proteins known to be associated with cytoskeletal rearrangement, are involved in tumor progression in various cancer types. However, little is known about the roles of PFDNs in hepatocellular carcinoma (HCC). Herein, we investigated the transcriptional and survival data of PFDNs from The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate the potential functions of PFDN1/2/3/4. We also detected the expression of PFDN1/2/3/4 via immunohistochemistry (IHC), Western blotting, and real-time PCR in our clinical samples. We found that the PFDN family showed elevated expression in HCC tissues, while only PFDN1/2/3/4 were found to be significantly correlated with poor prognosis of patients with HCC in the TCGA database. Further investigation was associated with PFDN1-4. We found that the expression of PFDN1/2/3/4 was significantly associated with advanced clinicopathologic features. Apart from the TCGA database, IHC, real-time PCR, and immunoblotting identified the overexpression of PFDN1/2/3/4 in HCC tissues and HCC cell lines. Taken together, these results indicated that PFDN1/2/3/4 might be novel prognostic biomarkers and treatment targets for patients with HCC.

Blocking hepatocarcinogenesis by a cytochrome P450 family member with female-preferential expression

OBJECTS

The incidence of hepatocellular carcinoma (HCC) shows an obvious male dominance in rodents and humans. We aimed to identify the key autosomal liver-specific sex-related genes and investigate their roles in hepatocarcinogenesis.

DESIGN

Two HCC cohorts (n=551) with available transcriptome and metabolome data were used. Class comparisons of omics data and ingenuity pathway analysis were performed to explore sex-related molecules and their associated functions. Functional assays were employed to investigate roles of the key candidates, including cellular assays, molecular assays and multiple orthotopic HCC mouse models.

RESULTS

A global comparison of multiple omics data revealed 861 sex-related molecules in non-tumour liver tissues between female and male HCC patients, which denoted a significant suppression of cancer-related diseases and functions in female liver than male. A member of cytochrome P450 family, CYP39A1, was one of the top liver-specific candidates with significantly higher levels in female vs male liver. In HCC tumours, CYP39A1 expression was dramatically reduced in over 90% HCC patients. Exogenous CYP39A1 significantly blocked tumour formation in both female and male mice and partially reduced the sex disparity of hepatocarcinogenesis. The HCC suppressor role of CYP39A1 did not rely on its known P450 enzyme activity but its C-terminal region, by which CYP39A1 impeded the transcriptional activation activity of c-Myc, leading to a significant inhibition of hepatocarcinogenesis.

CONCLUSIONS

The liver-specific CYP39A1 with female-preferential expression was a strong suppressor of HCC development. Strategies to up-regulate CYP39A1 might be promising methods for HCC treatment in both women and men in future.

A Novel mRNA Signature Related to Immunity to Predict Survival and Immunotherapy Response in Hepatocellular Carcinoma

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and the incidence and mortality rates are increasing. Given the limited treatments of HCC and promising application of immunotherapy for cancer, we aimed to identify an immune-related prognostic signature that can predict overall survival (OS) rates and immunotherapy response in HCC.

METHODS

The initial signature development was conducted using a training dataset from the Cancer Genome Atlas followed by independent internal and external validations from that resource and the Gene Expression Omnibus. A signature based nomogram was generated using multivariate Cox regression analysis. The associations of signature score with tumor immune phenotype and response to immunotherapy were analyzed using single-sample gene set enrichment analysis and tumor immune dysfunction and exclusion algorithm. A cohort from Zhongshan Hospital was employed to verify the predictive robustness of the signature regarding prognostic risk and immunotherapy response.

RESULTS

The prognostic signature, IGSHCC, consisting of 22 immune-related genes, had independent prognostic ability, with training and validation cohorts. Also, IGSHCC stratified HCC patients with different outcomes in subgroups. The prognostic accuracy of IGSHCC was better than three reported prognostic signatures. The IGSHCC-based nomogram had high accuracy and significant clinical benefits in predicting 3- and 5-year OS. IGSHCC reflected distinct immunosuppressive phenotypes in low- and high-score groups. Patients with low IGSHCC scores were more likely than those with high scores to benefit from immunotherapy.

CONCLUSIONS

IGSHCC predicted HCC prognosis and response to immunotherapy, and contributed to individualized clinical management.

Expression, Prognostic Value, and Functional Mechanism of Polarity-Related Genes in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and poor prognoses around the world. Within-cell polarity is crucial to cell development and function maintenance, and some studies have found that it is closely related to cancer initiation, metastasis, and prognosis. The aim of our research was to find polarity-related biomarkers which improve the treatment and prognosis of HCC. For the knowledge-driven analysis, 189 polarity-related genes (PRGs) were retrieved and curated manually from the molecular signatures database and reviews. Meanwhile, in the data-driven part, genomic datasets and clinical records of HCC was obtained from the cancer genome atlas database. The potential candidates were considered in the respect to differential expression, mutation rate, and prognostic value. Sixty-one PRGs that passed the knowledge and data-driven screening were applied for function analysis and mechanism deduction. Elastic net model combing least absolute shrinkage and selection operator and ridge regression analysis refined the input into a 12-PRG risk model, and its pharmaceutical potency was evaluated. These findings demonstrated that the integration of multi-omics of PRGs can help us in untangling the liver cancer pathogenesis as well as illustrate the underlying mechanisms and therapeutic targets.

Molecular subtypes based on Wnt-signaling gene expression predict prognosis and tumor microenvironment in hepatocellular carcinoma

Based on increasing research evidence, hepatocellular carcinoma (HCC) is heterogeneous, and genetic profiling has led to the identification of multiple subtypes of this disease. To advance our knowledge and the ability to use individualized medicine in the treatment of HCC, it is essential to perform a complete and methodical characterization of various molecular subtypes. The canonical Wnt/β-catenin pathway is an evolutionarily conserved complicated signaling mechanism that plays a role in carcinogenesis and progression of HCC. In this study, we acquired RNA sequencing, somatic mutation, and clinical data from 701 patients from The Cancer Genome Atlas and Gene Expression Omnibus databases and stratified patients into two subgroups: WNT-high and WNT-low. In general, the WNT-high subtype is associated with an immunosuppressive microenvironment, poor prognosis, cancer-related pathways, and a low response to immune checkpoint therapy. We also found that WNT3 is negatively linked to CD8⁺ T-cell infiltration using multiple immunofluorescence assays. Finally, we developed a WNT-related prognostic model to predict the survival time of patients with HCC. In summary, we developed a new classification scheme for HCC based on Wnt signaling signatures. This classification produced substantial clinical effects, both in terms of assessing patient prognosis and immunotherapy administered to patients with HCC.

Integrated Bioinformatics and Experimental Analysis Identified TRIM28 a Potential Prognostic Biomarker and Correlated with Immune Infiltrates in Liver Hepatocellular Carcinoma

Background

Since the 1970s, liver hepatocellular carcinoma (LIHC) has experienced a constant rise in incidence and mortality rates, making the identification of LIHC biomarkers very important. Tripartite Motif-Containing 28 (TRIM28) is a protein-coding gene which encodes the tripartite motif-containing proteins (TRIMs) family and is associated with specific chromatin regions. TRIM28 expression and its prognostic value and impact on the immune system in LIHC patients are being investigated for the first time.

Methods

The TRIM28 expression data from TCGA database was used to analyze TRIM28 expression, clinicopathological information, gene enrichment, and immune infiltration and conduct additional bioinformatics analysis. R language was used for statistical analysis. TIMER, CIBERSORT, and ssGSEA were used to assess immune responses of TRIM28 in LIHC. Next, the results were validated using GEPIA, ROC analysis, and immunohistochemical staining pictures from the THPA. GSE14520, GSE63898, and GSE87630 datasets were analyzed using ROC analysis to further evaluate TRIM28's diagnostic value. To ultimately determine TRIM28 expression, we performed qRT-PCR (quantitative real-time polymerase chain reaction).

Results

High TRIM28 expression level was associated with T classification, pathologic stage, histologic grade, and serum AFP levels. In patients with LIHC, TRIM28 was an independent risk factor for a poor prognosis. The pathways ligand-receptor interaction, which is critical in LIHC patients, were closely associated with TRIM28 expression, and the function of DC could be suppressed by overexpression of TRIM28. As a final step, our results were validated by GEO data and qRT-PCR.

Conclusions

TRIM28 will shed new light on LIHC mechanisms. As an effective diagnostic and intervention tool, this gene will be able to diagnose and treat LIHC at an early stage.

Protein Tyrosine Kinase 7 (PTK7) Promotes Metastasis in Hepatocellular Carcinoma via SOX9 Regulation and TGF-β Signaling

BACKGROUND & AIMS

Metastasis is found in most advanced hepatocellular carcinoma (HCC) patients, and it drives tumor recurrence and systemic failure. There is no effective treatment owing to its complex biological features. Many of the molecular drivers of metastasis are crucial players in normal physiology but behave unconventionally during cancer progression. Targeting these molecular drivers for therapy and differentiating them from a physiological background require a detailed examination of the novel mechanisms involved in their activation during metastasis.

METHODS

Publicly available transcriptomic data such as that of TCGA-LIHC and Gene Expression Omnibus were utilized to identify novel targets upregulated in advanced and metastatic HCC. Validation of candidates was assisted by immunohistochemistry performed on tissue microarrays derived from more than 100 HCC patients. Expression of protein tyrosine kinase 7 (PTK7) was studied under the treatment of transforming growth factor-β1 and knockdown of SRY-Box Transcription Factor 9 (SOX9) to delineate upstream regulation, while CRISPR-mediated knockout and lentiviral overexpression of PTK7 in HCC cells were performed to study their functional and signaling consequences. Manipulated HCC cells were injected into mice models either by orthotopic or tail-vein injection to observe for any in vivo pro-metastatic effects.

RESULTS

PTK7 was discovered to be the kinase most significantly upregulated in advanced and metastatic HCC, at both transcriptomic and proteomic level. Bioinformatic analyses and functional assays performed in HCC cell lines revealed transforming growth factor-β signaling and SOX9 to be important activators of PTK7 expression. Functionally, enrichment of PTK7 expression could positively regulate metastatic potential of HCC cells in vitro and in lung metastasis models performed in immunodeficient mice. The up-regulation of PTK7 recruited the epithelial-mesenchymal transition components, zinc finger protein SNAI2 (SLUG) and zinc finger E-box-binding homeobox 1 (ZEB1).

CONCLUSIONS

Our study proposes PTK7 as a novel molecular driver in metastatic HCC, particularly in a transforming growth factor-β-activated microenvironment. The preferential expression of PTK7 resulted in a previously unobserved regulatory effect on the recruitment of epithelial-mesenchymal transition components, which established PTK7 as a potential determinant of specific epithelial-mesenchymal transition status. Therefore, our data support the continual development of PTK7-targeted agents as antimetastatic therapies.

Identification and validation in a novel quantification system of the glutamine metabolism patterns for the prediction of prognosis and therapy response in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) has one of the highest mortality rates worldwide. Abnormal glutamine metabolism (GM) has been reported to be involved in HCC progression. The current study sought to examine the predictive value of GM in HCC patient's prognosis and therapy response.

METHODS

The RNA-sequencing data and clinical information of HCC samples were obtained from The Cancer Genome Atlas (TCGA) database (N=377) and Gene Expression Omnibus (GEO) database (N=242). By analyzing a data set from TCGA, we showed that the GM landscape of HCC patients was developed based on the non-negative matrix factorization (NMF) algorithm. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analyses were used to construct a risk model. The accuracy of the model, which was based on the GM-related genes (GMRGs), was verified by Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves. We also verified the reliability of the model based on GEO data. Finally, the immune infiltration analysis, pathway enrichment analysis, and treatment response prediction results were compared to each other in the 2 risk groups.

RESULTS

In our study, the HCC samples were divided into 2 GM-related patterns; that is, C1 and C2. The multi-analysis revealed that the GM-related patterns were associated with the pathologic stage, T stages, N stages, histologic grade, and the tumor immune microenvironment (TIME). Next, the prognostic model containing 5 GMRGs (i.e., aldehyde dehydrogenase 5 family member A1, ASNSD1, carbamoyl-phosphate synthetase 1, GMPS, and PPAT) was constructed to calculate the risk score. The high-risk group of HCC patients had significantly worse overall survival (OS) than the low-risk group in both datasets (P<0.001). Multivariate Cox regression uncover the riskScores may serve as an independent prognostic marker for HCC patients [TCGA: hazard ratio (HR) =2.909 (1.940-4.362), P<0.001; GEO: HR =2.911 (1.753-5.848), P=0.043]. Finally, we found that the prognostic model was significantly correlated with the pathologic stage and TIME of the HCC patients in both databases. Moreover, the prognostic model may guide the immunotherapy, chemotherapy, and targeted drugs choice.

CONCLUSIONS

In summary, we developed a GM-related 5-gene risk-score model, which may be a useful tool for predicting prognosis and guiding the treatment of HCC patients.

Trip13 Depletion in Liver Cancer Induces a Lipogenic Response Contributing to Plin2-Dependent Mitotic Cell Death

Aberrant energy metabolism and cell cycle regulation both critically contribute to malignant cell growth and both processes represent targets for anticancer therapy. It is shown here that depletion of the AAA+-ATPase thyroid hormone receptor interacting protein 13 (Trip13) results in mitotic cell death through a combined mechanism linking lipid metabolism to aberrant mitosis. Diminished Trip13 levels in hepatocellular carcinoma cells result in insulin-receptor-/Akt-pathway-dependent accumulation of lipid droplets, which act as functional acentriolar microtubule organizing centers disturbing mitotic spindle polarity. Specifically, the lipid-droplet-coating protein perilipin 2 (Plin2) is required for multipolar spindle formation, induction of DNA damage, and mitotic cell death. Plin2 expression in different tumor cells confers susceptibility to cell death induced by Trip13 depletion as well as treatment with paclitaxel, a spindle-interfering drug commonly used against different cancers. Thus, assessment of Plin2 levels enables the stratification of tumor responsiveness to mitosis-targeting drugs, including clinically approved paclitaxel and Trip13 inhibitors currently under development.

Identification and Validation of an Inflammatory Response-Related Polygenic Risk Score as a Prognostic Marker in Hepatocellular Carcinoma

Aims

We hypothesized that the expression patterns of inflammatory response-related genes may be a potential tool for hepatocellular carcinoma (HCC) risk scoring.

Background

Inflammatory response plays a pivotal role in the pathogenesis of HCC.

Objective

To establish and validate a hallmark inflammatory response gene-based polygenic risk score as a prognostic tool in HCC.

Methods

We screened differentially expressed inflammatory response genes and established an inflammatory response-related polygenic risk score (IRPRS) in an HCC-related dataset. Patients with HCC were categorized into high- and low-risk groups according to the median IRPRS, and the overall survival between the two groups was compared. The IRPRS was validated in an independent external dataset. Tumor-infiltrating lymphocytes (TILs) in high- and low-risk groups were compared, and gene set enrichment analysis was performed to characterize high-risk HCC identified using this IRPRS.

Results

Four differentially expressed hallmark inflammatory response genes (CD14, AQP9, SERPINE1, and ITGA5) were identified to construct the IRPRS. Patients in the high-risk group had significantly shorter overall survival than those in the low-risk group in both the training set and the test set. Furthermore, the IRPRS remained an independent prognostic factor compared to the routine clinicopathological characteristics. Many cancer-related hallmark gene sets and TILs were significantly enriched in the high-risk group.

Conclusions

We established and validated a four-hallmark inflammatory response gene-based polygenic risk score, which could successfully divide patients with HCC into high-risk and low-risk groups. These two risk groups of HCC possess significantly distinct prognostic and biological characteristics.

Overexpression of SRD5A3 in Hepatocellular Carcinoma and Its Molecular Mechanism: A Study of Bioinformatics Exploration Analysis with Experimental Verification

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, and more prevalent among males than females. However, the biological role of enzyme 5α-reductase (SRD5A3), which plays a critical role in the androgen receptor signaling pathway during HCC development, remains poorly understood.

METHODS

ONCOMINE, GEPIA, UALCAN, and Kaplan-Meier Plotter were used to analyze the expression and prognostic value of SRD5A3 in HCC. STRING and Metascape were applied to analyze potential target and molecular pathways underlying SRD5A3 in HCC. A real-time quantitative reverse transcription-polymerase chain reaction was used to validate the downstream target expression of SRD5A3.

RESULTS

The expression of SRD5A3 was significantly overexpressed in HCC tissues compared with normal tissues, while the expression of SRD5A1 and SRD5A2 were downregulated in multiple public datasets. It may be that the low methylation of the SRD5A3 promoter leads to its overexpression. The level of SRD5A3 tended to be higher expressed in clinical samples with advanced stage and positive node metastasis. Furthermore, the patients with higher SRD5A3 were remarkably associated with poorer overall survival and disease-free survival in the TCGA data. In addition, the increased mRNA expression of SRD5A3 could predict poorer overall survival in Kaplan-Meier Plotter database including different patient cohorts. Moreover, HCC patients with higher level of SRD5A3 had significantly shorter recurrence-free survival, progression-free survival, and disease-specific survival. Furthermore, enrichment analysis demonstrated that multiple processes, such as steroid hormone biosynthesis, lipid biosynthetic process, and androgen metabolic process, were affected by SRD5A1-3 alterations. In vitro experiments showed that the expression of SRD5A3 was increased in HCC tissues than that in adjacent tissues. SRD5A3 silencing promoted the expression of DOLK in two HCC cell lines.

CONCLUSIONS

This study identified SRD5A3/DOLK as a novel axis to regulate HCC development.

Deep View of HCC Gene Expression Signatures and Their Comparison with Other Cancers

BACKGROUND

Gene expression signatures correlate genetic alterations with specific clinical features, providing the potential for clinical usage. A plethora of HCC-dependent gene signatures have been developed in the last two decades. However, none of them has made its way into clinical practice. Thus, we investigated the specificity of public gene signatures to HCC by establishing a comparative transcriptomic analysis, as this may be essential for clinical applications.

METHODS

We collected 10 public HCC gene signatures and evaluated them by utilizing four different (commercial and non-commercial) gene expression profile comparison tools: Oncomine Premium, SigCom LINCS, ProfileChaser (modified version), and GENEVA, which can assign similar pre-analyzed profiles of patients with tumors or cancer cell lines to our gene signatures of interests. Among the query results of each tool, different cancer entities were screened. In addition, seven breast and colorectal cancer gene signatures were included in order to further challenge tumor specificity of gene expression signatures.

RESULTS

Although the specificity of the evaluated HCC gene signatures varied considerably, none of the gene signatures showed strict specificity to HCC. All gene signatures exhibited potential significant specificity to other cancers, particularly for colorectal and breast cancer. Since signature specificity proved challenging, we furthermore investigated common core genes and overlapping enriched pathways among all gene signatures, which, however, showed no or only very little overlap, respectively.

CONCLUSION

Our study demonstrates that specificity, independent validation, and clinical use of HCC genetic signatures solely relying on gene expression remains challenging. Furthermore, our work made clear that standards in signature generation and statistical methods but potentially also in tissue preparation are urgently needed.

rtcisE2F promotes the self-renewal and metastasis of liver tumor-initiating cells via N6-methyladenosine-dependent E2F3/E2F6 mRNA stability

Liver cancer is highly heterogeneous, and the tumor tissue harbors a variety of cell types. Liver tumor initiating cells (TICs) well contribute to tumor heterogeneity and account for tumor initiation and metastasis, but the molecular mechanisms of liver TIC self-renewal are elusive. Here, we identified a functional read-through rt-circRNA, termed rtcisE2F, that is highly expressed in liver cancer and liver TICs. rtcisE2F plays essential roles in the self-renewal and activities of liver TICs. rtcisE2F targets E2F6 and E2F3 mRNAs, attenuates mRNA turnover, and increases E2F6/E2F3 expression. Mechanistically, rtcisE2F functions as a scaffold of N-methyladenosine (m⁶A) reader IGF2BP2 and E2F6/E2F3 mRNA. rtcisE2F promotes the association of E2F6/E2F3 mRNAs with IGF2BP2, and inhibits their association with another m⁶A reader, YTHDF2. IGF2BP2 inhibits E2F6/E2F3 mRNA decay, whereas YTHDF2 promotes E2F6/E2F3 mRNA decay. By switching m⁶A readers, rtcisE2F enhances E2F6/E2F3 mRNA stability. E2F6 and E2F3 are both required for liver TIC self-renewal and Wnt/β-catenin activation, and inhibition of these pathways is a potential strategy for preventing liver tumorigenesis and metastasis. In conclusion, the rtcisE2F-IGF2BP2/YTHDF2-E2F6/E2F3-Wnt/β-catenin axis drives liver TIC self-renewal and initiates liver tumorigenesis and metastasis, and may provide a strategy to eliminate liver TICs.

Serological responses to human virome define clinical outcomes of Italian patients infected with SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic respiratory infectious disease COVID-19. However, clinical manifestations and outcomes differ significantly among COVID-19 patients, ranging from asymptomatic to extremely severe, and it remains unclear what drives these disparities. Here, we studied 159 sequentially enrolled hospitalized patients with COVID-19-associated pneumonia from Brescia, Italy using the VirScan phage-display method to characterize circulating antibodies binding to 96,179 viral peptides encoded by 1,276 strains of human viruses. SARS-CoV-2 infection was associated with a marked increase in immune antibody repertoires against many known pathogenic and non-pathogenic human viruses. This antiviral antibody response was linked to longitudinal trajectories of disease severity and was further confirmed in additional 125 COVID-19 patients from the same geographical region in Northern Italy. By applying a machine-learning-based strategy, a viral exposure signature predictive of COVID-19-related disease severity linked to patient survival was developed and validated. These results provide a basis for understanding the role of memory B-cell repertoire to viral epitopes in COVID-19-related symptoms and suggest that a unique anti-viral antibody repertoire signature may be useful to define COVID-19 clinical severity.

Single-cell biology uncovers apoptotic cell death and its spatial organization as a potential modifier of tumor diversity in HCC

BACKGROUND AND AIMS

HCC is a highly aggressive and heterogeneous cancer type with limited treatment options. Identifying drivers of tumor heterogeneity may lead to better therapeutic options and favorable patient outcomes. We investigated whether apoptotic cell death and its spatial architecture is linked to tumor molecular heterogeneity using single-cell in situ hybridization analysis.

APPROACH AND RESULTS

We analyzed 254 tumor samples from two HCC cohorts using tissue microarrays. We developed a mathematical model to quantify cellular diversity among HCC samples using two tumor markers, cyclin-dependent kinase inhibitor 3 and protein regulator of cytokinesis 1 as surrogates for heterogeneity and caspase 3 (CASP3) as an apoptotic cell death marker. We further explored the impact of potential dying-cell hubs on tumor cell diversity and patient outcome by density contour mapping and spatial proximity analysis. We also developed a selectively controlled in vitro model of cell death using CRISPR/CRISPR-associated 9 to determine therapy response and growth under hypoxic conditions. We found that increasing levels of CASP3⁺ tumor cells are associated with higher tumor diversity. Interestingly, we discovered regions of densely populated CASP3⁺ , which we refer to as CASP3⁺ cell islands, in which the nearby cellular heterogeneity was found to be the greatest compared to cells farther away from these islands and that this phenomenon was associated with survival. Additionally, cell culture experiments revealed that higher levels of cell death, accompanied by increased CASP3 expression, led to greater therapy resistance and growth under hypoxia.

CONCLUSIONS

These results are consistent with the hypothesis that increased apoptotic cell death may lead to greater tumor heterogeneity and thus worse patient outcomes.

Comprehensive characterization of enhancer RNA in hepatocellular carcinoma reveals three immune subtypes with implications for immunotherapy

Hepatocellular carcinoma (HCC) is highly heterogeneous. Molecular subtyping for guiding immunotherapy is warranted. Previous studies have indicated that enhancer RNAs (eRNAs) are involved in tumor heterogeneity and immune infiltration. However, the eRNA landscape and its correlation with immune infiltration in HCC remain unknown. Here we first revealed the genome-wide eRNA landscape in two HCC cohorts. Then we divided individuals with HCC into three immune-related clusters (C1, C2, and C3) based on eRNA expression profiles. The prognosis, biological properties, immune infiltration, clinical features, genomic features, and drug response were analyzed. C1 was enriched in immune infiltration and potentially sensitive to immune checkpoint inhibitors (ICIs). C2 displayed features of immune depletion, high proliferation activity, malignant clinical features, and the worst prognosis. C2 may benefit from targeted therapy. C3 presented moderate immune infiltration, metabolism-related signatures, and the best prognosis. Transarterial chemoembolization (TACE) may be effective for C3. Finally, we constructed a 51-eRNA classifier for subtype prediction and validated its efficacy in The Cancer Genome Atlas (TCGA) cohort and Sun Yat-sen University Cancer Center (SYSUCC) cohort. Our results provide a novel method for immune classification of HCC, shed new light on tumor heterogeneity, and may aid in HCC immunotherapy.

RPNs Levels Are Prognostic and Diagnostic Markers for Hepatocellular Carcinoma

The ribophorin family (RPN) is an essential regulatory subunit of the proteasome. By influencing the ubiquitin-proteasome system activity, ribophorins (RPNs) are responsible for almost all physiology and pathology processes of mammalian cells. Nevertheless, little is known about the role of RPNs in HCC. In this work, we first evaluated the transcriptional levels and the prognostic and diagnostic value of RPNs based on the public database. Firstly, we found all RPNs were surprisingly consistently upregulated in HCC tissues. Moreover, the RPNs' expression pattern is correlated with HCC tumor grade. The TCGA HCC platforms' data indicated that RPN2, RPN3, RPN6, RPN9, RPN10, RPN11, and RPN12 have robust diagnosis values. Then, survival analysis revealed that the high expression of RPN1, RPN2, RPN4, RPN5, RPN6, RPN9, and RPN11 was correlated with unfavourable HCC overall survival. Then, genetic alteration, immune infiltration feature, gene-genes network, and functional enrichment for RPNs indicated that RPNs have many potential biosynthesis activities expert for UPS functions. Moreover, western blot and qRT-PCR results confirmed these results. The silencing of RPN6 and RPN9 significantly reduced HCC cells' proliferation, migration, and invasion ability in vitro. An in vivo tumor model further validated the oncogene effect of RPN6 on HCC cell growth. Moreover, RPN6 and RPN9 could promote cell migratory and invasive potential by affecting the epithelial-mesenchymal transition (EMT) process. In summary, this study suggests that the RPN family has the potential to be potential biomarkers and targets for 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.