Biomarkers in Hepatocellular Carcinoma: Diagnosis, Prognosis and Treatment Response Assessment

PVALB Was Identified as an Independent Prognostic Factor for HCC Closely Related to Immunity, and Its Absence Accelerates Tumor Progression by Regulating NK Cell Infiltration

Purpose

Hepatocellular carcinoma is the most common primary liver cancer, with poor prognosis. Complex immune microenvironment of the liver is linked to the development of HCC. PVALB is a calcium-binding protein which has been described as a cancer suppressor gene in thyroid cancer and glioma. Nevertheless, the role of PVALB in HCC is unknown.

Materials and Methods

We obtained data from TCGA and GSE54236 datasets. MCP-counter, WGCNA and LASSO model were applied to identify PVALB. With UALCAN, MethSurv, and other websites, we probed the expression, methylation and survival of PVALB. LinkedOmics and GSEA were adopted for functional analysis, while TIMER, TISIDB, Kaplan-Meier plotter, TIDE databases were utilized to evaluate the relevance of PVALB to the tumor immune microenvironment and predict immunotherapy efficacy. TargetScan, DIANA, LncRNASNP2 databases and relevant experiments were employed to construct ceRNA network. Finally, molecular docking and drug sensitivity of PVALB were characterized by GeneMANIA, CTD, and so on.

Results

PVALB was recognized as a gene associated with HCC and NK cell. Its expression was down-regulated in HCC tissue, which lead to adverse prognosis. Besides, the hypomethylation of PVALB was related to its reduced expression. Notably, PVALB was tightly linked to immune, and its reduced expression attenuated the anticancer effect of NK cells via the Fas/FasL pathway, leading to a adverse outcome. The lnc-YY1AP1-3/hsa-miR-6735-5p/PVALB axis may regulate the PVALB expression. Finally, we found immunotherapy might be a viable treatment option.

Conclusion

In a word, PVALB is a prognostic indicator, whose low expression facilitates HCC progression by impacting NK cell infiltration.

Understanding Gene Involvement in Hepatocellular Carcinoma: Implications for Gene Therapy and Personalized Medicine

Hepatocellular carcinoma (HCC) is the dominant type of liver cancers and is one of the deadliest health threats globally. The conventional therapeutic options for HCC are hampered by low efficiency and intolerable side effects. Gene therapy, however, now offers hope for the treatment of many disorders previously considered incurable, and gene therapy is beginning to address many of the shortcomings of conventional therapies. Herein, we summarize the involvement of genes in the pathogenesis and prognosis of HCC, with a special focus on dysregulated signaling pathways, genes involved in immune evasion, and non-coding RNAs as novel two-edged players, which collectively offer potential targets for the gene therapy of HCC. Herein, the opportunities and challenges of HCC gene therapy are discussed. These include innovative therapies such as genome editing and cell therapies. Moreover, advanced gene delivery technologies that recruit nanomedicines for use in gene therapy for HCC are highlighted. Finally, suggestions are offered for improved clinical translation and future directions in this area of endeavor.

Impact of Prospero Homeobox-1 (PROX-1) οn the Oncogenic Phenotypes of Hepatocellular Carcinoma Cells

BACKGROUND/AIM

Transcriptional factor prospero homeobox-1 (PROX-1) is crucial for the embryonic development of various organs and cell fate specification. It exhibits either an oncogenic or tumor suppressive activity depending on cancer types. However, the relationship between PROX-1 and hepatocellular carcinoma (HCC) remains obscure. This study was conducted to investigate the effect of PROX-1 on the invasive and oncogenic phenotypes of human HCC cells.

MATERIALS AND METHODS

The effect of PROX-1 on tumor cell behavior was investigated by using a pcDNA-myc vector and a small interfering RNA in HepG2 and Huh7 human HCC cell lines. Flow cytometry, migration, invasion, proliferation, and tube formation assays were performed. PROX-1 expression in human HCC cells was explored by western blotting.

RESULTS

PROX-1 overexpression enhanced tumor cell proliferation and inhibited apoptosis and cell cycle arrest by modulating the activities of caspase-3, PARP, and cyclin-dependent kinase inhibitors, including p21, p27, and p57 in HCC cells. After PROX-1 overexpression, the number of migrating and invading HCC cells significantly increased, and the expression levels of N-cadherin and Snail increased in HCC cells. PROX-1 overexpression enhanced angiogenesis through increased VEGF-A and VEGF-C expression and decreased angiostatin expression. PROX-1 overexpression also increased the phosphorylation of glycogen synthase kinase-3β (GSK-3β) and forkhead box O1 (FOXO1) in HCC cells. After PROX-1 knockdown, their phosphorylation was reversed.

CONCLUSION

PROX-1 overexpression is associated with the invasive and oncogenic phenotypes of human HCC cells via GSK-3β and FOXO1 phosphorylation.

Biomarker in Hepatocellular Carcinoma

Liver cancer is one of the most prevalent types of cancer and a major contributor to the socioeconomic burden worldwide. The pathogenesis of hepatocellular carcinoma (HCC) is contributed by various etiological factors like virus infection, excessive alcohol consumption, exposure to toxins, or metabolic disorders. Majority of patients are diagnosed with late-stage HCC, which restricts its management to only palliative care. HCC, if diagnosed early, increases the survival and quality of life. Currently available biomarker (alpha-fetoproteins) have several limitations, that impede the early diagnosis and staging of cancer. This warrants the continous search in pursuit of a novel biomarker. Several research works in diverse areas have contributed to the identification of various novel biomarkers that have shown multifaceted application in early disease diagnosis, which further aid in targeted and effective therapy that can prevent cancer progression. This improves the overall health status of the patient along with significant reduction in caretaker's burden. With the aid of novel technologies, several biomarkers have been investigated and validated in mutliple preliminary research works. Therefore in this review, we have outlined various novel biomarkers that showed promising outcomes in their trials and we have highlighted the developing areas that act as game changers in cancer diagnosis and management.

Hepatocellular Carcinoma: Current Drug Therapeutic Status, Advances and Challenges

Hepatocellular carcinoma (HCC) is the most common form of liver cancer, accounting for ~90% of liver neoplasms. It is the second leading cause of cancer-related deaths and the seventh most common cancer worldwide. Although there have been rapid developments in the treatment of HCC over the past decade, the incidence and mortality rates of HCC remain a challenge. With the widespread use of the hepatitis B vaccine and antiviral therapy, the etiology of HCC is shifting more toward metabolic-associated steatohepatitis (MASH). Early-stage HCC can be treated with potentially curative strategies such as surgical resection, liver transplantation, and radiofrequency ablation, improving long-term survival. However, most HCC patients, when diagnosed, are already in the intermediate or advanced stages. Molecular targeted therapy, followed by immune checkpoint inhibitor immunotherapy, has been a revolution in HCC systemic treatment. Systemic treatment of HCC especially for patients with compromised liver function is still a challenge due to a significant resistance to immune checkpoint blockade, tumor heterogeneity, lack of oncogenic addiction, and lack of effective predictive and therapeutic biomarkers.

A meta-analysis: laparoscopic versus open liver resection for large hepatocellular carcinoma

BACKGROUND

The indication of laparoscopic liver resection (LLR) for treating large hepatocellular carcinoma (HCC) is controversial. In this study, we compared the short-term and long-term outcomes of LLR and open liver resection (OLR) for large HCC.

MATERIAL AND METHODS

We searched eligible articles about LLR versus OLR for large HCC in PubMed, Cochrane Library, and EMBASE and performed a meta-analysis.

RESULTS

Eight publications involving 1,338 patients were included. Among them, 495 underwent LLR and 843 underwent OLR. The operation time was longer in the LLR group (MD: 22.23, 95% CI: 4.14-40.33, p = 0.02). but the postoperative hospital stay time was significantly shorter (MD : -4.88, CI: -5.55 to -4.23, p < 0.00001), and the incidence of total postoperative complications and major complications were significantly fewer (OR: 0.49, 95% CI:0.37-0.66, p < 0.00001; OR: 0.54, 95% CI:0.36 - 0.82, p = 0.003, respectively). Patients in the laparoscopic group had no significant difference in intraoperative blood loss, intraoperative transfusion rate, resection margin size, R0 resection rate, three-year overall survival (OS) and three-year disease-free survival (DFS).

CONCLUSION

LLR for large HCC is safe and feasible. This surgical strategy will not affect the long-term outcomes of patients.

Bioengineered Organoids Offer New Possibilities for Liver Cancer Studies: A Review of Key Milestones and Challenges

Hepatic cancer is widely regarded as the leading cause of cancer-related mortality worldwide. Despite recent advances in treatment options, the prognosis of liver cancer remains poor. Therefore, there is an urgent need to develop more representative in vitro models of liver cancer for pathophysiology and drug screening studies. Fortunately, an exciting new development for generating liver models in recent years has been the advent of organoid technology. Organoid models hold huge potential as an in vitro research tool because they can recapitulate the spatial architecture of primary liver cancers and maintain the molecular and functional variations of the native tissue counterparts during long-term culture in vitro. This review provides a comprehensive overview and discussion of the establishment and application of liver organoid models in vitro. Bioengineering strategies used to construct organoid models are also discussed. In addition, the clinical potential and other relevant applications of liver organoid models in different functional states are explored. In the end, this review discusses current limitations and future prospects to encourage further development.

Methylated ctDNA Quantification: Noninvasive Approach to Monitoring Hepatocellular Carcinoma Burden

BACKGROUND

Noninvasive, precision monitoring of hepatocellular carcinoma (HCC) treatment efficacy would greatly facilitate personalized therapy and improve patient outcomes. We hypothesize that quantifying methylated circulating tumor DNA (ctDNA) can be used to effectively monitor HCC burden without the need for biopsy.

STUDY DESIGN

Blood samples were collected from 25 patients, 21 with HCC and 4 with benign liver masses, at various timepoints throughout the course of treatment at a high-volume academic medical center. Quantification of methylated ctDNA molecules assessed CpG sites on more than 550 preselected cancer-specific amplicons. The tumor methylation score (TMS) was calculated by measuring the difference between the amount of methylation in the plasma and buffy coat with a normal cutoff value of 120 or less.

RESULTS

Among 10 patients with surgical HCC (5 surgical resections and 5 liver transplants), TMS revealed a statistically significant, rapid postoperative decline in 9. One patient who had a persistently elevated TMS on postoperative day 1 was subsequently found to have had metastatic disease. Patients in the negative control cohort all had normal-range pre- and postoperative TMS. Preoperative TMS correlated moderately with tumor burden on pathology (Spearman r = 0.54) of surgical specimens. From 11 subjects undergoing systemic therapy or Y90 radioembolization, analysis of 16 time periods demonstrated that the change in TMS (ΔTMS) was better associated with tumor progression than the change in Δalpha-fetoprotein (area under the curve 0.800 and 0.783, respectively). A composite score combining ΔTMS and Δalpha-fetoprotein further improved performance for detecting tumor progression with an area under the curve of 0.892.

CONCLUSIONS

These findings indicate that ctDNA methylation scores can effectively evaluate changes in tumor burden without the need for tumor biopsy.

Network pharmacology analysis and experimental verification of the antitumor effect and molecular mechanism of isocryptomerin on HepG2 cells

Isocryptomerin (ISO) is a flavonoid isolated from the natural medicine Selaginellae Herba, which has various pharmacological activities. This study investigated the antitumor effect and underlying molecular mechanism of ISO on hepatocellular carcinoma (HCC) HepG2 cells. The cell viability assay revealed that ISO has a considerable killing effect on HCC cell lines. The apoptosis assay showed that ISO induced mitochondria-dependent apoptosis through the Bad/cyto-c/cleaved (cle)-caspase-3/cleaved (cle)-PARP pathway. The network pharmacological analysis found 13 key target genes, and epidermal growth factor receptor (EGFR), AKT, mitogen-activated protein kinase (MAPK), and reactive oxygen species (ROS) signaling pathways were strongly associated with ISO against HCC. Further verification of the results showed that ISO induced apoptosis by increasing p-p38 and p-JNK expression and decreasing p-EGFR, p-SRC, p-ERK, and p-STAT3 expression. Furthermore, ISO induced G0/G1 phase arrest by downregulating p-AKT, Cyclin D, and CDK 4 expression and upregulating p21 and p27 expression in HepG2 cells. Moreover, ISO inhibited HepG2 cell migration by decreasing p-GSK-3β, β-catenin, and N-cadherin expression and increasing E-cadherin expression. Additionally, ISO promoted ROS accumulation in HepG2 cells, and ISO-induced apoptosis, arrest cell cycle, and inhibition of migration were reversed by an ROS scavenger, N-acetyl- l-cysteine. Overall, ISO induced cell apoptosis and cell cycle arrest and inhibited cell migration by ROS-mediated EGFR, AKT, and MAPK signaling pathways in HepG2 cells.

Early-stage hepatocellular carcinoma screening in patients with chronic hepatitis B in China: a cost-effectiveness analysis

Aim: To evaluate the cost-effectiveness of seven screening strategies for chronic hepatitis B (CHB) patients in China. Methods: A discrete event simulation model combining a decision tree and Markov structure was developed to simulate a CHB cohort aged ≥40 years on a lifetime horizon and evaluate the costs and health outcomes (quality-adjusted life years [QALYs] gained) of ultrasonography (US), alpha-fetoprotein (AFP), protein induced by vitamin K absence-II (PIVKA-II), AFP+US, AFP+PIVKA-II, GAAD (a diagnostic algorithm based on gender and age combined with results of AFP and PIVKA-II) and GAAD+US. Epidemiologic, clinical performance, utility and cost data were obtained from the literature, expert interviews and real-world data. Uncertainties on key parameters were explored through deterministic and probabilistic sensitivity analyses (DSA and PSA). Results: Compared with other strategies, GAAD+US detected the most HCC patients at early stage, and GAAD was the screening strategy with the lowest average cost per HCC case diagnosed. Using 3× China's 2022 GDP per capita ($38,233.34) as the threshold, the three strategies of US, GAAD and GAAD+US formed a cost-effectiveness frontier. Screening with US, GAAD, or GAAD+US was associated with costs of $6110.46, $7622.05 and $8636.32, and QALYs of 13.18, 13.48 and 13.52, respectively. The ICER of GAAD over US was $4993.39/QALY and the ICER of GAAD+US over GAAD was $26,691.45/QALY, which was less than 3× GDP per capita. Both DSA and PSA proved the stability of the results. Conclusion: GAAD+US was the most cost-effective strategy for early HCC diagnosis among CHB patients which could be considered as the liver cancer screening scheme for the high-risk population in China.

Cost-Effectiveness Comparison of Carbon-Ion Radiation Therapy and Transarterial Chemoembolization for Hepatocellular Carcinoma

Purpose

Carbon-ion radiation therapy (CIRT) is a treatment option for patients with hepatocellular carcinoma (HCC) that results in better outcomes with fewer side effects despite its high cost. This study aimed to evaluate the cost-effectiveness of CIRT for HCC from medical and economic perspectives by comparing CIRT and transarterial chemoembolization (TACE) in patients with localized HCC who were ineligible for surgery or radiofrequency ablation.

Methods and Materials

This study included 34 patients with HCC who underwent either CIRT or TACE at Gunma University between 2007 and 2016. Patient characteristics were employed to select each treatment group using the propensity score matching method. Life years were used as the outcome indicator. The CIRT technical fee was ¥3,140,000; however, a second CIRT treatment on the same organ within 2 years was performed for free.

Results

Our study showed that CIRT was dominant over TACE, as the CIRT group had a higher life year (point estimate, 2.75 vs 2.41) and lower total cost (mean, ¥4,974,278 vs ¥5,284,524). We conducted a sensitivity analysis to validate the results because of the higher variance in medical costs in the TACE group, which demonstrated that CIRT maintained its cost effectiveness with a high acceptability rate.

Conclusions

CIRT is a cost-effective treatment option for localized HCC cases unsuitable for surgical resection.

Optimizing hepatocellular carcinoma disease staging systems by incorporating tumor micronecrosis: A multi-institutional retrospective study

Tumor micronecrosis is a pathological feature that reflects malignant biological behavior in hepatocellular carcinoma (HCC). However, whether micronecrosis can optimize HCC staging systems remains unilluminated. A total of 1632 HCC patients who underwent curative hepatectomy in four institutions from January 2014 to December 2021 were enrolled in this study. Independent prognostic factors were identified, and optimized staging models were established using a training cohort (n = 934). The performance of optimized staging models was validated using an external cohort consisting of cases from three other institutions (n = 232). In addition, patients from our prospectively collected database (n = 379) tested the application effectiveness of the models. Harrel's c-statistics and the corrected Akaike information criterion (AICc) were used to assess the performance of staging models. In most of Barcelona Clinic Liver Cancer (BCLC) and tumor (T) stages, HCC patients with tumor micronecrosis showed poorer prognosis than those without. Tumor micronecrosis, microvascular invasion, multiple tumors and tumor size >2 cm were independent prognostic-related factors. The BCLC and T staging models incorporating tumor micronecrosis showed better performance than the original systems (c-statistic, 0.712 and 0.711 vs. 0.664 and 0.679; AICc, 2314.8 and 2322.3 vs. 2338.2 and 2338.1; respectively). Furthermore, the external validation cohort confirmed that the optimized staging models had improved efficiency compared with the original ones. Moreover, the prospective cohort demonstrated the applicability of the optimized staging systems. Tumor micronecrosis plays a stage-ascending role in HCC patients. The BCLC and T staging systems incorporating tumor micronecrosis can improve the prognosis stratification efficiency of patients.

Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients

Background

Liver hepatocellular carcinoma (LIHC) is a solid primary malignancy with poor prognosis. This study discovered key prognostic genes based on T cell exhaustion and used them to develop a prognostic prediction model for LIHC.

Methods

SingleR's annotations combined with Seurat was used to automatically annotate the single-cell clustering results of the LIHC dataset GSE166635 downloaded from the Gene Expression Omnibus (GEO) database and to identify clusters related to exhausted T cells. Patients were classified using ConsensusClusterPlus package. Next, weighted gene co-expression network analysis (WGCNA) package was employed to distinguish key gene module, based on which least absolute shrinkage and selection operator (Lasso) and multi/univariate cox analysis were performed to construct a RiskScore system. Kaplan-Meier (KM) analysis and receiver operating characteristic curve (ROC) were employed to evaluate the efficacy of the model. To further optimize the risk model, a nomogram capable of predicting immune infiltration and immunotherapy sensitivity in different risk groups was developed. Expressions of genes were measured by quantitative real-time polymerase chain reaction (qRT-PCR), and immunofluorescence and Cell Counting Kit-8 (CCK-8) were performed for analyzing cell functions.

Results

We obtained 18,413 cells and clustered them into 7 immune and non-immune cell subpopulations. Based on highly variable genes among T cell exhaustion clusters, 3 molecular subtypes (C1, C2 and C3) of LIHC were defined, with C3 subtype showing the highest score of exhausted T cells and a poor prognosis. The Lasso and multivariate cox analysis selected 7 risk genes from the green module, which were closely associated with the C3 subtype. All the patients were divided into low- and high-risk groups based on the medium value of RiskScore, and we found that high-risk patients had higher immune infiltration and immune escape and poorer prognosis. The nomogram exhibited a strong performance for predicting long-term LIHC prognosis. In vitro experiments revealed that the 7 risk genes all had a higher expression in HCC cells, and that both liver HCC cell numbers and cell viability were reduced by knocking down MMP-9.

Conclusion

We developed a RiskScore model for predicting LIHC prognosis based on the scRNA-seq and RNA-seq data. The RiskScore as an independent prognostic factor could improve the clinical treatment for LIHC patients.

Establishment of a prognosis predictive model for liver cancer based on expression of genes involved in the ubiquitin-proteasome pathway

BACKGROUND

The ubiquitin-proteasome pathway (UPP) has been proven to play important roles in cancer.

AIM

To investigate the prognostic significance of genes involved in the UPP and develop a predictive model for liver cancer based on the expression of these genes.

METHODS

In this study, UPP-related E1, E2, E3, deubiquitylating enzyme, and proteasome gene sets were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, aiming to screen the prognostic genes using univariate and multivariate regression analysis and develop a prognosis predictive model based on the Cancer Genome Atlas liver cancer cases.

RESULTS

Five genes (including autophagy related 10, proteasome 20S subunit alpha 8, proteasome 20S subunit beta 2, ubiquitin specific peptidase 17 like family member 2, and ubiquitin specific peptidase 8) were proven significantly correlated with prognosis and used to develop a prognosis predictive model for liver cancer. Among training, validation, and Gene Expression Omnibus sets, the overall survival differed significantly between the high-risk and low-risk groups. The expression of the five genes was significantly associated with immunocyte infiltration, tumor stage, and postoperative recurrence. A total of 111 differentially expressed genes (DEGs) were identified between the high-risk and low-risk groups and they were enriched in 20 and 5 gene ontology and KEGG pathways. Cell division cycle 20, Kelch repeat and BTB domain containing 11, and DDB1 and CUL4 associated factor 4 like 2 were the DEGs in the E3 gene set that correlated with survival.

CONCLUSION

We have constructed a prognosis predictive model in patients with liver cancer, which contains five genes that associate with immunocyte infiltration, tumor stage, and postoperative recurrence.

Mechanisms and therapeutic prospect of the JAK-STAT signaling pathway in liver cancer

Liver cancer (LC) poses a significant global health challenge due to its high incidence and poor prognosis. Current systemic treatment options, such as surgery, chemotherapy, radiofrequency ablation, and immunotherapy, have shown limited effectiveness for advanced LC patients. Moreover, owing to the heterogeneous nature of LC, it is crucial to uncover more in-depth pathogenic mechanisms and develop effective treatments to address the limitations of the existing therapeutic modalities. Increasing evidence has revealed the crucial role of the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway in the pathogenesis of LC. The specific mechanisms driving the JAK-STAT pathway activation in LC, participate in a variety of malignant biological processes, including cell differentiation, evasion, anti-apoptosis, immune escape, and treatment resistance. Both preclinical and clinical investigations on the JAK-STAT pathway inhibitors have exhibited potential in LC treatment, thereby opening up avenues for the development of more targeted therapeutic strategies for LC. In this study, we provide an overview of the JAK-STAT pathway, delving into the composition, activation, and dynamic interplay within the pathway. Additionally, we focus on the molecular mechanisms driving the aberrant activation of the JAK-STAT pathway in LC. Furthermore, we summarize the latest advancements in targeting the JAK-STAT pathway for LC treatment. The insights presented in this review aim to underscore the necessity of research into the JAK-STAT signaling pathway as a promising avenue for LC therapy.

Developing risk models and subtypes of autophagy-associated LncRNAs for enhanced prognostic prediction and precision in therapeutic approaches for liver cancer patients

Background

Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC).

Methods

We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using consensus clustering to identify subgroups related to ARLncRNAs.

Results

The screening process identified 27 ARLncRNAs, with 13 being associated with HCC prognosis. Consequently, a set of signatures comprising 8 ARLncRNAs was successfully constructed as independent prognostic factors for HCC. Patients in the high-risk group showed very poor prognoses in most clinical categories. The Riskscore was closely related to immune cell scores, such as macrophages, and the DEGs between different groups were implicated in metabolism, cell cycle, and mitotic processes. Notably, high-risk group patients demonstrated a significantly lower IC50 for Paclitaxel, suggesting that Paclitaxel could be an ideal treatment for those at elevated risk for HCC. We further identified C2 as the Paclitaxel subtype, where patients exhibited higher Riskscores, reduced survival rates, and more severe clinical progression.

Conclusion

The 8 signatures based on ARLncRNAs present novel targets for prognostic prediction in HCC. The drug candidate Paclitaxel may effectively treat HCC by impacting ARLncRNAs expression. With the identification of ARLncRNAs-related isoforms, these results provide valuable insights for clinical exploration of autophagy mechanisms in HCC pathogenesis and offer potential avenues for precision medicine.

The chemokine CCL20 can assist AFP in serological diagnosis of hepatocellular carcinoma

The chemokine 20 (CCL20) is a member of the CC chemokine family and plays a role in tumor immunity and autoimmune disease. This work investigated the value of CCL20 as a serum diagnostic marker for primary hepatocellular carcinoma (HCC). Based on the data of hepatocellular carcinoma patients in the TCGA database, the up-regulated genes encoding secretory proteins were analyzed in each pathological stage, and the candidate marker CCL20 gene was selected. Serum concentrations of CCL20 in patients with primary HCC, benign liver disease, and healthy subjects were analyzed by enzyme-linked immunosorbent assay (ELISA). The ROC curve evaluated the efficacy of CCL20 alone or in combination with AFP in the diagnosis of HCC. It was found the expression of CCL20 in HCC patients was significantly higher than that in the benign liver disease group and healthy controls (P < 0.05); The AUC of ROC curve to distinguish HCC patients from healthy controls was 0.859, the sensitivity was 73.42%, and the specificity was 86.84%. After combination with AFP, the AUC increased to 0.968, the sensitivity was 88.16%, and the specificity was 97.37%. Although CCL20 was increased in the serum of patients with benign liver diseases, combined with AFP, the AUC to distinguish HCC patients from non-HCC cohorts (benign liver disease group and healthy control group) was 0.902, with a sensitivity of 91.67% and a specificity of 75.26%. Collectively, serum CCL20 is closely related to the occurrence of HCC, and detection of serum CCL20 can assist AFP in improving the diagnostic sensitivity of HCC.

PGK1 is a potential biomarker for early diagnosis and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a common type of liver cancer, is increasing in incidence worldwide. An early diagnosis of hepatocellular carcinoma (HCC) is still challenging: Currently, few biomarkers are available to diagnose the early stage of HCC, therefore, additional prognostic biomarkers are required to identify potential risk factors. The present study analyzed gene expression levels of HCC tissue samples and the protein expression levels obtained from the GSE46408 HCC dataset using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The metabolically associated differentially expressed genes (DEGs), including DEGs involved in the glucose metabolism pathway, were selected for further analysis. Phosphoglycerate kinase 1 (PGK1), a glycolytic enzyme, was determined as a potential prognostic biomarker through Kaplan-Meier curve and clinical association variable analyses. This was also verified based on the expression levels of PGK1 in tumor tissue and protein expression levels in several liver cancer cell lines. PGK1 mRNA demonstrated a high level of expression in HCC tissue and was significantly associated with a poor prognosis, showing a negative association with survival time. In addition, as an independent risk factor, PGK1 may potentially be a valuable prognostic biomarker for patients with HCC. Furthermore, expression of PGK1 was associated with the early stages (stage I and T1) of HCC. Moreover, PGK1 mRNA expression levels demonstrated a positive association with progression of liver cancer. The results suggested that PGK1 mRNA may be involved in the degree of HCC malignancy and may be a future potential prognostic biomarker for HCC progression.

Blockade of PD-1 and CTLA-4: A potent immunotherapeutic approach for hepatocellular carcinoma

Immune checkpoints (ICPs) can promote tumor growth and prevent immunity-induced cancer cell apoptosis. Fortunately, targeting ICPs, such as programmed cell death 1 (PD-1) or cytotoxic T lymphocyte associated protein 4 (CTLA-4), has achieved great success in the past few years and has gradually become an effective treatment for cancers, including hepatocellular carcinoma (HCC). However, many patients do not respond to ICP therapy due to acquired resistance and recurrence. Therefore, clarifying the specific mechanisms of ICP in the development of HCC is very important for enhancing the efficacy of anti-PD-1 and anti-CTLA-4 therapy. In particular, antigen presentation and interferon-γ (IFN-γ) signaling were reported to be involved in the development of resistance. In this review, we have explained the role and regulatory mechanisms of ICP therapy in HCC pathology. Moreover, we have also elaborated on combinations of ICP inhibitors and other treatments to enhance the antitumor effect. Collectively, recent advances in the pharmacological targeting of ICPs provide insights for the development of a novel alternative treatment for HCC.

Biomarker discovery and validation for gastrointestinal tumors: A comprehensive review of colorectal, gastric, and liver cancers

Gastrointestinal (GI) malignancies, encompassing gastric, hepatic, colonic, and rectal cancers, are prevalent forms of cancer globally and contribute substantially to cancer-related mortality. Although there have been improvements in methods for diagnosing and treating GI cancers, the chances of survival for these types of cancers are still extremely low. According to the World Cancer Research International Fund's most recent figures, stomach cancer was responsible for roughly one million deaths worldwide in 2020. This emphasizes the importance of developing more effective tools for detecting, diagnosing, and predicting the outcome of these cancers at an early stage. Biomarkers, quantitative indications of biological processes or disease states, have emerged as promising techniques for enhancing the diagnosis and prognosis of GI malignancies. Recently, there has been a considerable endeavor to discover and authenticate biomarkers for various GI cancers by the utilization of diverse methodologies, including genomics, proteomics, and metabolomics. This review provides a thorough examination of the current state of biomarker research in the field of gastrointestinal malignancies, with a specific emphasis on colorectal, stomach, and liver cancers. A thorough literature search was performed on prominent databases such as PubMed, Scopus, and Web of Science to find pertinent papers published until November, 2023 for the purpose of compiling this review. The diverse categories of biomarkers, encompassing genetic, epigenetic, and protein-based biomarkers, and their potential utility in the fields of diagnosis, prognosis, and treatment selection, are explored. Recent progress in identifying and confirming biomarkers, as well as the obstacles that persist in employing biomarkers in clinical settings are emphasized. The utilization of biomarkers in GI cancers has significant potential in enhancing patient outcomes. Ongoing research is expected to uncover more efficient biomarkers for the diagnosis and prognosis of these cancers.

A Facile Strategy for Multiplex Protein Detection by a Fluorescent Microsphere-Based Digital Immunoassay

The digital immunoassay is a highly sensitive detection technique based on single-molecule counting and is widely used in the ultrasensitive detection of biomarkers. Herein, we developed a fluorescent microsphere-based digital immunoassay (FMDIA) by employing fluorescent microspheres as both the carriers for immunoreaction and fluorescent reports for imaging. In this approach, the target protein in the sample was captured by fluorescent microspheres to form a biotin-labeled sandwich immunocomplex, and then, the fluorescent microspheres containing the target protein molecules were captured by adding streptavidin-coated magnetic beads (SA-MBs). By counting the proportion of fluorescence-positive magnetic beads, the concentration of the target protein can be precisely quantified. As a proof of concept, α fetoprotein (AFP) and human interleukin-6 (IL-6) were used to assess the analytical performance of the proposed FMDIA, and limit of detection (LOD) values of 21 pg/mL (0.30 pM) and 0.19 pg/mL (7.3 fM) were achieved, respectively. The results of AFP detection in serum samples of patients and healthy people were consistent with the reference values given by the hospital. Furthermore, by adding fluorescent microspheres of various colors for encoding, the proposed FMDIA can easily realize the simultaneous detection of multiple proteins without the need to introduce multiple modified magnetic beads. This multiplex protein detection strategy, in which the reactions are first carried out on the fluorescent microspheres and then magnetic beads are used to capture the fluorescent reporters containing the target molecules, provides a new idea for digital assays.

Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence

BACKGROUND

Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed.

METHODS

In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered.

RESULTS

Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF).

CONCLUSIONS

The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.

68Ga-Labeled TMTP1 Modified with d-Amino Acid for Positron Emission Tomography Diagnosis of Highly Metastatic Hepatocellular Carcinoma

TMTP1 (NVVRQ) has been proven to selectively target various highly metastatic tumor cells. Nonetheless, existing TMTP1 probes encounter challenges such as rapid blood clearance, limited tumor uptake, and inadequate suitability for therapeutic interventions. To overcome these constraints, we designed and synthesized eight peptide probes, employing innovative chemical modification strategies involving d-amino acid modification and retro-inverso isomerization. Notably, [68Ga]TV2 exhibited particularly impressive performance, displaying an 88.88, 76.90, and 90.32% improvement in uptake at 15, 30, and 60 min, respectively, while maintaining a high target-to-nontarget ratio. Further research has demonstrated that [68Ga]TV2 also exhibits remarkable diagnostic potential for detecting in situ microtumors in the liver. The results suggest that through the implementation of innovative chemical modification strategies, we successfully developed a peptide precursor, NOTA-G-NVvRQ, with specific affinity for highly metastatic tumors, enhanced in vivo pharmacokinetic profile, and heightened stability in vivo, rendering it well suited for prospective investigations in combination therapy studies.

Application of Machine Learning Techniques to Assess Alpha-Fetoprotein at Diagnosis of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver tumor and is associated with high mortality rates. Approximately 80% of cases occur in cirrhotic livers, posing a significant challenge for appropriate therapeutic management. Adequate screening programs in high-risk groups are essential for early-stage detection. The extent of extrahepatic tumor spread and hepatic functional reserve are recognized as two of the most influential prognostic factors. In this retrospective multicenter study, we utilized machine learning (ML) methods to analyze predictors of mortality at the time of diagnosis in a total of 208 patients. The eXtreme gradient boosting (XGB) method achieved the highest values in identifying key prognostic factors for HCC at diagnosis. The etiology of HCC was found to be the variable most strongly associated with a poorer prognosis. The widely used Barcelona Clinic Liver Cancer (BCLC) classification in our setting demonstrated superiority over the TNM classification. Although alpha-fetoprotein (AFP) remains the most commonly used biological marker, elevated levels did not correlate with reduced survival. Our findings suggest the need to explore new prognostic biomarkers for individualized management of these patients.

APOB is a potential prognostic biomarker in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is significantly associated with adverse prognostic outcomes. The development and progression of different types of human tumors are significantly influenced by APOB. Nevertheless, the significance and pathomechanisms of APOB in HCC have not been conclusively determined. We assessed APOB expression levels in HCC using three publicly available databases of TIMER2.0, UALCAN and Human Protein Atlas. To identify the biological function of APOB, we conducted enrichment analysis via LinkedOmics. Moreover, UALCAN was employed to assess the relationship between APOB expression and clinicopathological features among HCC patients. Additionally, the Kaplan-Meier plotter was utilized to investigate the prognostic relevance of APOB in HCC. To explore potential regulatory ncRNAs that could bind to APOB, we utilized StarBase and GEPIA. Furthermore, the correlation between APOB expression and immune cell infiltration, as well as immune checkpoint genes, was investigated using Spearman's correlation analysis in TISIDB, GEPIA, and TIMER2.0. The findings of our investigation showed a notable decrease in the expression levels of APOB among individuals diagnosed with HCC. Moreover, a noteworthy correlation was observed between the expression of APOB and immune checkpoint genes, alongside the occurrence of immune cell infiltration. The levels of APOB expression in HCC tissues also showed correlations with various clinicopathological features. According to Cox regression analysis, decreased APOB expression emerged as a potential autonomous predictor for OS, RFS, DSS, and PFS among HCC patients. Furthermore, we identified six potential pathways associated with non-coding RNA (ncRNA) as the most promising pathway for APOB in HCC. Our results illuminate the possible involvement of APOB in HCC and offer understanding into its governing mechanisms and medical importance.

Targeting ALK averts ribonuclease 1-induced immunosuppression and enhances antitumor immunity in hepatocellular carcinoma

Tumor-secreted factors contribute to the development of a microenvironment that facilitates the escape of cancer cells from immunotherapy. In this study, we conduct a retrospective comparison of the proteins secreted by hepatocellular carcinoma (HCC) cells in responders and non-responders among a cohort of ten patients who received Nivolumab (anti-PD-1 antibody). Our findings indicate that non-responders have a high abundance of secreted RNase1, which is associated with a poor prognosis in various cancer types. Furthermore, mice implanted with HCC cells that overexpress RNase1 exhibit immunosuppressive tumor microenvironments and diminished response to anti-PD-1 therapy. RNase1 induces the polarization of macrophages towards a tumor growth-promoting phenotype through activation of the anaplastic lymphoma kinase (ALK) signaling pathway. Targeting the RNase1/ALK axis reprograms the macrophage polarization, with increased CD8+ T- and Th1- cell recruitment. Moreover, simultaneous targeting of the checkpoint protein PD-1 unleashes cytotoxic CD8+ T-cell responses. Treatment utilizing both an ALK inhibitor and an anti-PD-1 antibody exhibits enhanced tumor regression and facilitates long-term immunity. Our study elucidates the role of RNase1 in mediating tumor resistance to immunotherapy and reveals an RNase1-mediated immunosuppressive tumor microenvironment, highlighting the potential of targeting RNase1 as a promising strategy for cancer immunotherapy in HCC.

Knockdown of liver cancer cell-secreted exosomal PSMA5 controls macrophage polarization to restrain cancer progression by blocking JAK2/STAT3 signaling

INTRODUCTION

Tumor-associated macrophages, a major component of the tumor microenvironment, undergo polarization into M2 macrophages (M2), and thereby exert an immunosuppressive effect to induce cancer metastasis. This study strives to uncover a molecular mechanism underlying this event in hepatocellular carcinoma (HCC).

METHODS

Proteasome subunit alpha 5 (PSMA5) expression in liver hepatocellular carcinoma (LIHC) tissues and its association with LIHC patients were predicted using StarBase. PSMA5 level in human HCC cells was manipulated via transfection. Exosomes were isolated from HCC cells, and internalized into macrophages which were cocultured with HCC cells. Exosome internalization was observed after fluorescence labeling. HCC cell migration and invasion were evaluated by wound healing and Transwell assays. Xenograft assay was performed to investigate the role of PSMA5 in in vitro tumorigenesis. M2 polarization was assessed by enzyme-linked immunosorbent assay, quantitative reverse transcription polymerase chain reaction, and immunohistochemistry. PSMA5 expression in exosomes and Janus Kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) activation in macrophages and tumors were detected by Western blot analysis.

RESULTS

High PSMA5 expression was observed in LIHC tissues and associated with compromised survival of LIHC patients. PSMA5 knockdown inhibited HCC cell migration and invasion. PSMA5 knockdown in HCC cells downregulated PSMA5 level in exosomes from these HCC cells. HCC cell-isolated exosomes were successfully internalized into macrophages, and facilitated M2 polarization and JAK2/STAT3 pathway activation. HCC cell-secreted exosomal PSMA5 knockdown inhibited the exosome-induced effect on macrophages, and attenuated the promotion induced by exosome-treated macrophages on HCC cell migration/invasion and tumorigenesis along with in vivo M2 polarization and JAK2/STAT3 pathway activation.

CONCLUSION

HCC cell-secreted exosomal PSMA5 knockdown hinders M2 polarization to suppress cancer progression by restraining JAK2/STAT3 signaling.

Prediagnostic plasma metabolite concentrations and liver cancer risk: a population-based study of Chinese men

BACKGROUND

Previous metabolic profiling of liver cancer has mostly used untargeted metabolomic approaches and was unable to quantitate the absolute concentrations of metabolites. In this study, we examined the association between the concentrations of 186 targeted metabolites and liver cancer risk using prediagnostic plasma samples collected up to 14 years prior to the clinical diagnosis of liver cancer.

METHODS

We conducted a nested case-control study (n = 322 liver cancer cases, n = 322 matched controls) within the Shanghai Men's Health Study. Conditional logistic regression models adjusted for demographics, lifestyle factors, dietary habits, and related medical histories were used to estimate the odds ratios. Restricted cubic spline functions were used to characterise the dose-response relationships between metabolite concentrations and liver cancer risk.

FINDINGS

After adjusting for potential confounders and correcting for multiple testing, 28 metabolites were associated with liver cancer risk. Significant non-linear relationships were observed for 22 metabolites. The primary bile acid biosynthesis and phenylalanine, tyrosine and tryptophan biosynthesis were found to be important pathways involved in the aetiology of liver cancer. A metabolic score consisting of 10 metabolites significantly improved the predictive ability of traditional epidemiological risk factors for liver cancer, with an optimism-corrected AUC increased from 0.84 (95% CI: 0.81-0.87) to 0.89 (95% CI: 0.86-0.91).

INTERPRETATION

This study characterised the dose-response relationships between metabolites and liver cancer risk, providing insights into the complex metabolic perturbations prior to the clinical diagnosis of liver cancer. The metabolic score may serve as a candidate risk predictor for liver cancer.

FUNDING

National Key Project of Research and Development Program of China [2021YFC2500404, 2021YFC2500405]; US National Institutes of Health [subcontract of UM1 CA173640].

The role of competing endogenous RNA network in the development of hepatocellular carcinoma: potential therapeutic targets

The current situation of hepatocellular carcinoma (HCC) management is challenging due to its high incidence, mortality, recurrence and metastasis. Recent advances in gene genetic and expression regulation have unveiled the significant role of non-coding RNA (ncRNA) in various cancers. This led to the formulation of the competing endogenous RNA (ceRNA) hypothesis, which posits that both coding RNA and ncRNA, containing miRNA response elements (MRE), can share the same miRNA sequence. This results in a competitive network between ncRNAs, such as lncRNA and mRNA, allowing them to regulate each other. Extensive research has highlighted the crucial role of the ceRNA network in HCC development, impacting various cellular processes including proliferation, metastasis, cell death, angiogenesis, tumor microenvironment, organismal immunity, and chemotherapy resistance. Additionally, the ceRNA network, mediated by lncRNA or circRNA, offers potential in early diagnosis and prevention of HCC. Consequently, ceRNAs are emerging as therapeutic targets for HCC. The complexity of these gene networks aligns with the multi-target approach of traditional Chinese medicine (TCM), presenting a novel perspective for TCM in combating HCC. Research is beginning to show that TCM compounds and prescriptions can affect HCC progression through the ceRNA network, inhibiting proliferation and metastasis, and inducing apoptosis. Currently, the lncRNAs TUG1, NEAT1, and CCAT1, along with their associated ceRNA networks, are among the most promising ncRNAs for HCC research. However, this field is still in its infancy, necessitating advanced technology and extensive basic research to fully understand the ceRNA network mechanisms of TCM in HCC treatment.

Real-time monitoring of glucose metabolism and effects of metformin on HepG2 cells using 13C in-cell NMR spectroscopy

Metformin is currently a strong candidate antitumor agent for multiple cancers, and has the potential to inhibit cancer cell viability, growth, and proliferation. Metabolic reprogramming is a critical feature of cancer cells. However, the effects of metformin which targets glucose metabolism on HepG2 cancer cells remain unclear. In this study, to explore the effects of metformin on glucose metabolism in HepG2 cells, we conducted real-time metabolomic monitoring of live HepG2 cells treated with metformin using 13C in-cell NMR spectroscopy. Metabolic tracing with U-13C6-glucose revealed that metformin significantly increased the production of 13C-G3P and 13C-glycerol, which were reported to attenuate liver cancer development, but decreased the production of potential oncogenesis-supportive metabolites, including 13C-lactate, 13C-alanine, 13C-glycine, and 13C-glutamate. Moreover, the expression levels of enzymes associated with the measured metabolites were carried out. The results showed that the levels of ALT1, MCT4, GPD2 and MPC1 were greatly reduced, which were consistent with the changes of measured metabolites in 13C in-cell NMR spectroscopy. Overall, our approach directly provides fundamental insights into the effects of metformin on glucose metabolism in live HepG2 cells, and highlights the potential mechanism of metformin, including the increase in production of G3P and glycerol derived from glucose, as well as the inhibition of glucose incorporation into lactate, alanine, glutamate, and glycine.

Novel prognostic factors after radical resection of hepatocellular carcinoma: Updating an old issue

In this editorial, I comment on the article by Li et al published in the recent issue of the World Journal of Gastrointestinal Surgery in 2023, investigating the role of some novel prognostic factors for early survival after radical resection of liver cancer. Liver cancer is an important burden among Asian and Western populations, despite recent advances in both medicine (from virus eradication to systemic target therapies) and surgery. However, survival after proven radical surgery remains poor, with recurrences being the rule. Many prognostic scores have been developed and validated to select those patients who will best benefit from radical liver surgery, although the final general and oncological outcomes continue to be highly jeopardized. Unfortunately, no single biomarker can resolve all these issues for hepatocellular carcinoma, and it remains to be proven whether some of them maintain predictive power in the long-term follow-up. In the ongoing era of "precision" medicine, the novel prognostic markers, including immune inflammatory and nutritional indexes could be of great help in better stratify surgical candidates.

Midkine (MDK) in Hepatocellular Carcinoma: More than a Biomarker

Midkine (MDK) is a multifunctional secreted protein that can act as a cytokine or growth factor regulating multiple signaling pathways and being implicated in fundamental cellular processes, such as survival, proliferation, and migration. Although its expression in normal adult tissues is barely detectable, MDK serum levels are found to be elevated in several types of cancer, including hepatocellular carcinoma (HCC). In this review, we summarize the findings of recent studies on the role of MDK in HCC diagnosis and progression. Overall, studies show that MDK is a powerful biomarker for HCC early diagnosis, as it can differentiate not only between HCC patients and normal individuals but also between HCC patients and patients with other liver pathologies. It is correlated with high recurrence rates and was shown to be valuable for the diagnosis of early-stage HCC, even in patients negative for α-fetoprotein (AFP), the most commonly used biomarker for HCC diagnosis. A comparison with AFP reveals that MDK is inferior to AFP with regard to specificity but significantly superior with regard to sensitivity, which further indicates the need for using both biomarkers for more effective HCC diagnosis.

γδ T cells: Major advances in basic and clinical research in tumor immunotherapy

ABSTRACT

γδ T cells are a kind of innate immune T cell. They have not attracted sufficient attention because they account for only a small proportion of all immune cells, and many basic factors related to these cells remain unclear. However, in recent years, with the rapid development of tumor immunotherapy, γδ T cells have attracted increasing attention because of their ability to exert cytotoxic effects on most tumor cells without major histocompatibility complex (MHC) restriction. An increasing number of basic studies have focused on the development, antigen recognition, activation, and antitumor immune response of γδ T cells. Additionally, γδ T cell-based immunotherapeutic strategies are being developed, and the number of clinical trials investigating such strategies is increasing. This review mainly summarizes the progress of basic research and the clinical application of γδ T cells in tumor immunotherapy to provide a theoretical basis for further the development of γδ T cell-based strategies in the future.

LINC00665: A Promising Biomarker in Gastrointestinal Tumors

An increasing volume of studies has reported that long non-codingRNAs (lncRNAs) are involved in the carcinogenesis of many different cancers. Especially in gastrointestinal tumors, lncRNAs are found to participate in various physiological and pathological processes. LncRNAs can regulate gene expression at multiple levels, including transcriptional, post-transcription, translational, and post-translational levels. Long intergenic non-protein coding RNA 665(LINC00665), a novel cancer-related lncRNA, is frequently dysregulated in multiple gastrointestinal tumors, including gastric and colorectal cancers, hepatocellular carcinoma, and so on. In this review, we analyzed the expression and prognostic value of LINC00665 in human gastrointestinal tumors, systematically summarized the current literature about the clinical significance of this lncRNA, and explored the regulatory mechanisms of LINC00665 as a competing endogenous RNA (ceRNA) in tumor progression. Consequently, we concluded that LINC00665 might act as a prognostic biomarker and a potential target for gastrointestinal tumor diagnosis and treatment.

Unleashing the potential of gene signatures as prognostic and predictive tools: A step closer to personalized medicine in hepatocellular carcinoma (HCC)

Hepatocellular carcinoma (HCC) is one of the growing malignancies globally, affecting a myriad of people and causing numerous cancer-related deaths. Despite therapeutic improvements in treatment strategies over the past decades, HCC still remains one of the leading causes of person-years of life lost. Numerous studies have been conducted to assess the characteristics of HCC with the aim of predicting its prognosis and responsiveness to treatment. However, the identified biomarkers have shown limited sensitivity, and the translation of these findings into clinical practice has faced challenges. The development of sequencing techniques has facilitated the exploration of a wide range of genes, leading to the emergence of gene signatures. Although several studies assessed differentially expressed genes in normal and HCC tissues to find the unique gene signature with prognostic value, to date, no study has reviewed the task, and to the best of our knowledge, this review represents the first comprehensive analysis of relevant studies in HCC. Most gene signatures focused on immune-related genes, while others investigated genes related to metabolism, autophagy, and apoptosis. Even though no identical gene signatures were found, NDRG1, SPP1, BIRC5, and NR0B1 were the most extensively studied genes with prognostic value. Finally, despite challenges such as the lack of consistent patterns in gene signatures, we believe that comprehensive analysis of pertinent gene signatures will bring us a step closer to personalized medicine in HCC, where treatment strategies can be tailored to individual patients based on their unique molecular profiles.

Identification of metabolism terms significantly affecting hepatocellular carcinoma immune microenvironment and immunotherapy response

Metabolic pathways exert a significant influence on the onset and progression of cancer. Public data on hepatocellular carcinoma (HCC) patients were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Analysis was performed in R software using different R packages. Here, we integrated the data from multiple independent HCC cohorts, including TCGA-LIHC, ICGC-FR and ICGC-JP. Then, the enrichment score of 21 metabolism-related pathways was quantified using the ssGSEA algorithm. Next, univariate Cox regression analysis was applied to identify the metabolic terms with significant correlation to patient survival. Finally, a prognosis model based on linoleic acid metabolism, sphingolipid metabolism and regulation of lipolysis in adipocytes was established, which showed good performance in predicting patients' survival. Furthermore, we conducted a biological enrichment analysis to delineate the biological disparities between high- and low-risk patients. Notably, we discerned differences in the microenvironments between these two patient groups. We also found that low-risk patients could potentially respond better to immunotherapy. Drug sensitivity analysis suggested that low-risk patients are more susceptible to bexarotene and erlotinib, yet exhibit resistance to ATRA and bleomycin. Furthermore, through the use of LASSO logistic regression analysis, we identified 19 characteristic genes, which could robustly indicate the risk groups. Our research underscores the role of linoleic acid metabolism, sphingolipid metabolism and the regulation of lipolysis in adipocytes in HCC, pointing towards potential avenues for future research.

Identification of secretory pathway-related genes based on Random Forest algorithm to predict the prognosis and immunotherapy response of hepatocellular carcinoma

BACKGROUND

The dysfunction of secretory pathways may represent biomarkers or therapeutic targets of cancer. The hepatocellular carcinoma (HCC) phenotype was studied in relation to the genes in the secretory pathway and to screen for a combination of genes that may be a viable therapeutic target for HCC and connected to the pathophysiological features of the tumor.

METHODS

Using the HCC information from The Cancer Genome Atlas, somatic mutation and prognostic association analysis were performed on the secretory pathway genes. Based on prognostic genes in the secretory pathway, the samples were consensus clustered, and a Random Forest model was built. The clinical characteristics, tumor mutation burden, functional status and potential responses to immunotherapy and tumor suppressor medications of various subtypes and risk groups were discussed.

RESULTS

Of the 84 genes for secretory pathway, 32 were prognostic genes related to HCC, which divided HCC into two categories: C1 and C2. By comparing the two types of HCC samples, it was found that the survival outcome of C1 was inferior, with stronger adaptive and innate immunity, but less sensitive to immunotherapy than C2. The constructed prognostic signature included seven of the 32 prognostic genes in the secretory pathway, which showed significant correlation with the prognosis, somatic mutation, biological pathway status, potential response to immunotherapy and sensitivity of 72 tumor suppressor drugs from different HCC cohorts, and had a feasible prognostic effect for 31 types of cancer and immunotherapy cohorts.

CONCLUSIONS

In this study, HCC was divided into two molecular subtypes according to prognostic genes in the secretory pathway, and seven of them were combined into one signature, which produced significant results in evaluating the prognosis of different HCC cohorts, pan-cancer cohorts and immunotherapy cohorts, and had potential guiding significance for prophylactic immunotherapy in patients with HCC.

Detection of Circulating Cell-free DNA to Diagnose Hepatocellular Carcinoma in Chinese Population: A Systematic Review and Meta-analysis

BACKGROUND

Cell-free circulating DNA has been known for many years, but this knowledge has not been beneficial for diagnosis. In this meta-analysis, we examine the diagnostic role of circulating cell-free DNA in HCC patients to find a reliable biomarker for the early detection of HCC.

MATERIALS AND METHODS

We performed a systematic literature search using Science Direct, Web of Science, PubMed/Medline, Scopus, Google Scholar, and Embase, up to April 1st, 2022. Meta-Disc V.1.4 and Comprehensive Meta-Analysis V.3.3 software calculated the pooled specificity, sensitivity, area under the curve (AUC), diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR) Q*index, and summary receiver-- operating characteristic (SROC) for the role of cfDNA as a biomarker for HCC patients. Moreover, the subgroup analyses have been performed based on sample types (serum/plasma) and detection methods (MS-PCR/methylation).

RESULTS

A total of 7 articles (9 studies) included 697 participants (485 cases and 212 controls). The overall pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were 0.706 (95% CI: 0.671 - 0.739), 0.905 (95% CI: 0.865 - 0.937), 6.66 (95% CI: 4.36 - 10.18), 0.287 (95% CI: 0.185 - 0.445), 28.40 (95% CI: 13.01 - 62.0), and 0.93, respectively. We conducted a subgroup analysis of diagnostic value, which showed that the plasma sample had a better diagnostic value compared to the serum.

CONCLUSION

This meta-analysis showed that cfDNA could be a fair biomarker for diagnosing HCC patients.

Comparison between models for detecting hepatocellular carcinoma in patients with chronic liver diseases of various etiologies: ASAP score versus GALAD score

BACKGROUND

Diagnostic panels based on multiple biomarkers and clinical characteristics are considered more favorable than individual biomarker to diagnose hepatocellular carcinoma (HCC). Based on age, sex, alpha-fetoprotein (AFP), and protein induced by vitamin K absence II (PIVKA-II) with/without AFP-L3, ASAP and GALAD models are potential diagnostic panels. The diagnostic performances of these two panels were compared relative to HCC detection among patients with various etiologies of chronic liver diseases (CLDs).

METHODS

A multicenter case-control study recruited CLDs patients with and without HCC from 14 Chinese hospitals. The etiologies of CLDs included hepatitis B virus (HBV), hepatitis C virus (HCV), alcoholic liver disease (ALD), and nonalcoholic fatty liver disease (NAFLD). Using area under the receiver operating characteristic curve (AUC) values, the diagnostic performances of ASAP and GALAD models were compared to detect HCC among patients with various etiologies of CLDs.

RESULTS

Among 248 HCC patients and 722 CLD controls, the ASAP model demonstrated the highest AUC (0.886) to detect HCC at any stage, outperforming the GALAD model (0.853, P = 0.001), as well as any individual biomarker (0.687-0.799, all P < 0.001). In the subgroup analysis of various CLDs etiologies, the ASAP model outperformed the GALAD model to HCC independent of CLDs etiology. In addition, the ASAP model performed better in detecting early-stage (BCLC stage 0/A) HCC versus the GALAD model.

CONCLUSIONS

Despite using one less laboratory variable (AFP-L3), the ASAP model demonstrated better diagnostic performance than the GALAD model to detect all-stage HCC among patients with various etiologies of CLDs-related HCC.

Exosomal DNA: Role in Reflecting Tumor Genetic Heterogeneity, Diagnosis, and Disease Monitoring

Extracellular vesicles (EVs), with exosomes at the forefront, are key in transferring cellular information and assorted biological materials, including nucleic acids. While exosomal RNA has been thoroughly examined, exploration into exosomal DNA (exoDNA)-which is stable and promising for cancer diagnostics-lags behind. This hybrid genetic material, combining contributions from both nuclear and mitochondrial DNA (mtDNA), is rooted in the cytoplasm. The enigmatic process concerning its cytoplasmic encapsulation continues to captivate researchers. Covering the entire genetic landscape, exoDNA encases significant oncogenic alterations in genes like TP53, ALK, and IDH1, which is vital for clinical assessment. This review delves into exosomal origins, the ins and outs of DNA encapsulation, and exoDNA's link to tumor biology, underscoring its superiority to circulating tumor DNA in the biomarker arena for both detection and therapy. Amidst scientific progress, there are complexities in the comprehension and practical application of the exoDNA surface. Reflecting on these nuances, we chart the prospective research terrain and potential pitfalls, forging a path for future inquiry. By illuminating both the known and unknown facets of exoDNA, the objective of this review is to provide guidance to the field of liquid biopsy (LB) while minimizing the occurrence of avoidable blind spots and detours.

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

Development and Validation of a Prognostic Model for Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma Based on Preoperative Serum Prealbumin

Purpose

We aimed to develop a prognostic nomogram utilizing preoperative serum prealbumin levels to predict the overall survival (OS) in patients undergoing transarterial chemoembolization (TACE) for unresectable hepatocellular carcinoma (HCC).

Patients and Methods

A total of 768 individuals with unresectable HCC who underwent TACE at three medical facilities in Suzhou between January 2007 December 2018 were included. The patient cohort was assigned to a training set (n = 461) and a validation set (n = 307). Cox regression analysis identified independent prognostic factors, which were then used to construct a prognostic nomogram. Internal validation was performed in the testing group, and its effectiveness and capability were evaluated with reference to the concordance index (C-index), area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

Independent risk factors identified through Cox regression analyses included the BCLC stage, cirrhosis, invasion, tumor number, preoperative serum PALB, performance status (PS), and tumor size. The nomogram demonstrated a C-index of 0.734 (95% confidence interval (CI): 0.710-0.758) in the training set and 0.717 (95% CI: 0.678-0.756) in the validation set, indicating strong discriminatory ability. The nomogram also demonstrated favorable discriminatory performance with AUC values of 0.873, 0.820, and 0.833 for 1-, 2-, and 3-year OS, respectively, in the training set, and 0.854, 0.765, and 0.724 in the validation set. The AUC value of the nomogram (0.843) was significantly higher than that of the four conventional staging systems. Moreover, calibration graphs confirmed a strong concordance between the predicted and observed results. Furthermore, DCA underscored the significant clinical utility of the nomogram. Additionally, the low-risk group exhibited considerably superior rates of survival compared to the high-risk group.

Conclusion

The developed nomogram demonstrated excellent prognostic capability, which served as a valuable tool for personalized clinical decision-making for patients with HCC.

Nanoprojectile Secondary Ion Mass Spectrometry Enables Multiplexed Analysis of Individual Hepatic Extracellular Vesicles

Extracellular vesicles (EVs) are nanoscale lipid bilayer particles secreted by cells. EVs may carry markers of the tissue of origin and its disease state, which makes them incredibly promising for disease diagnosis and surveillance. While the armamentarium of EV analysis technologies is rapidly expanding, there remains a strong need for multiparametric analysis with single EV resolution. Nanoprojectile (NP) secondary ion mass spectrometry (NP-SIMS) relies on bombarding a substrate of interest with individual gold NPs resolved in time and space. Each projectile creates an impact crater of 10-20 nm in diameter while molecules emitted from each impact are mass analyzed and recorded as individual mass spectra. We demonstrate the utility of NP-SIMS for statistical analysis of single EVs derived from normal liver cells (hepatocytes) and liver cancer cells. EVs were captured on antibody (Ab)-functionalized gold substrate and then labeled with Abs carrying lanthanide (Ln) MS tags (Ab@Ln). These tags targeted four markers selected for identifying all EVs, and specific to hepatocytes or liver cancer. NP-SIMS was used to detect Ab@Ln-tags colocalized on the same EV and to construct scatter plots of surface marker expression for thousands of EVs with the capability of categorizing individual EVs. Additionally, NP-SIMS revealed information about the chemical nanoenvironment where targeted moieties colocalized. Our approach allowed analysis of population heterogeneity with single EV resolution and distinguishing between hepatocyte and liver cancer EVs based on surface marker expression. NP-SIMS holds considerable promise for multiplexed analysis of single EVs and may become a valuable tool for identifying and validating EV biomarkers of cancer and other diseases.

miR-4270 suppresses hepatocellular carcinoma progression by inhibiting DNMT3A-mediated methylation of HGFAC promoter

Background

miR-4270 is a regulatory factor has been linked with the progression of various cancers, such as nasopharyngeal carcinoma, hepatocellular carcinoma (HCC), and gastric cancer. However, the underlying mechanisms through which miR-4270 modulates HCC development are not fully understood.

Methods

miR-4270 expression levels were analyzed in various HCC cell lines and tissue samples. An online bioinformatics tool was then utilized to predict the miR-4270 target gene. The binding relationship between miR-4270 and its target gene DNMT3A was verified using dual-luciferase reporter and Ago2-RIP assays. Then, co-immunoprecipitation (Co-IP) and chromatin immunoprecipitation (ChIP) assays were conducted to investigate the association between DNMT3A and the hepatocyte growth factor activator (HGFAC) promoter region. To assess the methylation level of the HGFAC promoter, methylation-specific PCR (MSP) was employed. Furthermore, rescue analyses were carried out to evaluate the functional relevance of miR-4270 and HGFAC in the modulation of the malignant properties of HCC cells. Finally, HepG2 cells overexpressing miR-4270 were subcutaneously injected into nude mice to estimate the impact of miR-4270 on the xenograft tumor growth of HCC.

Results

A substantial miR-4270 downregulation was revealed in HCC patient samples and cell lines. miR-4270 upregulation suppressed both cell proliferation and invasion while promoting apoptosis. At the molecular level, miR-4270 was found to bind to the 3'untranslated region (3'UTR) of DNMT3A, thereby inhibiting DNMT3A-mediated methylation of the HGFAC promoter. Functional assays indicated that inhibition of miR-4270 stimulated HCC cell growth, an effect counteracted by overexpression of HGFAC. In vivo assays further verified that miR-4270 effectively suppressed the progression of HCC xenograft tumors.

Conclusions

miR-4270 was found to mitigate the malignant characteristics of HCC by inhibiting DNMT3A-mediated methylation of the HGFAC promoter, suggesting a potential therapeutic avenue for the management of HCC.

Incorporation of protein induced by vitamin K absence or antagonist-II into transplant criteria expands beneficiaries of liver transplantation for hepatocellular carcinoma: a multicenter retrospective cohort study in China

INTRODUCTION

In order to maximize the utilization of precious donor liver, precisely determining potential hepatocellular carcinoma (HCC) candidates who will benefit from liver transplantation (LT) is essential. As a crucial diagnostic biomarker for HCC, protein induced by vitamin K absence or antagonist-II (PIVKA-II) has become one of the key indicators for assessing tumor recurrence risk after LT. This study aims to investigate the role of PIVKA-II in recipient selection and prognostic stratification.

METHODS

The clinicopathologic data of HCC patients undergoing LT from 2015 to 2020 in six Chinese transplant centers were collected. Univariate and multivariate analyses were performed to determine risk factors for disease free survival (DFS). Based on these risk factors, survival analysis was made by Kaplan-Meier method and their value in prognostic stratification was assessed.

RESULTS

A total of 522 eligible HCC patients with pre-LT PIVKA-II records were finally included in this study. Tumor burden>8 cm, α-fetoprotein>400 ng/ml, histopathologic grade III and PIVKA-II>240 mAU/ml were identified as independent risk factors for DFS. DFS of patients with PIVKA-II≤240 mAU/ml ( N =288) were significantly higher than those with PIVKA-II>240 mAU/ml ( N =234) (1-year, 3-year, and 5-year DFS: 83.2, 77.3, and 75.9% vs. 75.1, 58.5, and 50.5%; P <0.001). Compared with Hangzhou criteria ( N =305), incorporating PIVKA-II into Hangzhou criteria (including tumor burden, α-fetoprotein, and histopathologic grade) increased the number of patients with eligibility for LT by 21.6% but achieved comparable DFS and overall survival.

CONCLUSIONS

Incorporating PIVKA-II into existing LT criteria could increase the number of eligible HCC patients without compromising post-LT outcomes.

Development and validation of a model to predict the risk of distant metastases from hepatocellular carcinoma: a real-world retrospective study

PURPOSE

This study aimed to construct a novel clinical prediction model to predict the risk of distant metastases (DM) in hepatocellular carcinoma (HCC).

METHODS

We included 3869 HCC patients, comprising 3076 patients from the Surveillance, Epidemiology, and End Results (SEER) database and 793 patients from a hospital in China. Variables with a P-value < 0.05 in the univariate logistic analysis were entered into the multivariate analysis to determine the independent predictive factors for DM in HCC. A nomogram was created based on the independent predictive factors. The predictive performance of the model was assessed using the receiver operating characteristics (ROCs) curve, decision curve analysis (DCA), calibration curves, and clinical impact curve analysis (CIC). Additionally, we developed a user-friendly web-based calculator based on the model.

RESULTS

The multivariate logistic regression analysis revealed that tumor size (P < 0.001), type of treatment (P < 0.001), T stage (P = 0.001), N stage (P < 0.001), and grade (P = 0.043) were identified as independent predictive factors. A nomogram was constructed based on these factors. The area under the ROC curves (AUC) value was 0.845 (95% CI 0.815-0.874) for the training set, 0.818 (95% CI 0.774-0.863) for the internal validation set, and 0.823 (95% CI 0.770-0.876) for the external validation set. Moreover, DCA analysis, calibration curves, and CIC analysis demonstrated the favorable predictive performance of the nomogram. Finally, a more user-friendly web-based calculator was developed.

CONCLUSION

We developed a nomogram and showed its favorable predictive performance in predicting DM in HCC. Furthermore, we developed a more user-friendly web-based calculator, which has the potential to aid clinicians in individualized diagnosis and make better clinical decisions for HCC patients.

Cellular gp96 upregulates AFP expression by blocking NR5A2 SUMOylation and ubiquitination in hepatocellular carcinoma

Alpha-fetoprotein (AFP) is the most widely used biomarker for the diagnosis of hepatocellular carcinoma (HCC). However, a substantial proportion of HCC patients have either normal or marginally increased AFP levels in serum, and the underlying mechanisms are not fully understood. In the present study, we provided in vitro and in vivo evidence that heat shock protein gp96 promoted AFP expression at the transcriptional level in HCC. NR5A2 was identified as a key transcription factor for the AFP gene, and its stability was enhanced by gp96. A further mechanistic study by co-immunoprecipitation, GST pull-down, and molecular docking showed gp96 and the SUMO E3 ligase RanBP2 competitively binding to NR5A2 at the sites spanning from aa 507 to aa 539. The binding of gp96 inhibited SUMOylation, ubiquitination, and subsequent degradation of NR5A2. In addition, clinical analysis of HCC patients indicated that gp96 expression in tumors was positively correlated with serum AFP levels. Therefore, our study uncovered a novel mechanism that gp96 regulates the stability of its client proteins by directly affecting their SUMOylation and ubiquitination. These findings will help in designing more accurate AFP-based HCC diagnosis and progression monitoring approaches.

Effect of RPL27 knockdown on the proliferation and apoptosis of human liver cancer cells

RPL27 is linked to the development of various diseases including malignant tumors. RPL27 may play an oncogenic function in hepatocellular carcinoma (HCC), but this is unknown. So, the aim of this study was to investigate how the human liver cancer cell lines SNU449 and HepG2 responded to RPL27 knockdown in terms of proliferation and apoptosis. SNU449 and HepG2 were cultured and infected with shCon and shRPL27 lentiviral particles to induce RPL27 knockdown, and then RPL27 expression was detected using qPCR and Western blot. Cell proliferation was measured using CCK8, cell cloning, cell scraping, and transwell migration and invasion, while apoptosis was measured using flow cytometry (FCM). The qPCR revealed that mRNA expression of RPL27 decreased after knocking down RPL27 in cells. The CCK8 and cell cloning assay confirmed that knocking down RPL27 significantly reduced cell viability. The cell scratch assay and transwell assays showed that the proliferation rate decreased after knocking down RPL27. A substantial increase in apoptotic cells was discovered by FCM. According to WB, RPL27 knockdown increased the expression of Bax and Caspase-3 while decreasing the expression of bcl-2. The findings showed that RPL27 knockdown inhibited cell proliferation in SNU449 and HepG2 via inducing apoptosis, proving that RPL27 is a novel gene linked with HCC and is crucial for both proliferation and apoptosis. These outcomes imply that RPL27 may be a potential target for liver cancer diagnosis and therapy.

The prognostic nutritional index and tumor pathological characteristics predict the prognosis of elderly patients with early-stage hepatocellular carcinoma after surgery

The elderly comprises over one-third of hepatocellular carcinoma (HCC) patients, however, they are not adequately represented in prognostic studies. The study aims to determine the prognostic significance of the preoperative prognostic nutritional index (PNI) and develop nomograms for predicting their recurrence-free and overall survival (RFS and OS). The study consisted of 282 elderly patients (aged ≥ 65 years) with early-stage HCC (China Liver Cancer Staging System: I-IIA) after curative resection (R0). They were randomly divided into a training (n = 197) and a test cohort (n = 85). The patients were stratified into two groups: PNI-low (PNI ≤ 49.05) and PNI-high (PNI > 49.05) based on a cut-off value. Most patients' demographics and perioperative outcomes were comparable, while patients in the PNI-high group were younger (P = 0.002), heavier (P < 0.001), and had lower comorbidity rates (P = 0.003). Although the tumor stages were earlier in the PNI-low group (P < 0.001), patients' OS (5-year OS: 48.9% vs. 93.1%) and RFS (5-year RFS: 27.3% vs. 75.7%) were significantly worse compared to the PNI-high group (both P < 0.0001). Patients' OS and RFS nomograms were developed by incorporating independent survival predictors including chronic obstructive pulmonary disease (COPD), age ≥ 75 years, PNI-low, tumor presence of satellite nodules, capsule, and microvascular invasion. The nomograms showed good calibration and discrimination, with all C-indexes ≥ 0.75 and calibration plots essentially coinciding with the diagonal. In conclusion, for elderly HCC patients, COPD, age ≥ 75 years, PNI-low, and tumor presence of satellite nodules, capsule, and microvascular invasion were independent prognostic factors. The nomogram could accurately predict the prognosis of these patients.