Heat-shock protein 90α is a potential prognostic and predictive biomarker in hepatocellular carcinoma: a large-scale and multicenter study

Expression and Clinical Significance of Nuclear Phosphoglucomutase-1 in Hepatocellular Carcinoma

This study aimed to evaluate the predictive values of phosphoglucomutase-1 (PGM1) expression for prognosis in patients with hepatocellular carcinoma (HCC). PGM1 expression was assessed by immunohistochemistry in tissue microarrays. The relationship of PGM1 expression level with pathologic parameters and prognosis values was respectively analyzed by χ 2 test and Cox regression. The accuracy of independent risk factors in predicting prognosis was calculated by receiver operating characteristic curve. HCC patient-derived xenograft models were performed to evaluate the nuclear PGM1 antitumor effect. The results showed that PGM1 expression was low in HCC tissues. Nuclear PGM1 was an independent prognostic factor for overall survival and time to recurrence. Cox regression showed that nuclear PGM1, serum α-fetoprotein, liver cirrhosis, and TNM staging stage were independent risk predictors for HCC. Receiver operating characteristic curve demonstrated that combination of independent predictors had better prognostic value than TNM staging alone. Moreover, patient-derived xenograft models showed antitumor effect of nuclear PGM1. We found that low expression of nuclear PGM1 was detected in HCC tissues and associated with poor prognostic. Nuclear PGM1 was an independent prognostic factor in patients with HCC. Furthermore, nuclear PGM1 combining other independent risk factors showed a better prognostic value. Nuclear PGM1 was a useful prognostic biomarker for patients with HCC.

Unraveling the role of ADAMs in clinical heterogeneity and the immune microenvironment of hepatocellular carcinoma: insights from single-cell, spatial transcriptomics, and bulk RNA sequencing

Background

Hepatocellular carcinoma (HCC) is a prevalent and heterogeneous tumor with limited treatment options and unfavorable prognosis. The crucial role of a disintegrin and metalloprotease (ADAM) gene family in the tumor microenvironment of HCC remains unclear.

Methods

This study employed a novel multi-omics integration strategy to investigate the potential roles of ADAM family signals in HCC. A series of single-cell and spatial omics algorithms were utilized to uncover the molecular characteristics of ADAM family genes within HCC. The GSVA package was utilized to compute the scores for ADAM family signals, subsequently stratified into three categories: high, medium, and low ADAM signal levels through unsupervised clustering. Furthermore, we developed and rigorously validated an innovative and robust clinical prognosis assessment model by employing 99 mainstream machine learning algorithms in conjunction with co-expression feature spectra of ADAM family genes. To validate our findings, we conducted PCR and IHC experiments to confirm differential expression patterns within the ADAM family genes.

Results

Gene signals from the ADAM family were notably abundant in endothelial cells, liver cells, and monocyte macrophages. Single-cell sequencing and spatial transcriptomics analyses have both revealed the molecular heterogeneity of the ADAM gene family, further emphasizing its significant impact on the development and progression of HCC. In HCC tissues, the expression levels of ADAM9, ADAM10, ADAM15, and ADAM17 were markedly elevated. Elevated ADAM family signal scores were linked to adverse clinical outcomes and disruptions in the immune microenvironment and metabolic reprogramming. An ADAM prognosis signal, developed through the utilization of 99 machine learning algorithms, could accurately forecast the survival duration of HCC, achieving an AUC value of approximately 0.9.

Conclusions

This study represented the inaugural report on the deleterious impact and prognostic significance of ADAM family signals within the tumor microenvironment of HCC.

Elucidating the multifaceted role of MGAT1 in hepatocellular carcinoma: integrative single-cell and spatial transcriptomics reveal novel therapeutic insights

Background

Glycosyltransferase-associated genes play a crucial role in hepatocellular carcinoma (HCC) pathogenesis. This study investigates their impact on the tumor microenvironment and molecular mechanisms, offering insights into innovative immunotherapeutic strategies for HCC.

Methods

We utilized cutting-edge single-cell and spatial transcriptomics to examine HCC heterogeneity. Four single-cell scoring techniques were employed to evaluate glycosyltransferase genes. Spatial transcriptomic findings were validated, and bulk RNA-seq analysis was conducted to identify prognostic glycosyltransferase-related genes and potential immunotherapeutic targets. MGAT1's role was further explored through various functional assays.

Results

Our analysis revealed diverse cell subpopulations in HCC with distinct glycosyltransferase gene activities, particularly in macrophages. Key glycosyltransferase genes specific to macrophages were identified. Temporal analysis illustrated macrophage evolution during tumor progression, while spatial transcriptomics highlighted reduced expression of these genes in core tumor macrophages. Integrating scRNA-seq, bulk RNA-seq, and spatial transcriptomics, MGAT1 emerged as a promising therapeutic target, showing significant potential in HCC immunotherapy.

Conclusion

This comprehensive study delves into glycosyltransferase-associated genes in HCC, elucidating their critical roles in cellular dynamics and immune cell interactions. Our findings open new avenues for immunotherapeutic interventions and personalized HCC management, pushing the boundaries of HCC immunotherapy.

Lasso-Cox interpretable model of AFP-negative hepatocellular carcinoma

BACKGROUND

In AFP-negative hepatocellular carcinoma patients, markers for predicting tumor progression or prognosis are limited. Therefore, our objective is to establish an optimal predicet model for this subset of patients, utilizing interpretable methods to enhance the accuracy of HCC prognosis prediction.

METHODS

We recruited a total of 508 AFP-negative HCC patients in this study, modeling with randomly divided training set and validated with validation set. At the same time, 86 patients treated in different time periods were used as internal validation. After comparing the cox model with the random forest model based on Lasso regression, we have chosen the former to build our model. This model has been interpreted with SHAP values and validated using ROC, DCA. Additionally, we have reconfirmed the model's effectiveness by employing an internal validation set of independent periods. Subsequently, we have established a risk stratification system.

RESULTS

The AUC values of the Lasso-Cox model at 1, 2, and 3 years were 0.807, 0.846, and 0.803, and the AUC values of the Lasso-RSF model at 1, 2, and 3 years were 0.783, 0.829, and 0.776. Lasso-Cox model was finally used to predict the prognosis of AFP-negative HCC patients in this study. And BCLC stage, gamma-glutamyl transferase (GGT), diameter of tumor, lung metastases (LM), albumin (ALB), alkaline phosphatase (ALP), and the number of tumors were included in the model. The validation set and the separate internal validation set both indicate that the model is stable and accurate. Using risk factors to establish risk stratification, we observed that the survival time of the low-risk group, the middle-risk group, and the high-risk group decreased gradually, with significant differences among the three groups.

CONCLUSION

The Lasso-Cox model based on AFP-negative HCC showed good predictive performance for liver cancer. SHAP explained the model for further clinical application.

Exploiting gender-based biomarkers and drug targets: advancing personalized therapeutic strategies in hepatocellular carcinoma

This review systematically examines gender differences in hepatocellular carcinoma (HCC), identifying the influence of sex hormones, genetic variance, and environmental factors on the disease's epidemiology and treatment outcomes. Recognizing the liver as a sexually dimorphic organ, we highlight how gender-specific risk factors, such as alcohol consumption and obesity, contribute differently to hepatocarcinogenesis in men and women. We explore molecular mechanisms, including the differential expression of androgen and estrogen receptors, which mediate diverse pathways in tumor biology such as cell proliferation, apoptosis, and DNA repair. Our analysis underscores the critical need for gender-specific research in liver cancer, from molecular studies to clinical trials, to improve diagnostic accuracy and therapeutic effectiveness. By incorporating a gender perspective into all facets of liver cancer research, we advocate for a more precise and personalized approach to cancer treatment that acknowledges gender as a significant factor in both the progression of HCC and its response to treatment. This review aims to foster a deeper understanding of the biological and molecular bases of gender differences in HCC and to promote the development of tailored interventions that enhance outcomes for all patients.

Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study

Background: Hepatocellular carcinoma (HCC), the predominant malignancy of the digestive tract, ranks as the third most common cause of cancer-related mortality globally, significantly impeding human health and lifespan. Emerging immunotherapeutic approaches have ignited fresh optimism for patient outcomes. This investigation probes the link between 731 immune cell phenotypes and HCC through Mendelian Randomization and single-cell sequencing, aiming to unearth viable drug targets and dissect HCC's etiology. Methods: We conducted an exhaustive two-sample Mendelian Randomization analysis to ascertain the causal links between immune cell features and HCC, utilizing publicly accessible genetic datasets to explore the causal connections of 731 immune cell traits with HCC susceptibility. The integrity, diversity, and potential horizontal pleiotropy of these findings were rigorously assessed through extensive sensitivity analyses. Furthermore, single-cell sequencing was employed to penetrate the pathogenic underpinnings of HCC. Results: Establishing a significance threshold of pval_Inverse.variance.weighted at 0.05, our study pinpointed five immune characteristics potentially elevating HCC risk: B cell % CD3- lymphocyte (TBNK panel), CD25 on IgD+ (B cell panel), HVEM on TD CD4+ (Maturation stages of T cell panel), CD14 on CD14+ CD16- monocyte (Monocyte panel), CD4 on CD39+ activated Treg ( Treg panel). Conversely, various cellular phenotypes tied to BAFF-R expression emerged as protective elements. Single-cell sequencing unveiled profound immune cell phenotype interactions, highlighting marked disparities in cell communication and metabolic activities. Conclusion: Leveraging MR and scRNA-seq techniques, our study elucidates potential associations between 731 immune cell phenotypes and HCC, offering a window into the molecular interplays among cellular phenotypes, and addressing the limitations of mono-antibody therapeutic targets.

Pathways and molecules for overcoming immunotolerance in metastatic gastrointestinal tumors

Worldwide, gastrointestinal (GI) cancer is recognized as one of the leading malignancies diagnosed in both genders, with mortality largely attributed to metastatic dissemination. It has been identified that in GI cancer, a variety of signaling pathways and key molecules are modified, leading to the emergence of an immunotolerance phenotype. Such modifications are pivotal in the malignancy's evasion of immune detection. Thus, a thorough analysis of the pathways and molecules contributing to GI cancer's immunotolerance is vital for advancing our comprehension and propelling the creation of efficacious pharmacological treatments. In response to this necessity, our review illuminates a selection of groundbreaking cellular signaling pathways associated with immunotolerance in GI cancer, including the Phosphoinositide 3-kinases/Akt, Janus kinase/Signal Transducer and Activator of Transcription 3, Nuclear Factor kappa-light-chain-enhancer of activated B cells, Transforming Growth Factor-beta/Smad, Notch, Programmed Death-1/Programmed Death-Ligand 1, and Wingless and INT-1/beta-catenin-Interleukin 10. Additionally, we examine an array of pertinent molecules like Indoleamine-pyrrole 2,3-dioxygenase, Human Leukocyte Antigen G/E, Glycoprotein A Repetitions Predominant, Clever-1, Interferon regulatory factor 8/Osteopontin, T-cell immunoglobulin and mucin-domain containing-3, Carcinoembryonic antigen-related cell adhesion molecule 1, Cell division control protein 42 homolog, and caspases-1 and -12.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma

Glioma is a common intracranial tumor and is generally associated with poor prognosis. Recently, numerous studies illustrated the importance of 5-methylcytosine (m5C) RNA modification to tumorigenesis. However, the prognostic value and immune correlation of m5C in glioma remain unclear. We obtained RNA expression and clinical information from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) datasets to analyze. Nonnegative matrix factorization (NMF) was used to classify patients into two subgroups and compare these patients in survival and clinicopathological characteristics. CIBERSORT and single-sample gene-set algorithm (ssGSEA) methods were used to investigate the relationship between m5C and the immune environment. The Weighted correlation network analysis (WGCNA) and univariate Cox proportional hazard model (CoxPH) were used to construct a m5C-related signature. Most of m5C RNA methylation regulators presented differential expression and prognostic values. There were obvious relationships between immune infiltration cells and m5C regulators, especially NSUN7. In the m5C-related module from WGCNA, we found SEPT3, CHI3L1, PLBD1, PHYHIPL, SAMD8, RAP1B, B3GNT5, RER1, PTPN7, SLC39A1, and MXI1 were prognostic factors for glioma, and they were used to construct the signature. The great significance of m5C-related signature in predicting the survival of patients with glioma was confirmed in the validation sets and CGGA cohort.

Thymidine kinase 1 appears to be a marker for the prognosis of hepatocellular carcinoma based on a large-scale, multicenter study

INTRODUCTION

The aim of this study was to investigate the role of thymidine kinase 1 (TK1) levels in hepatocellular carcinoma (HCC) prognosis and to develop a nomogram for predicting HCC prognosis.

METHOD

In this study, 1066 HCC patients were enrolled between August 2018 and April 2022. TK1 levels were measured within one week before enrollment, and the relationship with HCC prognosis was evaluated. Next, all patients were randomly assigned to the training set (70%, n = 746) and the validation set (30%, n = 320). We used multivariate Cox analysis to find independent prognostic factors in the training set to construct a nomogram. The predictive power of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The optimal critical value of TK1 was determined as 2.35 U/L using X-tile software.

RESULT

Before and after propensity score matching (PSM), the median overall survival (mOS) of the low-TK1 group (< 2.35 U/L) remained significantly longer than that of the high-TK1 group (≥ 2.35 U/L) (48.1 vs 16.5 months, p < 0.001; 75.7 vs 19.8 months, p = 0.001). Moreover, multivariate Cox analysis showed that the low TK1 level was an independent positive prognostic indicator. Additionally, the area under the ROC curve for predicting the 1-year, 2-year, and 3-year survival rates was 0.770, 0.758, and 0.805, respectively.

CONCLUSIONS

TK1 could serve as a prognostic marker for HCC. In addition, the nomogram showed good predictive capability for HCC prognosis.

Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

Purpose

The accurate prediction of non-cirrhotic hepatocellular carcinoma (NCHCC) risk facilitates improved surveillance strategy and decreases cancer-related mortality. This study aimed to explore the correlation between immunogenic cell death (ICD) and NCHCC prognosis using The Cancer Genome Atlas (TCGA) datasets, and the potential prognostic value of ICD-related genes in NCHCC.

Methods

Clinical and transcriptomic data of patients with NCHCC patients were retrieved from TCGA database. Weighted gene co-expression network analysis was performed to obtain the NCHCC phenotype-related module genes. Consensus clustering analysis was performed to classify the patients into two clusters based on intersection genes among differentially expressed genes (DEGs) between cancer and adjacent tissues, NCHCC phenotype-related genes, and ICD-related genes. NCHCC-derived tissue microarray was used to evaluate the correlation of the expression levels of key genes with NCHCC prognosis using immunohistochemical staining.

Results

Cox regression analyses were performed to construct a prognostic risk score model comprising three genes (TMC7, GRAMD1C, and GNPDA1) based on DEGs between two clusters. The model stratified patients with NCHCC into two risk groups. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. Univariable and multivariable Cox regression analyses revealed that these signature genes are independent predictors of OS. Functional analysis revealed differential immune status between the two risk groups. Next, a nomogram was constructed, which demonstrated the potent distinguishing ability of the developed model based on receiver operating characteristic curves. In vitro functional validation revealed that the migration and invasion abilities of HepG2 and Huh7 cells were upregulated upon GRAMD1C knockdown but downregulated upon TMC7 knockdown.

Conclusion

This study developed a prognostic model comprising three genes, which can aid in predicting the survival of patients with NCHCC and guide the selection of drugs and molecular markers for NCHCC.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.

Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review

Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, accounting for approximately 90% of all primary liver cancers, with high mortality and a poor prognosis. A large number of predictive models have been applied that integrate multiple clinical factors and biomarkers to predict the prognosis of HCC. Nomograms, as easy-to-use prognostic predictive models, are widely used to predict the probability of clinical outcomes. We searched PubMed with the keywords "hepatocellular carcinoma" and "nomogram", and 974 relative literatures were retrieved. According to the construction methodology and the real validity of the nomograms, in this study, 97 nomograms for HCC were selected in 77 publications. These 97 nomograms were established based on more than 100,000 patients, covering seven main prognostic outcomes. The research data of 56 articles are from hospital-based HCC patients, and 13 articles provided external validation results of the nomogram. In addition to AFP, tumor size, tumor number, stage, vascular invasion, age, and other common prognostic risk factors are included in the HCC-related nomogram, more and more biomarkers, including gene mRNA expression, gene polymorphisms, and gene signature, etc. were also included in the nomograms. The establishment, assessment and validation of these nomograms are also discussed in depth. This study would help clinicians construct and select appropriate nomograms to guide precise judgment and appropriate treatments.

Clinicopathological and prognostic significance of heat shock proteins in hepatocellular carcinoma: a systematic review and meta-analysis

Background

Overexpression of heat shock proteins (HSPs) has been observed in a wide range of human tumors, and there is an increasing evidence demonstrated that HSPs play a key role in tumor progression. Several studies were conducted to explore the clinicopathological characteristics and prognostic value of HSPs in hepatocellular carcinoma (HCC), but the results remain controversial. To address this gap, we conducted a systematic review and meta-analysis.

Methods

The eligible literature was obtained from PubMed, Cochrane library, Web of science, Embase, Chinese National Knowledge Infrastructure and Wan Fang databases. We used the odds ratio (OR) and hazard ratio (HR) as the suitable parameters to assess the clinicopathological features and prognostic value of HSPs in HCC patients.

Results

The meta-analysis results showed that HSPs expression was associated with overall survival (OS) of HCC patients (HR = 1.61, 95%CI = 1.22-2.13, P=0.001, I 2 = 62.7%). In addition, the pooled results suggested that HSPs expression was significantly correlated with tumor differentiation (OR = 1.33, 95%CI = 1.08-1.65, P = 0.907), vascular invasion (OR = 1.31, 95%CI = 1.02-1.69, P = 0.921) and lymphatic metastasis (OR=1.98, 95%CI= 1.70-2.31, P = 0.740). Meanwhile, the subgroup analysis showed a significant correlation between the expression of HSP27 (HR=1.69, 95%CI = 1.24-2.31, P = 0.674) and HSP90α (HR=2.03, 95%CI = 1.73-2.40, P = 0.743) with OS of HCC patients.

Conclusions

Our meta-analysis confirms that HSPs expression is closely associated with a worse prognosis in HCC patients, and may be directly involved in tumor differentiation and distant metastasis. In addition, the subgroup analysis results demonstrate that the expression of HSP27 and HSP90α can be served as potential prognostic predictors of HCC.

Beyond atezolizumab plus bevacizumab in patients with advanced hepatocellular carcinoma: overall efficacy and safety of tyrosine kinase inhibitors in a real-world setting

BACKGROUND

In patients with advanced hepatocellular carcinoma (HCC) progressing after atezolizumab and bevacizumab, the optimal therapeutic sequence is still unclear and no second-line agent has proven its efficacy.

OBJECTIVES

The aim of this retrospective multicenter real-world cohort study was to provide an evaluation of the efficacy and safety of the use of second-line tyrosine kinase inhibitors (TKIs) in this population.

METHODS

All patients with advanced HCC, treated in first-line setting by atezolizumab-bevacizumab, and who received at least one dose of treatment with TKI were included in this study. All the data were retrospectively collected from medical records. The primary outcome was progression-free survival (PFS). Secondary outcomes were overall survival (OS), overall global survival (OGS), and safety. A total of 82 patients were included in this study.

RESULTS

Patients were assigned to the regorafenib group (n = 29, 35.4%) or other TKI (sorafenib n = 41, lenvatinib n = 8, or cabozantinib n = 4) group (n = 53). PFS was not significantly different between the two groups [2.6 versus 2.8 months, HR 1.07 (95% CI: 0.61-1.86), p = 0.818]. Median PFS rates were 2.6, 4.4, and 2.8 months in sorafenib-, lenvatinib-, and cabozantinib group, respectively. OS was statistically different between the regorafenib group and other TKI group [15.8 versus 7.0 months, HR 0.40 (95% CI: 0.20-0.79), p = 0.023]. When adjusting on confounding factors, there was still a difference in OS favoring the regorafenib group (adjusted hazard ratio 0.35, p = 0.019). OGS of patients who received regorafenib was improved compared to other TKI [18.6 versus 15.0 months, HR 0.42 (95% CI: 0.22-0.84), p = 0.036]. Twenty percent of patients had grade 3 and none had grade 4 or 5 adverse events. In patients who experienced disease progression and fit for a third-line treatment, 80% and 50% received cabozantinib in regorafenib group and other TKI group, respectively.

CONCLUSION

Efficacy of any TKI in the second-line setting was not affected by atezolizumab-bevacizumab treatment as first-line therapy. The safety profile in the second-line setting was consistent with the results shown in pivotal studies. PFS rates of patients were similar, regardless of TKI type. Regorafenib was associated with better OS and OGS rates compared to other TKI. These data need to be confirmed in prospective comparative studies.

Development and Validation of Novel Models Including Tumor Micronecrosis for Predicting the Postoperative Survival of Patients with Hepatocellular Carcinoma

Background

The heterogeneity of hepatocellular carcinoma (HCC) leads to the unsatisfying predictive performance of current staging systems. HCC patients with pathological tumor micronecrosis have an immunosuppressive microenvironment. We aimed to develop novel prognostic models by integrating micronecrosis to predict the survival of HCC patients after hepatectomy more precisely.

Methods

We enrolled 765 HCC patients receiving curative hepatic resection. They were randomly divided into a training cohort (n= 536) and a validation cohort (n = 229). We developed two prognostic models for postoperative recurrence-free survival (RFS) and overall survival (OS) based on independent factors identified through multivariate Cox regression analyses. The predictive performance was assessed using the Harrell concordance index (C-index) and the time-dependent area under the receiver operating characteristic curve, compared with six conventional staging systems.

Results

The RFS and OS nomograms were developed based on tumor micronecrosis, tumor size, albumin-bilirubin grade, tumor number and prothrombin time. The C-indexes for the RFS nomogram and OS nomogram were respectively 0.66 (95% CI, 0.62-0.69) and 0.74 (95% CI, 0.69-0.79) in the training cohort, which was significantly better than those of the six common staging systems (0.52-0.61 for RFS and 0.53-0.63 for OS). The results were further confirmed in the validation group, with the C-indexes being 0.66 and 0.77 for the RFS and OS nomograms, respectively.

Conclusion

The two nomograms could more accurately predict RFS and OS in HCC patients receiving curative hepatic resection, thereby aiding in formulating personalized postoperative follow-up plans.

Alterations of the gut microbiota associated with the occurrence and progression of viral hepatitis

Background

Gut microbiota is the largest population of microorganisms and is closely related to health. Many studies have explored changes in gut microbiota in viral hepatitis. However, the correlation between gut microbiota and the occurrence and progression of viral hepatitis has not been fully clarified.

Methods

PubMed and BioProject databases were searched for studies about viral hepatitis disease and 16S rRNA gene sequencing of gut microbiota up to January 2023. With bioinformatics analyses, we explored changes in microbial diversity of viral hepatitis, screened out crucial bacteria and microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting risks for the occurrence and progression of viral hepatitis based on ROC analysis.

Results

Of the 1389 records identified, 13 studies met the inclusion criteria, with 950 individuals including 656 patient samples (HBV, n = 546; HCV, n = 86; HEV, n = 24) and 294 healthy controls. Gut microbial diversity is significantly decreased as the infection and progression of viral hepatitis. Alpha diversity and microbiota including Butyricimonas, Escherichia-Shigella, Lactobacillus, and Veillonella were identified as the potential microbial markers for predicting the risk of development of viral hepatitis (AUC>0.7). Microbial functions including tryptophan metabolism, fatty acid biosynthesis, lipopolysaccharide biosynthesis, and lipid metabolism related to the microbial community increased significantly as the development of viral hepatitis.

Conclusions

This study demonstrated comprehensively the gut microbiota characteristics in viral hepatitis, screened out crucial microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting the risk of viral hepatitis.

Research progress of targeted therapy combined with immunotherapy for hepatocellular carcinoma

Hepatocellular carcinoma is a common gastrointestinal malignancy with a high mortality rate and limited treatment options. Molecularly targeted drugs combined with immune checkpoint inhibitors have shown unique advantages over single-agent applications, significantly prolonging patient survival. This paper reviews the research progress of molecular-targeted drugs combined with immune checkpoint inhibitors in the treatment of hepatocellular carcinoma and discusses the effectiveness and safety of the combination of the two drugs to provide a reference for the further application of molecular-targeted drugs combined with immune checkpoint inhibitors in clinical practice.

Identification of novel prognostic biomarkers in the TF-enhancer-target regulatory network in hepatocellular carcinoma and immune infiltration analysis

Background: Hepatocellular carcinoma (HCC) remains notorious for its high malignancy, poor prognosis and high mortality. The exploration of novel therapeutic agents for HCC has remained challenging due to its complex aetiology. Therefore, it is necessary to elucidate the pathogenesis and mechanism of HCC for clinical intervention.

Methods: We collected data from several public data portals and systematically analysed the association between transcription factors (TFs), eRNA-associated enhancers and downstream targets. We next filtered the prognostic genes and established a novel prognosis-related nomogram model. Moreover, we explored the potential mechanisms of the identified prognostic genes. The expression level was validated by several ways.

Results: We first constructed a significant TF-enhancer-target regulatory network and identified DAPK1 as a coregulatory differentially expressed prognosis-related gene. We combined common clinicopathological factors and built a prognostic nomogram model for HCC. We found that our regulatory network was correlated with the processes of synthesizing various substances. Moreover, we explored the role of DAPK1 in HCC and found that it was associated with immune cell infiltration and DNA methylation. Several immunostimulators and targeting drugs could be promising immune therapy targets. The tumor immune microenvironment was analyzed. Finally, the lower DAPK1 expression in HCC was validated via the GEO database, UALCAN cohort, and qRT-PCR.

Conclusion: In conclusion, we established a significant TF-enhancer-target regulatory network and identified downregulated DAPK1 as an important prognostic and diagnostic gene in HCC. Its potential biological functions and mechanisms were annotated using bioinformatics tools.

Dialog beyond the Grave: Necrosis in the Tumor Microenvironment and Its Contribution to Tumor Growth

Damage-associated molecular patterns (DAMPs) are endogenous molecules released from the necrotic cells dying after exposure to various stressors. After binding to their receptors, they can stimulate various signaling pathways in target cells. DAMPs are especially abundant in the microenvironment of malignant tumors and are suspected to influence the behavior of malignant and stromal cells in multiple ways often resulting in promotion of cell proliferation, migration, invasion, and metastasis, as well as increased immune evasion. This review will start with a reminder of the main features of cell necrosis, which will be compared to other forms of cell death. Then we will summarize the various methods used to assess tumor necrosis in clinical practice including medical imaging, histopathological examination, and/or biological assays. We will also consider the importance of necrosis as a prognostic factor. Then the focus will be on the DAMPs and their role in the tumor microenvironment (TME). We will address not only their interactions with the malignant cells, frequently leading to cancer progression, but also with the immune cells and their contribution to immunosuppression. Finally, we will emphasize the role of DAMPs released by necrotic cells in the activation of Toll-like receptors (TLRs) and the possible contributions of TLRs to tumor development. This last point is very important for the future of cancer therapeutics since there are attempts to use TLR artificial ligands for cancer therapeutics.

Evaluation of Lactate Dehydrogenase and Alkaline Phosphatase as Predictive Biomarkers in the Prognosis of Hepatocellular Carcinoma and Development of a New Nomogram

Background

Tumor proliferation is frequently accompanied by aberrant enzyme production. We aim to investigate the potential predictive value of both plasma alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) in patients with HCC and to develop a nomogram to assess the prognosis of HCC.

Methods

The trial involved 2327 patients between May 2015 and March 2022. Within 7 days of enrollment, the levels of ALP and LDH were measured, and their association with survival was assessed. And we had developed and validated a new nomogram based on ALD and ALP.

Results

Using X-tile software, the optimal cut-off values were determined to be ALP = 172 U/L and LDH = 241 U/L. The high ALP (≥ 172), LDH (≥ 241), and ALP/LDH (≥ 0.91) groups had lower median overall survival (mOS) than low ALP (< 172), LDH (< 241), and ALP/LDH (< 0.91) groups (all p < 0.001). In addition, elevated ALP and LDH levels are independent negative prognostic indicators. Moreover, we established that the area under the curve (AUC) values of the predicted 1-, 2-, and 3-year survival rates of receiver operating characteristic curve (ROC) based on the nomogram were 0.79, 0.77, and 0.74, respectively. In addition, the calibration curves and decision curve analyses (DCA) demonstrated that this model possessed strong predictive capability.

Conclusion

ALP, LDH and ALP/LDH can be employed as biomarkers for predicting the prognosis of HCC. Furthermore, the nomograph based on ALH and ALP demonstrates good HCC prediction performance. For HCC patients with high ALH or ALP or ALP/LDH, close surveillance program and adjuvant therapy should be considered.

A radiomics nomogram for predicting cytokeratin 19-positive hepatocellular carcinoma: a two-center study

Objectives

We aimed to construct and validate a radiomics-based nomogram model derived from gadoxetic acid-enhanced magnetic resonance (MR) images to predict cytokeratin (CK) 19-positive (+) hepatocellular carcinoma (HCC) and patients' prognosis.

Methods

A two-center and time-independent cohort of 311 patients were retrospectively enrolled (training cohort, n = 168; internal validation cohort, n = 72; external validation cohort, n = 71). A total of 2286 radiomic features were extracted from multisequence MR images with the uAI Research Portal (uRP), and a radiomic feature model was established. A combined model was established by incorporating the clinic-radiological features and the fusion radiomics signature using logistic regression analysis. Receiver operating characteristic curve (ROC) was used to evaluate the predictive efficacy of these models. Kaplan-Meier survival analysis was used to assess 1-year and 2-year progression-free survival (PFS) and overall survival (OS) in the cohort.

Results

By combining radiomic features extracted in DWI phase, arterial phase, venous and delay phase, the fusion radiomics signature achieved AUCs of 0.865, 0.824, and 0.781 in the training, internal, and external validation cohorts. The final combined clinic-radiological model showed higher AUC values in the three datasets compared with the fusion radiomics model. The nomogram based on the combined model showed satisfactory prediction performance in the training (C-index, 0.914), internal (C-index, 0.855), and external validation (C-index, 0.795) cohort. The 1-year and 2-year PFS and OS of the patients in the CK19+ group were 76% and 73%, and 78% and 68%, respectively. The 1-year and 2-year PFS and OS of the patients in the CK19-negative (-) group were 81% and 77%, and 80% and 74%, respectively. Kaplan-Meier survival analysis showed no significant differences in 1-year PFS and OS between the groups (P = 0.273 and 0.290), but it did show differences in 2-year PFS and OS between the groups (P = 0.032 and 0.040). Both PFS and OS were lower in CK19+ patients.

Conclusion

The combined model based on clinic-radiological radiomics features can be used for predicting CK19+ HCC noninvasively to assist in the development of personalized treatment.

A retrospective cohort study on the association between early coagulation disorder and short-term all-cause mortality of critically ill patients with congestive heart failure

Purpose

Coagulation disorder in congestive heart failure (CHF) has been well-documented. The prognostic value of a composite coagulation disorder score, which combines the absolute platelet count, international normalized ratio (INR), and activated partial thromboplastin time (APTT), has not been assessed in CHF. The present study endeavored to explore the association between the coagulation disorder score and adverse outcomes of critically ill patients with CHF.

Methods

Patients diagnosed with CHF in the Medical Information Mart for Intensive Care III (MIMIC-III) database were included in the present retrospective cohort study. The coagulation disorder score was calculated according to the abnormalities of the absolute platelet count, INR, and APTT within 24 h after intensive care unit admission. The primary outcomes were the short-term all-cause mortality, including 30-, 90-day and in-hospital mortalities. The Kaplan-Meier (K-M) survival curve and the Cox proportional hazard model were performed to assess the correlation between coagulation disorder score and outcome events.

Results

A total of 6,895 patients were enrolled in this study and divided into four groups according to the coagulation disorder score. K-M survival curve preliminarily indicated that subjects with higher coagulation disorder score presented lower survival rate and shorter survival time. After adjustment for potential confounders, the multivariate Cox analysis further illustrated that elevated coagulation disorder score as a quartile variable was significantly associated with increased all-cause mortality (quartile 4 vs. quartile 1, 30-day: HR [95% CI], 1.98 [1.50, 2.62], 90-day: HR [95% CI], 1.88 [1.49, 2.37], in-hospital: HR [95%CI], 1.93 [1.42, 2.61]).

Conclusion

In critically ill patients with CHF, ones with high coagulation disorder score tend to be worse clinical prognosis, which would be a promising biomarker and helpful for the management of CHF patients.