Prognostic Predictor For Hepatocellular Carcinoma Research Articles

A novel single-cell model reveals ferroptosis-associated biomarkers for individualized therapy and prognostic prediction in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a prevalent malignancy with a pressing need for improved therapeutic response and prognosis prediction. This study delves into a novel predictive model related to ferroptosis, a regulated cell death mechanism disrupting metabolic processes.ResultsSingle-cell sequencing data analysis identified subpopulations of HCC cells exhibiting activated ferroptosis and distinct gene expression patterns compared to normal tissues. Utilizing the LASSO-Cox algorithm, we constructed a model with 10 single-cell biomarkers associated with ferroptosis, namely STMN1, S100A10, FABP5, CAPG, RGCC, ENO1, ANXA5, UTRN, CXCR3, and ITM2A. Comprehensive analyses using these biomarkers revealed variations in immune infiltration, tumor mutation burden, drug sensitivity, and biological functional profiles between risk groups. Specific associations were established between particular immune cell subtypes and certain gene expression patterns. Treatment response analyses indicated potential benefits from anti-tumor immune therapy for the low-risk group and chemotherapy advantages for the high-risk group.ConclusionsThe integration of this single-cell level model with clinicopathological features enabled accurate overall survival prediction and effective risk stratification in HCC patients. Our findings illuminate the potential of ferroptosis-related genes in tailoring therapy and prognosis prediction for HCC, offering novel insights into the intricate interplay among ferroptosis, immune response, and HCC progression.

Integrated analysis of single-cell and bulk RNA sequencing data reveals a cellular senescence-related signature in hepatocellular carcinoma.

The mortality of hepatocellular carcinoma (HCC) is on the rise globally, particularly in the Western world, with etiology gradually shifting from virus-related liver diseases to metabolic disorders such as non-alcoholic fatty liver disease. Early detection of HCC is challenging, and effective prognostic indicators are currently lacking, urgently necessitating reliable markers to assist in treatment planning and clinical management. Here, we introduce hepatocellular carcinoma senescence genes (HSG) to assess cellular senescence in HCC and devise a hepatocellular carcinoma senescence score (HSS) for prognostic prediction. Higher HSS levels signify poorer prognosis and increased tumor proliferation activity. Additionally, we observe alterations in the tumor immune microenvironment with higher HSS levels, such as increased infiltration of Treg, potentially providing a basis for immunotherapy. Furthermore, we identify key genes, such as PTTG1, within the senescence gene set and demonstrate their regulatory roles in HCC cells and Treg through experimentation. In summary, we establish a scoring system based on hepatocellular carcinoma senescence genes for prognostic prediction in HCC, potentially offering guidance for clinical treatment planning.

Identification of a novel matrix metalloproteinases-related prognostic signature in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide. Cancer cells' local infiltration, proliferation, and spread are mainly influenced by the protein hydrolyzing function of different matrix metalloproteinases (MMPs). However, no study has determined the relationship between MMPs and prognostic prediction in HCC. Expression profiles of mRNA and MMPs-related genes were obtained from publicly available databases. Cox regression and LASSO Cox regression analysis were used to identify and predict MMPs-related prognostic signature and construct predictive models for overall survival (OS). A nomogram was used to validate the accuracy of the prediction model. Drug prediction was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) dataset, and single-cell clustering analysis was performed to further understand the significance of the MMPs-related signature. A MMPs-related prognostic signature (including RNPEPL1, ADAM15, ADAM18, ADAMTS5, CAD, YME1L1, AMZ2, PSMD14, and COPS6) was identified. Using the median value, HCC patients in the high-risk group showed worse OS than those in the low-risk group. Immune microenvironment analysis showed that patients in the high-risk group had higher levels of M0 and M2 macrophages. Drug sensitivity analysis revealed that the IC50 values of sorafenib, cisplatin, and cytarabine were higher in the high-risk group. Finally, the single-cell cluster analysis results showed that YME1L1 and COPS6 were the major genes expressed in the monocyte cluster. A novel MMPs-related signature can be used to predict the prognosis of HCC. The findings of this research could potentially impact the predictability of the prognosis and treatment of HCC.

Exploring the role of NAA40 in immune infiltrates and prognostic prediction in hepatocellular carcinoma

Exploring the role of NAA40 in immune infiltrates and prognostic prediction in hepatocellular carcinoma

Albumin-Based Liver Reserve Models vs. MELD 3.0 in Prognostic Prediction for Hepatocellular Carcinoma Patients with Renal Insufficiency.

The severity of liver functional reserve is an important prognostic predictor in hepatocellular carcinoma (HCC). The albumin-bilirubin (ALBI), easy (EZ)-ALBI, platelet-albumin-bilirubin (PALBI), platelet-albumin (PAL) score, and MELD 3.0 score are used to evaluate the severity of liver dysfunction. However, their prognostic role in HCC patients, specifically with renal insufficiency (RI), is unclear. We aimed to investigate the predictive accuracy of the five models in these patients. A total of 1120 newly diagnosed HCC patients with RI were enrolled. A multivariate Cox proportional analysis was used to identify independent predictors associated with survival. In the Cox model, older age, an α-fetoprotein ≥20 ng/mL, vascular invasion, a medium and high tumor burden score, poor performance status, a higher ALBI grade, an EZ-ALBI grade, a PALBI grade, a PAL grade, and MELD 3.0 score were all independently associated with decreased overall survival (all p < 0.001). Among the five liver reserve models, the ALBI grade is the best surrogate marker to represent liver functional reserve in terms of outcome prediction. The albumin-based liver reserve models (ALBI, EZ-ALBI, PALBI, and PAL) and MELD 3.0 are all feasible prognostic markers to indicate liver injury, specifically in HCC patients with RI. Among them, the ALBI grade is the most robust tool for survival prediction in these patients.

A novel metabolism-related gene signature in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) remains a global challenge as it is the sixth most common neoplasm worldwide and the third leading cause of cancer-related death. A key feature of HCC is abnormal metabolism, which promotes cancer cell proliferation, survival, invasion, and metastasis. However, the significance of metabolism-related genes (MRGs) in HCC remains to be elucidated. Here, we aim to establish a novel metabolism-related prognostic signature for the prediction of patient outcomes and to investigate the value of MRG expression in the prognostic prediction of HCC. In our research, a Metabolism-Related Risk Score (MRRS) model was constructed using 14 MRGs (DLAT, SEPHS1, ACADS, UCK2, GOT2, ADH4, LDHA, ME1, TXNRD1, B4GALT2, AK2, PTDSS2, CSAD, and AMD1). The Kaplan-Meier curve confirmed that the MRRS has a high accuracy in predicting the prognosis of HCC patients (p<0.001). According to the MRRS model, the area under the curve (AUC) values for predicting the prognosis of patients with hepatocellular carcinoma at 1, 3, and 5 years reached 0.829, 0.760, and 0.739, respectively. Functional analyses revealed that signaling pathways associated with the cell cycle were largely enriched by differential genes between high and low-risk groups. In addition, dendritic cells (DCs) (p<0.001), CD4+ T cells (p<0.01), CD8+ T cells (p<0.001), B cells (p<0.001), neutrophils (p<0.001), macrophages (p<0.001) had a higher proportion of infiltrates in high-risk populations. Low GOT2 expression is associated with poor prognosis in patients with hepatocellular carcinoma. Knockdown of GOT2 significantly increased the migration capacity of the Huh7 and MHCC97H hepatocellular carcinoma lines. Our research reveals that GOT2 is negatively related to the survival of patients with hepatocellular carcinoma and GOT2 may contribute to tumor progression by inhibiting the ability of tumor cells to migrate.

A Novel Gene Pair CSTF2/DPE2A Impacts Prognosis and Cell Cycle of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), one of the commonest cancers at present, possesses elevated mortality. This study explored the predictive value of CSTF2/PDE2A for HCC prognosis. In this study, clinical information and RNA sequencing expression profiles of HCC patients were acquired from common databases. Kaplan-Meier curve compound with time-dependent ROC curve, nomogram model, and univariate/multivariate Cox analysis were carried out to access the prediction capacity of CSTF2/PDE2A. The immune status, tumor microenvironment, drug sensitivity, biological function and pathway between HCC and adjacent non-tumorous tissue were analyzed and compared. Finally, RT-qPCR, Western blot, and apoptosis assays were performed to verify the effect on HCC cells of CSTF2/PDE2A. The optimal cut-off value of CSTF2, PDE2A and CSTF2/PDE2A was 6.95, 0.95 and 3.63, respectively. In TCGA and ICGC cohorts, the high group of CSTF2/PDE2A presented higher OS compared to low group. The area under the curve (AUC) for OS at 1-, 2-, and 3-years predicted by CSTF2/PDE2A were 0.731/0.695, 0.713/0.732 and 0.689/0.755, higher than the counterparts of the single gene CSTF2 and PDE2A. Multivariate Cox analysis revealed that CSTF2/PDE2A (HR = 1.860/3.236, 95% CI = 1.265-2.733/1.575-6.645) was an independent prognostic factor for HCC. The OS nomogram model created according to five independent factors including CSTF2/PDE2A showed excellent capacity for HCC prognosis. Furthermore, the immune status of the CSTF2/PDE2A high group was deleted, cell cycle-related genes and chemotherapy resistance were increased. Finally, cell experiments revealed distinct differences in the proliferation, apoptosis, protein and mRNA expression of HCC cells after si-CSTF2 transfection compared with the negative control. Taken together, the gene pair CSTF2/PDE2A is able to forecast the prognosis of HCC and regulates cell cycle, which is promising as a novel prognostic predictor of HCC.

Surgical resection versus transarterial chemoembolization for patients with hepatocellular carcinoma beyond Milan criteria: prognostic role of tumor burden score

Tumor burden score (TBS) has been recently introduced to indicate the extent of tumor burden in different cancers, but its role in advanced hepatocellular carcinoma (HCC) is unclear. We aimed to determine the prognostic role of TBS in patients with HCC beyond the Milan criteria receiving surgical resection (SR) or transarterial chemoembolization (TACE). A total of 1303 newly diagnosed HCC patients beyond Milan criteria receiving SR or TACE as the primary therapy were retrospectively analyzed. Independent prognostic predictors were examined by the multivariate Cox proportional hazards model. SR was associated with better overall survival compared with TACE in these patients. Multivariate Cox analysis of the entire cohort revealed that age > 66 years (hazard ratio [HR]: 1.145, 95% confidence interval [CI]: 1.004–1.305, p = 0.043), serum α-fetoprotein > 200 ng/mL (HR: 1.602, 95% CI: 1.402–1.831, p < 0.001), performance status 2–4 (HR: 1.316, 95% CI: 1.115–1.553, p < 0.001), medium TBS (HR: 1.225, 95% CI:1.045–1.436, p = 0.012), high TBS (HR: 1.976, 95% CI: 1.637–2.384, p < 0.001), albumin-bilirubin (ALBI) grade 2–3 (HR: 1.529, 95% CI: 1.342–1.743, p < 0.001), presence of vascular invasion (HR: 1.568, 95% CI: 1.354–1.816, p < 0.001), and TACE (HR: 2.396, 95% CI: 2.082–2.759, p < 0.001) were linked with decreased survival. SR consistently predicted a significantly better survival in different TBS subgroups. TBS is a feasible and independent prognostic predictor in HCC beyond the Milan criteria. SR provides better long-term outcome compared with TACE in these patients independent of TBS grade, and should be considered as the primary treatment modality in this special patient group.

Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis.

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan-Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored.

A nomogram based on 4-lncRNAs signature for improving prognostic prediction of hepatocellular carcinoma.

Long noncoding RNAs (lncRNAs) with abnormal expression are frequently seen in hepatocellular cancer patients (HCC). Previous studies have reported the correlation between lncRNA and prognosis processes of HCC patients. In this research, a graphical nomogram with lncRNAs signatures, T, M phases was developed using the rms R package to estimate the survival rates of HCC patients in year 1, 3, and 5. To find the prognostic lncRNA and create the lncRNA signatures, univariate Cox survival analysis and multivariate Cox regression analysis were chosen. The rms R software package was used to build a graphical nomogram based on lncRNAs signatures to predict the survival rates in of HCC patients in 1, 3, and 5years. Using "edgeR", "DEseq" R packages to find the differentially expressed genes (DEGs). Firstly, a total of 5581 DEGs including 1526 lncRNAs and 3109 mRNAs were identified through bioinformatic analysis, of which 4 lncRNAs (LINC00578, RP11-298O21.2, RP11-383H13.1, RP11-440G9.1) were identified to be strongly related to the prognosis of liver cancer (P < 0.05). Moreover, we constructed a 4-lncRNAs signature by using the calculated regression coefficient. 4-lncRNAs signature is identified to significantly correlated with clinical and pathological characteristics (such as T stage, and death status of HCC patients). A prognostic nomogram on the base of 4-lncRNAs markers was built, which is capable to accurately predict the 1-year, 3-year, and 5-year survival of HCC patients after the construction of the 4-lncRNAs signature linked with prognosis of HCC.

Development and validation of robust metabolism-related gene signature in the prognostic prediction of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism-related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism-related genes associated with prognosis, a risk score based on metabolism-related genes was formulated via the LASSO-Cox and LASSO-pcvl algorithms. According to the risk score, patients were stratified into low- and high-risk groups, and further analysis and validation were accordingly conducted. The results revealed that high-risk patients had a significantly worse 5-year overall survival (OS) than low-risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302-0.651; p< 0.001) This observation was confirmed in the external TCGA-LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299-0.681; p< 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism-based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.

Integrative analysis of the characteristic of lipid metabolism-related genes for the prognostic prediction of hepatocellular carcinoma: Retraction.

Integrative analysis of the characteristic of lipid metabolism-related genes for the prognostic prediction of hepatocellular carcinoma: Retraction.

DLGAP4 acts as an effective prognostic predictor for hepatocellular carcinoma and is closely related to tumour progression

Disc large associated protein 4 (DLGAP4) plays an important role in neurological diseases, but the role and mechanism of DLGAP4 in hepatocellular carcinoma (HCC) remain unclear. In this study, the prognostic effect of DLGAP4 on HCC patients was investigated by means of bioinformatics. The correlation of DLGAP4 expression with the prognosis of HCC patients was evaluated by TCGA data analysis, and the correlation between DLGAP4 expression and the clinical characteristics of HCC patients was evaluated by the Wilcoxon signed rank test and logistic regression analysis. Kaplan‒Meier and Cox regression methods were used to assess the effect of DLGAP4 expression level on overall survival, and nomograms were used to illustrate the correlation between DLGAP4 gene expression and HCC risk. The genes related to DLGAP4 in HCC were screened, and GO/KEGG enrichment analysis was performed. Furthermore, in vitro and in vivo experiments were conducted to detect the effect of DLGAP4 expression on the proliferation, migration and metastasis of HCC cells. We also examined the effect of DLGAP4 expression on enriched pathway proteins to explore the possible mechanism. The expression levels of DLGAP4 were significantly higher in HCC cell lines and tissue samples than in normal liver cell lines and tissues. The expression of DLGAP4 was significantly associated with clinical characteristics. Survival analysis showed that high expression of DLGAP4 was associated with a poor prognosis in HCC. Multivariate analysis showed that high expression of DLGAP4 was an independent risk factor affecting the overall survival rate in HCC patients. By means of ROC curve analysis and nomograms, we determined the value of DLGAP4 expression in the diagnosis and prognosis evaluation of HCC. GO/KEGG enrichment analysis showed that the PPAR signalling pathway was differentially enriched in patients with high expression of DLGAP4. According to in vitro and in vivo experiments, DLGAP4 knockdown inhibited the proliferation and metastasis of HCC cells and decreased the expression of PPARβ/δ protein. In contrast, overexpression of DLGAP4 promoted the proliferation and metastasis of HCC cell, and increased the expression of PPARβ/δ protein.In contrast, overexpression of DLGAP4 promoted the proliferation and metastasis of HCC cells and increased the expression of PPARβ/δ protein. The results show a close correlation between DLGAP4 expression and clinicopathological features of HCC, and DLGAP4 can be used as a prediction biomarker of HCC.

Homotypic cell-in-cell structures as an adverse prognostic predictor of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant liver tumors. A homotypic cell-in-cell structure (hoCIC) refers to one or more cells internalized into the same type as their neighbors, which predominantly occurs in multiple tumors. The objective of this study was to investigate the prognostic value of hoCICs in HCC and its relationship with other clinicopathological features. By immunostaining analysis of a panel of HCC tissues, we found that hoCICs were prevalent in tumor tissues (54/90) but not in para-tumor tissues (17/90). The presence of hoCICs in tumor tissues was closely associated with E-cadherin expression. The presence of CICs was identified as significantly associated with poor survival rates of patients with HCC, comparable to traditional clinicopathological parameters, such as histological grade [hazard ratio (HR) = 0.734, p = 0.320]. Multivariate Cox regression analysis further confirmed that CICs were an independent risk factor for poor survival (HR = 1.902, p = 0.047). In addition, hoCICs were the predominant contributor in a nomogram model constructed for survival prediction at 1, 3, and 5 years [the areas under the curve (AUCs) were 0.760, 0.733, and 0.794, respectively]. Stratification analysis indicated that hoCICs tend to selectively affect patients with high-grade disease (HR = 2.477, p = 0.009) and at the early TNM stage (HR = 2.351, p = 0.05). Thus, hoCICs predict poor survival of patients with HCC, particularly those with higher grades and at an early stage.

Integrative analysis of the characteristic of lipid metabolism-related genes for the prognostic prediction of hepatocellular carcinoma.

Background:Dysregulation of lipid metabolism is implicated in the progression of hepatocellular carcinoma (HCC). We therefore investigated the molecular characteristics of lipid-metabolism-related genes in HCC.Methods:Multi-dimensional bioinformatics analysis was conducted to comprehensively identify the lipid metabolism-related genes (LMRGs) from public databases, as well as the clinical information, immune features, and biological characteristics of HCC. The IMGR were then used to classify HCC into molecular phenotypes. Six lipid metabolism-related genes sets with the potential to predict the prognosis of HCC patients were identified.Results:A total of 770 HCC patients with complete clinical information and corresponding 776 LMRGs were downloaded from 3 databases. Univariate cox and non-negative matrix factorization analyses were used to classify HCC patients into 2 clusters. This molecular classification was associated with overall survival, clinical characteristics, and immune cells. The biological function of the differentially expressed LMRGs in the 2 clusters showed the genes associated with tumor-related metabolism pathways. A combination of multivariate/univariate cox regression and least absolute shrinkage and selection operator analyses were conducted to build a 6 LMRGs signature (6-IS) to predict the prognosis of HCC. The 6-IS signature was found to be an independent prognostic factor. Performance of the 6-IS prognostic signature was verified in a validation set and compared with an external data set. Results revealed the 6-IS signature could effectively predict the prognosis of patients with HCC.Conclusions:This study provides new insights into the role of LMRG in the pathogenesis of HCC and presents a novel prognostic signature 6-IS monitoring the clinical course of HCC.

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

BackgroundOncoprotein-Induced Transcript 3 Protein (OIT3) was identified as a liver-specific gene with abnormal expression in hepatocellular carcinoma (HCC). Herein, we aimed to examine the function and specific mechanism of OIT3 in HCC.MethodsBioinformatic analyses and tissue microarray via immunohistochemistry were used to validate the expression of OIT3 in HCC. The biofunctions of OIT3 in HCC were determined in vitro and in vivo. The mechanism was confirmed by RNA-Sequence and Western blotting. The uni- and multivariate analyses were used to identify the independent predictors for HCC.ResultsLow expression of OIT3 was observed in HCC and predicted a poor clinical outcome. Ectopic expression of OIT3 could inhibit the proliferation, migration, and invasion abilities of HCC cells. Mechanistically, OIT3 upregulated the expression of ALOX15 and CYP4F3, thus inducing arachidonic acid increase, ROS accumulation, and lipid peroxidation, and eventually causing ferroptosis. OIT3 was validated as a prognostic predictor for HCC patients.ConclusionsOur findings revealed a novel role of OIT3 in the process of tumorigenesis of HCC. OIT3 inhibited reproliferation, migration, and invasion of HCC cells by triggering ferroptosis, which indicates that OIT3 could serve as a potential biomarker in HCC.

Prothrombin induced by vitamin K Absence-II versus alpha-fetoprotein in detection of both resectable hepatocellular carcinoma and early recurrence after curative liver resection: A retrospective cohort study

BackgroundAlpha-fetoprotein (AFP) and prothrombin induced by vitamin K absence-II (PIVKA-II) are two commonly used biomarkers for detection and prognostic prediction of hepatocellular carcinoma (HCC). This study sought to evaluate and compare the use of these two biomarkers to detect HCC, as well as predict postoperative early recurrence (within 2 years after HCC resection). MethodsData on consecutive patients who underwent curative resection for HCC between 2014 and 2020 was prospectively collected and reviewed. Serum AFP and PIVKA-II levels within one week before surgery or at the time of detection of early recurrence were assessed; preoperative AFP positivity (≥20 ng/ml) and preoperative PIVKA-II positivity (≥40 mAU/ml) were examined relative to recurrence using univariate and multivariate Cox-regression analyses. ResultsAmong 751 patients who underwent curative HCC resection, 589 (78.4%) patients had preoperative PIVKA-II positivity versus 498 (66.3%) patients had preoperative AFP positivity (P < 0.001). With a median follow-up of 41.6 months, 370 (50.1%) patients had an early HCC recurrence; among patients with an early recurrence, the proportion of patients with PIVKA-II positivity versus AFP positivity (76.5% vs. 60.0%, P = 0.002) was higher. On multivariate analysis, preoperative PIVKA-II positivity, but not preoperative AFP positivity was an independent risk factor to predict early recurrence after HCC resection. ConclusionsAFP and PIVKA-II are useful biomarkers to detect resectable HCC and predict early recurrence after HCC resection, with the latter showing higher rates of positivity. Preoperative PIVKA-II positivity was independently associated with early recurrence following HCC resection.

Combination of Preoperative Circulating Tumor Cell Count and Neutrophil-Lymphocyte Ratio for Prognostic Prediction in Hepatocellular Carcinoma Patients after Curative Hepatectomy.

Background Both the preoperative neutrophil-lymphocyte ratio (NLR) and circulating tumor cell count (CTC) are associated with poor prognosis in hepatocellular carcinoma (HCC). The purpose of this study was to explore the prognostic value of these two indices (CTC-NLR) in HCC. Methods We retrospectively collected demographic and clinical data, including NLR and CTC, from 97 patients with HCC who underwent curative hepatectomy at our institution from March 2014 to May 2017. X-Tile software was used to confirm the optimal cut-off value of NLR and CTC for predicting overall survival (OS) in this study. OS were also analyzed using Kaplan-Meier and Cox regression methods. Based on preoperative CTC and NLR, patients were divided into three groups: CTC-NLR (0), CTC-NLR (1), and CTC-NLR (2). Relationships of CTC-NLR with clinicopathological factors and survival were evaluated. Results Preoperatively, CTC positively correlated with NLR. Patients with NLR and CTC higher than the cut-offs had shorter OS than patients with low NLR and CTC. Kaplan-Meier analysis, and log-rank tests revealed significantly lower OS among patients with CTC-NLR scores of 0, 1, and 2. Uni- and multivariate analyses showed that CTC-NLR (hazard ratio 2.050, P = 0.005), CTC (hazard ratio 2.285, P = 0.032), and NLR (hazard ratio 1.902, P = 0.048) were independent predictor of OS. A time-dependent ROC curve indicated that the prognostic efficacy of the CTC-NLR at 1 year (0.714) was better than that of NLR (0.687) and CTC (0.590); the prognostic efficacy of the CTC-NLR at 2 years (0.746) was better than that of NLR (0.711) and CTC (0.601); the prognostic efficacy of the CTC-NLR at 3 years (0.742) was better than that of NLR (0.694) and CTC (0.629). Conclusions HCC patients with higher NLR and CTC tend to show shorter OS. Preoperative CTC-NLR may be associated with poor survival and might be a reliable prognostic predictor in HCC after curative hepatectomy.

A multiomic ferroptosis-associated prognostic signature incorporating epigenetic and transcriptional biomarkers for hepatocellular carcinoma.

BackgroundFerroptosis was reported to have tremendous promise in the treatment and prognosis of hepatocellular carcinoma (HCC). Here, we identified a novel ferroptosis-related prognostic signature incorporating epigenetic and transcriptional biomarkers could help predicting survival of patients with HCC.MethodsWe employed multi-omics and clinical data from The Cancer Genome Atlas (TCGA) database to identify the ferroptosis-associated methylation CpG sites associated with HCC survival using sure independence screening (SIS). Then we utilized Kaplan-Meier curves to evaluate the prognostic significance of gene expression and DNA methylation. Receiver operating characteristic (ROC) curve was used predicting the 3- and 5-year survival. Mediation analysis of ferroptosis-related methylation and transcriptional score was performed.ResultsWe firstly identified 114 significant CpG sites under the criteria of false discovery rate (FDR) <0.05 in training set. Then we screened out 5 candidate CpG sites in validation set for multivariate screening and stepwise regression. We found that the high-risk group had significantly shorter survival time than the low-risk group in the prognostic signature combined with epigenetic and transcriptional scores (HR =2.72 95% CI: 2.01–3.68, P=8.75E-11). And the predictive model involving clinical information, gene expression, and methylation data performed best for 3-year survival prediction (AUC =0.672) and 5-year survival prediction (AUC =0.742).ConclusionsOur results suggested a signature combining clinical information, ferroptosis-related gene expression, and methylation presented a superior ability for prognostic prediction in HCC, which may bring us novel tool and targets in the treatment of HCC.

Identification and Validation of Prognosis-Related Necroptosis Genes for Prognostic Prediction in Hepatocellular Carcinoma.

Background The prediction of hepatocellular carcinoma (HCC) survival is challenging because of its rapid progression. In recent years, necroptosis was found to be involved in the progression of multiple cancer types. However, the role of necroptosis in HCC remains unclear. Methods Clinicopathological parameters and transcriptomic data of 370 HCC patients were obtained from TCGA-LIHC dataset. Prognosis-related necroptosis genes (PRNGs) were identified and utilized to construct a LASSO risk model. The GEO cohorts (GSE54236 and GSE14520) were used for external validation. We evaluated the distribution of HCC patients, the difference in prognosis, and the accuracy of the prognostic prediction of the LASSO risk model. The immune microenvironment and functional enrichment of different risk groups were further clarified. Finally, we performed a drug sensitivity analysis on the PRNGs that constructed the LASSO model and verified their mRNA expression levels in vitro. Results: A total of 48 differentially expressed genes were identified, 23 of which were PRNGs. We constructed the LASSO risk model using nine genes: SQSTM1, FLT3, HAT1, PLK1, MYCN, KLF9, HSP90AA1, TARDBP, and TNFRSF21. The outcomes of low-risk patients were considerably better than those of high-risk patients in both the training and validation cohorts. In addition, stronger bile acid metabolism, xenobiotic metabolism, and more active immune cells and immune functions were observed in low-risk patients, and high expressions of TARDBP, PLK1, and FLT3 were associated with greater drug sensitivity. With the exception of FLT3, the mRNA expression of the other eight genes was verified in Huh7 and 97H cells. Conclusions. The PRNG signature provides a novel and effective method for predicting the outcome of HCC as well as potential targets for further research.