Predictor For Hepatocellular Carcinoma Patients Research Articles

Hepatocellular Carcinoma Subtyping and Prognostic Model Construction Based on Chemokine-Related Genes

Background: Chemokines not only regulate immune cells but also play significant roles in development and treatment of tumors and patient prognoses. However, these effects have not been fully explained in hepatocellular carcinoma (HCC). Materials and Methods: We conducted a clustering analysis of chemokine-related genes. We then examined the differences in survival rates and analyzed immune levels using single-sample Gene Set Enrichment Analysis (ssGSEA) for each subtype. Based on chemokine-related genes of different subtypes, we built a prognostic model in The Cancer Genome Atlas (TCGA) dataset using the survival package and glmnet package and validated it in the Gene Expression Omnibus (GEO) dataset. We used univariate and multivariate regression analyses to select independent prognostic factors and used R package rms to draw a nomogram reflecting patient survival rates at 1, 3, and 5 years. Results: We identified two chemokine subtypes and, after screening, found that Cluster1 had higher survival rates than Cluster2. In addition, in terms of immune evaluation, stromal evaluation, ESTIMATE evaluation, immune abundance, immune function, and expressions of various immune checkpoints, immune levels of Cluster1 were significantly better than those of Cluster2. The immunophenoscore (IPS) of HCC patients in Cluster1 was significantly higher than that in Cluster2. Furthermore, we established a prognostic model consisting of 9 genes, which correlated with chemokines. Through testing, Riskscore was revealed as an independent prognostic factor, and the model could effectively predict HCC patients’ prognoses in both TCGA and GEO datasets. Conclusion: This study resulted in the development of a novel prognostic model related to chemokine genes, providing new targets and theoretical support for HCC patients.

Risk Predictive Model Based on Three DDR-Related Genes for Predicting Prognosis, Therapeutic Sensitivity, and Tumor Microenvironment in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Tumor mutational load, genomic instability, and tumor-infiltrating lymphocytes were associated with DNA damage response and repair gene changes. The goal of this study is to estimate the chances of patients with HCC surviving their disease by constructing a DNA damage repair- (DDR-) related gene profile. The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) provided us with the mRNA expression matrix as well as clinical information relevant to HCC patients. Using Cox regression and LASSO analysis, DEGs strongly related to general survival were discovered in the differentially expressed gene (DEG) study. In order to assess the model's accuracy, Kaplan-Meier (KM) and receiver operating characteristic (ROC) were used. In order to compute the immune cell infiltration score and immune associated pathway activity, a single-sample gene set enrichment analysis was performed. A three-gene signature (CDC20, TTK, and CENPA) was created using stability selection and LASSO COX regression. In comparison to the low-risk group, the prognosis for the high-risk group was surprisingly poor. In the ICGC datasets, the predictive characteristic was confirmed. A receiver operating characteristic (ROC) curve was calculated for each cohort. The risk mark for HCC patients is a reliable predictor according to multivariate Cox regression analysis. According to ssGSEA, this signature was highly correlated with the immunological state of HCC patients. There was a significant correlation between the expression levels of prognostic genes and cancer cells' susceptibility to antitumor therapies. Overall, a distinct gene profile associated with DDR was identified, and this pattern may be able to predict HCC patients' long-term survival, immune milieu, and chemotherapeutic response.

A Three-Gene Signature for Predicting the Prognosis of Patients Treated with Transarterial Chemoembolization (TACE) and Identification of PD-184352 as a Potential Drug to Reverse Nonresponse to TACE.

Background Transarterial chemoembolization (TACE) is a first-line treatment for patients with unresectable hepatocellular carcinoma (HCC). Owing to differences in its efficacy across individuals, determining the indicators of patient response to TACE and finding approaches to reversing nonresponse thereto are necessary. Methods Transcriptome data were obtained from the GSE104580 dataset, in which patients were marked as having TACE response or nonresponse. We identified differentially expressed genes (DEGs) and performed Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. We screened genes with a prognostic value for TACE in the HIF-1 signaling pathway by univariate regression analysis. By using least absolute shrinkage and selection operator (LASSO) Cox regression, we established a multigene signature in GSE14520, which we verified using a drug sensitivity test. The Connectivity Map (CMap) database was used to find potential drugs to reverse nonresponse to TACE. Results We constructed a prognostic signature consisting of three genes (erythropoietin (EPO), heme oxygenase 1 (HMOX1), and serine protease inhibitor 1 (SERPINE1)) that we validated by drug sensitivity test. After dividing patients treated with TACE into high- and low-risk groups based on this new signature, we showed that overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group and that the risk score was an independent predictor of OS in patients treated with TACE. Based on our CMap findings, we speculated that PD-184352, an inhibitor of mitogen-activated protein kinase (MEK), had potential as a drug treatment to reverse nonresponse to TACE. We confirmed this speculation by using PD-184352 in a cell promotion experiment in a TACE environment. Conclusion We constructed a TACE-specific three-gene signature that could be used to predict HCC patients' responses to and prognosis after TACE treatment. PD-184352 might have potential as a drug to improve TACE efficacy.

Pyroptosis related genes signature predicts prognosis and immune infiltration of tumor microenvironment in hepatocellular carcinoma

Little is known on the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of hepatocellular carcinoma (HCC). In this study, a specific PRGs prognostic model was developed with an aim to improve therapeutic efficiency among HCC patients. In total, 42 PRGs that were differentially expressed between HCC tissues and adjacent tissues and we exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). There were significant differences in clinical features including T stage, grade, gender, and stage among different clusters. Kaplan–Meier curve analysis showed that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. Based on the univariate analysis and multivariate analysis, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from International Cancer Genome Consortium (ICGC)-LIRI-JP cohort and Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group. Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of HCC patients. Then, ROC curve and survival analysis were performed to evaluate the prognostic prediction value of the model. Finally, we constructed a PRGs clinical characteristics nomogram to further predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency between high-risk group and low-risk group. This work suggests PRGs signature played a crucial role in predicting the prognosis, infiltration of cancer microenvironment, and sensitivity of chemotherapeutic agents.

LncRNA HULC and miR-122 Expression Pattern in HCC-Related HCV Egyptian Patients

Hepatocellular carcinoma (HCC) is a highly prevalent malignancy. It is a common type of cancer in Egypt due to chronic virus C infection (HCV). Currently, the frequently used lab test is serum α-fetoprotein. However, its diagnostic value is challenging due to its low sensitivity and specificity. Genetic biomarkers have recently provided new insights for cancer diagnostics. Herein, we quantified Lnc HULC and miR-122 gene expression to test their potential in diagnosis. Both biomarkers were tested in the sera of 60 HCC patients and 60 with chronic HCV using real-time RT-PCR. miR-122 was highly expressed in HCV patients with a significant difference from the HCC group (p = 0.004), which points towards its role in prognosis value as a predictor of HCC in patients with chronic HCV. HULC was more highly expressed in HCC patients than in the HCV group (p = 0.018), indicating its potential use in screening and the early diagnosis of HCC. The receiver operating characteristic (ROC) curve analysis showed their reliable sensitivity and specificity. Our results reveal that miR-122 can act as a prognostic tool for patients with chronic HCV. Furthermore, it is an early predictor of HCC. LncRNA HULC can be used as an early diagnostic tool for HCC.

Transcriptome sequencing of hepatocellular carcinoma uncovers multiple types of dysregulated ncRNAs.

Transcriptome profiling of hepatocellular carcinoma (HCC) by next-generation sequencing (NGS) technology has been broadly performed by previous studies, which facilitate our understanding of the molecular mechanisms of HCC formation, progression, and metastasis. However, few studies jointly analyze multiple types of noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and micro-RNAs (miRNAs), and further uncover their implications in HCC. In this study, we observed that the circRNA cZRANB1 and lncRNA DUXAP10 were not only significantly upregulated in tumor tissues, but also higher expressed in blood exosomes of HCC as compared with healthy donors. From the analysis of subclass-associated dysregulated ncRNAs, we observed that DLX6-AS1, an antisense RNA of DLX6, and the sense gene DLX6 were highly expressed in S1, a subclass with a more invasive/disseminative phenotype. High correlation between DLX6-AS1 and DLX6 suggested that DLX6-AS1 may function via promoting the transcription of DLX6. Integrative analysis uncovers circRNA–miRNA, lncRNA–miRNA, and competing endogenous RNA networks (ceRNAs). Specifically, cZRANB1, LINC00501, CTD-2008L17.2, and SLC7A11-AS1 may function as ceRNAs that regulate mRNAs by competing the shared miRNAs. Further prognostic analysis demonstrated that the dysregulated ncRNAs had the potential to predict HCC patients’ overall survival. In summary, we identified some novel circRNAs and miRNAs, and dysregulated ncRNAs that could participate in HCC tumorigenesis and progression by inducing transcription of their neighboring genes, increasing their derived miRNAs, or acting as miRNA sponges. Moreover, our systematic analysis provides not only rich data resources for related researchers, but also new insights into the molecular basis of how different ncRNAs coordinately or antagonistically participate in the pathogenesis process of diseases.

A Genomic Instability-Related Long Noncoding RNA Signature for Predicting Hepatocellular Carcinoma Prognosis.

Background Long noncoding RNAs (lncRNAs) are found to be novel biomarkers for hepatocellular carcinoma (HCC) and play an important role in tumor progression. We established a genomic instability-related long noncoding RNA signature (GIlncSig) as an independent prognosis factor and also investigated its impact on prognosis significance. Method Somatic mutation profiles, clinical characteristics, and RNA sequencing data were obtained from The Cancer Genome Atlas (TCGA) database. Lasso regression was used to construct GIlncSig. KEGG was used to identify the possible biological pathways. ESTIMATE and CIBERSORT algorithms were used to calculate the immune microenvironment scores and proportion of immune cells in HCC patients. The expression of LINC00501 was conducted by qRT-PCR. Cell proliferation was measured by EdU, CCK-8, and colony formation assay, and cell migration and invasion ability were measured by wound healing and transwell assay. Results 135 genomic instability-related lncRNAs were identified, and GIlncSig was constructed using 13 independent lncRNAs with significant prognosis values. Based on the GIlncSig, high-risk group had worse clinical outcomes than low-risk group, while high-risk group also had higher UBQLN4, KRAS, ARID1A, and PIK3CA expression. Moreover, the efficiency of GIlncSig combining single-gene mutation was higher than single-gene mutation alone such as TP53. The results of CIBERSORT and ESTIMATE showed that GS group and GU group had significantly different immune infiltration. In addition, LINC00501 was identified as a potential biomarker in HCC with strong relationship with clinical characteristics. In vitro assays validated that LINC00501 promoted proliferation and migration of HCC cell lines. Conclusion Our results showed that GIlncSig serves as a potential independent prognosis factor to predict HCC patients' prognosis for exploring potential mechanism and therapy strategy. Besides, LINC00501 plays an important role in the progression of HCC, which may be a potential therapy target.

Early complete tumor response as a survival predictor in hepatocellular carcinoma patients receiving stereotactic body radiation therapy

Background and PurposeTo evaluate the different response patterns after Stereotactic Body Radiation Therapy (SBRT) and their predictive value in local control and progression of hepatocellular carcinoma (HCC). Materials and methodsSeventy-two HCC patients who were treated with SBRT during 2015–2020 were included in this retrospective study. The assessment was made using MRI, CT, and PET-CT. Local and systemic responses were determined according to modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria during follow up. Patients were categorized as early responders (complete response during 6 months after radiotherapy) or non-early responders (the rest of the patients). Prognostic factors were determined using multivariate logistic models. ResultsThe median follow-up was 24.0 months (range, 7.7–74.5 months). We found that 84.7%（61/72) of patients achieved a complete response. Early responses occurred in 45 patients (45/72, 62.5%), and they had 1-, 2-, and 5- year intrahepatic outfield-free survival (OutFFS) rates of 86.2%, 80.3%, and 76.3% vs. 55.3%, 44.7%, and 33.5% in non-early responses patients, whereas the 1-, 2-, and 5- year distant metastasis-free survival (DMFS) were 95.5%, 84.5% and 79.5% and 74.1%, 56.2% and 56.2%, respectively. The 1-, 2-, and 5-year overall survival (OS) were 97.7%, 92.1%, 79.1%, and 85.2%, 53.8%, and 40.3%, respectively. Multivariate analysis revealed that early tumor response was an independent predictor of OutFFS, DMFS, and OS. ConclusionsEarly complete tumor response within 6 months after radiotherapy predicted better intrahepatic outfield-free survival, distant metastasis-free survival, and overall survival outcomes. Confirmation is warranted for early response on SBRT to guide decision making.

Integrated Multi-Omics Data Analysis Reveals Associations Between Glycosylation and Stemness in Hepatocellular Carcinoma.

BackgroundGlycosylation plays an essential role in driving the progression and treatment resistance of hepatocellular carcinoma (HCC). However, its function in regulating the acquisition and maintenance of the cancer stemness-like phenotype in HCC remains largely unknown. There is also very little known about how CAD and other potential glycosylation regulators may influence stemness. This study explores the relationship between glycosylation and stemness in HCC.MethodsGene set variance analysis (GSVA) was used to assess the TCGA pan-cancer enrichment in glycosylation-related pathways. Univariate, LASSO, and multivariate COX regression were then used to identify prognostic genes in the TCGA-LIHC and construct a prognostic signature. HCC patients were classified into high- and low-risk subgroups based on the signature. The relationship between gene expression profiles and stemness was confirmed using bulk and single-cell RNA-sequencing data. The role of CAD and other genes in regulating the stemness of HCC was also validated by RT-qPCR, CCK-8, and colony formation assay. Copy number variation (CNV), immune infiltration, and clinical features were further analyzed in different subgroups and subsequent gene expression profiles. Sensitive drugs were also screened.ResultsIn the pan-cancer analysis, HCC was shown to have specific glycosylation alterations. Five genes, CAD, SLC51B, LGALS3, B3GAT3, and MT3, identified from 572 glycosylation-related genes, were used to construct a gene signature and predict HCC patient survival in the TCGA cohort. The results demonstrated a significant positive correlation between patients in the high-risk group and both elevated gene expression and HCC dedifferentiation status. A significant reduction in the stemness-related markers, CD24, CD44, CD20, FOXM1, and EpCAM, was found after the knockdown of CAD and other genes in HepG2 and Huh7 cells. Frequent mutations increased CNVs, immune-suppressive responses, and poor prognosis were also associated with the high-risk profile. The ICGC-LIRI-JP cohort confirmed a similar relationship between glycosylation-related subtypes and stemness. Finally, 84 sensitive drugs were screened for abnormal glycosylation of HCC, and carfilzomib was most highly correlated with CAD.ConclusionsGlycosylation-related molecular subtypes are associated with HCC stemness and disease prognosis. These results provide new directions for further research on the relationship between glycosylation and stemness phenotypes.

Identification of a new five-gene risk score for risk stratification and prognosis prediction in HCC

Approximately 80% of primary liver cancer (PLC) is hepatocellular carcinoma (HCC), and the prognosis of HCC patients is unfavorable. Further studies are required to develop new prognostic tools for predicting the HCC patients’ prognosis. The univariate Cox and LASSO regression were utilized to develop the multi-gene risk score. Single-sample Gene Set Enrichment Analysis (ssGSEA) was employed to assess differences of immune functions and cells. The model performance was evaluated by calibration curve and receiver operating characteristic curve (ROC). And qRT-PCR was utilized to evaluate the genes expression in clinical samples. Finally, a novel five-gene (KIF20A, CENPA, HMMR, G6PD, and ADH4) risk score was developed. Based on the median value of patients’ risk scores, patients were divided into two groups: high-risk group and low-risk group. The Overall survival (OS) of patients in high-risk group was obviously poorer than that in the low-risk group. And the five-gene risk score was an independent risk factor correlated with patients’ OS. Besides, a nomogram consisting of TNM stage and risk score was established. The results of decision curve, calibration curve, and ROC presented that the prognostic risk score and the nomogram had great predictive capability. Besides, ADH4’s mRNA was reduced in HCC tissues, while the mRNA of KIF20A, CENPA, HMMR, and G6PD were overexpressed in HCC tissues. We developed a novel five-gene risk score that could predict HCC patients’ prognosis. And these five genes could be promising therapeutic targets for HCC. The five-gene risk score and nomogram may be useful prognostic tools for HCC.

Alpha-Fetoprotein Ratio Predicts Alpha-Fetoprotein Positive Hepatocellular Cancer Patient Prognosis after Hepatectomy.

Background This study was conducted to investigate the effect of alpha-fetoprotein (AFP) ratio on the prognosis of AFP-positive hepatocellular carcinoma (HCC) patients after hepatectomy. Methods We retrospectively included 879 HCC patients with AFP-positive who underwent hepatectomy from February 2012 to October 2017 and randomly divided into training cohort and validation cohort. AFP ratio was equal to the AFP level within one week before hepatectomy to AFP level within 20-40 days after surgery. The end point of follow-up was disease-free survival (DFS) and overall survival (OS). Results AFP ratio was not associated with clinical characteristics in training cohort and validation cohort. According to the X-tile software, the optimum cut-off point was 17.8 for AFP ratio. Significant differences between AFP ratio high and AFP ratio low were observed in DFS and OS in both cohort (p < 0.05). Kaplan-Meier curves and receiver-operating curves were showed that AFP ratio was better than AFP level preoperation in predicting the prognosis of AFP-positive HCC patients after hepatectomy. The multivariate analysis demonstrated that AFP ratio was a significant independent risk factor for both OS and DFS in HCC patients with AFP-positive. Conclusions AFP ratio might be a prognosis predictor for HCC patients with AFP-positive after hepatectomy.

Epithelial Growth Factor Receptor Mutations Predict Hepatocellular Carcinoma Patient’s Prognosis

Objective: Hepatocellular Carcinoma (HCC) is an aggressive malignancy and the second leading cause of cancer-related death in the world. Epithelial Growth Factor Receptor (EGFR) overexpression has been demonstrated in HCC patients frequently and is a poor prognostic factor. EGFR is a promising target for cancer treatment, but its variety in response by mutation status. Patients and Methods: We retrospective analyzed 28 Hepatocellular Carcinoma (HCC) patients with good quality DNA samples, 5 hepatic dysplasia tissues, and 5 cholangiocarcinoma samples from 1996 to 2002. EGFR mutation status was detected by ARMS and Scorpions real-time PCR, using therascreen® EGFR RGQ PCR Kit. T790M, exon 19 deletions, L858R, L861Q, G719X, S786I, and exon 20 insertions can be detected. Clinical data of 28 HCC patients were collected for the longest period of 17 years. Results: All patients received liver segmentectomy. No one received tyrosine kinase inhibitor treatment during the following period. All liver samples had T790M mutation detected. Based on overall survival, we calculated cut-off points, by ROC curve. In HCC patients with a higher percentage of T790M or G719X mutations had better overall survival (P=0.001, and 0.036). Patients with exon 19 deletion showed a trend of better overall survival. HCC patients with exon 19 deletions, L858R and G719X mutations showed trends of longer Disease Free Survival (DFS) and HCC patients with higher S768I mutation had a trend of shorter DFS. HCC samples from chronic hepatitis B carriers had a higher T790M mutation rate and the ones from chronic hepatitis C carriers showed the opposite in T790M mutation with statistics significant (P=0.013). No EGFR mutations contribute to HCC recurrence. Conclusion: EGFR mutation status can predict HCC patient’s prognosis post liver segmentectomy.

Development and validation of LRP1B mutation-associated prognostic model for hepatocellular carcinoma.

Purpose: To develop a lipoprotein receptor-related protein 1B (LRP1B) gene mutation-based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categorized into high- and low-risk groups. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. The functions of LRP1B and risk factors in the model were verified using both in vivo and in vitro experiments. Results: It could be demonstrated that LRP1B was a potential negative predictor for HCC patients prognosis with high mutation frequency. The functions of LRP1B were verified with ELISA and Quantitative Real-time PCR method based on clinic-recruited HCC participants. Eleven genes displayed significant differences according to LRP1B status, which could better predict HCC patient prognosis. The functions of these genes were examined using HCC cell line HCCLM3, suggesting they played a pivotal role in determining HCC cell proliferation and apoptosis. From the immune cell infiltration ratio analysis, there was a significant difference in the infiltration degree of seven types of immune cells and two immune checkpoints between high- and low-risk HCC patients. Conclusion: The present study hypothesized a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for HCC, which provided a systematic reference for future understanding of clinical research.

A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients.

Hepatocellular carcinoma (HCC) is 1 of the deadliest malignancies worldwide. Despite significant advances in diagnosis and treatment, the mortality rate from HCC persists at a substantial level. Construction of a prognostic model that can reliably predict HCC patients’ overall survival is urgently needed.Two RNA-seq dataset (the Cancer Genome Atlas and International Cancer Genome Consortium) and 1 microarray dataset (GSE14520) were included in our study. RNA-binding proteins (RBPs) in HCC patients was examined by differentially expressed genes analysis, functional enrichment analysis and protein-protein interaction network analysis. Subsequently, the Cancer Genome Atlas dataset was randomly divided into training and testing cohort with a prognostic model developed in the training cohort. In order to evaluate the prognostic value of the model, a comprehensive survival assessment was conducted.Five RBPs (ribosomal protein L10-like, enhancer of zeste homolog 2 (EZH2), peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A), zinc finger protein 239, interferon-induced protein with tetratricopeptide repeats 1) were used to construct the model. The model accurately predicted the prognosis of liver cancer patients in both the training cohort and validation cohort. HCC patients could be assigned into a high-risk group and a low-risk group by this model, and the overall survival of these 2 groups was significantly different (P < .05). Furthermore, the risk scores obtained by this model were highly correlated with immune cell infiltration.The prognostic model helps to identify HCC patients at high risk of mortality, which optimizes decision-making for individualized treatment.

Skeletal Muscle Volume Is an Independent Predictor of Survival after Sorafenib Treatment Failure for Hepatocellular Carcinoma.

Simple SummarySkeletal muscle volume has been reported as a prognostic factor for patients with hepatocellular carcinoma receiving sorafenib. In this study, we show that skeletal muscle volume is not only a predictor of overall survival but also of post-progression survival, which represents survival time following confirmation of progressive disease. We may be able to prolong survival by upregulating skeletal muscle volume, especially in hepatocellular carcinoma patients with skeletal muscle depletion.Few studies exist on the relationship between post-progression survival (PPS) and skeletal muscle volume in hepatocellular carcinoma (HCC) patients receiving sorafenib. This study aimed to analyze the effects of muscle volume on clinical outcomes. We retrospectively enrolled 356 HCC patients. Various clinical parameters, including skeletal muscle index, were analyzed as predictors of overall survival (OS), progression-free survival (PFS), and PPS. Patients with high muscle volume showed longer survival or PPS than those with low muscle volume (median survival time: 12.8 vs. 9.5 months, p = 0.005; median PPS: 8.2 vs. 6.3 months, p = 0.015); however, no differences in PFS were found. Multivariate analysis indicated that muscle volume was an independent predictor of PPS and OS. Skeletal muscle volume was a PPS predictor in HCC patients receiving sorafenib. Therefore, survival can be prolonged by the upregulation of skeletal muscle volume, especially in HCC patients with skeletal muscle depletion.

The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) using methods in systems biology. Differentially expressed mRNAs, miRNAs, and long non-coding RNAs (lncRNAs) were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas (TCGA) database. Subsequently, a prognosis-associated mRNA co-expression network, an mRNA–miRNA regulatory network, and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis. Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs. An expression module including 120 mRNAs was significantly correlated with HCC patient survival. Combined with patient survival data, several mRNAs and miRNAs, including CHST4, SLC22A8, STC2, hsa-miR-326, and hsa-miR-21 were identified from the network to predict HCC patient prognosis. Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC. Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways. The present study provides a bioinformatics method for biomarker screening, leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.

Combination of albumin-bilirubin grade and clinically significant portal hypertension predicts the prognosis of patients with hepatocellular carcinoma after liver resection.

There is little information concerning whether incorporating clinically significant portal hypertension (CSPH) into albumin-bilirubin (ALBI) grading could improve its predictive capacity. In this study, we investigated the predictive ability of ALBI grade plus CSPH (ALBI-P score) for patients with hepatocellular carcinoma (HCC) after liver resection. Data from 1,679 patients were retrospectively reviewed. The ALBI-P score was calculated from the ALBI grade and a point for CSPH (0 for absence of CSPH and 1 for presence of CSPH). Independent risk factors for recurrence-free survival (RFS) and overall survival (OS) were analyzed. Multivariate analysis suggested that the ALBI-P score was an independent risk factor for both postoperative recurrence (HR = 1.441, 95% CI = 1.328-1.563, P < 0.001) and mortality (HR = 1.332, 95% CI = 1.156-1.535, P < 0.001). Both the RFS and OS of patients with an ALBI-P score of 1 were significantly better than those of patients with ALBI-P scores of 2 and 3 (5-year RFS of 38.9%, 26.1%, and 14.7%, respectively, P < 0.001; 5-year OS of 52.7%, 42.6%, and 29.3%, P < 0.001). When the ALBI-P score and BCLC stage were combined, the ALBI-P-BCLC score showed the highest area under the receiver operating characteristic curve to predict both postoperative recurrence and mortality compared with BCLC stage alone, BCLC stage combined with ALBI grade, or platelet-albumin-bilirubin grade. These results suggested incorporating CSPH into the ALBI grade could strengthen its prognostic power. The ALBI-P score may serve as a surrogate marker to predict HCC patient outcomes after liver resection.

Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients

BackgroundLiver cancer is one of the most common malignancies worldwide. HCC (hepatocellular carcinoma) is the predominant pathological type of liver cancer, accounting for approximately 75–85 % of all liver cancers. Lipid metabolic reprogramming has emerged as an important feature of HCC. However, the influence of lipid metabolism-related gene expression in HCC patient prognosis remains unknown. In this study, we performed a comprehensive analysis of HCC gene expression data from TCGA (The Cancer Genome Atlas) to acquire further insight into the role of lipid metabolism-related genes in HCC patient prognosis.MethodsWe analyzed the mRNA expression profiles of 424 HCC patients from the TCGA database. GSEA(Gene Set Enrichment Analysis) was performed to identify lipid metabolism-related gene sets associated with HCC. We performed univariate Cox regression and LASSO(least absolute shrinkage and selection operator) regression analyses to identify genes with prognostic value and develop a prognostic model, which was tested in a validation cohort. We performed Kaplan-Meier survival and ROC (receiver operating characteristic) analyses to evaluate the performance of the model.ResultsWe identified three lipid metabolism-related genes (ME1, MED10, MED22) with prognostic value in HCC and used them to calculate a risk score for each HCC patient. High-risk HCC patients exhibited a significantly lower survival rate than low-risk patients. Multivariate Cox regression analysis revealed that the 3-gene signature was an independent prognostic factor in HCC. Furthermore, the signature provided a highly accurate prediction of HCC patient prognosis.ConclusionsWe identified three lipid-metabolism-related genes that are upregulated in HCC tissues and established a 3-gene signature-based risk model that can accurately predict HCC patient prognosis. Our findings support the strong links between lipid metabolism and HCC and may facilitate the development of new metabolism-targeted treatment approaches for HCC.

Eight-gene metabolic signature related with tumor-associated macrophages predicting overall survival for hepatocellular carcinoma

BackgroundIn recent years, the relationship between tumor-associated macrophages (TAMs) and solid tumors has become a research hotspot. This study aims to explore the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma (HCC) to provide new methods of treatment for HCC.MethodsThe study selected 343 HCC patients with complete survival information (survival time > = 1 month) in the Cancer Genome Atlas (TCGA) as study subjects. Kaplan-Meier survival analysis assisted in determining the relationship between macrophage infiltration and overall survival (OS), and Pearson correlation tests were used to identify metabolic reprogramming genes (MRGs) associated with tumor macrophage abundance. Lasso regression algorithms were used on prognosis-related MRGs identified by Kaplan-Meier survival analysis and univariate Cox regression analysis to construct a risk score; another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) was used to verify prognostic signature externally.ResultsA risk score composed of 8 metabolic genes could accurately predict the OS of a training cohort (TCGA) and a testing cohort (ICGC). The risk score could be widely used for people with different clinical characteristics, and it is a predictor that is independent of other clinical factors that affect prognosis. As expected, compared with the low-risk group, the high-risk group exhibited an obviously higher macrophage abundance, together with a positive correlation between the risk score and the expression levels of three commonly used immune checkpoints (PD1, PDL1, and CTLA4).ConclusionOur study constructed and validated a novel eight-gene signature for predicting HCC patient OS, which may contribute to clinical treatment decisions.

A novel immune-related genes prognosis biomarker for hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is closely associated with the immune microenvironment. To identify the effective population before administering treatment, the establishment of prognostic immune biomarkers is crucial for early HCC diagnosis and treatment.Results: A total of 335 IRGs identified from 788 overlapping IRGs were associated with the survival of HCC. A prognostic immunoscore model was identified. The Kaplan-Meier survival curves and time-dependent ROC analysis revealed a powerful prognostic performance of immunoscore signature via multi validation. Besides, the immunoscore signature exhibited a better predictive power compared to other prognostic signatures. Gene set enrichment analysis showed multiple signaling differences between the high and low immunoscore group. Furthermore, immunoscore was significantly associated with multiple immune cells and immune infiltration in the tumor microenvironment.Conclusions: We identified the immunoscore as a robust marker for predicting HCC patient survival.Methods: Three sets of immune-related genes (IRGs) were integrated to identify the overlapping IRGs. Weighted gene co-expression network analysis was performed to obtain the survival-related IRGs. Further, the prognostic immunoscore model was constructed via LASSO-penalized Cox regression analysis. Then the prognostic performance of immunoscore was evaluated. In addition, ESTIMATE and CIBERSORT algorithms were applied to explore the relationship between immunoscore and tumor immune microenvironment.