Prognostic Significance In Hepatocellular Carcinoma Research Articles

Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis.Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER).Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration.Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.

Identification and validation of a five-gene prognostic signature for hepatocellular carcinoma

BackgroundARID1A is a commonly mutated tumor suppressor gene found in all human cancer types, but its clinical significance, oncogenic functions, and relevant mechanisms in hepatocellular carcinoma (HCC) are not well understood.ObjectiveWe aimed to improving the prognosis risk classification of HCC from the perspective of ARID1A mutations.Materials and methodsWe examined the interaction between ARID1A mutations and the overall survival via Kaplan-Meier survival analysis. We used gene set enrichment analysis (GSEA) to elucidate the influence of ARID1A mutations on signaling pathways. A prognostic model was constructed using LASSO and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was used to estimate the performance and accuracy of the model.ResultsHCC patients with ARID1A mutations presented poor prognosis. By GSEA, we showed that genes upregulated by reactive oxygen species (ROS) and regulated by MYC were positively correlated with ARID1A mutations. A prognostic signature consisting of 5 genes (SRXN1, LDHA, TFDP1, PPM1G, and EIF2S1) was constructed in our research. The signature showed good performance in predicting overall survival (OS) for HCC patients by internal and external validation.ConclusionOur research proposed a novel and robust approach for the prognostic risk classification of HCC patients, and this approach may provide new insights to improve the treatment strategy of HCC.

Development and Validation of a Metabolic Gene-Based Prognostic Signature for Hepatocellular Carcinoma

BackgroundHepatocellular carcinoma (HCC) is a malignant tumor with great variation in prognosis among individuals. Changes in metabolism influence disease progression and clinical outcomes. The objective of this study was to determine the overall survival (OS) risk of HCC patients from a metabolic perspective.Patients and MethodsThe model was constructed using the least absolute shrinkage and selection operator (LASSO) COX regression based on The Cancer Genome Atlas (TCGA, n=342) dataset. The International Cancer Genome Consortium (ICGC, n=232), GSE14520 (n=242) datasets, and a clinical cohort (n=64) were then used to assess the prognostic value of the signature.ResultsA 10 metabolic gene-based signature was constructed and verified as a robust and independent prognostic classifier in public and real-world validation cohorts. Meanwhile, the signature enabled the identification of HCC molecular subtypes, yielding an AUC value of 0.678 [95% CI: 0.592–0.763]. Besides, the signature was associated with metabolic processes like glycolysis, supported by a clear correlation between the risk score and expression of rate-limiting enzymes. Furthermore, high-risk tumor was likely to have a high tumor infiltration status of immunosuppressive cells, as well as elevated expression of some immune checkpoint molecules. For final clinical translation, a nomogram integrating the signature and tumor stage was established, and showed improved predictive accuracy of 3- and 5-year OS and brought more net benefit to patients.ConclusionWe developed a prognostic signature based on 10 metabolic genes, which has proven to be an independent and reliable prognostic predictor for HCC and reflects the metabolic and immune characteristics of tumors.

Development and validation of a robust epithelial-mesenchymal transition (EMT)-related prognostic signature for hepatocellular carcinoma

ObjectiveEpithelial-to-mesenchymal transition (EMT) is an essential biological process of cancer progression associated with increased metastatic potential and initiation. Herein, we aimed to develop and validate a robust EMT-related prognostic signature that could predict the prognosis of patients with hepatocellular carcinoma (HCC). MethodsMessenger RNA expression matrix and clinicopathological data were retrieved from The Cancer Genome Atlas (TCGA) and identified differentially expressed genes (DEGs) between HCC tissues and adjacent non-tumor samples. Univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analysis were performed to establish a prognosis signature. Kaplan–Meier survival curve, time-dependent receiver operating characteristic (ROC), multivariate Cox regression analysis, nomogram, C-index, and decision curve analysis (DCA) were performed to investigate the prognostic performance of the signature. The prognostic performance of the new signature was further validated in an independent external cohort. A support vector machine (SVM) approach was performed to evaluate the diagnostic value of the identified genes. ResultsA seven-gene signature was formulated to classify patients into high-risk and low-risk groups with discrepant overall survival (OS) in two cohorts (all P < 0.0001), and the former illustrated shorter survival time than the latter even stratified by various groups. The new signature has presented an excellent performance for predicting survival prognosis. Multivariate analysis showed that the signature was an independent risk factor for HCC. The SVM classifier based on the seven genes presented an excellent diagnostic power in differentiating early HCC and normal tissues. Gene Set Enrichment Analyses (GSEA) demonstrated multiple biological processes and pathways to provide novel insights into the development of HCC. ConclusionWe established and validated a prognostic signature based on EMT-related genes with good predictive value for HCC survival. The diagnostic performance of the signature had been demonstrated to accurately distinguish early HCC from control individuals.

Identification and Validation of Ubiquitin-Specific Proteases as a Novel Prognostic Signature for Hepatocellular Carcinoma.

PurposeUbiquitin-specific proteases (USPs), as a sub-family of deubiquitinating enzymes (DUBs), are responsible for the elimination of ubiquitin-triggered modification. USPs are recently correlated with various malignancies. However, the expression features and clinical significance of USPs have not been systematically investigated in hepatocellular carcinoma (HCC).MethodsGenomic alterations and expression profiles of USPs were investigated in CbioPortal and The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) dataset. Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to establish a risk signature for HCC prognosis in TCGA LIHC cohort. Subsequently, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves and univariate/multivariate analyses were performed to evaluate the prognostic significance of the risk signature in TCGA LIHC and international cancer genome consortium (ICGC) cohorts. Furthermore, we explored the alterations of the signature genes during hepatocarcinogenesis and HCC progression in GSE89377. In addition, the expression feature of USP39 was further explored in HCC tissues by performing western blotting and immunohistochemistry.ResultsGenomic alterations and overexpression of USPs were observed in HCC tissues. The consensus analysis indicated that the USPs-overexpressed sub-Cluster was correlated with aggressive characteristics and poor prognosis. Cox regression with LASSO algorithm identified a risk signature formed by eight USPs for HCC prognosis. High-risk group stratified by the signature score was correlated with advanced tumor stage and poor survival HCC patients in TCGA LIHC cohort. In addition, the 8-USPs based signature could also robustly predict overall survival of HCC patients in ICGC(LIRI-JP) cohort. Furthermore, gene sets enrichment analysis (GSEA) showed that the high-risk score was associated with tumor-related pathways. According to the observation in GSE89377, USP39 expression was dynamically increased with hepatocarcinogenesis and HCC progression. The overexpression of USP39 was further determined in a local HCC cohort and correlated with poor prognosis. The co-concurrence analysis suggested that USP39 might promote HCC by regulating cell-cycle- and proliferation- related genes.ConclusionThe current study provided a USPs-based signature, highlighting its robust prognostic significance and targeted value for HCC treatment.

ZNF674-AS1 antagonizes miR-423-3p to induce G0/G1 cell cycle arrest in non-small cell lung cancer cells

BackgroundZNF674-AS1, a recently characterized long noncoding RNA, shows prognostic significance in hepatocellular carcinoma and glioma. However, the expression and function of ZNF674-AS1 in non-small cell lung cancer (NSCLC) are unclear.MethodsIn this work, we investigated the expression of ZNF674-AS1 in 83 pairs of NSCLC specimens and adjacent noncancerous lung tissues. The clinical significance of ZNF674-AS1 in NSCLC was analyzed. The role of ZNF674-AS1 in NSCLC growth and cell cycle progression was explored.ResultsOur data show that ZNF674-AS1 expression is decreased in NSCLC compared to normal tissues. ZNF674-AS1 downregulation is significantly correlated with advanced TNM stage and decreased overall survival of NSCLC patients. Overexpression of ZNF674-AS1 inhibits NSCLC cell proliferation, colony formation, and tumorigenesis, which is accompanied by a G0/G1 cell cycle arrest. Conversely, knockdown of ZNF674-AS1 enhances the proliferation and colony formation of NSCLC cells. Biochemically, ZNF674-AS1 overexpression increases the expression of p21 through downregulation of miR-423-3p. Knockdown of p21 or overexpression of miR-423-3p blocks ZNF674-AS1-mediated growth suppression and G0/G1 cell cycle arrest. In addition, ZNF674-AS1 expression is negatively correlated with miR-423-3p in NSCLC specimens.ConclusionsZNF674-AS1 suppresses NSCLC growth by downregulating miR-423-3p and inducing p21. This work suggests the therapeutic potential of ZNF674-AS1 in the treatment of NSCLC.

Expression of osteopontin in hepatocellular carcinoma

Introduction Hepatocellular carcinoma (HCC) is a complex disease entity with multiple steps and associated with many risk factors and cofactors. Osteopontin (OPN) is a secreted glycosylated phosphoprotein encoded by the secreted phosphoprotein 1 gene. OPN has been implicated as an essential mediator and potential therapeutic target of cancer metastasis. Objectives This study aimed to determine the expression of OPN in HCC and to correlate the findings with different clinicopathological characteristics as well as p53 expression. Materials and methods HCC specimens (n=84) were immunostained using antibodies against OPN and p53. The association between OPN and clinicopathological parameters including grade, number of tumors, tumor size, and vascular invasion as well as p53 expression were evaluated. Results OPN showed positivity (+1 and +2) in 53 (63.1%) cases. OPN expression was directly proportional with tumor size, tumor grade, and vascular invasion, as high OPN expression showed a highly significant association with large-sized tumors (&gt;5 cm in diameter), high-grade HCC, and vascular invasion. No significant association was detected between OPN expression and age (P=0.738), sex (P=0.835), and number of tumors (P=0.358). OPN expression was directly proportional to p53 expression, as p53 positivity was observed in high-grade tumors with vascular invasion (P&lt;0.001). Conclusion OPN expression may have prognostic significance in HCC, as overexpression of OPN is associated with high-grade, large-size tumors and vascular invasion as well as high p53 expression.

Identification and validation of redox-immune based prognostic signature for hepatocellular carcinoma.

The intimate interaction between redox signaling and immunity has been widely revealed. However, the clinical application of relevant therapeutic is unavailable due to the absence of validated markers that stratify patients. Here, we identified novel biomarkers for prognosis prediction in hepatocellular carcinoma (HCC). Prognostic redox-immune-related genes for predicting overall survival (OS) of HCC were identified using datasets from TCGA, LIRI-JP, and GSE14520. LASSO Cox regression was employed to construct the signature model and generate a risk score in the TCGA cohort. The signature contained CDO1, G6PD, LDHA, GPD1L, PPARG, FABP4, CCL20, SPP1, RORC, HDAC1, STC2, HDGF, EPO, and IL18RAP. Patients in the high-risk group had a poor prognosis compared to the low-risk group. Univariate and multivariate Cox regressions identified this signature as an independent factor for predicting OS. Nomogram constructed by multiple clinical parameters showed good performance for predicting OS indicated by the c-index, the calibration curve, and AUC. GSEA showed that oxidoreductase activity and peroxisome-related metabolic pathways were enriched in the low-risk group, while glycolysis activity and hypoxia were higher in the high-risk group. Furthermore, immune profiles analysis showed that the immune score and stromal score were significantly decreased in the high-risk group in the TCGA cohort. There was a considerably lower infiltration of anti-tumor immune cells while a higher proportion of pro-tumor immune cells in silico. Immune markers were distinctly expressed between the subgroups, and redox-sensitive immunoregulatory biomarkers were at higher levels in the high-risk group. Altogether, we identified a redox-immune prognostic signature. A more severe redox perturbation-driven immunosuppressive environment in the high-risk group stratified by the signature may account for poor survival. This may provide a clue to the combined therapy targeting redox and immune in HCC.

Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

Identification and Validation of the N6-Methyladenosine RNA Methylation Regulator YTHDF1 as a Novel Prognostic Marker and Potential Target for Hepatocellular Carcinoma.

Purpose: N6-methyladenosine (m6A) RNA methylation has been implicated in various malignancies. This study aimed to identify the m6A methylation regulator-based prognostic signature for hepatocellular carcinoma (HCC) as well as provide candidate targets for HCC treatment.Methods: The least absolute shrinkage and selection operator (LASSO) analyses were performed to identify a risk signature in The Cancer Genome Atlas (TCGA) datasets. The risk signature was further validated in International Cancer Genome Consortium (ICGC) and Pan-Cancer Analysis of Whole Genomes (PCAWG) datasets. Following transfection of short hairpin RNA (shRNA) targeting YTHDF1, the biological activities of HCC cells were evaluated by Cell Counting Kit-8 (CCK-8), wound-healing, Transwell, flow cytometry, and xenograft tumor assays, respectively. The potential mechanisms mediated by YTHDF1 were predicted by overrepresentation enrichment analysis (ORA)/gene set enrichment analysis (GSEA) and validated by Western blotting.Results: Overexpression of m6A RNA methylation regulators was correlated with malignant clinicopathological characteristics of HCC patients. The Cox regression and LASSO analyses identified a risk signature with five m6A methylation regulators (KIAA1429, ZC3H13, YTHDF1, YTHDF2, and METTL3). In accordance with HCC cases in TCGA, the prognostic value of risk signature was also determined in ICGC and PCAWG datasets. Following analyzing the expression and clinical implications in TCGA and Gene Expression Omnibus (GEO), YTHDF1 was chosen for further experimental validation. Knockdown of YTHDF1 significantly inhibited the proliferation, migration, and invasion of HCC cells, as well as enhanced the apoptosis in vitro. Moreover, silencing YTHDF1 repressed the growth of xenograft tumors in vivo. Mechanism investigation indicated that YTHDF1 might promote the aggressive phenotypes by facilitating epithelial–mesenchymal transition (EMT) and activating AKT/glycogen synthase kinase (GSK)-3β/β-catenin signaling.Conclusion: The current study identified a robust risk signature consisting of m6A RNA methylation regulators for HCC prognosis. In addition, YTHDF1 was a potential molecular target for HCC treatment.

Comprehensive Evaluation of Endocytosis-Associated Protein SCAMP3 in Hepatocellular Carcinoma.

BackgroundSecretory carrier membrane proteins 3 (SCAMP3) is an endocytosis-associated protein involved in regulating endosomal pathways and the trafficking of vital signaling receptors. This study aimed to comprehensively assess the role of SCAMP3 in hepatocellular carcinoma (HCC) by integrated bioinformatics analysis.MethodsIn this study, bioinformatics databases were used to explore the differential expression status and prognostic value of SCAMP3 gene in HCC, and bioinformatics analyses of survival data and interactors of SCAMP3 were conducted to predict the prognostic value of SCAMP3 in HCC.ResultsUsing the TCGA data, our data shows that SCAMP3 mRNA expression is most significantly different between liver and hepatocellular carcinoma tissues and higher expression of SCAMP3 has unfavorable prognostic significance in HCC. Tumor grade, stage, and gender also showed a significant relevance with SCAMP3 expression. High SCAMP3 expression of males revealed significantly poorer survival and progression compared with low SCAMP3 expression of males. BioGRID statistics explores 79 unique interactions with SCAMP3 and multiple post translational modifications. Further analysis finds that SOCS2 may negatively correlate with SCAMP3, while GBA, MX1, and DDOST positively correlate with SCAMP3. Moreover, ncRNA analysis shows that SCAMP3 gene expression is positively associated with lncRNA SBF2-AS1 and negatively related with Has-miR-145. The expressions of SBF2-AS1 and Has-miR-145 are also significantly related with survival in HCC.DiscussionSCAMP3 expression can be affected by multiple genes or ncRNAs expression that are associated with survival, thus suggesting that SCAMP3 can be used as a clinical diagnosis and prognostic biomarker in HCC.

A robust nine-gene prognostic signature associated with tumour doubling time for hepatocellular carcinoma

BackgroundTumour doubling time (TDT) is an indicator reflecting tumour growth rate, however, the prognostic genes associated with the TDT in hepatocellular carcinoma (HCC) have not been fully identified. Materials and methodsWe obtained mRNA expression profiles and tumour doubling time from GSE54236 and used the Pearson correlation test to identify tumour doubling time-related genes (TDTRGs). We extracted TDTRGs from The Cancer Genome Atlas (TCGA) and identified prognostic genes using univariate Cox regression analysis and Kaplan–Meier survival analysis. Lasso and multivariate Cox regression analysis assisted in constructing the signature and International Cancer Genome Consortium (ICGC) served as an external validation. ResultsWe identified a total of 296 genes associated with tumour doubling time and developed a prognostic signature consisting of 9 genes. Patients were divided into high- and low-risk groups according to the uniform cutoff (0.85). Regardless of the clinical characteristics of the patients, the group at high risk exhibited obviously lower overall survival (OS) than did the group with low risk in both TCGA and ICGC cohorts. The prognostic model showed superior accuracy in both TCGA and ICGC cohorts, as confirmed by receiver operating characteristic (ROC) curve analysis. The univariate together with multivariate Cox regression analysis further suggested the ability of the signature to predict prognosis independently. ConclusionA novel prognostic signature for HCC was developed and validated in the study, which may be beneficial to improve the treatment strategy of HCC.

High CENPM mRNA expression and its prognostic significance in hepatocellular carcinoma: a study based on data mining

BackgroundHepatocellular carcinoma (HCC) is a high mortality disease, the fifth most general cancer worldwide, and the second leading to cancer-related deaths, with more than 500,000 new patients diagnosed each year. First, the high expression of centromere M (CENPM) in mammary gland tissue of b-catenin transformed mice was identified.Materials and methodsIn our study, we evaluated the expression of CENPM in hepatocellular carcinoma based on data obtained from an online database. Multivariate analysis showed that the expression of CENPM and M classification was an independent prognostic factor for patients with hepatocellular carcinoma.ResultsSurvival analysis showed that patients with high CENPM had a worse prognosis than patients with low CENPM (P < 0.01). A multivariate Cox regression hazard model showed that B cells, CD8+ T cells, macrophages, and dendritic cells infiltrated by immune cells were statistically significant in liver cancer (P < 0.05). Using the network, the 50 most frequently changed neighbor genes of CENPM were shown, and the most common change was RAD21 (18.3%).ConclusionOur study found that the expression of CENPM was significantly increased in patients with hepatocellular carcinoma, and it was related to a variety of clinical characteristics, its correlation with the level of immune infiltration and poor prognosis, so CENPM can be used as a useful prognosis for patients’ markers and HCC.

Construction of a lipid metabolism-related and immune-associated prognostic signature for hepatocellular carcinoma.

BackgroundHepatocellular carcinoma (HCC) is one of the most lethal malignancies. We aimed to identify a robust lipid metabolism‐related signature associated with the HCC microenvironment to improve the prognostic prediction of HCC patients.MethodsWe analyzed the gene expression profiles of lipid metabolism from Molecular Signatures Database and information of patients from The Cancer Genome Atlas. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and principal component analysis (PCA) were employed for functional annotation. Quantitative real‐time polymerase chain reaction (qRT‐PCR) was employed to verify the expression of model genes in HCC and adjacent tissues.ResultsAs a result, a lipid metabolism‐related signature consisting of acyl‐CoA synthetase long‐chain family member 6 (ACSL6), lysophosphatidylcholine acyltransferase 1, phospholipase A2 group 1B, lecithin‐cholesterol acyltransferase (LCAT), and sphingomyelin phosphodiesterase 4 (SMPD4) was identified among HCC patients. Lysophosphatidylcholine acyltransferase 1, PLA2G1B, and SMPD4 were proved significantly high expression while ACSL6 and LCAT were remarkably low expression in our 15 pairs of matched HCC and normal tissues by qRT‐PCR. Under different conditions, the overall survival (OS) of patients in low‐risk group was prolonged than that in high‐risk group. Moreover, the as‐constructed signature was an independent factor, which was remarkably associated with gender, histologic grade, and platelet level of HCC patients. In addition, the receiver operating characteristic (ROC) curve analysis confirmed the good potency of the model. Functional enrichment analysis further revealed that lower fatty acid (FA) oxidation and higher infiltration of immunocytes were detected in patients from the high‐risk group compared with those in the low‐risk group.ConclusionsOur findings indicate that the lipid metabolism‐related signature shows prognostic significance for HCC.

A Four-Methylated lncRNAs-Based Prognostic Signature for Hepatocellular Carcinoma.

Currently, an increasing number of studies suggest that long non-coding RNAs (lncRNAs) and methylation-regulated lncRNAs play a critical role in the pathogenesis of various cancers including hepatocellular carcinoma (HCC). Therefore, methylated differentially expressed lncRNAs (MDELs) may be critical biomarkers of HCC. In this study, 63 MDELs were identified by screening The Cancer Genome Atlas (TCGA) HCC lncRNAs expression data set and lncRNAs methylation data set. Based on univariate and multivariate survival analysis, four MDELs (AC025016.1, LINC01164, LINC01183 and LINC01269) were selected to construct the survival prognosis prediction model. Through the PI formula, the study indicates that our new prediction model performed well and is superior to the traditional staging method. At the same time, compared with the previous prediction models reported in the literature, the results of time-dependent receiver operating characteristic (ROC) curve analysis show that our 4-MDELs model predicted overall survival (OS) stability and provided better prognosis. In addition, we also applied the prognostic model to Cancer Cell Line Encyclopedia (CCLE) cell lines and classified different hepatoma cell lines through the model to evaluate the sensitivity of different hepatoma cell lines to different drugs. In conclusion, we have established a new risk scoring system to predict the prognosis, which may have a very important guiding significance for the individualized treatment of HCC patients.

Whole exome sequencing identifies clinically relevant mutational signatures in resected hepatocellular carcinoma

AbstractBackground &amp; AimsMost patients develop recurrent disease despite curative surgery for hepatocellular carcinoma (HCC). No standard adjuvant therapy is available and molecular predictors of outcome are poorly understood.MethodsWe conducted a multicentre pilot study on patients with localized HCC following surgical resection. Patients received up to 6 months of oral gefitinib as adjuvant therapy. Clinical end points included recurrence‐free survival (RFS) and overall survival (OS), and exploratory analyses were conducted from whole exome sequencing data.ResultsA total of 65 patients were screened for the study, of which 40 were eligible. The median age was 63 years (range, 44‐80). Most patients (80%) were positive for hepatitis B or C. With a median follow‐up of 4.5 years, the median RFS was 24 months. Median OS was not reached. High Child‐Pugh score and advanced T‐stage (3‐4) were independent predictors for both OS and RFS. Mutational signatures for exposure to aristolochic acid (AA), as characterized by a majority of T:A &gt; A:T mutations, were observed in 18 cases (55%). HCC without AA mutagenesis was associated with early recurrences or death within 2 years of surgery (P = .037). HCC with high T &gt; A mutations was associated with better OS (HR 0.29, 95% CI 0.09‐0.90, P = .033). Conversely, HCC with high C &gt; T mutations was associated with worse OS (HR 4.55, 95% CI 1.20‐17.31, P = .026).ConclusionsMutational signatures may carry prognostic significance in HCC following curative resection. Patient outcomes with gefitinib as adjuvant therapy after resection for HCC were modest, highlighting the need for urgent research in this area of unmet clinical need. ClinicalTrials.gov number, NCT00282100.

Development and validation of a novel ten-gene prognostic signature for hepatocellular carcinoma.

e16665 Background: Hepatocellular carcinoma (HCC) remains a major challenge for public health worldwide and long-term outcomes remained dismal despite availability of curative treatment. We aimed to construct a multi-gene model for prognosis prediction to inform clinical management of HCC. Methods: RNA-seq data of paired tumor and normal tissue samples of HCC patients from the TCGA and GEO database were used to identify differentially expressed genes (DEGs). DEGs shared by both cohorts along with patients’ survival data of the TCGA cohort were further analyzed using univariate Cox regression and LASSO Cox regression to build a prognostic 10-gene signature, followed by validation of the signature via ICGC cohort and identification of independent prognostic predictors. A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: Of 571 patients (70.93% men and 29.07% women; median age [IQR], 65 [56-72] years), a signature of 10 genes was constructed using the training cohort. In the testing and validation cohorts, the signature significantly stratified patients into low- vs high-risk groups in terms of overall survival across and within subpopulations with stage I/II and III/IV disease and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 0.13 [95% CI, 0.07-0.24; P &lt; 0 .001] to 0.38 [95% CI, 0.2-0.71; P &lt; 0.001]) after adjusting for clinicopathological factors. Prognosis was significantly worse in the high-risk group than in the low-risk group across cohorts (P &lt; 0.001 for all). The 10-gene signature achieved a higher accuracy (C-index, 0.84; AUCs for 1-, 3- and 5-year OS, 0.84, 0.81 and 0.85, respectively) than 8 previously reported multigene signatures (C-index range, 0.67 to 0.73; AUCs range, 0.68 to 0.79, 0.68 to 0.80 and 0.67 to 0.78, respectively) for estimation of survival in comparable cohorts. A nomogram incorporating tumor stage and signature-based risk group showed better predictive performance for 1- and 3- year survival than for 5 year survival. Moreover, GSEA revealed that the pathways related to cell cycle regulation were more prominently enriched in the high-risk group while the low-risk group had higher enrichment of metabolic process. Conclusions: Taken together, we established a robust 10-gene signature and a nomogram to predict overall survival of HCC patients, which may help recognize high-risk patients potentially benefiting from more aggressive treatment.

Identification and Validation of Immune-Related Gene Prognostic Signature for Hepatocellular Carcinoma.

Immune-related genes (IRGs) have been identified as critical drivers of the initiation and progression of hepatocellular carcinoma (HCC). This study is aimed at constructing an IRG signature for HCC and validating its prognostic value in clinical application. The prognostic signature was developed by integrating multiple IRG expression data sets from TCGA and GEO databases. The IRGs were then combined with clinical features to validate the robustness of the prognostic signature through bioinformatics tools. A total of 1039 IRGs were identified in the 657 HCC samples. Subsequently, the IRGs were subjected to univariate Cox regression and LASSO Cox regression analyses in the training set to construct an IRG signature comprising nine immune-related gene pairs (IRGPs). Functional analyses revealed that the nine IRGPs were associated with tumor immune mechanisms, including cell proliferation, cell-mediated immunity, and tumorigenesis signal pathway. Concerning the overall survival rate, the IRGPs distinctly grouped the HCC samples into the high- and low-risk groups. Also, we found that the risk score based on nine IRGPs was related to clinical and pathologic factors and remained a valid independent prognostic signature after adjusting for tumor TNM, grade, and grade in multivariate Cox regression analyses. The prognostic value of the nine IRGPs was further validated by forest and nomogram plots, which revealed that it was superior to the tumor TNM, grade, and stage. Our findings suggest that the nine-IRGP signature can be effective in determining the disease outcomes of HCC patients.

Expression, clinicopathologic and prognostic significance of plasminogen activator inhibitor 1 in hepatocellular carcinoma.

Thus far, biological roles of plasminogen activator inhibitor 1 (PAI1) in hepatocellular carcinoma (HCC) remain controversial. Moreover, its expression, clinicopathologic and prognostic significance in HCC have not been comprehensively investigated, therefore needing further evidence. PAI1 expression was measured, using tissue microarray-based immunohistochemical staining, in matched HCC and adjacent liver samples from 178 patients with HCC after curative resection. The correlations of PAI1 H-scores with clinicopathologic variables and survival were further evaluated. Its prognostic value was finally confirmed in some public databases. It was found that PAI1 expression was significantly higher in HCC than in adjacent liver tissues. Moreover, high PAI1 expression was more frequent in those with multiple lesions. Univariate analyses showed that PAI1 expression was negatively associated with both overall and relapse-free survival. Although PAI1 expression was not statistically significant in multivariate Cox regression test, combination of it with TNM stage effectively distinguished survival and relapse, and served as an independent prognostic factor. In the online available datasets of HCC and liver cancer used, SERPINE1, the gene encoding PAI1, was also revealed to be prognostic. Our data suggested that high PAI1 expression was predictive for unfavorable biological behavior and long-term prognosis in HCC.

A novel seven-gene signature as Prognostic Biomarker in Hepatocellular Carcinoma.

Purpose: Our study is designed to develop and certify a promising prognostic signature for hepatocellular carcinoma (HCC).Materials and methods: We retrospectively analyzed mRNA expression profiles and clinicopathological data fetched from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. We formulated a prognostic seven-gene signature composed of differentially expressed mRNAs (DEmRNAs) between HCC and nonneoplastic tissues through univariate Cox regression analysis. The receiver operating characteristic (ROC) curve, survival analysis and multivariate Cox regression analysis as well as nomograms were utilized to assess the prognostic performance of the seven-gene signature.Results: The risk score based on a seven-gene signature categorized HCC subjects into a high- and low-risk group. There was significantly discrepant overall survival (OS) between patients in both groups and the corresponding ROC curve revealed a satisfactory predictive performance in HCC survival in both TCGA and GSE76427 cohort. Multivariate Cox regression analysis demonstrated that a seven-gene signature was an independently prognostic factor for HCC. Nomograms combining this prognostic signature with significant clinical characteristics conferred a crucial reference to predict the 1-,3- and 5 years OS.Conclusions: Our study defined a promising seven-gene signature and nomogram model to forecast the OS of HCC patients, which is instrumental in clinical decision and personalized therapy.