Value In Hepatocellular Carcinoma Research Articles

Benefits and Harms of Hepatocellular Carcinoma Screening Outreach in Patients with Cirrhosis: A Multi-Center Randomized Clinical Trial.

The value of hepatocellular carcinoma (HCC) screening is defined by the balance of benefits from early tumor detection vs harms due to false positive results. We evaluated the value of a mailed outreach strategy for HCC screening in patients with cirrhosis. We conducted a multi-center pragmatic randomized clinical trial comparing mailed outreach for HCC screening (n = 1436) and usual care with visit-based screening (n = 1436) among patients with cirrhosis at three health systems from March 2018 to September 2021. Outcomes of interest were early-stage HCC detection (ie, screening benefit) and diagnostic evaluation for false positive or indeterminate results (ie, screening harm). Screening harm was categorized as mild, moderate, and severe based on number and type of diagnostic exams. All patients were included in intention-to-screen analyses. Of 125 patients diagnosed with HCC (67 outreach and 58 usual care), 71.2% were found at an early stage per the Milan Criteria. Early tumor detection did not significantly differ between the outreach and usual care arms (64.2% vs 79.3%, p = .06). The proportion of patients with physical harms also did not differ between the outreach and usual care arms (10.8% vs 10.7%, p = .95) with 5.9% in both arms having mild harms, 4.0% and 3.8% respectively with moderate harms, and 0.9% and 1.0% respectively with severe harms. Most patients enrolled in HCC screening were detected at an early stage, and a minority experienced physical harms. A mailed outreach strategy did not significantly increase early HCC detection or physical harms compared to usual care. NCT02582918 and NCT03756051.

Prognostic significance of pan-immune-inflammation value in hepatocellular carcinoma treated by curative radiofrequency ablation: potential role for individualized adjuvant systemic treatment

Background This study aimed to explore the prognostic role of pan-immune-inflammation value (PIV) and develop a new risk model to guide individualized adjuvant systemic treatment following radiofrequency ablation (RFA) for early-stage hepatocellular carcinoma (HCC). Materials and methods Patients with early-stage HCC treated by RFA were randomly divided into training cohort A (n = 65) and testing cohort B (n = 68). Another 265 counterparts were enrolled into external validating cohort C. Various immune-inflammatory biomarkers (IIBs) were screened in cohort A. Prognostic role of PIV was evaluated and validated in cohort B and C, respectively. A nomogram risk model was built in cohort C and validated in pooled cohort D. Clinical benefits of adjuvant anti-angiogenesis therapy plus immune checkpoint inhibitor (AA-ICI) following RFA was assessed in low- and high-risk groups. Results The cutoff point of PIV was 120. High PIV was an independent predictor of unfavorable recurrence-free survival (RFS) and overall survival (OS). RFS and OS rates of patients with high PIV were significantly lower than those with low PIV both in cohort B (P RFS=0.016, P OS=0.011) and C (P RFS<0.001, P OS<0.001). The nomogram model based on PIV, tumor number and BCLC staging performed well in risk stratification in external validating cohort C. Adjuvant AA-ICI treatment showed an added benefit in OS (p = 0.011) for high-risk patients. Conclusions PIV is a feasible independent prognostic factor for RFS and OS in early-stage HCC patients who received curative RFA. The proposed PIV-based nomogram risk model could help clinicians identify high-risk patients and tailor adjuvant systemic treatment and disease follow-up scheme.

Developing a comprehensive cell-free DNA (cfDNA) epigenetic signature (ep-Sig) with diagnostic and prognostic value in hepatocellular carcinoma (HCC).

553 Background: HCC is one of the deadliest tumors with poor outcomes often diagnosed in late stages. Current surveillance strategies (alfa-fetoprotein (AFP) plus ultrasound (USG)) for high-risk population (HRP) have poor sensitivity (SN) and reasonable specificity (SP) of 61% and 92%, respectively. Triple phase computed tomography (CT) imaging has good sensitivity and specificity (≈ 90% for both) but the evidence does not support its use for routine surveillance. All these modalities do not have prognostic (survival) value. We attempted to develop a non-invasive, cfDNA-based tests to improve the diagnostic value of the current standard of care modalities (AFP and USG) and help in predicting the outcomes at diagnosis. We hypothesized that RNA and gene expression in HCC tissues is a surrogate for epigenome (DNA-methylation changes). Methods: We curated a 21-gene cfDNA ep-Sig from the literature with a proven prognostic value. Evidence suggests epigenetic changes in every gene of this panel affect HCC outcomes influencing clinicopathological characteristics (large tumor size, vascular invasion, advanced staging) or early post-operative recurrence, and survival. We compared RNA and gene expression of the genes in the ep-Sig between normal (NT) and HCC tissues using a web-based tool, TNMplot.com that uses the data from the Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO) or Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories was used create this tool. It uses Mann-Whitney or Kruskal-Wallis tests to compute statistical significance. Results: RNA expression of all the genes from the ep-Sig was significantly higher in HCC than NT (1.133, p=5.84e-15). Gene expression was 19/21 genes was higher in HCC tissues (1.15, p=1.3e-08). The other 2 genes were not detected in the tested samples. Conclusions: Our ep-Sig which has a proven prognostic value and reliable prevalence in HCC patients. This study is the first step in developing clinically useful blood-based test that can revamp the current diagnostic and screening practices when used alongside AFP and USG.

Diagnostic role and immune correlates of programmed cell death-related genes in hepatocellular carcinoma

Programmed cell death (PCD) is thought to have multiple roles in tumors. Here, the roles of PCD-related genes were comprehensively analyzed to evaluate their values in hepatocellular carcinoma (HCC) diagnosis and prognosis. Gene expression and single-cell data of HCC patients, and PCD-related genes were collected from public databases. The diagnostic and prognostic roles of differentially expressed PCD-related genes in HCC were explored by univariate and multivariate Cox regression analyses. Single-cell data were further analyzed for the immune cells and expression of feature genes. Finally, we evaluated the expression of genes by quantitative real-time polymerase chain reaction and Western blot, and the proportion of immune cells was detected by flow cytometry in HCC samples. We obtained 52 differentially expressed PCD-related genes in HCC, based on which the consensus clustering analysis cluster 2 was found to have a worse prognosis than cluster 1. Then 10 feature genes were identified using LASSO analysis, and programmed cell death index (PCDI) was calculated to divided HCC patients into high-PCDI and low-PCDI groups. Worse prognosis was observed in high-PCDI group. Cox regression analysis showed that PCDI is an independent prognostic risk factor for HCC patients. Additionally, SERPINE1 and G6PD of feature genes significantly affect patient survival. Macrophages and Tregs were significantly positively correlated with PCDI. G6PD mainly expressed in macrophages, SERPINE1 mainly expressed in fibroblast. The experimental results confirmed the high expression of SERPINE1 and G6PD in HCC compared with the control, and the infiltration level of macrophages and Treg in HCC was also obviously elevated. PCDI may be a new predictor for the diagnosis of patients with HCC. The association of SERPINE1 and G6PD with the immune environment will provide new clues for HCC therapy.

Radiomics nomogram for prediction of glypican-3 positive hepatocellular carcinoma based on hepatobiliary phase imaging.

The hepatobiliary-specific phase can help in early detection of changes in lesion tissue density, internal structure, and microcirculatory perfusion at the microscopic level and has important clinical value in hepatocellular carcinoma (HCC). Therefore, this study aimed to construct a preoperative nomogram for predicting the positive expression of glypican-3 (GPC3) based on gadoxetic acid-enhanced (Gd-EOB-DTPA) MRI hepatobiliary phase (HBP) radiomics, imaging and clinical feature. We retrospectively included 137 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI and subsequent liver resection or puncture biopsy at our hospital from January 2017 to December 2021 as training cohort. Subsequently collected from January 2022 to June 2023 as a validation cohort of 49 patients, Radiomic features were extracted from the entire tumor region during the HBP using 3D Slicer software and screened using a t-test and least absolute shrinkage selection operator algorithm (LASSO). Then, these features were used to construct a radiomics score (Radscore) for each patient, which was combined with clinical factors and imaging features of the HBP to construct a logistic regression model and subsequent nomogram model. The clinicoradiologic, radiomics and nomogram models performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). In the validation cohort,the nomogram performance was assessed by the area under the curve (AUC). In the training cohort, a total of 1688 radiomics features were extracted from each patient. Next, radiomics with ICCs<0.75 were excluded, 1587 features were judged as stable using intra- and inter-class correlation coefficients (ICCs), 26 features were subsequently screened using the t-test, and 11 radiomics features were finally screened using LASSO. The nomogram combining Radscore, age, serum alpha-fetoprotein (AFP) >400ng/mL, and non-smooth tumor margin (AUC=0.888, sensitivity 77.7%, specificity 91.2%) was superior to the radiomics (AUC=0.822, sensitivity 81.6%, specificity 70.6%) and clinicoradiologic (AUC=0.746, sensitivity 76.7%, specificity 64.7%) models, with good consistency in calibration curves. DCA also showed that the nomogram had the highest net clinical benefit for predicting GPC3 expression.In the validation cohort, the ROC curve results showed predicted GPC3-positive expression nomogram model AUC, sensitivity, and specificity of 0.800, 58.5%, and 100.0%, respectively. HBP radiomics features are closely associated with GPC3-positive expression, and combined clinicoradiologic factors and radiomics features nomogram may provide an effective way to non-invasively and individually screen patients with GPC3-positive HCC.

Comprehensive analysis of basement membrane and immune checkpoint related lncRNA and its prognostic value in hepatocellular carcinoma via machine learning

BackgroundHepatocellular carcinoma (HCC), which is characterized by its high malignancy, generally exhibits poor response to immunotherapy. As part of the tumor microenvironment, basement membranes (BMs) are involved in tumor development and immune activities. Presently, there is no integrated analysis linking the basement membrane with immune checkpoints, especially from the perspective of lncRNA. MethodsBased on transcriptome data from The Cancer Genome Atlas, BMs-related and immune checkpoint-related lncRNAs were identified. By applying univariable Cox regression and Machine learning (LASSO and SVM-RFE algorithm), a 10-lncRNA prognosis signature was constructed. The prognostic significance of this signature was assessed by survival analysis. GSEA, ssGSEA, and drug sensitivity analysis were conducted to investigate potential functional pathways, immune status, and clinical implications of guiding individual treatments in HCC. Finally, the promoting migration effect of LINC01224 was validated via in vitro experiments. ResultsThe multiple Cox regression, receiver operating characteristic curves, and stratified survival analysis of clinical subgroups exhibited the robust prognostic ability of the lncRNA signature. Results of the GSEA and drug sensitivity analysis revealed significant differences in potential functional pathways and response to drugs between the two risk groups. In addition, the risk level of HCC patients was distinctly correlated with immune cell infiltration status. More importantly, LINC01224 was independently associated with the OS of HCC patients (P < 0.05), suppressing the expression of LINC01224 inhibited the migration of HCC cells. ConclusionThis study developed a reliable signature for the prognosis of HCC based on BM and immune checkpoint related lncRNA, revealing that LINC01224 might be a prognostic biomarker for HCC associated with the progression of HCC.

Identification and verification of novel immune-related ferroptosis signature with excellent prognostic predictive and clinical guidance value in hepatocellular carcinoma.

Background: Immunity and ferroptosis often play a synergistic role in the progression and treatment of hepatocellular carcinoma (HCC). However, few studies have focused on identifying immune-related ferroptosis gene biomarkers. Methods: We performed weighted gene co-expression network analysis (WGCNA) and random forest to identify prognostic differentially expressed immune-related genes (PR-DE-IRGs) highly related to HCC and characteristic prognostic differentially expressed ferroptosis-related genes (PR-DE-FRGs) respectively to run co-expression analysis for prognostic differentially expressed immune-related ferroptosis characteristic genes (PR-DE-IRFeCGs). Lasso regression finally identified 3 PR-DE-IRFeCGs for us to construct a prognostic predictive model. Differential expression and prognostic analysis based on shared data from multiple sources and experimental means were performed to further verify the 3 modeled genes' biological value in HCC. We ran various performance testing methods to test the model's performance and compare it with other similar signatures. Finally, we integrated composite factors to construct a comprehensive quantitative nomogram for accurate prognostic prediction and evaluated its performance. Results: 17 PR-DE-IRFeCGs were identified based on co-expression analysis between the screened 17 PR-DE-FRGs and 34 PR-DE-IRGs. Multi-source sequencing data, QRT-PCR, immunohistochemical staining and testing methods fully confirmed the upregulation and significant prognostic influence of the three PR-DE-IRFeCGs in HCC. The model performed well in the performance tests of multiple methods based on the 5 cohorts. Furthermore, our model outperformed other related models in various performance tests. The immunotherapy and chemotherapy guiding value of our signature and the comprehensive nomogram's excellent performance have also stood the test. Conclusion: We identified a novel PR-DE-IRFeCGs signature with excellent prognostic prediction and clinical guidance value in HCC.

Study on F9 gene expression downregulation and its clinical value in hepatocellular carcinoma

Objective: To analyze the expression levels of the F9 gene and F9 protein in hepatocellular carcinoma by combining multiple gene chip data, real-time fluorescence quantitative PCR (RT qPCR), and immunohistochemistry. Additionally, explore their correlation with the occurrence and development of hepatocellular carcinoma, as well as with various clinical indicators and prognosis. Methods: The mRNA microarray dataset from the GEO database was analyzed to identify the F9 gene with significant expression differences associated with hepatocellular carcinoma. Liver cancer and adjacent tissues were collected from 18 cases of hepatocellular carcinoma. RT-qPCR method was used to detect the F9 gene expression level. Immunohistochemistry was used to detect the F9 protein level. Combined with the TCGA database information, the correlation between F9 gene expression level and prognostic and clinicopathological parameters was analyzed. The biological function of F9 co-expressed genes associated with hepatocellular carcinoma was analyzed by the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Statistical analysis was performed using Graphpad Prism software. Results: Meta-analysis results showed that the expression of the F9 gene was lower in HCC tissues than in non-cancerous tissues. Immunohistochemistry results were basically consistent with those of RT-qPCR. The data obtained from TCGA showed that the F9 gene had lower expression values in stages III-IV, T3-T4, and patients with vascular invasion. A total of 127 genes were selected for bioinformatics analysis as co-expressed genes of F9, which were highly enriched in redox processes and metabolic pathways. Conclusion: This study validates that the F9 gene and F9 protein are lower in HCC. The down-regulation of the F9 gene predicts adverse outcomes, which may provide a new therapeutic target for HCC.

Comprehensive analysis identifies CLEC1B as a potential prognostic biomarker in hepatocellular carcinoma

BackgroundC-type lectin domain family 1 member B (CLEC1B, encoding the CLEC-2 protein), a member of the C-type lectin superfamily, is a type II transmembrane receptor involved in platelet activation, angiogenesis, and immune and inflammatory responses. However, data regarding its function and clinical prognostic value in hepatocellular carcinoma (HCC) remain scarce.MethodsThe expression of CLEC1B was explored using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RT-qPCR, western blot, and immunohistochemistry assays were employed to validate the downregulation of CLEC1B. Univariate Cox regression and survival analyses were used to evaluate the prognostic value of CLEC1B. Gene Set Enrichment Analysis (GSEA) was conducted to investigate the potential association between cancer hallmarks and CLEC1B expression. The TISIDB database was applied to search for the correlation between immune cell infiltration levels and CLEC1B expression. The association between CLEC1B and immunomodulators was conducted by Spearman correlation analysis based on the Sangerbox platform. Annexin V-FITC/PI apoptosis kit was used for the detection of cell apoptosis.ResultsThe expression of CLEC1B was low in various tumors and exhibited a promising clinical prognostic value for HCC patients. The expression level of CLEC1B was tightly associated with the infiltration of various immune cells in the HCC tumor microenvironment (TME) and positively correlated with a bulk of immunomodulators. In addition, CLEC1B and its related genes or interacting proteins are implicated in multiple immune-related processes and signaling pathways. Moreover, overexpression of CLEC1B significantly influenced the treatment effects of sorafenib on HCC cells.ConclusionsOur results reveal that CLEC1B could serve as a potential prognostic biomarker and may be a novel immunoregulator for HCC. However, its function in immune regulation should be further explored.

SNRPD1 inhibition suppresses the proliferation of hepatocellular carcinoma and promotes autophagy through the PI3K/AKT/mTOR/4EBP1 pathway

BackgroundSmall nuclear ribonucleoprotein Sm D1 (SNRPD1) has been reported as an oncogene in some solid cancers. Our previous study suggested that SNRPD1 has diagnostic and prognostic value in hepatocellular carcinoma (HCC), but its role in tumor growth and biological behavior remains unknown. In this study, we aimed to unravel the role and mechanism of SNRPD1 in HCC. MethodsWe investigated the SNRPD1 mRNA level in adjacent normal liver tissues and HCC tissues with different tumor stages in the UALCAN database. The associations between SNRPD1 mRNA expression and HCC prognosis were investigated in TCGA database. Then, 52 pairs of frozen HCC tissues and corresponding adjacent normal liver tissues were collected to perform qPCR and immunohistochemistry assay. Next, we carried out a series of experiments in vitro and in vivo to investigate the effects of SNRPD1 expression on cell invasion, migration, proliferation, autophagy, and the PI3K/AKT/mTOR pathway. ResultsThe bioinformatics analysis and qPCR in our patient cohort demonstrated that the SNRPD1 mRNA level in HCC tissues was higher than in adjacent normal tissues. In addition, the immunohistochemistry assay exhibited an increased SNRPD1 protein level with the tumor stage increase. Survival analysis suggested that higher expression of SNRPD1 was significantly associated with unfavorable prognosis of patients with HCC. The functional experiments in vitro indicated that SNRPD1 knockdown suppressed the cellular proliferation, migration, and invasion capacities. Furthermore, SNRPD1 inhibition induced cellular apoptosis and arrested the HCC cells at the G0/G1 phase of the cell cycle. Mechanistic analyses demonstrated that SNRPD1 knockdown induced the increase of autophagic vacuoles and the expression of autophagy-related genes (ATG5, ATG7, and ATG12) and blocked the PI3K/AKT/mTOR/4EBP1 pathway in vitro. Moreover, SNRPD1 inhibition suppressed tumor growth and expression of the Ki67 protein in vivo. ConclusionsSNRPD1 may serve as an oncogene in HCC and promote tumor proliferation via inhibiting autophagy induced through the PI3K/Akt/mTOR/4EBP1 pathway.

Identification of PPT1 as a lysosomal core gene with prognostic value in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most frequent cancer worldwide with a poor prognosis. Unfortunately, there are few reports on effective biomarkers for HCC, identification of novel cancer targets is urgently needed. Lysosomes are central organelles for degradation and recycling processes in cells, and how lysosome-related genes are involved in the progression of hepatocellular carcinoma remains unclear. The aim of the present study was to identify key lysosome-related genes affecting HCC. In the present study, lysosome-related genes involved in HCC progression were screened based on the TCGA (The Cancer Genome Atlas) dataset. Differentially expressed genes (DEGs) were screened, and core lysosomal genes were obtained in combination with prognostic analysis and protein interaction networks. Two genes were associated with survival, and their prognostic value was validated by prognostic profiling. After mRNA expression validation and IHC, the palmitoyl protein thioesterase 1 (PPT1) gene was identified as an important lysosomal-related gene. We demonstrated that PPT1 promotes the proliferation of HCC cells in vitro. In addition, quantitative proteomics and bioinformatics analysis confirmed that PPT1 acts by affecting the metabolism, localization, and function of various macromolecular proteins. The present study reveals that PPT1 could be a promising therapeutic target for the treatment of HCC. These findings provided new insights into HCC and identified candidate gene prognosis signatures for HCC.

Tumor–stroma ratio predicts prognosis and PD-L1 expression in hepatocellular carcinoma

BackgroundWith the in-depth research on the tumor microenvironment, the tumor stroma is considered to play a leading role in malignant tumor behavior, and PD-L1 is also related to the tumor stroma. The tumor–stroma ratio (TSR) has been regarded as a novel prognostic factor in many cancers. Our study aims to assess the TSR and PD-L1 clinical value in hepatocellular carcinoma (HCC) patients.MethodsNinety-five patients who were diagnosed with HCC were included in our study. TSR was estimated on HCC specimen hematoxylin–eosin staining (HE) sections, and the optimal TSR cut-off value was determined by receiver operating characteristic (ROC) curves. The correlation between the TSR and clinicopathologic features was also calculated. Immunohistochemistry (IHC) staining was also carried out to analyze the PD-L1 expression level in HCCs.ResultsThe optimal TSR cut-off value was 0.525. The median OS of the stroma-high and stroma-low groups was 27 and 36 months, respectively. The median RFS of the stroma-high and stroma-low groups was 14.5 and 27 months, respectively. In the Cox multivariate analysis, the TSR was an independent prognostic factor for HCC overall survival (OS) and recurrence-free survival (RFS) in patients who underwent liver resection. IHC staining revealed TSR-high HCC samples with high PD-L1-positive cell expression.ConclusionsOur results suggest that the TSR can predict the prognosis of HCC patients who underwent liver resection. The TSR is related to PD-L1 expression and may be a therapeutic target that can dramatically improve HCC patients’ clinical outcomes.

Profiling and integrated analysis of transcriptional addiction gene expression and prognostic value in hepatocellular carcinoma.

Transcriptional dysregulation caused by genomic and epigenetic alterations in cancer is called "transcriptional addiction". Transcriptional addiction is an important pathogenic factor of tumor malignancy. Hepatocellular carcinoma (HCC) genomes are highly heterogeneous, with many dysregulated genes. Our study analyzed the possibility that transcriptional addiction-related genes play a significant role in HCC. All data sources for conducting this study were public cancer databases and tissue microarrays. We identified 38 transcriptional addiction genes, and most were differentially expressed genes. Among patients of different groups, there were significant differences in overall survival rates. Both nomogram and risk score were independent predictors of HCC outcomes. Transcriptional addiction gene expression characteristics determine the sensitivity of patients to immunotherapy, cisplatin, and sorafenib. Besides, HDAC2 was identified as an oncogene, and its expression was correlated with patient survival time. Our study conclusively demonstrated that transcriptional addiction is crucial in HCC. We provided biomarkers for predicting the prognosis of HCC patients, which can more precisely guide the patient's treatment.

D-dimer for assessment of treatment response, and survival to drug-eluting beads transarterial chemoembolization in hepatocellular carcinoma.

D-dimer exhibits a certain prognostic value in hepatocellular carcinoma (HCC) patients who underwent hepatectomy and microwave ablation, while its value in estimating the clinical benefit of drug-eluting beads transarterial chemoembolization (DEB-TACE) remains unclear. Hence, this study aimed to investigate the correlation of D-dimer with tumor features, response and survival to DEB-TACE in HCC patients. Fifty-one HCC patients treated with DEB-TACE were recruited. Their serum samples at baseline and after DEB-TACE were collected and proposed for D-dimer detection by the immunoturbidimetry method. Elevated D-dimer levels were related to a higher Child‒Pugh stage (P=0.013), tumor nodule number (P=0.031), largest tumor size (P=0.004), and portal vein invasion (P=0.050) in HCC patients. Then, patients were classified by the median value of D-dimer, and it was observed that patients with D-dimer >0.7mg/L achieved a lower complete response rate (12.0% vs. 46.2%, P=0.007) but a similar objective response rate (84.0% vs. 84.6%, P=1.000) compared to those with D-dimer ≤0.7mg/L. The Kaplan‒Meier curve showed that D-dimer >0.7mg/L (vs. ≤0.7mg/L) was related to shorter overall survival (OS) (P=0.013). Further univariate Cox regression analyses showed that D-dimer >0.7mg/L (vs. ≤0.7mg/L) was related to unfavorable OS [hazard ratio (HR): 5.524, 95% confidence interval (CI): 1.209-25.229, P=0.027], but it failed to independently estimate OS (HR: 10.303, 95%CI: 0.640-165.831, P=0.100) in multivariate Cox regression analyses. Moreover, D-dimer was elevated during DEB-TACE therapy (P<0.001). D-dimer may be helpful for monitoring prognosis to DEB-TACE therapy in HCC, while further large-scale-study validation is warranted.

Characterization of a lipid droplet and endoplasmic reticulum stress related gene risk signature to evaluate the clinical and biological value in hepatocellular carcinoma

IntroductionLipid metabolism and endoplasmic reticulum (ER) stress play an important role in the progression and metastasis of hepatocellular carcinoma (HCC). We aimed to establish lipid droplet (LD)-associated and ER stress-related gene risk signature as prognostic indicators.Materials and methodsLiterature searches for LD-associated proteins was screened and validated in The Cancer Genome Atlas (TCGA) and International Cancer Genome Collaboratory (ICGC) databases. A total of 371 samples were enrolled from the TCGA RNA-seq dataset (training cohort) and 240 samples from IGGC RNA-seq dataset (validation cohort). A 10-gene risk signature was established by the last absolute shrinkage and selection operator (LASSO) regression analysis. The prognostic value of the risk signature was evaluated by Cox regression, Kaplan–Meier and ROC Curve analyses. Biological features associated with LD and ER stress-related factors were explored by functional analysis and in vitro experiment.ResultsBased on the medical literatures, 124 lipid droplet-associated proteins were retrieved, and three genes failed to establish a valid prognostic model. ER stress was considered as an important component by functional analysis. A 10-gene risk signature compared the clinicopathology characteristics, immunosuppressive events and a nomogram in HCC patients.ConclusionLD-associated and ER stress-related gene risk signatures highlighted poor prognosis for clinicopathological features, positively correlate with macrophages and T cell immunoglobulin and mucin-3 (TIM-3) expression in the tumor microenvironment, and might act as independent prognostic factors.

An immune infiltration-related long non-coding RNAs signature predicts prognosis for hepatocellular carcinoma.

Background: With a high incidence and dismal survival rate, hepatocellular carcinoma (HCC) tops the list of the world's most frequent malignant tumors. Immunotherapy is a new approach to cancer treatment, and its effect on prolonging overall survival (OS) varies from patient to patient. For a more effective prognosis and treatment of HCC, we are committed to identifying immune infiltration-related long non-coding RNAs (IIRLs) with prognostic value in hepatocellular carcinoma. Methods: In our study, we calculated immune scores of 369 hepatocellular carcinoma samples from the Cancer Genome Atlas (TCGA) database by using an estimation algorithm, and obtained long non-coding RNAs (lncRNAs) associated with immune infiltration by using Weighted Gene Co-expression Network analysis (WGCNA). For training cohort, univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis were used to determine prognostic IIRLs, we established a prognostic IIRLs signature. By testing cohort and entire cohort, we confirmed that the signature is practical. The prognosis of people with different clinicopathological stages and risk scores were predicted by the nomogram we constructed. In addition, Immune cell infiltration analysis and prediction of therapeutic drugs were performed. Results: 93 IIRLs were obtained by WGCNA. Furthermore, the prognostic value of these IIRLs were evaluated by using univariate Cox, Lasso and multivariate Cox analysis. Four IIRLs were used to create a signature with a prognosis. Time-related receiver operating characteristic (ROC) curve revealed that this model had an acceptable prognostic value for HCC patients. By using univariate and multivariate Cox regression analysis, this risk score has been shown to be an independent prognostic factor for HCC. The nomogram we made showed good predictions. Except for that, the treatment with immune checkpoint inhibitors (ICI) was likely to be more effective for low-risk patients. Conclusion: Based on four IIRLs, a prognostic signature was created in this research showed good accuracy in predicting OS. This study also provided valuable references for Immunotherapy of hepatocellular carcinoma.

Comprehensive Analysis of the Expression and Prognosis for Lipid Metabolism-Related Genes in Hepatocellular Carcinoma.

Hao DingBackground This study aimed to screen potential key genes associated with lipid metabolism and to evaluate their expressions and prognosis values in hepatocellular carcinoma (HCC). Methods Data sets GSE6764, GSE14520, and GSE112790 were used to identify the common differentially expressed genes (DEGs). Protein-protein interaction (PPI) network was constructed by STRING database. Hub genes in PPI network were identified and subjected to functional enrichment analysis to screen lipid metabolism-related genes. The expressions of selected genes and their associations with prognosis were analyzed using UALCAN, The Human Protein Atlas, and Kaplan-Meier plotter databases. The transcriptional factor (TF)-gene regulatory network was constructed using NetworkAnalyst. Results A total of 331 common DEGs including 106 upregulated and 225 downregulated genes were identified. PPI network analysis showed that 76 genes with high degrees were identified as hub genes, among which 14 genes were lipid metabolism-related genes. PON1, CYP2C9, and SPP1 were found to be the independent prognostic markers. Key TFs with close interactions with these prognostic genes, including HINFP, SRF, YY1, and NR3C1, were identified from the TF-gene regulatory network. Conclusion This study presented evidence for the prognostic capabilities of lipid metabolism-related genes in HCC, and newly identified HINFP and NR3C1 as potential biomarkers for HCC.

Radiomics signature based on CECT for non-invasive prediction of response to anti-PD-1 therapy in patients with hepatocellular carcinoma

This study aimed to develop a radiomics signature (RS) based on contrast-enhanced computed tomography (CECT) and evaluate its potential predictive value in hepatocellular carcinoma (HCC) patients receiving anti-PD-1 therapy. CECT scans of 76 HCC patients who received anti-PD-1 therapy were obtained in this study (training group = 53 and validation group = 23). The least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics features of primary and metastatic lesions and establish a RS to predict lesion-level response. Then, a nomogram combined the mean RS (MRS) and clinical variables with patient-level response as the end point. In the lesion-level analysis, the area under the curves (AUCs) of RS in the training and validation groups were 0.751 (95% CI, 0.668-0.835) and 0.734 (95% CI, 0.604-0.864), respectively. In the patient-level analysis, the AUCs of the nomogram in the training and validation groups were 0.897 (95% CI, 0.798-0.996) and 0.889 (95% CI, 0.748-1.000), respectively. The nomogram stratified patients into low- and high-risk groups, which showed a significant difference in progression-free survival (PFS) (p<0.05). The RS is a noninvasive biomarker for predicting anti-PD-1 therapy response in patients with HCC. The nomogram may be of clinical use for identifying high-risk patients and formulating individualised treatments.

Identification of senescence-associated long non-coding RNAs to predict prognosis and immune microenvironment in patients with hepatocellular carcinoma.

Background: Cellular senescence plays a complicated and vital role in cancer development because of its divergent effects on tumorigenicity. However, the long non-coding RNAs (lncRNAs) associated with tumor senescence and their prognostic value in hepatocellular carcinoma (HCC) remain unexplored. Methods: The trans-cancer oncogene-induced senescence (OIS) signature was determined by gene set variation analysis (GSVA) in the cancer genome atlas (TCGA) dataset. The OIS-related lncRNAs were identified by correlation analyses. Cox regression analyses were used to screen lncRNAs associated with prognosis, and an optimal predictive model was created by regression analysis of the least absolute shrinkage and selection operator (LASSO). The performance of the model was evaluated by Kaplan-Meier survival analyses, nomograms, stratified survival analyses, and receiver operating characteristic curve (ROC) analyses. Gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) were carried out to explore the functional relevance and immune cell infiltration, respectively. Results: Firstly, we examined the pan-cancer OIS signature, and found several types of cancer with OIS strongly associated with the survival of patients, including HCC. Subsequently, based on the OIS signature, we identified 76 OIS-related lncRNAs with prognostic values in HCC. We then established an optimal prognostic model based on 11 (including NRAV, AC015908.3, MIR100HG, AL365203.2, AC009005.1, SNHG3, LINC01138, AC090192.2, AC008622.2, AL139423.1, and AC026356.1) of these lncRNAs by LASSO-Cox regression analysis. It was then confirmed that the risk score was an independent and potential risk indicator for overall survival (OS) (HR [95% CI] = 4.90 [2.74-8.70], p < 0.001), which outperforms those traditional clinicopathological factors. Furthermore, patients with higher risk scores also showed more advanced levels of a proinflammatory senescence-associated secretory phenotype (SASP), higher infiltration of regulatory T (Treg) cells and lower infiltration of naïve B cells, suggesting the regulatory effects of OIS on immune microenvironment. Additionally, we identified NRAV as a representative OIS-related lncRNA, which is over-expressed in HCC tumors mainly driven by DNA hypomethylation. Conclusion: Based on 11 OIS-related lncRNAs, we established a promising prognostic predictor for HCC patients, and highlighted the potential immune microenvironment-modulatory roles of OIS in HCC, providing a broad molecular perspective of tumor senescence.

GNB2L1 gene expression and clinical value in hepatocellular carcinoma based on bioinformatics

Objective: To analyze guanine nucleotide-binding protein subunit beta-2-like 1 (GNB2L1) expression based on bioinformatics, so as to evaluate its role and its relationship with survival rate during the occurrence and development of hepatocellular carcinoma. Methods: GEPIA, UALCAN and HPA databases were used to analyze the expression level of GNB2L1 and its relationship with HCC survival rate. Mutations in the GNB2L1 gene and their impact on survival were analyzed using the cBioPortal database. LinkedOmics database was used to analyze GNB2L1-related genes in HCC. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed simultaneously. STEING database was used to construct the GNB2L1 protein interaction network. TIMER database was used to analyze the relationship between GNB2L1 gene expression and immune infiltration in hepatocellular carcinoma. Differential expression of GNB2L1 in plasma platelets of HCC patients and healthy controls was analyzed using mRNA-based sequencing technology. Data between groups were compared using an independent-samples t-test. Results: GNB2L1 expression level was significantly increased in HCC tissues (P<0.05), and its expression was significantly correlated with body weight, classification and stage (P<0.05). The overall survival rate was higher in GNB2L1 low expression group (P<0.001). GNB2L1 and its related genes were related to biological process regulation, metabolic process, protein binding, oxidative phosphorylation, JAK-STAT signaling pathway, Ras signaling pathway and so on. GNB2L1 had interaction with RPS12, RPS11 and RPL19, and participated in multiple biological processes such as liver regeneration and positive regulation of endogenous apoptotic signaling pathway. GNB2L1 expression was significantly positively correlated with the infiltration degree of various immune cells in HCC (P<0.05). Cox regression analysis showed that GNB2L1 was an independent risk factor for lower survival rate in patients with HCC [Hazard ratio (95% confidence interval)=1.456 (1.034~2.051), P=0.031]. GNB2L1expression levels were significantly higher in platelets of HCC patients than that of healthy controls (10.40±1.36 vs. 9.58±0.51, t=2.194, P=0.037). Conclusion: GNB2L1 has high expression and close relationship to survival rate in HCC. Therefore, GNB2L1 may be a potential biomarker of HCC.