Risk stratification of patients with hepatocellular carcinoma undergoing trans arterial chemoembolization using an alpha-fetoprotein model

Abstract Background Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. There are multiple factors that could affect the malignancy and progression of HCC including tumor number, size, and macrovascular invasion. The alpha-fetoprotein (AFP) model was validated as a predictor for HCC recurrence post-liver transplantation, especially in France. However, the AFP model has not been studied on patients with HCC undergoing locoregional treatment. This study aimed to assess the prognostic value of the AFP model in patients with HCC undergoing trans arterial chemoembolization (TACE). This cohort study was conducted at Ain Shams University Hospitals, Cairo, Egypt. We included all newly diagnosed patients with HCC who were fit for TACE from January 2012 to January 2017. The AFP model was calculated for each patient before TACE. Subsequently, we classified them into low- and high-risk groups for TACE. The patients were followed up by AFP level and triphasic spiral CT performed 1 month after TACE to evaluate the response then at 4 months and 7 months post TACE to evaluate the local and distant recurrence. Results One hundred and thirty-two patients were included in the study. Complete response (CR) was achieved nonsignificantly at a higher percentage in the low-risk group in comparison with the high-risk group. One- and three-year recurrence-free survivals (RFS) were longer in the low-risk group in comparison with the high-risk group (50% and 24.1% vs. 29.1% and 16.2%, respectively). One- and three-year overall survival (OS) rates were 97% and 37.3% in the low-risk group vs. 98.1% and 11.6% in the high-risk group, respectively, without statistical significance. On classifying patients with AFP levels < 100 IU/mL into low- and high-risk patients, CR was achieved in a significantly higher percentage in the low-risk group in comparison with the high-risk group(P < 0.05). Recurrence occurred nonsignificantly in a less percentage in low than high-risk group. The median OS was significantly higher in the low-risk group in comparison with that in the high-risk group (18 vs. 16 months respectively) (P < 0.01). Conclusion The AFP model may have a prognostic value for patients with HCC undergoing TACE especially in patients with an AFP level < 100 IU/mL.

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Contrast-enhanced ultrasound–based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation

British Journal of Radiology ◽

10.1259/bjr.20210748 ◽

2021 ◽

Author(s):

Hui Huang ◽

Si-min Ruan ◽

Meng-fei Xian ◽

Ming-de Li ◽

Mei-qing Cheng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Risk Group ◽

Early Recurrence ◽

High Risk Group ◽

Low Risk ◽

Primary Hepatocellular Carcinoma ◽

Contrast Enhanced Ultrasound ◽

Combined Model ◽

Contrast Enhanced

Objectives: This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation. Methods: This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort (n = 139) and a test cohort (n = 76). Four representative images—grayscale ultrasound, arterial phase, portal venous phase and delayed phase —were extracted from each CEUS video. Ultrasomics features were extracted from tumoral and peritumoral area inside the region of interest. Logistic-regression was used to establish models, including a tumoral model, a peritumoral model and a combined model with additional clinical risk factors. The performance of the three models in predicting recurrence within 2 years was verified. Results: The combined model performed best in predicting recurrence within 2 years, with an area under the curve (AUC) of 0.845, while the tumoral model had an AUC of 0.810 and the peritumoral model one of 0.808. For prediction of recurrence-free survival, the 2 year cumulative recurrence rate was significant higher in the high-risk group (76.5%) than in the low-risk group (9.5%; p < 0.0001). Conclusion: These CEUS ultrasomics models, especially the combined model, had good efficacy in predicting early recurrence of HCC. The combined model has potential for individual survival assessment for HCC patients undergoing resection or ablation. Advances in knowledge: CEUS ultrasomics had high sensitivity, specificity and PPV in diagnosing early recurrence of HCC, and high efficacy in predicting early recurrence of HCC (AUC > 0.8). The combined model performed better than the tumoral ultrasomics model and peritumoral ultrasomics model in predicting recurrence within 2 years. Recurrence was more likely to occur in the high-risk group than in the low-risk group, with 2-year cumulative recurrence rates respectively 76.5% and 9.5% (p < 0.0001).

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A radiomics nomogram for prediction of overall survival in hepatocellular carcinoma after hepatectomy

10.21203/rs.3.rs-28085/v1 ◽

2020 ◽

Author(s):

Qinqin Liu ◽

Jing Li ◽

Fei Liu ◽

Weilin Yang ◽

Jingjing Ding ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Clinicopathological Factors ◽

Training Cohort ◽

Texture Parameters ◽

Radiomics Signature

Abstract Background Hepatocellular carcinoma (HCC) is associated with dismal prognosis, and prediction of the prognosis of HCC can assist the therapeutic decisions. More and more studies showed that the texture parameters of images can reflect the heterogeneity of the tumor, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of the study was to investigate the prognostic value of computed tomography (CT) texture parameters for patients with HCC after hepatectomy, and try to develop a radiomics nomograms by combining clinicopathological factors with radiomics signature.Methods 544 eligible patients were enrolled in the retrospective study and randomly divided into training cohort (n=381) and validation cohort (n=163). The regions of interest (ROIs) of tumor is delineated, then the corresponding texture parameters are extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in training cohort, and the radiomics score (Rad-score) was generated. According to the cut-off value of the Rad-score calculated by the receiver operating characteristic (ROC) curve, the patients were divided into high-risk group and low-risk group. The prognosis of the two groups was compared and validated in the validation cohort. Univariate and multivariable analyses by COX proportional hazard regression model were used to select the prognostic factors of overall survival (OS). The radiomics nomogram for OS were established based on the radiomics signature and clinicopathological factors. The Concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram.Result 7 texture parameters associated with OS were selected in the training, and the radiomics signature was formulated based on the texture parameters. The patients were divided into high-risk group and low-risk group by the cut-off values of the Rad-score of OS. The 1-, 3- and 5-year OS rate was 71.0%, 45.5% and 35.5% in the high-risk group, respectively, and 91.7%, 82.1% and 78.7%, in the low-risk group, respectively, with significant difference (P <0.001). COX regression model found that Rad-score was an independent prognostic factor of OS. In addition, the radiomics nomogram was developed based on five variables: α‐fetoprotein (AFP), platelet lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and Rad-score. The nomograms displayed good accuracy in predicting OS (C-index=0.747) in the training cohort and was confirmed in the validation cohort (C-index=0.777). The calibration plots also showed an excellent agreement between the actual and predicted survival probabilities. The DAC indicated that the radiomics nomogram showed better clinical usefulness than the clinicopathologic nomogram.Conclusion The radiomics signature is potential biomarkers of the prognosis of HCC after hepatectomy. Radiomics nomogram that integrated radiomics signature can provide more accurate estimate of OS for patients with HCC after hepatectomy.

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Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

10.21203/rs.3.rs-17851/v1 ◽

2020 ◽

Author(s):

Li Liu ◽

She Tian ◽

Zhu Li ◽

Yongjun Gong ◽

Hao Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Principal Component ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

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Identification of Novel Tumor Microenvironment-Related Long Non-coding RNAs to Predict the Prognosis for Hepatocellular Carcinoma

10.21203/rs.3.rs-877901/v1 ◽

2021 ◽

Author(s):

Shenglan Huang ◽

Jian Zhang ◽

Dan Li ◽

Xiaolan Lai ◽

Lingling Zhuang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Clinicopathological Parameters ◽

Kaplan Meier ◽

The Impact

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Tumor microenvironment (TME) plays a vital role in the tumor progression of HCC. Thus, we aimed to analyze the association of TME with HCC prognosis, and construct an TME-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We firstly assessed the stromal/immune /Estimate scores within the HCC microenvironment using the ESTIMATE algorithm based on TCGA database, and its associations with survival and clinicopathological parameters were also analyzed. Then, different expression lncRNAs were filtered out according to immune/stromal scores. Cox regression was performed to built an TME-related lncRNAs risk signature. Kaplan–Meier analysis was carried out to explored the prognostic values of the risk signature. Furthermore, we explored the biological functions and immune microenvironment feathers in high- and low risk groups. Lastly, we probed the association of the risk signature with the treatment responses to immune checkpoint inhibitors (ICIs) in HCC by comparing the immunophenoscore (IPS).Results: Stromal/immune /Estimate scores of HCC patients were obtained based on the ESTIMATE algorithm. The Kaplan-Meier curve analysis showed the high stromal/immune/ Estimate scores were significantly associated with better prognosis of the HCC patients. Then, six TME-related lncRNAs were screened for constructing the prognosis model. Kaplan-Meier survival curves suggested that HCC patients in high-risk group had worse prognosis than those with low-risk. ROC curve and Cox regression analyses demonstrated the signature could predict HCC survival exactly and independently. Function enrichment analysis revealed that some tumor- and immune-related pathways associated with HCC tumorigenesis and progression might be activated in high-risk group. We also discovered that some immune cells, which were beneficial to enhance immune responses towards cancer, were remarkably upregulated in low-risk group. Besides, there was closely correlation of immune checkmate inhibitors (ICIs) with the risk signature and the signature can be used to predict treatment response of ICIs.Conclusions: We analyzed the impact of the tumor microenvironment scores on the prognosis of patients with HCC. A novel TME-related prognostic risk signature was established, which may improve prognostic predictive accuracy and guide individualized immunotherapy for HCC patients.

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Living donor liver transplantation for advanced hepatocellular carcinoma including macrovascular invasion

Journal of Cancer Research and Clinical Oncology ◽

10.1007/s00432-021-03665-9 ◽

2021 ◽

Author(s):

Abu Bakar Hafeez Bhatti ◽

Wajih Naqvi ◽

Nusrat Yar Khan ◽

Haseeb Haider Zia ◽

Faisal Saud Dar ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Transplantation ◽

High Risk ◽

Living Donor ◽

Living Donor Liver Transplantation ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Donor Liver ◽

Donor Liver Transplantation

Abstract Background The indications for liver transplantation (LT) in patients with hepatocellular carcinoma (HCC) continue to evolve. The aim of this study was to report outcomes in patients who underwent living donor liver transplantation (LDLT) for HCC outside traditional criteria including macrovascular invasion (MVI). Methods We reviewed outcomes in patients who met the University of California San Francisco (UCSF) criteria (n = 159) and our center-specific criteria (UCSF+) (largest tumor diameter ≤ 10 cm, any tumor number, AFP ≤ 1000 ng/ml) (n = 58). We also assessed outcomes in patients with MVI (n = 27). Results The median follow was 28 (10.6–42.7) months. The 5 year overall survival and risk of recurrence (RR) in the UCSF and UCSF + group was 71% vs 69% (P = 0.7) and 13% vs 36% (P = 0.1) respectively. When patients with AFP > 600 ng/ml were excluded from the UCSF + group, RR was 27% (P = 0.3). Among patients with MVI who had downstaging (DS), 4/5(80%) in low-risk group (good response and AFP ≤ 100 ng/ml) and 2/10 (20%) in the high-risk group (poor response or AFP > 100 ng/ml) were alive at the last follow-up. When DS was not feasible, 3/3 (100%) in the low-risk group (AFP ≤ 100 ng/ml + Vp1-2 MVI) and 1/9 (9.1%) in the high-risk group (AFP > 100 or Vp3 MVI) were alive. The 5 year OS in the low-risk MVI group was 85% (P = 0.003). Conclusion With inclusion of AFP, response to downstaging and degree of MVI, acceptable survival can be achieved with LDLT for HCC outside traditional criteria.

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A Novel Nine-Gene Signature Associated With Immune Infiltration for Predicting Prognosis in Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.730732 ◽

2021 ◽

Vol 12 ◽

Author(s):

Rongqiang Liu ◽

ZeKun Jiang ◽

Weihao Kong ◽

Shiyang Zheng ◽

Tianxing Dai ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Liver Cancer ◽

Prognostic Value ◽

Risk Group ◽

Prognostic Significance ◽

Group Versus ◽

Gene Signature ◽

High Risk Group ◽

Data Sets

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, and its prognosis remains unsatisfactory. The identification of new and effective markers is helpful for better predicting the prognosis of patients with HCC and for conducting individualized management. The oncogene Aurora kinase A (AURKA) is involved in a variety of tumors; however, its role in liver cancer is poorly understood. The aim of this study was to establish AURKA-related gene signatures for predicting the prognosis of patients with HCC.Methods: We first analyzed the expression of AURKA in liver cancer and its prognostic significance in different data sets. Subsequently, we selected genes with prognostic value related to AURKA and constructed a gene signature based on them. The predictive ability of the gene signature was tested using the HCC cohort development and verification data sets. A nomogram was constructed by integrating the risk score and clinicopathological characteristics. Finally, the influence of the gene signature on the immune microenvironment in HCC was comprehensively analyzed.Results: We found that AURKA was highly expressed in HCC, and it exhibited prognostic value. We selected eight AURKA-related genes with prognostic value through the protein-protein interaction network and successfully constructed a gene signature. The nine-gene signature could effectively stratify the risk of patients with HCC and demonstrated a good ability in predicting survival. The nomogram showed good discrimination and consistency of risk scores. In addition, the high-risk group showed a higher percentage of immune cell infiltration (i.e., macrophages, myeloid dendritic cells, neutrophils, and CD4+T cells). Moreover, the immune checkpoints SIGLEC15, TIGIT, CD274, HAVCR2, and PDCD1LG2 were also higher in the high-risk group versus the low-risk group.Conclusions: This gene signature may be useful prognostic markers and therapeutic targets in patients with HCC.

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Development of a Novel Autophagy-Related Prognostic Signature and Nomogram for Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.591356 ◽

2020 ◽

Vol 10 ◽

Author(s):

Qiongxuan Fang ◽

Hongsong Chen

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Regression Analyses ◽

Gene Model

BackgroundHepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Autophagy plays a crucial role in the development and progression of HCC.MethodsUnivariate and Lasso Cox regression analyses were performed to determine a gene model that was optimal for overall survival (OS) prediction. Patients in the GSE14520 and GSE54236 datasets of the Cancer Genome Atlas (TCGA) were divided into the high-risk and low-risk groups according to established ATG models. Univariate and multivariate Cox regression analyses were used to identify risk factors for OS for the purpose of constructing nomograms. Calibration and receiver operating characteristic (ROC) curves were used to evaluate model performance. Real-time PCR was used to validate the effects of the presence or absence of an autophagy inhibitor on gene expression in HepG2 and Huh7 cell lines.ResultsOS in the high-risk group was significantly shorter than that in the low-risk group. Gene set enrichment analysis (GSEA) indicated that the association between the low-risk group and autophagy- as well as immune-related pathways was significant. ULK2, PPP3CC, and NAFTC1 may play vital roles in preventing HCC progression. Furthermore, tumor environment analysis via ESTIMATION indicated that the low-risk group was associated with high immune and stromal scores. Based on EPIC prediction, CD8+ T and B cell fractions in the TCGA and GSE54236 datasets were significantly higher in the low-risk group than those in the high-risk group. Finally, based on the results of univariate and multivariate analyses three variables were selected for nomogram development. The calibration plots showed good agreement between nomogram prediction and actual observations. Inhibition of autophagy resulted in the overexpression of genes constituting the gene model in HepG2 and Huh7 cells.ConclusionsThe current study determined the role played by autophagy-related genes (ATGs) in the progression of HCC and constructed a novel nomogram that predicts OS in HCC patients, through a combined analysis of TCGA and gene expression omnibus (GEO) databases.

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Ferroptosis regulator genes are a favorable biomarker for hepatocellular carcinoma

10.21203/rs.3.rs-139865/v1 ◽

2021 ◽

Author(s):

Yongfei He ◽

Shuqi Zhao ◽

Zhongliu Wei ◽

Xin Zhou ◽

Tianyi Liang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Clinical Information ◽

High Risk Group ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Regulator Genes ◽

The Relationship

Abstract BackgroundIn this study, we comprehensively analyzed the relationship between ferroptosis regulator genes (FRGs) and prognosis of hepatocellular carcinoma (HCC), determined the prognostics value of FRGs, established a prediction model, and explored the relationship with immunotherapy for HCC.MethodsThe mRNA transcriptional levels and clinical information of HCC were obtained from The Cancer Genome Atlas (TCGA) database. The 24 FRGs were combined with the differential expression genes (DEGs) of HCC for further analysis. The prognostics values of differential FRGs via the construction of model and validation by the Cox regression analysis.ResultThere were three genes (CARS1, FANCD2, and SLC7A11) were identified as independent risk factors for HCC, and a predictive model was constructed based on CARS1, FANCD2, and SLC7A11. The model showed that the low-risk group HCC patients with a more prolonged overall survival (OS) than the high-risk group (P=0.001). The high-risk group with higher expression of FRGs than the low-risk group. Finally, the relations between FGEs and immune infiltration showed that CARS1, FANCD2, and SLC7A11 had a positive relationship with macrophage infiltration. From these, three genes might be the potential therapeutic targets.ConclusionOur study indicated that CARS1, FANCD2, and SLC7A11 might have potential value for therapeutic strategies as practical and reliable prognostic tools for HCC.

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Clinical and pathologic factors associated with development of hepatocellular carcinoma in patients with hepatitis virus-related cirrhosis: A long-term follow-up study

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.4646 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 4646-4646

Author(s):

D. Morales-Espinosa ◽

A. Martínez-Velasco ◽

T. Cerón-Lizárraga ◽

V. Rosas-Camargo ◽

J. L. Rodríguez-Díaz ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Platelet Count ◽

High Risk ◽

Risk Group ◽

Hepatitis Virus ◽

High Risk Group ◽

Alpha Fetoprotein ◽

Biochemical Tests ◽

Histological Activity

4646 Background: Hepatocellular carcinoma (HCC) represents >90% of primary liver neoplasms and develops mainly in patients with liver cirrhosis. Risk factor identification for development of HCC in patients with cirrhosis is of great relevance due to its high incidence and poor prognosis when detected at advanced stages. The aim of our study was to identify HCC development-associated risk factors in a cohort of patients with hepatitis virus-related chronic liver disease and cirrhosis. Materials and Methods: Patients with diagnosis of hepatitis virus-related cirrhosis from January 1980 to January 2000 were included. Patients were followed with abdominal US and determination of AFP levels, physical examination, and biochemical tests every 3–6 months. The endpoint in this study was defined as development of HCC. Liver histology was evaluated according to the METAVIR. Results: 282 patients met the inclusion criteria; most (86%) had a serologic diagnosis of hepatitis C virus, and only 14% had hepatitis B virus at the time of diagnosis of cirrhosis, while 56 and 37% were classified as Child A and B, respectively; only 7% as Child C. Histological activity was mild in 59% of patients; moderate and severe in 41%. Mean annual incidence was 1.87%; 22 and 35% of patients developed HCC at 10 and 15 years of follow-up, respectively. Diagnosis of HCC was made by histopathology in 37% and by tumoral lesion-associated AFP elevation confirmed by imaging studies in 63%. In multivariate analysis, 3 variables were associated with HCC: moderate to severe histological activity; platelet count <105 103/mm3, and alpha- fetoprotein >5 ng/mL. We divided patients into two groups according to regression coefficient: low and high-risk; patients assigned to the low-risk group showed 5, 10, and 15-year HCC incidences of 3.4, 6.4, and 6.4%, respectively, in contrast to patients from the high-risk group, who showed incidences of 17.8, 33.5, and 56.8%, respectively. Conclusions: We found three HCC-associated variables: histological activity; platelet count and alpha-fetoprotein levels. Patients considered as high-risk for developing hepatocellular carcinoma must be considered candidates for closer follow-up. No significant financial relationships to disclose.

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Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes

Journal of Translational Medicine ◽

10.1186/s12967-020-02691-4 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Xinyu Gu ◽

Jun Guan ◽

Jia Xu ◽

Qiuxian Zheng ◽

Chao Chen ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Prognostic Model ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Low Risk ◽

Immune Microenvironment ◽

Kaplan Meier ◽

Immune Related Genes

Abstract Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms. Results Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan–Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures. Conclusions We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.

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