scholarly journals Endoplasmic Reticulum Stress-related Classification for Prognosis Prediction in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Genhao Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Er Stress ◽  
Risk Model ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Survival Outcomes ◽  
Related Feature ◽  
Stress Related Genes ◽  
And Gender ◽  
Selection Of

Abstract Background: Cancer cells under ER stress are common in hepatocellular carcinoma (HCC) and ER stress is strongly associated with poor prognosis. The aim of this study was to discover credible biomarkers for predicting prognosis of HCC based on ER stress-related genes (ERSRGs). Methods: Univariate Cox regression was performed to calculate the association between ERSRGs and survival outcomes of HCC patients in TCGA. Then LASSO-Cox regression strategy and stepwise Cox regression examination were performed to investigate the quality and establish the prognostic characteristics associated with prognosis. Finally, the model was subsequently validated in two additional independent HCC cohorts.Results: A novel seven-gene prognostic risk model based on ERSRGs was constructed and exhibited superior accuracy in forecasting the survival outcomes and 1-, 2-, 3- year survival rate of HCC patients. qRT-PCR was performed to validate the prognostic risk model in an independent clinical cohort containing 59 HCC patients and the results revealed that this signature had a good prognostic performance. Moreover, we found ER stress could affect the immune microenvironment in HCC and immune checkpoint inhibitors (ICIs) treatment was more effective for patients in high-risk subgroup. In addition, we identified 103 tumor-sensitive drugs in the CellMiner database that may be available for the treatment of HCC patients targeting ER stress and constructed a nomogram combining ER stress-related feature, TNM stage, age and gender. Conclusions: Our seven genetic risk model associated with ER stress can accurately predict survival outcome in HCC patients and facilitate the selection of the best individualized treatment targeting ER stress.

A long non-coding RNA-based signature predicts early recurrence in hepatocellular carcinoma

2021 ◽  
pp. 1-10
Author(s):  
Shuai He ◽  
Jin-Feng Li ◽  
Hao Tian ◽  
Ye Sang ◽  
Xiao-Jing Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Curative Resection ◽  
Risk Model ◽  
Cox Regression ◽  
Early Recurrence ◽  
Support Vector ◽  
Term Survival ◽  
Cox Regression Analysis ◽  
Non Coding Rna ◽  
Long Non Coding Rna

BACKGROUND: Early recurrence is the main obstacle for long-term survival of hepatocellular carcinoma (HCC) patients after curative resection. OBJECTIVE: We aimed to develop a long non-coding RNA (lncRNA) based signature to predict early recurrence. METHODS: Using bioinformatics analysis and quantitative reverse transcription PCR (RT-qPCR), we screened for lncRNA candidates that were abnormally expressed in HCC. The expression levels of candidate lncRNAs were analyzed in HCC tissues from 160 patients who underwent curative resection, and a risk model for the prediction of recurrence within 1 year (early recurrence) of HCCs was constructed with linear support vector machine (SVM). RESULTS: A lncRNA-based classifier (Clnc), which contained nine differentially expressed lncRNAs including AF339810, AK026286, BC020899, HEIH, HULC, MALAT1, PVT1, uc003fpg, and ZFAS1 was constructed. In the test set, this classifier reliably predicted early recurrence (AUC, 0.675; sensitivity, 72.0%; specificity, 63.1%) with an odds ratio of 4.390 (95% CI, 2.120–9.090). Clnc showed higher accuracy than traditional clinical features, including tumor size, portal vein tumor thrombus (PVTT) in predicting early recurrence (AUC, 0.675 vs 0.523 vs 0.541), and had much higher sensitivity than Barcelona Clinical Liver Cancer (BCLC; 72.0% vs 50.0%), albeit their AUCs were comparable (0.675 vs 0.678). Moreover, combining Clnc with BCLC significantly increased the AUC, compared with Clnc or BCLC alone in predicting early recurrence (all P< 0.05). Finally, logistic and Cox regression analysis suggested that Clnc was an independent prognostic factor and associated with the early recurrence and recurrence-free survival of HCC patients after resection, respectively (all P= 0.001). CONCLUSIONS: Our lncRNA-based classifier Clnc can predict early recurrence of patients undergoing surgical resection of HCC.

scholarly journals Construction and Validation of a Prognostic Risk Model Based on Autophagy-Related Genes in Hepatocellular Carcinoma Cells

2021 ◽  
Author(s):  
Rui Feng ◽  
Jian Li ◽  
Weiling Xuan ◽  
Hanbo Liu ◽  
Dexin Cheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Differential Expression ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis ◽  
Dismal Prognosis

Abstract Background Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer and the main cause of cancer mortality. Its high complexity and dismal prognosis bring dramatic difficulty to treatment. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into seeking novel biomarkers for HCC. Methods Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. GO and KEGG pathway analyses were conducted on these genes. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by ICGC validation set. Results Altogether, 42 autophagy-related differential genes were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model was constituted by 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, FKBP1A. Validation confirmed the accuracy and independence of this model in predicting HCC patient’s prognosis. Conclusion A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.

scholarly journals Prognostic Value and Regulatory Network of Immune-Related Genes in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiang Zhou ◽  
Keying Zhang ◽  
Fa Yang ◽  
Chao Xu ◽  
Jianhua Jiao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Cox Regression ◽  
Wilcoxon Test ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Hepatocellular carcinoma (HCC) is a disease with higher morbidity, mortality, and poor prognosis in the whole world. Understanding the crosslink between HCC and the immune system is essential for people to uncover a few potential and valuable therapeutic strategies. This study aimed to reveal the correlation between HCC and immune-related genes and establish a clinical evaluation model. Methods: We had analyzed the clinical information consisted of 373 HCC and 49 normal samples from the cancer genome atlas (TCGA). The differentially expressed genes (DEGs) were selected by the Wilcoxon test and the immune-related differentially expressed genes (IRDEGs) in DEGs were identified by matching DEGs with immune-related genes downloaded from the ImmPort database. Furthermore, the univariate Cox regression analysis and multivariate Cox regression analysis were performed to construct a prognostic risk model. Then, twenty-two types of tumor immune-infiltrating cells (TIICs) were downloaded from Tumor Immune Estimation Resource (TIMER) and were used to construct the correlational graphs between the TIICs and risk score by the CIBERSORT. Subsequently, the transcription factors (TFs) were gained in the Cistrome website and the differentially expressed TFs (DETFs) were achieved. Finally, the KEGG pathway analysis and GO analysis were performed to further understand the molecular mechanisms between DETFs and PDIRGs.Results: In our study, 5839 DEGs, 326 IRDEGs, and 31 prognosis-related IRDEGs (PIRDEGs) were identified. And 8 optimal PIRDEGs were employed to construct a prognostic risk model by multivariate Cox regression analysis. The correlation between risk genes and clinical characterizations and TIICs has verified that the prognostic model was effective in predicting the prognosis of HCC patients. Finally, several important immune-related pathways and molecular functions of the eight PIRDEGs were significantly enriched and there was a distinct association between the risk IRDEGs and TFs. Conclusion: The prognostic risk model showed a more valuable predicting role for HCC patients, and produced many novel therapeutic targets and strategies for HCC.

scholarly journals Akebia trifoliate (Thunb.) KoidzSeed Extract Inhibits the Proliferation of Human Hepatocellular Carcinoma Cell Lines via Inducing Endoplasmic Reticulum Stress

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Wen-Li Lu ◽  
Hong-Yan Ren ◽  
Cao Liang ◽  
Yuan-Yuan Zhang ◽  
Ji Xu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Endoplasmic Reticulum ◽  
Er Stress ◽  
Main Trend ◽  
G1 Phase ◽  
Cell Viability Assay ◽  
Cell Cycles ◽  
Viability Assay ◽  
M Phase ◽  
Stress Related Genes

Akebia Fructus has long been used for hepatocellular carcinoma (HCC) in China, while the molecular mechanism remains obscure. Our recent work found thatAkebia trifoliate (Thunb.) Koidzseed extract (ATSE) suppressed proliferation and induced endoplasmic reticulum (ER) stress in SMMC-7721. The present study aimed to throw more light on the mechanism. ER stress occurred after ATSE treatment in HepG2, HuH7, and SMMC-7721 cells, manifested as ER expansion, and SMMC-7721 was the most sensitive kind in terms of morphology. Cell viability assay showed that ATSE significantly inhibited cells proliferation. Flow cytometry analysis indicated that ATSE leads to an upward tendency of G0/G1 phase and a reduced trend of the continuous peak after G2/M phase in HepG2; ATSE promoted apoptosis in HuH7 and a notable reduction in G0/G1 phase; ATSE does not quite influence cell cycles of SMMC-7721. Western blot analysis showed an increased trend of the chosen ER stress-related proteins after different treatments but nonsignificantly; only HYOU1 and GRP78 were decreased notably by ATSE in HuH7. Affymetrix array indicated that lots of ER stress-related genes’ expressions were significantly altered, and downward is the main trend. These results suggest that ATSE have anticancer potency in HCC cells via partly inducing ER stress.

scholarly journals Identification of a N6-Methyladenosine (m6A)-Related lncRNA Signature for Predicting the Prognosis and Immune Landscape of Lung Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Chengyin Weng ◽  
Lina Wang ◽  
Guolong Liu ◽  
Mingmei Guan ◽  
Lin Lu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Risk Model ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Immune Functions ◽  
Kaplan Meier

Backgroundm6A-related lncRNAs emerged as potential targets for tumor diagnosis and treatment. This study aimed to identify m6A-regulated lncRNAs in lung squamous cell carcinoma (LUSC) patients.Materials and MethodsRNA sequencing and the clinical data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The m6A-related lncRNAs were identified by using Pearson correlation assay. Univariate and multivariate Cox regression analyses were utilized to construct a risk model. The performance of the risk model was validated using Kaplan–Meier survival analysis and receiver operating characteristics (ROC). Immune estimation of LUSC was downloaded from TIMER, and the correlations between the risk score and various immune cells infiltration were analyzed using various methods. Differences in immune functions and expression of immune checkpoint inhibitors and m6A regulators between high-risk and low-risk groups were further explored. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were utilized to explore the biological functions of AL122125.1.ResultsA total of 351 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs demonstrated prognostic values. A further multivariate Cox regression assay constructed a risk model consisting of two lncRNAs (AL122125.1 and HORMAD2-AS1). The Kaplan–Meier analysis and area under the curve indicated that this risk model could be used to predict the prognosis of LUSC patients. The m6A-related lncRNAs were immune-associated. There were significant correlations between risk score and immune cell infiltration, immune functions, and expression of immune checkpoint inhibitors. Meanwhile, there were significant differences in the expression of m6A regulators between the high- and low-risk groups. Moreover, GO and KEGG analyses revealed that the upregulated expression of AL122125.1 was tumor-related.ConclusionIn this study, we constructed an m6A-related lncRNA risk model to predict the survival of LUSC patients. This study could provide a novel insight to the prognosis and treatment of LUSC patients.

scholarly journals AC099850.3 Promotes Cell Proliferation and is One of Five Costimulatory Molecule-Related LncRNAs That Predict Overall Survival of Liver Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Meimei Liu ◽  
Qiong Fang ◽  
Yanping Huang ◽  
Jin Zhou ◽  
Qi Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Proliferation ◽  
Risk Model ◽  
Cox Regression ◽  
Costimulatory Molecule ◽  
Costimulatory Molecules ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Liver Hepatocellular Carcinoma

Abstract Background: Extensive research has revealed that costimulatory molecules play central roles in mounting anti-tumor immune responses and long non‐coding RNA (lncRNA) is an important regulatory factor in the development of various cancers. However, their roles in liver hepatocellular carcinoma (HCC) remain unexplored. In this study, we aimed to explore costimulatory molecule-related lncRNAs in HCC and construct a prognostic signature to predict prognosis and improve clinical outcomes with HCC patients.Methods: The data we used for bioinformatics analysis were downloaded from The Cancer Genome Atlas database. Costimulatory molecules were obtained from the known literature. The R software, SPSS and GraphPad Prism were used for mapping and statistical analysis.Results: A five costimulatory molecule-related lncRNAs based risk model was initially constructed through lasso and Cox regression analysis. Moreover, multivariate regression suggested that the risk score was a significant prognostic risk factor in HCC. Samples in high- and low-risk groups exhibited significantly different in gene set enrichment analysis and immune infiltration analysis. Importantly, we found that the AC099850.3 were significantly related to cell proliferation in HCC according to the colony formation and CCK8 assays.Conclusion: In summary, we first identified and validated a novel costimulatory molecule-related survival model and we found that AC099850.3 is closely associated with clinical stage and could remarkably facilitate cell proliferation in HCC, making it potential to be a novel therapeutic target.

scholarly journals A novel tumor doubling time-related immune gene signature for prognosis prediction in hepatocellular carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Genhao Zhang ◽  
Lisa Su ◽  
Xianping Lv ◽  
Qiankun Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Doubling Time ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Gene Signature ◽  
Tumor Doubling Time ◽  
Prognosis Prediction ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) has become a global health issue of wide concern due to its high prevalence and poor therapeutic efficacy. Both tumor doubling time (TDT) and immune status are closely related to the prognosis of HCC patients. However, the association between TDT-related genes (TDTRGs) and immune-related genes (IRGs) and the value of their combination in predicting the prognosis of HCC patients remains unclear. The current study aimed to discover reliable biomarkers for anticipating the future prognosis of HCC patients based on the relationship between TDTRGs and IRGs. Methods Tumor doubling time-related genes (TDTRGs) were acquired from GSE54236 by using Pearson correlation test and immune-related genes (IRGs) were available from ImmPort. Prognostic TDTRGs and IRGs in TCGA-LIHC dataset were determined to create a prognostic model by the LASSO-Cox regression and stepwise Cox regression analysis. International Cancer Genome Consortium (ICGC) and another cohort of individual clinical samples acted as external validations. Additionally, significant impacts of the signature on HCC immune microenvironment and reaction to immune checkpoint inhibitors were observed. Results Among the 68 overlapping genes identified as TDTRG and IRG, a total of 29 genes had significant prognostic relevance and were further selected by performing a LASSO-Cox regression model based on the minimum value of λ. Subsequently, a prognostic three-gene signature including HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1), C-type lectin domain family 1 member B (CLEC1B), and Collectin sub-family member 12 (COLEC12) was finally identified by stepwise Cox proportional modeling. The signature exhibited superior accuracy in forecasting the survival outcomes of HCC patients in TCGA, ICGC and the independent clinical cohorts. Patients in high-risk subgroup had significantly increased levels of immune checkpoint molecules including PD-L1, CD276, CTLA4, CXCR4, IL1A, PD-L2, TGFB1, OX40 and CD137, and are therefore more sensitive to immune checkpoint inhibitors (ICIs) treatment. Finally, we first found that overexpression of CLEC1B inhibited the proliferation and migration ability of HuH7 cells. Conclusions In summary, the prognostic signature based on TDTRGs and IRGs could effectively help clinicians classify HCC patients for prognosis prediction and individualized immunotherapies.

scholarly journals Distant Survival for Patients Undergoing Surgery Using Volatile versus IV Anesthesia for Hepatocellular Carcinoma with Portal Vein Tumor Thrombus:A Retrospective study.

2020 ◽  
Author(s):  
Xiao-Yan Meng ◽  
Xiu-Ping Zhang ◽  
Hong-Qian Wang ◽  
Weifeng Yu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Portal Vein ◽  
Cox Regression ◽  
Cancer Recurrence ◽  
Survival Outcomes ◽  
Free Survival ◽  
Cox Regression Analysis ◽  
Underlying Causes ◽  
The Impact

Abstract Background Whether anesthesia type is associate with the surgical outcome of Hepatocellular carcinoma (HCC) patients with portal vein tumor thrombus (PVTT) remains to be determined. This study aims to investigate the impact of volatile inhalational anesthesia (INHA) versus total IV anesthesia (TIVA) on the survival outcomes in HCC patients with PVTT. Methods A cohort of in-patients whom were diagnosed of HCC with PVTT in Eastern Hepatobiliary Surgery Hospital, Shanghai, China, from January 1, 2008 to December 24, 2012 were identified. Surgical patients receiving the INHA and TIVA were screened out. The overall survival (OS), recurrence-free survival (RFS) and several postoperative adverse events were compared according to anesthesia types. Results A total of 1513 patients were included in this study. After exclusions are applied, 263 patients remain in the INHA group and 208 in the TIVA group. Patients receiving INHA have a lower 5-year overall survival rate than that of patients receiving TIVA [12.6% (95% CI, 9.0 to 17.3) vs. 17.7% (95% CI, 11.3 to 20.8), P=0.024]. Results of multivariable Cox-regression analysis also identify that INHA anesthesia is significantly associated with mortality and cancer recurrence after surgery compare to TIVA, with HR (95%CI) of 1.303 (1.065, 1.595) and 1.265 (1.040, 1.539), respectively. Subgroup analysis suggested that in more severe cancer patients, the worse outcome related to INHA might be more significant. Conclusion This retrospective analysis identifies that TIVA has better survival outcomes compare to INHA in HCC patients with PVTT. Future prospective researches are urgent to verify this difference and figure out underlying causes of it.

scholarly journals A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7942
Author(s):  
Junjie Kong ◽  
Tao Wang ◽  
Shu Shen ◽  
Zifei Zhang ◽  
Xianwei Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Model ◽  
Cox Regression ◽  
Group Performance ◽  
Performance Status ◽  
Survival Rates ◽  
Eastern Cooperative Oncology Group ◽  
Staging System ◽  
Recurrence Free Survival ◽  
Free Survival

Liver resection surgery is the most commonly used treatment strategy for patients diagnosed with hepatocellular carcinoma (HCC). However, there is still a chance for recurrence in these patients despite the survival benefits of this procedure. This study aimed to explore recurrence-related genes (RRGs) and establish a genomic-clinical nomogram for predicting postoperative recurrence in HCC patients. A total of 123 differently expressed genes and three RRGs (PZP, SPP2, and PRC1) were identified from online databases via Cox regression and LASSO logistic regression analyses and a gene-based risk model containing RRGs was then established. The Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves showed that the model performed well. Finally, a genomic-clinical nomogram incorporating the gene-based risk model, AJCC staging system, and Eastern Cooperative Oncology Group performance status was constructed to predict the 1-, 2-, and 3-year recurrence-free survival rates (RFS) for HCC patients. The C-index, ROC analysis, and decision curve analysis were good indicators of the nomogram’s performance. In conclusion, we identified three reliable RRGs associated with the recurrence of cancer and constructed a nomogram that performed well in predicting RFS for HCC patients. These findings could enrich our understanding of the mechanisms for HCC recurrence, help surgeons predict patients’ prognosis, and promote HCC treatment.

scholarly journals Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
Jianbing Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Treatment Responses

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

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