Dataset Information

Endoplasmic Reticulum Stress-Related Signature for Predicting Prognosis and Immune Features in Hepatocellular Carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) with cancer cells under endoplasmic reticulum (ER) stress has a poor prognosis. This study is aimed at discovering credible biomarkers for predicting the prognosis of HCC based on ER stress-related genes (ERSRGs). We constructed a novel four-ERSRG prognostic risk model, including PON1, AGR2, SSR2, and TMCC1, through a series of bioinformatic approaches, which can accurately predict survival outcomes in HCC patients. Higher risk scores were linked to later grade, recurrence, advanced TNM stage, later T stage, and HBV infection. In addition, 20 fresh frozen tumors and normal tissues from HCC patients were collected and used to validate the genes expressed in the signature by qRT-PCR and immunohistochemical (IHC) assays. Moreover, we found the ER stress-related signature could reflect the infiltration levels of different immune cells in the tumor microenvironment (TME) and forecast the efficacy of immune checkpoint inhibitor (ICI) treatment. Finally, we created a nomogram incorporating this ER stress-related signature. In conclusion, our constructed four-gene risk model associated with ER stress can accurately predict survival outcomes in HCC patients, and the model's risk score is associated with the poor clinical classification.

SUBMITTER: Zhang G

PROVIDER: S-EPMC9393196 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Endoplasmic Reticulum Stress-Related Signature for Predicting Prognosis and Immune Features in Hepatocellular Carcinoma.

Zhang Genhao G Sun Jianping J

Journal of immunology research 20220814

Hepatocellular carcinoma (HCC) with cancer cells under endoplasmic reticulum (ER) stress has a poor prognosis. This study is aimed at discovering credible biomarkers for predicting the prognosis of HCC based on ER stress-related genes (ERSRGs). We constructed a novel four-ERSRG prognostic risk model, including PON1, AGR2, SSR2, and TMCC1, through a series of bioinformatic approaches, which can accurately predict survival outcomes in HCC patients. Higher risk scores were linked to later grade, re ...[more]

PMID: 36003068

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Project description:BackgroundHepatocellular carcinoma (HCC) remains one of the most common human cancers, the death cases induced by HCC are increasing these years. Endoplasmic reticulum stress (ERS) occurs when misfolded proteins cannot be disposed of properly. It is reported that ERS plays a crucial role in the pathogenesis of human malignant tumors. The aim of this study is to construct a novel gene signature based on ERS for predicting prognosis in HCC.MethodsThe data of HCC patients were downloaded from public databases. The Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to construct ERS-related gene signature. The cases were divided into high- and low-risk groups based on the ERS-related gene signature in The Cancer Genome Atlas (TCGA) cohort. Subsequently, the differences in messenger ribonucleic acid (mRNA) expression patterns, immune status, tumor mutation burden (TMB) and copy number variants (CNV) were investigated between high- and low-risk groups. Then, a predictive nomogram according to the ERS-related gene signature and clinicopathological variables was established. At last, we explored the biological functions of TMX1 which had the biggest coefficient and we investigated the effect of BRSK2 on apoptosis in HCC.ResultsIn our study, a 9-gene ERS-related gene signature was constructed. The results showed that patients in the low-risk group had a better prognosis than the high-risk group patients. The results of receiver operating characteristic (ROC) curves revealed that the area under the curve (AUC) was 0.784 at 1 year, 0.780 at 2 years, 0.793 at 3 years in the training set. While in validation cohort, this index was 0.694 at 1 year, 0.622 at 2 years, 0.613 at 3 years respectively. The analysis of immune status revealed an immunosuppressive microenvironment in the high-risk group. The analysis of TMB and CNV revealed that the high-risk group patients had a higher genomic mutation frequency. In Univariate Cox regression analysis, the hazard ratio of RiskScore was 2.718 [95% confidence interval (CI): 2.173-3.399]. In Multivariate Cox regression analysis, the hazard ratio of RiskScore was 2.422 (95% CI: 1.805-3.25). Then, we established a nomogram according to the RiskScore and Eastern Cooperative Oncology Group performance status. The AUCs of the nomogram were 0.851 at 1 year, 0.860 at 2 years, and 0.866 at 3 years. At last, we found that TMX1 knockdown can inhibit the proliferation and migration of Huh7 and HepG2 cells. In addition, BRSK2 knockdown could promote the apoptosis induced by ERS.ConclusionsIn our study, a novel ERS-related gene signature was constructed to predict the prognosis of HCC patients. In addition, TMX1 and BRSK2 could promote the progression of HCC. This study may provide a new understanding for HCC.

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most common tumors worldwide. Various factors in the tumor environment (TME) can lead to the activation of endoplasmic reticulum stress (ERS), thereby affecting the occurrence and development of tumors. The objective of our study was to develop and validate a radiogenomic signature based on ERS to predict prognosis and systemic combination therapy response.MethodsUsing data from The Cancer Genome Atlas Program (TCGA) as a training cohort and data from International cancer genome consortium (ICGC) as a testing cohort. Univariate Cox regression and multivariate Cox regression analysis were used to identify prognostic-related genes and construct a model. HCC single-cell data obtained from Gene Expression Omnibus (GEO) were used to map gene signatures and explore inter-cellular signaling communications. Finally, a radiogenomic signature was used to predict the objective response rate (ORR) and overall survival (OS).ResultsA total of four gene signatures related to ERS, including Stanniocalcin-2 (STC2), Melanoma-Associated Antigen 3 (MAGEA3), BR Serine/Threonine-Protein Kinase 2 (BRSK2), DEAD/H-Box Helicase 11 (DDX11) were identified. Macrophages were significantly different between high-risk and low-risk groups. The high-risk group showed higher targeting programmed cell death-1 (PD-1) and mutated tumor protein p53 (TP53) scores. Drug sensitivity analysis found that most sensitive drugs target the phosphatidylinositol 3-kinase/ mechanistic target of rapamycin (PI3K/mTOR) signaling pathway. Further research revealed the expression of STC2 in the endothelial cells (ECs), particularly plasmalemma vesicle associated protein (PLVAP) + ECs, and may regulate the reprogramming and function of macrophages. Furthermore, we identified nine radiomic features and established a radiogenomic signature based on ERS that can predict prognosis and response to systemic combination therapy. This signature can guide systemic combination therapy for patients with unresectable HCC.ConclusionsWe established an ERS prognostic model that can predict patient prognosis. We also found that ERS is closely related to the TME and is mainly manifested in the interaction between tumor-associated endothelial cells (TAEs) and tumor-associated macrophages (TAMs). Moreover, we constructed a radiogenomic signature based on the ERS. This signature can guide subsequent combination therapy for patients with unresectable HCC.

Project description:Liver cancer is the sixth most frequently diagnosed primary malignancy and ranks as the third leading cause of cancer-related death worldwide in 2020. ER stress also plays a vital role in the pathogenesis of malignancies. In the current study, we aimed to construct an endoplasmic reticulum stress-related genes (ERGs) signature to predict the overall survival (OS) of patients with HCC. Differentially expressed ERGs (DE-ERGs) were analyzed using The Cancer Genome Atlas (TCGA-LIHC cohort) and International Cancer Genome Consortium (ICGC-LIRI-JP cohort) databases. The prognostic gene signature was identified by the univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression analysis. The predictive ability of the model was evaluated by utilizing Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the underlying biological processes and signaling pathways. CIBERPORT and single-sample Gene Set Enrichment Analysis (ssGSEA) were implemented to estimate the immune status between the different risk groups. A total of 113 DE-ERGs were identified between 50 normal samples and 365 HCC samples in the TCGA-LIHC cohort, and 48 DE-ERGs were associated with OS through the univariate Cox regression. A six DE-ERGs (PPARGC1A, SQSTM1, SGK1, PON1, CDK1, and G6PD) signature was constructed and classified patients into high-risk and low-risk groups. The risk score was an independent prognostic indicator for OS (HR > 1, p < 0.001). The function enrichment analysis indicated that cell cycle, RNA degradation, protein localization, and cell division were the main biological processes. The high-risk group had higher immune cell infiltration levels than those of the low-risk group. We predicted the response to targeted therapy in high- and low-risk patients with HCC and found that the high-risk patients were more sensitive to pazopanib. At last, we verified the expression of the six gene patterns in HCC tissues by qRT-PCR and immunohistochemistry. This signature may be a potential tool to provide a choice for prognosis prediction and personal management of patients with HCC.

Project description:BackgroundsThe extended duration of endoplasmic reticulum stress (ERS) can impact the progression of hepatocellular carcinoma (HCC) and the efficacy of immunotherapies by interacting with immune cells that have infiltrated the tumor microenvironment (TME).Methods and resultsThe study utilized a training cohort of 364 HCC patients with complete information from The Cancer Genome Atlas Program (TCGA) database, and a validation cohort of 231 HCC patients from the International Cancer Genome Consortium (ICGC) database. The genes related to ERS exhibiting a strong correlation with overall survival (OS) were identified using univariate Cox regression analysis. A 13-gene predictive signature was then produced through the least absolute shrinkage and selection operator (LASSO) regression approach. The data revealed that the ERS-associated gene signature effectively stratified patients into high- or low-risk groups regarding OS in both the training and validation cohorts (P < 0.0001 and P = 0.00029, respectively). Using the multivariate method, it is still an independent prognostic factor in both the training and validation cohorts (P < 0.001 and P = 0.008, respectively). Moreover, several metabolic pathways were identified to be enriched among the 13 genes in the predictive signature. When the ERS-associated gene signature was combined with the tumor-node-metastasis (TNM) stage, the ERS nomogram performed better than either the gene signature or the TNM stage alone (C-index values: 0.731, 0.729, and 0.573, respectively). Further analysis revealed that patients in the high-risk group exhibited increased infiltration of immune cells. Additionally, GP6 was downregulated in HCC tissues among these signature genes (P < 0.05), which was related to poor OS.ConclusionsThe data suggest that this novel ERS-associated gene signature could contribute to personalized cancer management for HCC. Moreover, targeting GP6 inhibition might be a potential method for HCC therapy.

Project description:BackgroundOxidative stress and endoplasmic reticulum stress are important components of the cellular stress process, which plays a critical role in tumor initiation and progression.MethodsFirst, the correlation between oxidative stress and endoplasmic reticulum stress was detected in 68 human hepatocellular carcinoma (HCC) tissue microarray samples by immunohistochemistry. Differentially expressed oxidative stress- and endoplasmic reticulum stressrelated genes (OESGs) then were screened in HCC. Next, an OESGs prognostic signature was constructed for HCC in the training cohort (TCGA-LIHC from The Cancer Genome Atlas), by least absolute shrinkage and selection operator Cox and stepwise Cox regression analyses, and was verified in the external cohort (GSE14520 from the Gene Expression Omnibus). The MCP counter was employed to evaluate immune cell infiltration. The C-index was used to evaluate the predictive power of prognostic signature. Finally, a prognostic nomogram model was constructed to predict the survival probability of patients with HCC based on the results of Cox regression analysis.ResultsWe demonstrated a positive correlation between oxidative stress and endoplasmic reticulum stress in human HCC samples. We then identified five OESGs as a prognostic signature consisting of IL18RAP, ECT2, PPARGC1A, STC2, and NQO1 for HCC. Related risk scores correlated with tumor stage, grade, and response to transcatheter arterial chemoembolization therapy, and the higher risk score group had less T cells, CD8+ T cells, cytotoxic lymphocytes and natural killer cell infiltration. The C-index of our OESGs prognostic signature was superior to four previously published signatures. Furthermore, we developed a nomogram based on the OESGs prognostic signature and clinical parameters for patients with HCC that is an effective quantitative analysis tool to predict patient survival.ConclusionThe OESGs signature showed excellent performance in predicting survival and therapeutic responses for patients with HCC.

Dataset Information

Endoplasmic Reticulum Stress-Related Signature for Predicting Prognosis and Immune Features in Hepatocellular Carcinoma.

Publications

Endoplasmic Reticulum Stress-Related Signature for Predicting Prognosis and Immune Features in Hepatocellular Carcinoma.

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