Combating inconsistent evaluation of intra-tumor immune status by a novel transcriptomic signature in hepatocellular carcinoma.
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ABSTRACT:
SUBMITTER: Zhang L
PROVIDER: S-EPMC9911634 | biostudies-literature | 2023 Feb
REPOSITORIES: biostudies-literature
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Combating inconsistent evaluation of intra-tumor immune status by a novel transcriptomic signature in hepatocellular carcinoma.
Zhang Linmeng L Tang Ning N Yang Chen C Geng Haigang H Hang Hualian H Qin Wenxin W Wang Cun C
Signal transduction and targeted therapy 20230210 1
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Project description:Immunotherapy as a strategy to deal with cancer is increasingly being used clinically, especially in hepatocellular carcinoma (HCC). We aim to create an immunotherapy-related signature that can play a role in predicting HCC patients' survival and therapeutic outcomes. Immunotherapy-related genes were discovered first. Clinical information and gene expression data were extracted from GSE140901. By a series of bioinformatics methods to analyze, overlapping genes were used to build an immunotherapy-related signature that could contribute to predict both the prognosis of people with hepatocellular carcinoma and responder to immune checkpoint blockade therapy of them in TCGA database. Differences of the two groups in immune cell subpopulations were then compared. Furthermore, A nomogram was constructed, based on the immunotherapy-related signature and clinicopathological features, and proved to be highly predictive. Finally, immunohistochemistry assays were performed in HCC tissue and normal tissue adjacent tumors to verify the differences of the four genes expression. As a result of this study, a prognostic protein profile associated with immunotherapy had been created, which could be applied to predict patients' response to immunotherapy and may provide a new perspective as clinicians focus on non-apoptotic treatment for patients with HCC.
| S-EPMC10618198 | biostudies-literature
Project description:Intra-tumor heterogeneity poses a serious challenge in the treatment of cancer, including hepatocellular carcinoma (HCC). Recent developments in single-cell RNA sequencing (scRNA-seq) make it possible to examine the heterogeneity of tumor cells. The Gene Expression Omnibus (GEO) database was retrieved to obtain scRNA-seq data of 13 HCC and 8 para cancer samples, and the cells were clustered using FindNeighbors and FindClusters functions. Cell subsets were defined using the "Enricher" function of the clusterProfiler package. Monocle was used to predict cell developmental trajectory. The LIMMA package included in the R program was utilized to detect differentially expressed genes (DEGs) between HCC and paracancerous tissues. Univariate Cox analysis and Least Absolute and Selection Operator (Lasso) Cox regression analysis were conducted to establish a risk assessment model. Thirteen cell subpopulations were identified from the sequencing data of 64,634 single cells. Four cell subgroups (dendritic cells, hepatocytes, liver bud hepatic cells, and liver progenitor cells) in tumor tissues were highly enriched. Between HCC and para cancer tissues, 3024 DEGs were identified, and 641 were specific markers of four cell subgroups. To develop a prognostic risk model, 9 genes among the 641 genes were selected. In the training and validation sets, the model demonstrated a higher 5-year AUC and independently served as a prognostic marker as confirmed by multivariate and univariate Cox analyses. This study revealed the characteristics of different cell subpopulations of immune cells and tumor cells from the HCC microenvironment. We established a novel nine-gene prognostic model to determine the death risk of HCC patients. The discoveries in this research improved the current knowledge of HCC heterogeneity and may inspire future research.
| S-EPMC9638959 | biostudies-literature
Project description:Despite great advances in the treatment of liver hepatocellular carcinoma (LIHC), such as immunotherapy, the prognosis remains extremely poor, and there is an urgent need to develop novel diagnostic and prognostic markers. Recently, RNA methylation-related long non-coding RNAs (lncRNAs) have been demonstrated to be novel potential biomarkers for tumor diagnosis and prognosis as well as immunotherapy response, such as N6-methyladenine (m6A) and 5-methylcytosine (m5C). N7-Methylguanosine (m7G) is a widespread RNA modification in eukaryotes, but the relationship between m7G-related lncRNAs and prognosis of LIHC patients as well as tumor immunotherapy response is still unknown. In this study, based on the LIHC patients' clinical and transcriptomic data from TCGA database, a total of 992 m7G-related lncRNAs that co-expressed with 22 m7G regulatory genes were identified using Pearson correlation analysis. Univariate regression analysis was used to screen prognostic m7G-related lncRNAs, and the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to construct a 9-m7G-related-lncRNA risk model. The m7G-related lncRNA risk model was validated to exhibit good prognostic performance through Kaplan-Meier analysis and ROC analysis. Together with the clinicopathological features, the m7G-related lncRNA risk score was found to be an independent prognostic factor for LIHC. Furthermore, the high-risk group of LIHC patients was unveiled to have a higher tumor mutation burden (TMB), and their tumor microenvironment was more prone to the immunosuppressive state and exhibited a lower response rate to immunotherapy. In addition, 47 anti-cancer drugs were identified to exhibit a difference in drug sensitivity between the high-risk and low-risk groups. Taken together, the m7G-related lncRNA risk model might display potential value in predicting prognosis, immunotherapy response, and drug sensitivity in LIHC patients.
| S-EPMC9354608 | biostudies-literature
Project description:Purpose: The purpose of this study was to construct a novel risk scoring model with prognostic value that could elucidate tumor immune microenvironment of hepatocellular carcinoma (HCC). Samples and methods: Data were obtained through The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis were carried out to screen for glycolysis-related long noncoding RNAs (lncRNAs) that could provide prognostic value. Finally, we established a risk score model to describe the characteristics of the model and verify its prediction accuracy. The receiver operating characteristic (ROC) curves of 1, 3, and 5 years of overall survival (OS) were depicted with risk score and some clinical features. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and CIBERSORT analysis were employed to reveal the characteristics of tumor immune microenvironment in HCC. The nomogram was drawn by screening indicators with high prognostic accuracy. The correlation of risk signature with immune infiltration and immune checkpoint blockade (ICB) therapy was analyzed. After enrichment of related genes, active behaviors and pathways in high-risk groups were identified and lncRNAs related to poor prognosis were validated in vitro. Finally, the impact of MIR4435-2HG upon ICB treatment was uncovered. Results: After screening through multiple steps, four glycolysis-related lncRNAs were obtained. The risk score constructed with the four lncRNAs was found to significantly correlate with prognosis of samples. From the ROC curve of samples with 1, 3, and 5 years of OS, two indicators were identified with high prognostic accuracy and were used to draw a nomogram. Besides, the risk score significantly correlated with immune score, immune-related signature, infiltrating immune cells (i.e. B cells, etc.), and ICB key molecules (i.e. CTLA4,etc.). Gene enrichment analysis indicated that multiple biological behaviors and pathways were active in the high-risk group. In vitro validation results showed that MIR4435-2HG was highly expressed in the two cell lines, which had a significant impact on the OS of samples. Finally, we corroborated that MIR4435-2HG had intimate relationship with ICB therapy in hepatocellular carcinoma. Conclusion: We elucidated the crucial role of risk signature in immune cell infiltration and immunotherapy, which might contribute to clinical strategies and clinical outcome prediction of HCC.
| S-EPMC8100457 | biostudies-literature
Project description:BackgroundHypoxia and angiogenesis, as prominent characteristics of malignant tumors, are implicated in the progression of hepatocellular carcinoma (HCC). However, the role of hypoxia in the angiogenesis of liver cancer is unclear. Therefore, we explored the regulatory mechanisms of hypoxia-related angiogenic genes (HRAGs) and the relationship between these genes and the prognosis of HCC.MethodsThe transcriptomic and clinical data of HCC samples were downloaded from public datasets, followed by identification of hypoxia- and angiogenesis-related genes in the database. A gene signature model was constructed based on univariate and multivariate Cox regression analyses, and validated in independent cohorts. Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) curves were generated to evaluate the model's predictive capability. Gene set enrichment analysis (GSEA) was performed to explore signaling pathways regulated by the gene signature. Furthermore, the relationships among gene signature, immune status, and response to anti-angiogenesis agents and immune checkpoint blockade (ICB) were analyzed.ResultsThe prognostic model was based on three HRAGs (ANGPT2, SERPINE1 and SPP1). The model accurately predicted that low-risk patients would have longer overall survival than high-risk patients, consistent with findings in other cohorts. GSEA indicated that high-risk group membership was significantly associated with hypoxia, angiogenesis, the epithelial-mesenchymal transition, and activity in immune-related pathways. The high-risk group also had more immunosuppressive cells and higher expression of immune checkpoints such as PD-1 and PD-L1. Conversely, the low-risk group had a better response to anti-angiogenesis and ICB therapy.ConclusionsThe gene signature based on HRAGs was predictive of prognosis and provided an immunological perspective that will facilitate the development of personalized therapies.
| S-EPMC9468769 | biostudies-literature
Project description:BackgroundThis study clarified whether EMT-related genes can predict immunotherapy efficacy and overall survival in patients with HCC.MethodsThe RNA-sequencing profiles and patient information of 370 samples were derived from the Cancer Genome Atlas (TCGA) dataset, and EMT-related genes were obtained from the Molecular Signatures database. The signature model was constructed using the least absolute shrinkage and selection operator Cox regression analysis in TCGA cohort. Validation data were obtained from the International Cancer Genome Consortium (ICGC) dataset of patients with HCC. Kaplan-Meier analysis and multivariate Cox analyses were employed to estimate the prognostic value. Immune status and tumor microenvironment were estimated using a single-sample gene set enrichment analysis (ssGSEA). The expression of prognostic genes was verified using qRT-PCR analysis of HCC cell lines.ResultsA signature model was constructed using EMT-related genes to determine HCC prognosis, based on which patients were divided into high-risk and low-risk groups. The risk score, as an independent factor, was related to tumor stage, grade, and immune cells infiltration. The results indicated that the most prognostic genes were highly expressed in the HCC cell lines, but GADD45B was down-regulated. Enrichment analysis suggested that immunoglobulin receptor binding and material metabolism were essential in the prognostic signature.ConclusionOur novel prognostic signature model has a vital impact on immune status and prognosis, significantly helping the decision-making related to the diagnosis and treatment of patients with HCC.
| S-EPMC9349121 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) develops in the context of chronic inflammatory liver disease and has an extremely poor prognosis. An immunosuppressive tumor microenvironment may contribute to therapeutic failure in metastatic HCC. Here, we identified unique molecular signatures pertaining to HCC disease progression and tumor immunity by analyzing genome-wide RNA-Seq data derived from HCC patient tumors and non-tumor cirrhotic tissues. Unsupervised clustering of gene expression data revealed a gradual suppression of local tumor immunity that coincided with disease progression, indicating an increasingly immunosuppressive tumor environment during HCC disease advancement. IHC examination of the spatial distribution of CD8+ T cells in tumors revealed distinct intra- and peri-tumoral subsets. Differential gene expression analysis revealed an 85-gene signature that was significantly upregulated in the peri-tumoral CD8+ T cell-excluded tumors. Notably, this signature was highly enriched with components of underlying extracellular matrix, fibrosis, and epithelial-mesenchymal transition (EMT). Further analysis condensed this signature to a core set of 23 genes that are associated with CD8+ T cell localization, and were prospectively validated in an independent cohort of HCC specimens. These findings suggest a potential association between elevated fibrosis, possibly modulated by TGF-β, PDGFR, SHH or Notch pathway, and the T cell-excluded immune phenotype. Indeed, targeting fibrosis using a TGF-β neutralizing antibody in the STAM™ model of murine HCC, we found that ameliorating the fibrotic environment could facilitate redistribution of CD8+ lymphocytes into tumors. Our results provide a strong rationale for utilizing immunotherapies in HCC earlier during treatment, potentially in combination with anti-fibrotic therapies.
| S-EPMC6237857 | biostudies-literature
Project description:Little is known on the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of hepatocellular carcinoma (HCC). In this study, a specific PRGs prognostic model was developed with an aim to improve therapeutic efficiency among HCC patients. In total, 42 PRGs that were differentially expressed between HCC tissues and adjacent tissues and we exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). There were significant differences in clinical features including T stage, grade, gender, and stage among different clusters. Kaplan-Meier curve analysis showed that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. Based on the univariate analysis and multivariate analysis, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from International Cancer Genome Consortium (ICGC)-LIRI-JP cohort and Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group. Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of HCC patients. Then, ROC curve and survival analysis were performed to evaluate the prognostic prediction value of the model. Finally, we constructed a PRGs clinical characteristics nomogram to further predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency between high-risk group and low-risk group. This work suggests PRGs signature played a crucial role in predicting the prognosis, infiltration of cancer microenvironment, and sensitivity of chemotherapeutic agents.
| S-EPMC9491002 | biostudies-literature
Project description:BackgroundHepatocellular 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.MethodsTumor 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.ResultsAmong 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.ConclusionsIn summary, the prognostic signature based on TDTRGs and IRGs could effectively help clinicians classify HCC patients for prognosis prediction and individualized immunotherapies.
| S-EPMC8502295 | biostudies-literature
Project description:Disulfidptosis, a novel type of programmed cell death, has attracted researchers' attention worldwide. However, the role of disulfidptosis-related lncRNAs (DRLs) in liver hepatocellular carcinoma (LIHC) not yet been studied. We aimed to establish and validate a prognostic signature of DRLs and analyze tumor microenvironment (TME) and drug susceptibility in LIHC patients. RNA sequencing data, mutation data, and clinical data were obtained from the Cancer Genome Atlas Database (TCGA). Lasso algorithm and cox regression analysis were performed to identify a prognostic DRLs signature. Kaplan-Meier curves, principal component analysis (PCA), nomogram and calibration curve, function enrichment, TME, immune dysfunction and exclusion (TIDE), tumor mutation burden (TMB), and drug sensitivity analyses were analyzed. External datasets were used to validate the predictive value of DRLs. qRT-PCR was also used to validate the differential expression of the target lncRNAs in tissue samples and cell lines. We established a prognostic signature for the DRLs (MKLN1-AS and TMCC1-AS1) in LIHC. The signature could divide the LIHC patients into low- and high-risk groups, with the high-risk subgroup associated with a worse prognosis. We observed discrepancies in tumor-infiltrating immune cells, immune function, function enrichment, and TIDE between two risk groups. LIHC patients in the high-risk group were more sensitive to several chemotherapeutic drugs. External datasets, clinical tissue, and cell lines confirmed the expression of MKLN1-AS and TMCC1-AS1 were upregulated in LIHC and associated with a worse prognosis. The novel signature based on the two DRLs provide new insight into LIHC prognostic prediction, TME, and potential therapeutic strategies.
| S-EPMC10772085 | biostudies-literature