Dataset Information

Identification of Mitochondrial-Related Prognostic Biomarkers Associated With Primary Bile Acid Biosynthesis and Tumor Microenvironment of Hepatocellular Carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the leading causes of tumor-associated deaths worldwide. Despite great progress in early diagnosis and multidisciplinary tumor management, the long-term prognosis of HCC remains poor. Currently, metabolic reprogramming during tumor development is widely observed to support rapid growth and proliferation of cancer cells, and several metabolic targets that could be used as cancer biomarkers have been identified. The liver and mitochondria are the two centers of human metabolism at the whole organism and cellular levels, respectively. Thus, identification of prognostic biomarkers based on mitochondrial-related genes (Mito-RGs)-the coding-genes of proteins located in the mitochondria-that reflect metabolic changes associated with HCC could lead to better interventions for HCC patients. In the present study, we used HCC data from The Cancer Genome Atlas (TCGA) database to construct a classifier containing 10 Mito-RGs (ACOT7, ADPRHL2, ATAD3A, BSG, FAM72A, PDK3, PDSS1, RAD51C, TOMM34, and TRMU) for predicting the prognosis of HCC by using 10-fold Least Absolute Shrinkage and Selection Operation (LASSO) cross-validation Cox regression. Based on the risk score calculated by the classifier, the samples were divided into high- and low-risk groups. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), t-distributed stochastic neighbor embedding (t-SNE), and consensus clusterPlus algorithms were used to identify metabolic pathways that were significantly different between the high- and low-risk groups. We further investigated the relationship between metabolic status and infiltration of immune cells into HCC tumor samples by using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm combined with the Tumor Immune Estimation Resource (TIMER) database. Our results showed that the classifier based on Mito-RGs could act as an independent biomarker for predicting survival of HCC patients. Repression of primary bile acid biosynthesis plays a vital role in the development and poor prognosis of HCC, which provides a potential approach to treatment. Our study revealed cross-talk between bile acid and infiltration of tumors by immune cells, which may provide novel insight into immunotherapy of HCC. Furthermore, our research may provide a novel method for HCC metabolic therapy based on modulation of mitochondrial function.

SUBMITTER: Zhang T

PROVIDER: S-EPMC8047479 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis. Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER). Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine-cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration. Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.

Project description:BackgroundVarious studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression.MethodsWe integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients.ResultsTo describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration.ConclusionsTaken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.

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.

Dataset Information

Identification of Mitochondrial-Related Prognostic Biomarkers Associated With Primary Bile Acid Biosynthesis and Tumor Microenvironment of Hepatocellular Carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets