Pan-cancer analysis identifies FAM49B as an immune-related prognostic maker for hepatocellular carcinoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Xu F
PROVIDER: S-EPMC8692685 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Although a previous pan-cancer study has reported the expression patterns of ITIHs in various tumors, their analyses have been restricted to limited cancer types. We thus comprehensively analyzed the expression profiles and clinical significances of ITIHs in a broader spectrum of cancers from TCGA. Our results showed that ITIHs were primarily down-regulated in tested cancers. The ITIH members were associated with either survival advantage or disadvantage, depending on the cancer type tested and the genes queried. Importantly, we for the first time demonstrated that ITIH1 had substantially decreased expression in liver hepatocellular carcinoma (LIHC) compared with corresponding normal tissue, and its down-regulation adversely impacted patient outcome. Moreover, ITIH1 expression was consistently declining during the progression of LIHC. Further analysis revealed that ITIH1 may be involved in cellular metabolic processes. Our findings established ITIH1 as a potential diagnostic and prognostic biomarker for LIHC, which awaits future experimental validation.
| S-EPMC8109120 | biostudies-literature
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.
| S-EPMC10839619 | biostudies-literature
Project description:Liver hepatocellular carcinoma (LIHC) is the most prevalent primary cancer of the liver, and immune-related genes (IRGs) regulate its development. So far, there is still no precise biomarker that predicts response to immunotherapy in LIHC. Therefore, this research seeks to identify immunogenic prognostic biomarkers and explore potential predictors for the efficacy of anti-PD-1/PD-L1 therapies in LIHC. The clinical data and gene expression profiles of patients diagnosed with LIHC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Moreover, IRGs were obtained from the ImmPort database. We discovered 35 IRGs that were differentially expressed between LIHC tissues and corresponding normal tissues. Through univariate Cox regression analysis, eight prognostic differentially expressed IRGs (PDEIRGs) were identified. Further, three optimal PDEIRGs (BIRC5, LPA, and ROBO1) were identified and used to construct a prognostic risk signature of LIHC patients via multivariate Cox regression analysis. The signature was validated by ROC curves. Subsequently, based on gene set enrichment analysis (GSEA) analysis, two out of the three optimal PDEIRGs (BIRC5 and LPA) were significantly enriched in the mismatch repair (MMR) pathway. Moreover, the two PDEIRGs (BIRC5 and LPA) were significantly correlated with the expression of genes related to mismatch repair (MLH1, MSH2, MSH6, and PMS2). Furthermore, correlations between the two PDEIRGs (BIRC5 and LPA) and immune checkpoints of cancer treatment (such as CTLA4, PD-1, and PD-L1) were demonstrated. Hyperprogressive disease (HPD) is a novel pattern of tumor progression which has a close relationship with immune checkpoint inhibitors (ICIs) utilization. MDM2 family amplification might promote the HPD phenomenon. Finally, we found a positive regulatory relationship between HPD related gene (MDM2) and BIRC5. Notably, MDM2 can either interact directly with BIRC5 or indirectly via downstream transcription factors of BIRC5. Overall, our study uncovered a novel 3-immune-related prognostic genes in LIHC.
| S-EPMC7542239 | biostudies-literature
Project description:BackgroundDensity of tumor infiltrating lymphocytes (TIL) and expressions of certain immune-related genes have prognostic and predictive values in hepatocellular carcinoma (HCC); however, factors determining the immunophenotype of HCC patients are still unclear. In the current study, the transcript sequencing data of liver cancer were systematically analyzed to determine an immune gene marker for the prediction of clinical outcome of HCC.MethodsRNASeq data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and the samples were assigned into high-stage and low-stage groups. Immune pathway-related genes were screened from the Molecular Signatures Database v4.0 (MsigDB) database. LASSO regression analysis was performed to identify robust immune-related biomarkers in predicting HCC clinical outcomes. Moreover, an immune gene-related prognostic model was established and validated by test sets and Gene Expression Omnibus (GEO) external validation sets.ResultsWe obtained 319 immune genes from MsigDB, and the genes have different expression profiles in high-stage and low-stage of HCC. Univariate survival analysis found that 17 genes had a significant effect on HCC prognosis, among them, 13 (76.5%) genes were prognostically protective factors. Further lasso regression analysis identified seven potential prognostic markers (IL27, CD1D, NCOA6, CTSE, FCGRT, CFHR1, and APOA2) of robustness, most of which are related to tumor development. Cox regression analysis was further performed to establish a seven immune gene signature, which could stratify the risk of samples in training set, test set and external verification set (p < 0.01), and the AUC in both training set and test set was greater than 0.85, which also greater compared with previous studies.ConclusionThis study constructed a 7-immunogenic marker as novel prognostic markers for predicting survival of HCC patients.
| S-EPMC7258897 | biostudies-literature
Project description:Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and they play fundamental roles in immune regulation. Here we introduce an integrated algorithm, ImmLnc, for identifying lncRNA regulators of immune-related pathways. We comprehensively chart the landscape of lncRNA regulation in the immunome across 33 cancer types and show that cancers with similar tissue origin are likely to share lncRNA immune regulators. Moreover, the immune-related lncRNAs are likely to show expression perturbation in cancer and are significantly correlated with immune cell infiltration. ImmLnc can help prioritize cancer-related lncRNAs and further identify three molecular subtypes (proliferative, intermediate, and immunological) of non-small cell lung cancer. These subtypes are characterized by differences in mutation burden, immune cell infiltration, expression of immunomodulatory genes, response to chemotherapy, and prognosis. In summary, the ImmLnc pipeline and the resulting data serve as a valuable resource for understanding lncRNA function and to advance identification of immunotherapy targets.
| S-EPMC7035327 | biostudies-literature
Project description:It is crucial to grasp the characteristics of tumour immune microenvironment to improve effects of immunotherapy. In this study, the immune and stromal scores of 371 cases were calculated for quantitative analysis of immune and stromal cell infiltration in the tumour microenvironment of hepatocellular carcinoma (HCC). The weighted gene co-expression network analysis and protein-protein interaction network were analysed to identify immune microenvironment-related genes. The results showed that patients with high immune scores had a higher 4-year recurrence-free rate. TP53, CTNNB1, and AXIN1 mutations significantly varied with immune scores. In immune score-related modules analysis, Kyoto encyclopaedia of genes and genomes pathways and gene ontology terms were closely related to immune processes, tumorigenesis, and metastasis. Twelve new immune microenvironment-related genes were identified and had significantly positive correlations with seven immune checkpoint genes. In prognostic analysis, eleven immune microenvironment-related genes exhibited high expression, nine of which were validated in the GSE62232 dataset and were significantly associated with a good prognosis. Our findings suggest that calculating immune score and stromal score could help to determine tumour purity and immune cell infiltration in the tumour microenvironment. Nine immune microenvironment-related genes identified in this study had potential as prognostic markers for HCC.
| S-EPMC7138561 | biostudies-literature
Project description:Background Hepatocellular carcinoma (HCC) is one of the most common and deadly tumors worldwide. Immunotherapy has emerged as a promising strategy for HCC treatment, and understanding the immune microenvironment of HCC provides a theoretical basis for identifying new immune targets. However, the roles of immune components and their regulatory mechanisms in HCC require further clarified. Methods By analyzing HCC expression profiles from The Cancer Genome Atlas (TCGA) database, we depicted the proportion profile of immune cells for each sample using the software CIBERSORTx. Using R packages, we also characterized the distribution of differentially expressed genes (DEGs) in immune cells, calculated the correlation coefficient between immune cells and common DEGs, and analyzed their biology function by Gene-Ontology analysis. Results We found that seven immune cell types were related to the overall survival of HCC patients, and identified 3,692 differentially expressed immune-related genes, predominantly functioning in nucleic acid processing and metabolism. Moreover, 14 DEGs were identified as common candidates related to immune cells and overall survival. Conclusions Our study not only presents an overview of the immune features of the microenvironment of HCC, but also provides potential targets related to immune components.
| S-EPMC9372241 | biostudies-literature
Project description:Liver hepatocellular carcinoma (LIHC), an inflammation-associated cancer induced by a variety of etiological factors, is still one of the most prevalent and lethal cancers in human population. In this study, the expression profiles of immune-related genes (IRGs) were integrated with the overall survival (OS) of 378 LIHC patients based on the Cancer Genome Atlas (TCGA) dataset. Moreover, the differentially expressed and survival related IRGs among LIHC patients were predicted through the computational difference algorithm and COX regression analysis. As a result, 7 genes, including HSPA4, S100A10, FABP6, CACYBP, HDAC1, FCGR2B and SHC1, were retrieved to construct a predictive model associated with the overall survival (OS) of LIHC patients. Typically, the as-constructed model performed moderately in predicting prognosis, which was also correlated with tumor grade. Functional enrichment analysis revealed that the genes of high-risk group were actively involved in mRNA binding and the spliceosome pathway. Intriguingly, the prognostic index established based on IRGs reflected infiltration by multiple types of immunocytes. Our findings screen several IRGs with clinical significance, reveal the drivers of immune repertoire, and illustrate the importance of a personalized, IRG-based immune signature in LIHC recognition, surveillance, and prognosis prediction.
| S-EPMC7138589 | biostudies-literature
Project description:Background: Nuclear factor interleukin 3 (NFIL3) mainly focuses on the regulation of the circadian rhythm and immune system. However, the potential role of NFIL3 in human cancers has not been studied extensively. Methods: We retrieved original data from the TCGA, TARGET, and GTEx datasets via the UCSC Xena browser (http://genome.ucsc.edu/) and integrated them using R version 3.6.4. NFIL3 expression was assessed using resources such as UCSC, GEPIA (http://gepia.cancer-pku.cn/), Kaplan-Meier Plotter (KM Plotter; https://kmplot.com/), and the Human Protein Atlas (HPA; https://www.proteinatlas.org/) databases. To investigate the prognostic implications of NFIL3, we utilized GEPIA, Kaplan-Meier Plotter, and PrognoScan (http://www.abren.net/PrognoScan/) datasets. For a comprehensive analysis across multiple cancer types, we employed pan-cancer data from UCSC, examining associations between NFIL3 expression and genomic heterogeneity, tumor mutational burden (TMB), microsatellite instability (MSI), tumor purity, and neoantigens. Furthermore, we explored the relationships between NFIL3 expression and the infiltration of immune cells and the expression of immune checkpoint genes. In the context of ovarian cancer, we validated the expression and functional relevance of NFIL3. Cell Counting Kit 8 (CCK8) assays were conducted to assess cell proliferation, while scratch and transwell assays were employed to evaluate cell migration capabilities. We further examined the interaction between NFIL3 and the p53 signaling pathway through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot analysis, immunofluorescence confocal, and Coimmunoprecipitation (Co-IP) assays. Results: In general, NFIL3 expression in cancerous tissues exhibited diminished levels when compared to normal tissue samples. Notably, NFIL3 expression demonstrated a robust correlation with several pivotal aspects, including prognosis, immune cell infiltration, immune checkpoint-related genes, TMB, MSI, tumor purity, and the presence of neoantigens. Experimental investigations involving scratch assays, transwell assays, and assessments of cell proliferation in ovarian cancer cells have provided indications that NFIL3 may exert influence over cell migration and proliferation processes. Moreover, a substantial association between NFIL3 and the p53 signaling pathway was discerned through Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, with subsequent validation through qRT-PCR, Western blot analysis, immunofluorescence confocal, and co-immunoprecipitation (Co-IP) assays. Conclusions: Therefore, we concluded NFIL3 may serve as a possible prognostic and immunological pan-cancer biomarker.
| S-EPMC10861811 | biostudies-literature
Project description:PurposeThe tumor microenvironment (TME) plays a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, underlying compositions and functions that drive the establishment and maintenance of the TME classifications are less-well understood.MethodsA total of 766 HCC patients from three public cohorts were clustered into four immune-related subclasses based on 13 TME signatures (11 immune-related cells and 2 immune-related pathways) calculated by MCP-counter. After analyzing the landscapes of functional annotation, methylation, somatic mutation, and clinical characteristics, we built a TME-based Support Vector Machine of 365 patients (discovery phase) and 401 patients (validation phase). We applied this SVM model on another two independent cohorts of patients who received sorafenib/pembrolizumab treatment.ResultsAbout 33% of patients displayed an immune desert pattern. The other subclasses were different in abundance of tumor infiltrating cells. The Immunogenic subclass (17%) associated with the best prognosis presented a massive T cell infiltration and an activation of immune checkpoint pathway. The 13 TME signatures showed a good potential to predict the TME classification (average AUC = 88%). Molecular characteristics of immunohistochemistry from Zhejiang cohort supported our SVM classification. The optimum response to pembrolizumab (78%) and sorafenib (81%) was observed in patients belonging to the Immunogenic subclass.ConclusionsThe HCC patients from distinct immune subclass showed significant differences in clinical prognosis and response to personalized treatment. Based on tumor transcriptome data, our workflow can help to predict the clinical outcomes and to find appropriate treatment strategies for HCC patients.
| S-EPMC7933665 | biostudies-literature