Dataset Information

Comprehensive bioinformatic analysis of MMP1 in hepatocellular carcinoma and establishment of relevant prognostic model.

ABSTRACT: Matrix metalloproteinase 1 (MMP1) encodes endopeptidases associated with degradation of multiple components of the extracellular matrix. This function has increasingly been considered to play a major proteolysis role in tumor invasion and metastasis. However, the relationship between MMP1 gene expression, tumor-immune microenvironment and prognosis in hepatocellular carcinoma patients remains mostly unclear. This study focused on a comprehensive analysis of MMP1 in hepatocellular carcinoma, specifically the prognosis and tumor-immune microenvironment. MMP1 expression was analyzed using TCGA database and clinical samples. MMP1 associated mechanisms, pathways, mutations and prognosis in hepatocellular carcinoma were evaluated. We also analyzed the tumor-immune microenvironment and corresponding treatments. Our research demonstrated that MMP1 expression was upregulated in patients with hepatocellular carcinoma and correlated with poor survival. A prognostic model was established and its performance evaluated. We also found and report various correlations between MMP1 and immune-related cells/genes, as well the potential therapeutic agents. These findings indicate that MMP1 can potentially be a promising prognostic biomarker and indicator of the tumor-immune microenvironment status in hepatocellular carcinoma.

SUBMITTER: Dai L

PROVIDER: S-EPMC9365786 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Comprehensive bioinformatic analysis of MMP1 in hepatocellular carcinoma and establishment of relevant prognostic model.

Dai Lei L Mugaanyi Joseph J Cai Xingchen X Dong Mingjun M Lu Caide C Lu Changjiang C

Scientific reports 20220810 1

Matrix metalloproteinase 1 (MMP1) encodes endopeptidases associated with degradation of multiple components of the extracellular matrix. This function has increasingly been considered to play a major proteolysis role in tumor invasion and metastasis. However, the relationship between MMP1 gene expression, tumor-immune microenvironment and prognosis in hepatocellular carcinoma patients remains mostly unclear. This study focused on a comprehensive analysis of MMP1 in hepatocellular carcinoma, spec ...[more]

PMID: 35948625

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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:Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide and its incidence continues to increase year by year. Endoplasmic reticulum stress (ERS) caused by protein misfolding within the secretory pathway in cells and has an extensive and deep impact on cancer cell progression and survival. Growing evidence suggests that the genes related to ERS are closely associated with the occurrence and progression of HCC. This study aimed to identify an ERS-related signature for the prospective evaluation of prognosis in HCC patients. RNA sequencing data and clinical data of patients from HCC patients were obtained from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC). Using data from TCGA as a training cohort (n=424) and data from ICGC as an independent external testing cohort (n=243), ERS-related genes were extracted to identify three common pathways IRE1, PEKR, and ATF6 using the GSEA database. Through univariate and multivariate Cox regression analysis, 5 gene signals in the training cohort were found to be related to ERS and closely correlated with the prognosis in patients of HCC. A novel 5-gene signature (including HDGF, EIF2S1, SRPRB, PPP2R5B and DDX11) was created and had power as a prognostic biomarker. The prognosis of patients with high-risk HCC was worse than that of patients with low-risk HCC. Multivariate Cox regression analysis confirmed that the signature was an independent prognostic biomarker for HCC. The results were further validated in an independent external testing cohort (ICGC). Also, GSEA indicated a series of significantly enriched oncological signatures and different metabolic processes that may enable a better understanding of the potential molecular mechanism mediating the progression of HCC. The 5-gene biomarker has a high potential for clinical applications in the risk stratification and overall survival prediction of HCC patients. In addition, the abnormal expression of these genes may be affected by copy number variation, methylation variation, and post-transcriptional regulation. Together, this study indicated that the genes may have potential as prognostic biomarkers in HCC and may provide new evidence supporting targeted therapies in HCC.

Project description:BackgroundThe tumor immune microenvironment is pivotal in predicting clinical outcomes and therapeutic efficacy in cancer patients. This study aims to develop an immune prediction model (IPM) to effectively predict prognosis and immunotherapeutic response in patients with hepatocellular carcinoma (HCC).MethodsAn IPM was constructed and validated based on immune-related genes. The influence of IPM on the HCC immune microenvironment, as well as the possible mechanism, was comprehensively analyzed. The value of the model in predicting the response of HCC patients to immunotherapy was also evaluated.ResultsA novel IPM based on eight genes was developed and validated to predict the prognosis of HCC patients. These genes are matrix metalloproteinase 12 (MMP12), heme oxygenase 1 (HMOX1), C-X-C motif chemokine receptor 6 (CXCR6), hepatoma-derived growth factor (HDGF), placental growth factor (PGF), tyrosine kinase 2 (TYK2), retinoid X receptor beta (RXRB), and cyclin-dependent kinase 4 (CDK4). High-risk patients showed significantly poorer survival than low-risk patients. A nomogram was also established based on the IPM and tumor, node, metastasis (TNM) classification, which showed some net clinical benefit. Gene set enrichment analysis (GSEA) revealed several significantly enriched oncological signatures and immunologic signatures. Furthermore, high-risk patients were characterized by severe clinicopathological characteristics and immune cell infiltration. Finally, we found the that the IPM showed a significant positive correlation with programmed cell death 1 (PDCD1), cluster of differentiation 274 (CD274), and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) expression, suggesting a potentially enhanced effects of immunotherapy antibodies in HCC patients with a high risk score.ConclusionsA novel IPM that could predict clinical prognosis and immunotherapeutic response in HCC patients was developed. Our findings not only provide new insights into the identification of HCC patients with poor survival, but also deepen our understanding of the immune microenvironment, as well as the mechanism of immunotherapy, in HCC.

Dataset Information

Comprehensive bioinformatic analysis of MMP1 in hepatocellular carcinoma and establishment of relevant prognostic model.

Publications

Comprehensive bioinformatic analysis of MMP1 in hepatocellular carcinoma and establishment of relevant prognostic model.

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