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The basement membrane-related gene signature is associated with immunity and predicts survival accurately in hepatocellular carcinoma | Journal of Cancer Research and Clinical Oncology
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Aims Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Expression defects and turnover of basement membrane (BM) proteins are key pathogenic factors in cancer. It is still uncertain how the expression of BM-related genes (BMGs) in HCC relates to prognosis. Methods All of the HCC cohort
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pubmed, article, google, scholar, cas, carcinoma, hepatocellular, central, cancer, immune, basement, signature, cell, analysis, research, gene, chen, genes, prognosis, hcc, membrane, patients, zhang, liver, risk, access, microenvironment, cells, authors, content, information, expression, bmgs, prognostic, tumor, matrix, laminin, oncol, fujian, privacy, cookies, data, journal, predicts, manuscript, zhao, hongbin, infiltration, mol, httpsdoiorgs,
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month download article/chapter suppressing tgf-beta signal immune-related genes predict basement membrane genes basement membrane component tumor immune microenvironment cellular tumor microenvironment ctsa gene mutations full article pdf subgroup analysis showed renal cell carcinoma hypoxia-related signature extracellular matrix signatures good prediction ability privacy choices/manage cookies precision oncology access immune-related pathways predicts survival accurately multivariate cox analyses bm-related genes hepatocellular carcinoma patients basement membrane exhaustion markers primary liver cancer standalone prognostic indicator immune cell infiltration immune checkpoint inhibitors low risk based immune feature analysis clinical oncology aims bm-related degs selected expression pattern holds exclusive rights risk score based research systematically built low-risk groups article journal european economic area key pathogenic factors protein–protein interaction force-dependent breaching extracellular matrix natural science foundation providing technical guidance high-risk group prognostic model ferroptosis-related seer-based study human disease associations liver cancer immunity
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- Lee CK, Chan SL, Chon HJ (2022) Could we predict the response of immune checkpoint inhibitor treatment in hepatocellular carcinoma?
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headline:The basement membrane-related gene signature is associated with immunity and predicts survival accurately in hepatocellular carcinoma
description:Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Expression defects and turnover of basement membrane (BM) proteins are key pathogenic factors in cancer. It is still uncertain how the expression of BM-related genes (BMGs) in HCC relates to prognosis. All of the HCC cohort's RNA-seq and clinical information came from TCGA datasets. The least absolute shrinkage and selection operator (LASSO) regression algorithm was utilized to filter down the candidate genes and construct the prognostic model. Univariate and multivariate Cox analyses were run to examine if the risk score may serve as a standalone prognostic indicator. The single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze examine immune cell infiltration and pathway activity. Five genes and their risk coefficients were eventually identified and patients with HCC were classified as either high or low risk based on the median of risk scores. Multivariate Cox regression analysis found a significant correlation between risk score and OS (p < 0.001). Subgroup analysis showed that BMGs signature had good prediction ability for HCC patients in age, gender, T stage, and AJCC stage (all p < 0.05). According to the ssGSEA, the high-risk subgroup showed higher levels of immune cell infiltration and immune-related pathways were more engaged in the high-risk group. Our research systematically built a prognostic model using risk score based on BMGs signature in HCC patients. The immune feature analysis of the BMGs signature indicated a potential regulation between tumor immunity and BM in HCC.
datePublished:2022-12-27T00:00:00Z
dateModified:2022-12-27T00:00:00Z
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Hepatocellular carcinoma
Basement membrane
Extracellular matrix
Tumor microenvironment
Prediction model
Oncology
Cancer Research
Internal Medicine
Hematology
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headline:The basement membrane-related gene signature is associated with immunity and predicts survival accurately in hepatocellular carcinoma
description:Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Expression defects and turnover of basement membrane (BM) proteins are key pathogenic factors in cancer. It is still uncertain how the expression of BM-related genes (BMGs) in HCC relates to prognosis. All of the HCC cohort's RNA-seq and clinical information came from TCGA datasets. The least absolute shrinkage and selection operator (LASSO) regression algorithm was utilized to filter down the candidate genes and construct the prognostic model. Univariate and multivariate Cox analyses were run to examine if the risk score may serve as a standalone prognostic indicator. The single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze examine immune cell infiltration and pathway activity. Five genes and their risk coefficients were eventually identified and patients with HCC were classified as either high or low risk based on the median of risk scores. Multivariate Cox regression analysis found a significant correlation between risk score and OS (p < 0.001). Subgroup analysis showed that BMGs signature had good prediction ability for HCC patients in age, gender, T stage, and AJCC stage (all p < 0.05). According to the ssGSEA, the high-risk subgroup showed higher levels of immune cell infiltration and immune-related pathways were more engaged in the high-risk group. Our research systematically built a prognostic model using risk score based on BMGs signature in HCC patients. The immune feature analysis of the BMGs signature indicated a potential regulation between tumor immunity and BM in HCC.
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Hepatocellular carcinoma
Basement membrane
Extracellular matrix
Tumor microenvironment
Prediction model
Oncology
Cancer Research
Internal Medicine
Hematology
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