Construction and validation of an angiogenesis-related gene expression signature associated with clinical outcome and tumor immune microenvironment in glioma

Hu, Tianhao; Wang, Yutao; Wang, Xiaoliang; Wang, Run; Song, Yifu; Zhang, Li; Han, Sheng

doi:10.3389/fgene.2022.934683

Cited by 3 publications

(2 citation statements)

References 80 publications

(91 reference statements)

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“…The ESTIMATE algorithm was used to evaluate the tumor purity, immune score, stromal score and ESTIMATE score ( 35 ). According to the ‘CIBERSORT’ algorithm, LM22 gene signature matrix was employed to measure the relative proportion of 22 immunocytes per sample under 1000 permutations ( 36 – 38 ). The relative infiltration of 28 immunocyte subpopulations in BC TME were quantified by single-sample gene set enrichment (ssGSEA) ( 39 ).…”

Section: Methodsmentioning

confidence: 99%

Comprehensive analysis of nicotinamide metabolism-related signature for predicting prognosis and immunotherapy response in breast cancer

Cui

Ren²,

Dai³

et al. 2023

Front. Immunol.

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BackgroundBreast cancer (BC) is the most common malignancy among women. Nicotinamide (NAM) metabolism regulates the development of multiple tumors. Herein, we sought to develop a NAM metabolism-related signature (NMRS) to make predictions of survival, tumor microenvironment (TME) and treatment efficacy in BC patients.MethodsTranscriptional profiles and clinical data from The Cancer Genome Atlas (TCGA) were analyzed. NAM metabolism-related genes (NMRGs) were retrieved from the Molecular Signatures Database. Consensus clustering was performed on the NMRGs and the differentially expressed genes between different clusters were identified. Univariate Cox, Lasso, and multivariate Cox regression analyses were sequentially conducted to develop the NAM metabolism-related signature (NMRS), which was then validated in the International Cancer Genome Consortium (ICGC) database and Gene Expression Omnibus (GEO) single-cell RNA-seq data. Further studies, such as gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, SubMap, and Immunophenoscore (IPS) algorithm, cancer-immunity cycle (CIC), tumor mutation burden (TMB), and drug sensitivity were performed to assess the TME and treatment response.ResultsWe identified a 6-gene NMRS that was significantly associated with BC prognosis as an independent indicator. We performed risk stratification according to the NMRS and the low-risk group showed preferable clinical outcomes (P < 0.001). A comprehensive nomogram was developed and showed excellent predictive value for prognosis. GSEA demonstrated that the low-risk group was predominantly enriched in immune-associated pathways, whereas the high-risk group was enriched in cancer-related pathways. The ESTIMATE and CIBERSORT algorithms revealed that the low-risk group had a higher abundance of anti-tumor immunocyte infiltration (P < 0.05). Results of Submap, IPS, CIC, TMB, and external immunotherapy cohort (iMvigor210) analyses showed that the low-risk group were indicative of better immunotherapy response (P < 0.05).ConclusionsThe novel signature offers a promising way to evaluate the prognosis and treatment efficacy in BC patients, which may facilitate clinical practice and management.

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Section: Methodsmentioning

confidence: 99%

Comprehensive analysis of nicotinamide metabolism-related signature for predicting prognosis and immunotherapy response in breast cancer

Cui

Ren²,

Dai³

et al. 2023

Front. Immunol.

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“…Currently, there are numerous HNSC prognostic models, including those for iron, cellular focal, and copper death. Evidence has emerged to indicate that prognostic models produced by AAGs could be advantageous in evaluating the prognosis of patients with tumors such as gastric adenocarcinoma [ 27 ], cervical cancer [ 28 ], and glioma [ 29 ]. However, additional research is necessary to examine the efficiency of AAGs in the HNSC context.…”

Section: Discussionmentioning

confidence: 99%

Construction and assessment of an angiogenesis-related gene signature for prognosis of head and neck squamous cell carcinoma

Wang,

Zhang,

et al. 2024

Discov Onc

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Objective Angiogenesis-associated genes (AAGs) play a critical role in cancer patient survival. However, there are insufficient reports on the prognostic value of AAGs in head and neck squamous cell carcinoma (HNSC). Therefore, this study aimed to investigate the correlation between AAG expression levels and survival in HNSC patients, explore the predictive value of signature genes and lay the groundwork for future in-depth research. Methods Relevant data for HNSC were obtained from the databases. AAGs-associated signature genes linked to prognosis were screened to construct a predictive model. Further analysis was conducted to determine the functional correlation of the signature genes. Results The signature genes (STC1, SERPINA5, APP, OLR1, and PDGFA) were used to construct prognostic models. Patients were divided into high-risk and low-risk groups based on the calculated risk scores. Survival analysis showed that patients in the high-risk group had a significantly lower overall survival than those in the low-risk group (P < 0.05). Therefore, this prognostic model was an independent prognostic factor for predicting HNSC. In addition, patients in the low-risk group were more sensitive to multiple anti-cancer drugs. Functional correlation analysis showed a good correlation between the characteristic genes and HNSC metastasis, invasion, and angiogenesis. Conclusion This study established a new prognostic model for AAGs and may guide the selection of therapeutic agents for HNSC. These genes have important functions in the tumor microenvironment; it also provides a valuable resource for the future clinical trials investigating the relationship between HNSC and AAGs.

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Comprehensive investigation of tumor immune microenvironment and prognostic biomarkers in osteosarcoma through integrated bulk and single-cell transcriptomic analysis

Shi,

Zhang,

Guo

2024

Front. Immunol.

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Osteosarcoma (OS) is an aggressive and highly lethal bone tumor, highlighting the urgent need for further exploration of its underlying mechanisms. In this study, we conducted analyses utilizing bulk transcriptome sequencing data of OS and healthy control samples, as well as single cell sequencing data, obtained from public databases. Initially, we evaluated the differential expression of four tumor microenvironment (TME)-related gene sets between tumor and control groups. Subsequently, unsupervised clustering analysis of tumor tissues identified two significantly distinct clusters. We calculated the differential scores of the four TME-related gene sets for Clusters 1 (C1) and 2 (C2), using Gene Set Variation Analysis (GSVA, followed by single-variable Cox analysis. For the two clusters, we performed survival analysis, examined disparities in clinical-pathological distribution, analyzed immune cell infiltration and immune evasion prediction, assessed differences in immune infiltration abundance, and evaluated drug sensitivity. Differentially expressed genes (DEGs) between the two clusters were subjected to Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). We conducted Weighted Gene Co-expression Network Analysis (WGCNA) on the TARGET-OS dataset to identify key genes, followed by GO enrichment analysis. Using LASSO and multiple regression analysis we conducted a prognostic model comprising eleven genes (ALOX5AP, CD37, BIN2, C3AR1, HCLS1, ACSL5, CD209, FCGR2A, CORO1A, CD74, CD163) demonstrating favorable diagnostic efficacy and prognostic potential in both training and validation cohorts. Using the model, we conducted further immune, drug sensitivity and enrichment analysis. We performed dimensionality reduction and annotation of cell subpopulations in single cell sequencing analysis, with expression profiles of relevant genes in each subpopulation analyzed. We further substantiated the role of ACSL5 in OS through a variety of wet lab experiments. Our study provides new insights and theoretical foundations for the prognosis, treatment, and drug development for OS patients.

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Construction and validation of an angiogenesis-related gene expression signature associated with clinical outcome and tumor immune microenvironment in glioma

Cited by 3 publications

References 80 publications

Comprehensive analysis of nicotinamide metabolism-related signature for predicting prognosis and immunotherapy response in breast cancer

Comprehensive analysis of nicotinamide metabolism-related signature for predicting prognosis and immunotherapy response in breast cancer

Construction and assessment of an angiogenesis-related gene signature for prognosis of head and neck squamous cell carcinoma

Comprehensive investigation of tumor immune microenvironment and prognostic biomarkers in osteosarcoma through integrated bulk and single-cell transcriptomic analysis

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