Dataset Information

Novel Implication of the Basement Membrane for Breast Cancer Outcome and Immune Infiltration.

ABSTRACT: Therapeutic failure in breast cancer patients is largely attributed to postoperative advancement and therapy resistance. Nevertheless, an efficacious prognostic signature for recognizing this population is lacking. The basement membrane (BM) has been proven to be strongly involved in cancer progression and metastasis, and has the potential to be a powerful predictor in breast cancer. In this study, substantial bulk RNA transcriptomics, single cell RNA transcriptomics and clinical information were collected from TCGA-BRCA, METABRIC and GSE96058, and Kaplan-Meier survival curves, single cell analysis and in vitro experiments were conducted to validate the signature. From the results, a prognostic index, namely, the BMscore, was established with six pivotal BM genes, specifically LOXL1, FBLN1, FBLN5, SDC1, ADAMTS8 and PXDNL. Verification by independent cohorts showed that breast cancer patients with high BMscore had a distinctly worse outcome. By integrating the BMscore and clinical factors, we constructed a prognostic nomogram that displayed good predictive capability. Furthermore, we evaluated the implication of the BMscore in breast cancer immune infiltration. More importantly, a strongly positive correlation between the BMscore and EMT activity was revealed with immunohistochemistry and in vitro experiments. Taken together, we provided a novel BMscore gene signature for breast cancer patients to predict clinical prognosis and metastasis accurately, which may help with individualized clinical decision-making.

SUBMITTER: Tian W

PROVIDER: S-EPMC10086744 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Novel Implication of the Basement Membrane for Breast Cancer Outcome and Immune Infiltration.

Tian Wenwen W Luo Yongzhou Y Tang Yuhui Y Kong Yanan Y Wu Linyu L Zheng Shaoquan S Zou Yutian Y Zhang Chao C Xie Jindong J Deng Xinpei X Zhang Junsheng J Chen Nian N Xie Xiaoming X Wei Weidong W

International journal of biological sciences 20230305 5

Therapeutic failure in breast cancer patients is largely attributed to postoperative advancement and therapy resistance. Nevertheless, an efficacious prognostic signature for recognizing this population is lacking. The basement membrane (BM) has been proven to be strongly involved in cancer progression and metastasis, and has the potential to be a powerful predictor in breast cancer. In this study, substantial bulk RNA transcriptomics, single cell RNA transcriptomics and clinical information wer ...[more]

PMID: 37056938

Similar Datasets

Project description:BackgroundGlucosamine 6-phosphate N-acetyltransferase (GNPNAT1) is a crucial enzyme involving hexosamine biosynthesis pathway and is upregulated in breast cancer (BRCA). However, its biological function and mechanism on patients in BRCA have not been investigated.MethodsIn this study, the differential expression of GNPNAT1 was analyzed between BRCA tissues and normal breast tissues using the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, which was validated by quantitative real-time polymerase chain reaction, Western blot and immunohistochemistry. Then, the potential clinical value of GNPNAT1 in BRCA was investigated based on TCGA database. Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Gene Set Variation Analysis, were performed to explore the potential signaling pathways and biological functions involved in GNPNAT1 in BRCA. Tumor immune infiltration was analyzed using ESTIMATE, CIBERSORT and TISIDB database; and immune therapy response scores were assessed using TIDE. Finally, Western blot, Cell counting kit-8 and Transwell assay were used to determine the proliferation and invasion abilities of breast cancer cells with GNPNAT1 knockdown.ResultsGNPNAT1 was up-regulated in BRCA tissues compared with normal tissues which was subsequently verified in different cell lines and clinical tissue samples. Based on TCGA and GEO, the overexpression of GNPNAT1 in BRCA contributed to a significant decline in overall survive and disease specific survive. Functional enrichment analyses indicated that the enriched pathways in high GNPNAT1 expression group included citrate cycle, N-glycan biosynthesis, DNA repair, and basal transcription factors. Moreover, the overexpression of GNPNAT1 was negatively correlated with immunotherapy response and the levels of immune cell infiltration of CD8+ T cells, B cells, natural killer cells, dendritic cells and macrophages. Knockdown of GNPNAT1 impairs the proliferation and invasion abilities of breast cancer cells.ConclusionGNPNAT1 is a potential diagnostic, prognostic biomarker and novel target for intervention in BRCA.

Project description:IntroductionThis study investigated pyroptosis- and immunity-related long non-coding RNAs (lncRNAs) to identify promising therapeutic targets for breast cancer (BC), and constructed lncRNA signatures to determine the prognosis and immunotherapy responses of BC patients.MethodsPearson's correlation coefficient was used to identify pyroptosis- and immune-related differentially expressed lncRNAs (DE-pyrolncRNAs and DE-ImmlncRNAs, respectively). The Cancer Genome Atlas dataset was allocated to training and testing subsets. Prognostic lncRNA signatures were derived based on the training subset using univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator methods. Stepwise Cox regression was used to refine these signatures and to select the optimal lncRNA signature. The median risk score of the training subset was applied as a threshold to divide patients into high-risk (HR) and low-risk (LR) groups. The Wilcoxon test was used to reveal differences in immune scores, cell types, functions, and checkpoint genes between these groups. Single-cell sequencing data from GSE176078 were used to validate the immune cell infiltration landscape of the identified lncRNA signatures.ResultsWe identified a six-lncRNA pyroptosis-immune signature comprising MAPT.AS1, CTA.384, D8.34, RP11.561, I11.3, HID1.AS1, AC097713.3, and USP2.AS1. Patients in the HR group demonstrated inferior prognoses in the training, testing, and full datasets (P=3.622e-07, P=3.736e-03, and P=1.151e-08, respectively). Immune scores were significantly enhanced in the LR group, whereas tumor purity was elevated in the HR group. Fifty-eight immune scores showed significant differences between the groups (P<0.05). Immune function (APC coinhibition, CCR, and checkpoints) more significantly impaired in the HR group. Expression levels of 38 immune checkpoint genes, including KIR2DS4, KIR3DL2, CD40LG, KIR3DL1, and PDCD1, were significantly higher in the LR group. Conversely, the TDO2, PVR, and CD276 levels were elevated in the HR group. Single-cell sequencing data from GSE176078 showed sparse T cell, B cell, myeloid, and plasmablast clusters in the HR group, whereas the LR group displayed significant clustering of B cells, myeloids, and plasmablasts.ConclusionThe six-lncRNA pyroptosis-immune signature effectively predicted BC prognosis and highlighted distinct immune cell infiltration patterns. This holds promise for evaluating immunotherapy responses and guiding therapeutic target identification in BC.

Project description:ObjectiveInvasive pituitary adenomas (IPAs) are common tumors of the nervous system tumors for which invasive growth can lead to difficult total resection and a high recurrence rate. The basement membrane (BM) is a special type of extracellular matrix and plays an important role in the invasion of pituitary adenomas (PAs). The aim of this study was to develop a risk model for predicting the invasiveness of PAs by analyzing the correlation between the expression of BM genes and immune infiltration.MethodsFour datasets, featuring samples IPAs and non-invasive pituitary adenomas (NIPAs), were obtained from the Gene Expression Omnibus database (GEO). R software was then used to identify differentially expressed genes (DEGs) and analyze their functional enrichment. Protein-protein interaction (PPI) network was used to screen BM genes, which were analyzed for immune infiltration; this led to the generation of a risk model based on the correlation between the expression of BM genes and immunity. A calibration curve and receiver operating characteristic (ROC) curve were used to evaluate and validate the model. Subsequently, the differential expression levels of BM genes between IPA and NIPA samples collected in surgery were verified by Quantitative Polymerase Chain Reaction (qPCR) and the prediction model was further evaluated. Finally, based on our analysis, we recommend potential drug targets for the treatment of IPAs.ResultsThe merged dataset identified 248 DEGs that were mainly enriching in signal transduction, the extracellular matrix and channel activity. The PPI network identified 11 BM genes from the DEGs: SPARCL1, GPC3, LAMA1, SDC4, GPC4, ADAMTS8, LAMA2, LAMC3, SMOC1, LUM and THBS2. Based on the complex correlation between these 11 genes and immune infiltration, a risk model was established to predict PAs invasiveness. Calibration curve and ROC curve analysis (area under the curve [AUC]: 0.7886194) confirmed the good predictive ability of the model. The consistency between the qPCR results and the bioinformatics results confirmed the reliability of data mining.ConclusionUsing a variety of bioinformatics methods, we developed a novel risk model to predict the probability of PAs invasion based on the correlation between 11 BM genes and immune infiltration. These findings may facilitate closer surveillance and early diagnosis to prevent or treat IPAs in patients and improve the clinical awareness of patients at high risk of IPAs.

Project description:IntroductionBreast cancer (BC) is the most common malignancy in the world and has a high cancer-related mortality rate. Basement membranes (BMs) guide cell polarity, differentiation, migration and survival, and their functions are closely related to tumor diseases. However, few studies have focused on the association of basement membrane-related genes (BMRGs) with BC. This study aimed to explore the prognostic features of BMRGs in BC and provide new directions for the prevention and treatment of BC.MethodsWe collected transcriptomic and clinical data of BC patients from TCGA and GEO datasets and constructed a predictive signature for BMRGs by using univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The reliability of the model was further evaluated and validated by Kaplan-Meier survival curves and receiver operating characteristic curves (ROC). Column line plots and corresponding calibration curves were constructed. Possible biological pathways were investigated by enrichment analysis. Afterward, we assessed the mutation status by tumor mutational burden (TMB) analysis and compared different subtypes using cluster analysis. Finally, we examined drug treatment sensitivity and immunological correlation to lay the groundwork for more in-depth studies in this area.ResultsThe prognostic risk model consisted of 7 genes (FBLN5, ITGB2, LAMC3, MMP1, EVA1B, SDC1, UNC5A). After validation, we found that the model was highly reliable and could accurately predict the prognosis of BC patients. Cluster analysis showed that patients with cluster 1 had more sensitive drugs and had better chances of better clinical outcomes. In addition, TMB, immune checkpoint, immune status, and semi-inhibitory concentrations were significantly different between high and low-risk groups, with lower-risk patients having the better anti-cancer ability.DiscussionThe basement membrane-related gene signature that we established can be applied as an independent prognostic factor for BC and can provide a reference for individualized treatment of BC patients.

Dataset Information

Novel Implication of the Basement Membrane for Breast Cancer Outcome and Immune Infiltration.

Publications

Novel Implication of the Basement Membrane for Breast Cancer Outcome and Immune Infiltration.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets