Identification and validation of basement membrane‐associated gene AGRN as prognostic and immune‐associated biomarkers in colorectal cancer patients
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ABSTRACT:
SUBMITTER: Li J
PROVIDER: S-EPMC11345205 | biostudies-literature | 2024 Aug
REPOSITORIES: biostudies-literature
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Project description:Bladder cancer (BC) is fatal during muscle invasion and treatment progress is limited. In this study, we aimed to construct and validate basement membrane (BM)-associated gene prognosis to predict BC progression and tumor immune infiltration correlation. We choreographed BM-related genes in the Cancer Genome Atlas (TCGA) database using COX regression and least absolute shrinkage and selection operator (LASSO) analysis, and the predictive value of BM-related genes was further validated by the GSE32548, GSE129845, and immunohistochemistry staining. All analyses were performed with R-version 4.2.2, and its appropriate packages. Three genes were identified to construct a gene signature to predictive of BC prognosis. We divided the TCGA database into 2 groups, and patients in the high-risk group had worse overall survival (OS) than those in the low-risk group. In GSE32548, we confirmed that patients in the high-risk group had a poorer prognosis compared to those in the low-risk group in terms of OS. Immunohistochemical staining of EPEMP1, GPC2, and ITGA3 showed significantly higher expression at the protein level in BC tissues than in normal tissues. The Spearman analysis showed risk score was positively correlated with B cell naïve, Macrophages M2, and Mast cells resting. stromal score, immune score, and ESTIMATE scores were significantly higher in the high-risk group. drugs sensitivity analysis showed IC50 of Cisplatin, Gemcitabine, and Methotrexate in the high-risk group was significantly higher than that in the low-risk group. We identified 3 prognostic genes from a novel perspective of BM genes as effective risk stratification tools for BC patients.
| S-EPMC11398827 | biostudies-literature
Project description:Based on the importance of basement membrane (BM) in cancer invasion and metastasis, we constructed a BM-associated lncRNA risk model to group bladder cancer (BCa) patients. Transcriptional and clinical data of BCa patients were downloaded from The Cancer Genome Atlas (TCGA), and the expressed genes of BM-related proteins were obtained from the BM-BASE database. We download the GSE133624 chip data from the GEO database as an external validation dataset. We screened for statistically different BM genes between tumors and adjacent normal tissues. Co-expression analysis of lncRNAs and differentially expressed BM genes was performed to identify BM-related lncRNAs. Then, differentially expressed BM-related lncRNAs (DEBMlncRNAs) between tumor and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select lncRNAs for risk assessment. LASSO analysis was performed to build a prognostic model. We constructed a model containing 8 DEBMlncRNAs (AC004034.1, AL662797.1, NR2F1-AS1, SETBP1-DT, AC011503.2, AC093010.2, LINC00649 and LINC02321). The prognostic risk model accurately predicted the prognosis of BCa patients and revealed that tumor aggressiveness and distant metastasis were associated with higher risk scores. In this model, we constructed a nomogram to assist clinical decision-making based on clinicopathological characteristics such as age, T, and N. The model also showed good predictive power for the tumor microenvironment and mutational burden. We validated the expression of eight lncRNAs using the dataset GSE133624 and two human bladder cancer cell lines (5637, BIU-87) and examined the expression and cellular localization of LINC00649 and AC011503.2 using a human bladder cancer tissue chip. We found that knockdown of LINC00649 expression in 5637 cells promoted the proliferation of 5637 cells.Our eight DEBMlncRNA risk models provide new insights into predicting prognosis, tumor invasion, and metastasis in BCa patients.
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Project description:Pancreatic cancer (PC) is a common and highly malignant tumor. Basement membrane (BM) is formed by the crosslinking of extracellular matrix macromolecules and acts as a barrier against tumor cell metastasis. However, the role of BM in PC prognosis, immune infiltration, and treatment remains unclear. This study collected transcriptome and clinical survival data of PC via TCGA, GEO, and ICGC databases. PC patients (PCs) from the First Affiliated Hospital of Dalian Medical University were obtained as the clinical validation cohort. BM-related genes (BMRGs) were acquired from GeneCards and basement membraneBASE databases. A total of 46 differential-expressed BMRGs were identified. Then the BM-related prognostic model (including DSG3, MET, and PLAU) was built and validated. PCs with a low BM-related score had a better outcome and were more likely to benefit from oxaliplatin, irinotecan, and KRAS(G12C) inhibitor-12, and immunotherapy. Immune analysis revealed that BM-related score was positively correlated with neutrophils, cancer-associated fibroblasts, and macrophages infiltration, but negatively correlated with CD8+ T cells, NK cells, and B cells infiltration. PCs from the clinical cohort further verified that BM-related model could accurately predict PCs' outcomes. DSG3, MET, and PLAU were notably up-regulated within PC tissues and linked to a poor prognosis. In vitro experiments showed that DSG3 knockdown markedly suppressed the proliferation, migration, and invasion of PC cells. Molecular docking indicated that epigallocatechin gallate had a strong binding activity with DSG3, MET, and PLAU and may be used as a potential therapeutic agent for PC. In conclusion, this study developed a BM-related model associated with PC prognosis, immune infiltration, and treatment, which provided new insights into PC stratification and drug intervention.
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Project description:BACKGROUND Colorectal cancer (CRC) is a common malignant tumor with high incidence and mortality worldwide. The aim of this study was to evaluate the association between differentially expressed genes (DEGs), which may function as biomarkers for CRC prognosis and therapies, and the clinical outcome in patients with CRC. MATERIAL AND METHODS A total of 116 normal mucous tissue and 930 CRC tissue datasets were downloaded from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). After screening DEGs based on limma package in R. Gene Ontology (GO) and KEGG enrichment analysis as well as the protein-protein interaction (PPI) networks were performed to predict the function of these DEGs. Meanwhile, Cox proportional hazards regression was used to build a prognostic model of these DEGs. Then, Kaplan-Meier risk analysis was used to test the model in TCGA datasets and validation datasets. RESULTS In the present study, 300 DEGs with 100 upregulated genes and 200 downregulated genes were identified. The PPI networks including 162 DEGs and 256 nodes were constructed and 2 modules with high degree were selected. Moreover, 5 genes (MMP1, ACSL6, SMPD1, PPARGC1A, and HEPACAM2) were identified using the Cox proportional hazards stepwise regression. Kaplan-Meier risk curve in the TCGA and validation cohorts showed that high-risk group had significantly poor overall survival than the low-risk group. CONCLUSIONS Our study provided insights into the mechanisms of CRC formation and found 5 prognostic genes, which could potentially inform further studies and clinical therapies.
| S-EPMC6065283 | biostudies-other
Project description:BackgroundInflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic, inflammatory, and autoimmune disease, but its specific etiology and pathogenesis are still unclear. This study aimed to better discover the causative basement membrane (BM) genes of their subtypes and their associations.MethodsThe differential expression of BM genes between CD and UC was analyzed and validated by downloading relevant datasets from the GEO database. We divided the samples into 3 groups for comparative analysis. Construction of PPI networks, enrichment of differential gene functions, screening of Lasso regression models, validation of ROC curves, nomogram for disease prediction and other analytical methods were used. The immune cell infiltration was further explored by ssGSEA analysis, the immune correlates of hub BM genes were found, and finally, the hub central genes were screened by machine learning.ResultsWe obtained 6 candidate hub BM genes related to cellular immune infiltration in the CD and UC groups, respectively, and further screened the central hub genes ADAMTS17 and ADAMTS9 through machine learning. And in the ROC curve models, AUC > 0.7, indicating that this characteristic gene has a more accurate predictive effect on IBD. We also found that the pathogenicity-related BM genes of the CD and UC groups were mainly concentrated in the ADAMTS family (ADAMTS17 and ADAMTS9). Addition there are some differences between the two subtypes, and the central different hub BM genes are SPARC, POSTN, and ADAMTS2.ConclusionsIn the current study, we provided a nomogram model of CD and UC composed of BM genes, identified central hub genes, and clarified the similarities and differences between CD and UC. This will have potential value for preclinical, clinical, and translational guidance and differential research in IBD.
| S-EPMC10362583 | biostudies-literature
Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease of unknown etiology, characterized by diffuse alveolitis and alveolar structural damage. Due to the short median survival time and poor prognosis of IPF, it is particularly urgent to find new IPF biomarkers. Previous studies have shown that basement membranes (BMs) are associated with the development of IPF and tumor metastasis. However, there is still a lack of research on BMs-related genes in IPF. Therefore, we investigated the expression level of BMs genes in IPF and control groups, and explored their potential as biomarkers for IPF diagnosis. In this study, the GSE32537 and GSE53845 datasets were used as training sets, while the GSE24206, GSE10667 and GSE101286 datasets were used as validation sets. In the training set, seven immune biomarkers related to BMs were selected by differential expression analysis, machine learning algorithm (LASSO, SVM-RFE, Randomforest) and ssGSEA analysis. Further ROC analysis confirmed that seven BMs-related genes played an important role in IPF. Finally, four immune-related Hub genes (COL14A1, COL17A1, ITGA10, MMP7) were screened out. Then we created a logistic regression model of immune-related hub genes (IHGs) and used a nomogram to predict IPF risk. The nomogram model was evaluated to have good reliability and validity, and ROC analysis showed that the AUC value of IHGs was 0.941 in the training set and 0.917 in the validation set. Pan-cancer analysis showed that IHGs were associated with prognosis, immune cell infiltration, TME, and drug sensitivity in 33 cancers, suggesting that IHGs may be potential targets for intervention in human diseases including IPF and cancer.
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Project description:BackgroundThe tumor microenvironment (TME) is integral to tumor progression. However, its prognostic implications and underlying mechanisms in clear cell renal cell carcinoma (ccRCC) are not yet fully elucidated. This study aims to examine the prognostic significance of genes associated with immune-stromal scores and to explore their underlying mechanisms in ccRCC.MethodsData from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were subjected to analysis to compute immune and stromal scores utilizing the ESTIMATE algorithm. The weighted gene co-expression network analysis (WGCNA) was employed to identify gene modules associated with these scores. Differentially expressed genes were assessed using the limma package. Prognostic biomarkers were subsequently identified through univariate, LASSO, and multivariate Cox regression analyses, culminating in the development of a risk score model. Gene expression was confirmed in ccRCC cell lines (786-O, Caki-1) and tumor tissues. Functional assays, such as wound healing and Transwell assays, were employed to evaluate tumor invasion and migration. The prognostic accuracy was assessed through ROC curve analysis, and a nomogram integrating risk scores with clinical variables was constructed. Analyses of immune infiltration, human leukocyte antigen (HLA) expression, immune checkpoint expression, immunophenoscore (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and responses to six targeted therapies were conducted across different risk groups.ResultsTwelve critical prognostic markers, including CAPRIN1, CXCR3, FERMT3, HAPLN3, HBP1, MACF1, MPEG1, OSCAR, STAT1, UBA7, VAMP1, and VSIG4, were identified. The risk score model exhibited a high degree of predictive accuracy for survival outcomes in ccRCC. Immune profiling revealed significant differences in the TME between risk groups, with high-risk patients displaying elevated expression of HLA and immune checkpoints. Drug sensitivity analyses suggested that high-risk patients had a better response to erlotinib, temsirolimus, axitinib, and sunitinib, whereas low-risk patients demonstrated greater sensitivity to pazopanib. Variability in immunotherapy responsiveness between groups was observed based on IPS and TIDE analyses.ConclusionThis study highlights the prognostic value and TME-related mechanisms of immune-stromal score signatures in ccRCC, developing a risk score model and nomogram for predicting patient prognosis.
| S-EPMC11771106 | biostudies-literature
Project description:Background and aimsThe initiation of cellular senescence in response to protumorigenic stimuli counteracts malignant progression in (pre)malignant cells. Besides arresting proliferation, cells entering this terminal differentiation state adopt a characteristic senescence-associated secretory phenotype (SASP) which initiates alterations to their microenvironment and effects immunosurveillance of tumorous lesions. However, some effects mediated by senescent cells contribute to disease progression. Currently, the exploration of senescent cells' impact on the tumor microenvironment and the evaluation of senescence as possible target in colorectal cancer (CRC) therapy demand reliable detection of cellular senescence in vivo. Therefore, specific immunohistochemical biomarkers are required. Our aim is to analyze the clinical implications of senescence detection in colorectal carcinoma and to investigate the interactions of senescent tumor cells and their immune microenvironment in vitro and in vivo.MethodsSenescence was induced in CRC cell lines by low-dose-etoposide treatment and confirmed by Senescence-associated β-galactosidase (SA-β-GAL) staining and fluorescence activated cell sorting (FACS) analysis. Co-cultures of senescent cells and immune cells were established. Multiple cell viability assays, electron microscopy and live cell imaging were conducted. Immunohistochemical (IHC) markers of senescence and immune cell subtypes were studied in a cohort of CRC patients by analyzing a tissue micro array (TMA) and performing digital image analysis. Results were compared to disease-specific survival (DSS) and progression-free survival (PFS).ResultsVarying expression of senescence markers in tumor cells was associated with in- or decreased survival of CRC patients. Proximity analysis of p21-positive senescent tumor cells and cytotoxic T cells revealed a significantly better prognosis for patients in which these cell types have the possibility to directly interact. In vitro, NK-92 cells (mimicking natural killer T cells) or TALL-104 cells (mimicking both cytotoxic T cells and natural killer T cells) led to dose-dependent specific cytotoxicity in >75 % of the senescent CRC cells but <20 % of the proliferating control CRC cells. This immune cell-mediated senolysis seems to be facilitated via direct cell-cell contact inducing apoptosis and granule exocytosis.ConclusionCounteracting tumorigenesis, cellular senescence is of significant relevance in CRC. We show the dual role of senescence bearing both beneficial and malignancy-promoting potential in vivo. Absence as well as exceeding expression of senescence markers are associated with bad prognosis in CRC. The antitumorigenic potential of senescence induction is determined by tumor micromilieu and immune cell-mediated elimination of senescent cells.
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Project description:Osteosarcoma is the most common primary malignancy of the bones, and is associated with a high rate of metastasis and a poor prognosis. A tight association between the tumor microenvironment (TME) and osteosarcoma metastasis has been established. In the present study, the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was applied to calculate the immune and stromal scores of patients with osteosarcoma based on data from The Cancer Genome Atlas database. A metagene approach and deconvolution method were used to reveal distinct TME landscapes in patients with osteosarcoma. Bioinformatics analysis was used to identify differentially expressed genes (DEGs) associated with metastasis and immune infiltration in osteosarcoma, and a risk model was constructed using the DEGs with potential prognostic significance. Subsequently, gene set enrichment and Spearman's correlation analyses were used to delineate the biological processes associated with these prognostic biomarkers. Finally, immunohistochemical (IHC) analysis was performed to evaluate the expression levels of immune infiltrates and prognostic biomarkers in clinical osteosarcoma tissues. The results of the ESTIMATE demonstrated that patients with non-metastatic osteosarcoma presented with higher immune/stromal scores and a more favorable prognosis compared with those with metastatic osteosarcoma. The TME landscapes in patients with osteosarcoma suggested that high levels of tumor-infiltrating immune cells (TIICs) may suppress metastasis. Increased numbers of CD56bright natural killer cells, immature B cells, M1 macrophages and neutrophils, and lower levels of M2 macrophages were observed in the non-metastatic tissues compared with those in the metastatic tissues. A total of 69 DEGs were identified to be associated with metastasis and immune infiltration in osteosarcoma. Of these, GATA3, LPAR5, EVI2B, RIAM and CFH exhibited prognostic potential and were highly expressed in non-metastatic osteosarcoma tissues based on the IHC analysis results. These biomarkers were involved in various immune-related biological processes and were positively associated with multiple TIICs and immune signatures. The risk model constructed using these prognostic biomarkers demonstrated high predictive accuracy for the prognosis of osteosarcoma. In conclusion, the present study proposed a five-biomarker prognostic signature for the prediction of metastasis and immune infiltration in patients with osteosarcoma.
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