CD86 Molecule Might Be a Novel Immune-Related Prognostic Biomarker for Patients With Bladder Cancer by Bioinformatics and Experimental Assays.
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ABSTRACT:
SUBMITTER: Yan X
PROVIDER: S-EPMC8378807 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGlioblastoma (GBM), which has a poor prognosis, accounts for 31% of all cancers in the brain and central nervous system. There is a paucity of research on prognostic indicators associated with the tumor immune microenvironment in GBM patients. Accurate tools for risk assessment of GBM patients are urgently needed.MethodsIn this study, we used weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) methods to screen out GBM-related genes among immune-related genes (IRGs). Then, we used survival analysis and Cox regression analysis to identify prognostic genes among the GBM-related genes to further establish a risk signature, which was validated using methods including ROC analysis, stratification analysis, protein expression level validation (HPA), gene expression level validation based on public cohorts, and RT-qPCR. In order to provide clinicians with a useful tool to predict survival, a nomogram based on an assessment of IRGs and clinicopathological features was constructed and further validated using DCA, time-dependent ROC curve, etc. Results: Three immune-related genes were found: PPP4C (p < 0.001, HR = 0.514), C5AR1 (p < 0.001, HR = 1.215), and IL-10 (p < 0.001, HR = 1.047). An immune-related prognostic signature (IPS) was built to calculate risk scores for GBM patients; patients classified into different risk groups had significant differences in survival (p = 0.006). Then, we constructed a nomogram based on an assessment of the IRG-based signature, which was validated as a potential prediction tool for GBM survival rates, showing greater accuracy than the nomogram without the IPS when predicting 1-year (0.35 < Pt < 0.50), 3-year (0.65 < Pt < 0.80), and 5-year (0.65 < Pt < 0.80) survival.ConclusionsIn conclusion, we integrated bioinformatics and experimental approaches to construct an IPS and a nomogram based on IPS for predicting GBM prognosis. The signature showed strong potential for prognostic prediction and could help in developing more precise diagnostic approaches and treatments for GBM.
| S-EPMC9562700 | biostudies-literature
Project description:Bladder cancer (BC) is the most common malignant tumor of the urinary system and is associated with high morbidity and mortality; however, the molecular mechanism underlying its occurrence is not clear. In this study, the gene expression profile and related clinical information of GSE13507 were downloaded from the Gene Expression Omnibus (GEO) database. RNA sequencing (RNA-seq) expression data and related clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Overlapping genes were identified by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Then, we carried out functional enrichment analysis to understand the potential biological functions of these co-expressed genes. Finally, we performed a protein-protein interaction (PPI) analysis combined with survival analysis. Using the CytoHubba plug-in of Cytoscape, TROAP, CENPF, PRC1, AURKB, CCNB2, CDC20, TTK, CEP55, ASPM, and CDCA8 were identified as candidate central genes. According to the survival analysis, the high expression of TTK was related to the poor overall survival (OS) of patients with BC. TTK may also affect the bladder tumor microenvironment (TME) by affecting the number of immune cells. The expression level of TTK was verified by immunohistochemistry (IHC) and real-time quantitative polymerase chain reaction (RT-qPCR), and the tumor-promoting effect of TTK in BC cells was confirmed in vitro. Our results also identified the MSC-AS1/hsa-miR-664b-3p/TTK regulatory axis, which may also play an important role in the progression of BC, but further research is needed to verify this result. In summary, our results provide a new idea for accurate early diagnosis, clinical treatment, and prognosis of BC.
| S-EPMC8721040 | biostudies-literature
Project description:BackgroundMuscle-invasive bladder cancer (MIBC) with high tumor stages accounts for most bladder cancer patient mortality. Platinum-based chemotherapy provides insufficient survival benefits; however, immunotherapy is a promising option for MIBC.ResultsThere were 31 differentially expressed IRGs that significantly correlated with the clinical outcomes of MIBC patients. A prognostic signature based on 12 IRGs (MMP9, RBP7, ADIPOQ, AHNAK, OAS1, RAC3, SLIT2, EDNRA, IL34, PDGFD, PPY, IL17RD) performed moderately in prognostic predictions with area under the curve (AUC) equal to 0.76. The high-risk patient group presented worse survival outcomes (hazard ratio 1.197, 95% confidence interval 1.103-1.299, p < 0.001). Furthermore, immune cell infiltration analysis showed increased tumor infiltration of macrophages in the high-risk group.ConclusionThis novel prognostic signature can effectively divide MIBC patients into different risk groups, allowing for intensive treatment of high-risk individuals who have worse predicted survival outcomes.MethodsBioinformatics analyses were conducted using the Cancer Genome Atlas (TCGA) database. Differentially expressed genes and survival-associated immune-related genes (IRGs) were analyzed through a computational algorithm and Cox regression. The potential mechanisms of IRG expression were explored with transcription factors, and a prognosis classification based on IRG expression was developed to stratify patients into distinct risk groups.
| S-EPMC7880322 | biostudies-literature
Project description:BackgroundWe aimed to establish an immune-related gene (IRG) based signature that could provide guidance for clinical bladder cancer (BC) prognostic surveillance.MethodsDifferentially expressed IRGs and transcription factors (TFs) between BCs and normal tissues were extracted from transcriptome data downloaded from the TCGA database. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to identify related pathways based on differently expressed IRGs. Then, univariate Cox regression analysis was performed to investigate IRGs with prognostic values and LASSO penalized Cox regression analysis was utilized to develop the prognostic index (PI) model.ResultsA total of 411 BC tissue samples and 19 normal bladder tissues in the TCGA database were enrolled in this study and 259 differentially expressed IRGs were identified. Networks between TFs and IRGs were also provided to seek the upstream regulators of differentially expressed IRGs. By means of univariate Cox regression analysis, 57 IRGs were analyzed with prognostic values and 10 IRGs were finally identified by LASSO penalized Cox regression analysis to construct the PI model. This model could significantly classified BC patients into high-risk group and low-risk group in terms of OS (P=9.923e-07) and its AUC reached 0.711. By means of univariate and multivariate COX regression analysis, this PI was proven to be a valuable independent prognostic factor (HR =1.119, 95% CI =1.066-1.175, P<0.001). CMap database analysis was also utilized to screen out 10 small molecules drugs with the potential for the treatment of BC.ConclusionsOur study successfully provided a novel PI based on IRGs with the potential to predict the prognosis of BC and screened out 10 small molecules drugs with the potential to treat BC. Besides, networks between TFs and IRGs were also displayed to seek its upstream regulators for future researches.
| S-EPMC7658175 | biostudies-literature
Project description:Triple-negative breast cancer represents approximately 15–20% of all reported breast cancer cases, and is characterized by a shorter survival time and higher mortality rates compared to other breast cancer sub-types. Tumor microenvironment (TME) refers to the internal and external environment of tumor tissue. Increasing evidence indicates that a tumor’s microenvironment is tightly associated with the immunological surveillance and defense during the development of breast cancer. Although oncology studies employing digital dissection methodologies have provided some insight on the biological features of TME, the development of methods to investigate the cellular composition of the tumor microenvironment remain an important research priority. In this study, we extracted whole transcriptome from 30 Triple-negative breast cancer (TNBC) patients and then used bioinformatics approaches to characterize cell type content in tumor tissue compared with para-cancerous tissue. We identified 4 types of enriched immune cells and 6 types of downregulated immune cells in the tumor tissue samples. After comprehensive bioinformatics analyses, we developed an ‘immune infiltration score’ (IIS) to quantitatively model immune cell infiltration in TNBC. To demonstrate the utility of the IIS, we used 2 independent datasets for validation. We found that patients with a higher IIS showing a longer progression-free survival time and significantly better prognosis than those with a lower IIS value. In sum, we explored the immune infiltration landscape in 30 TNBC patients and provided a novel and reliable biomarker IIS to evaluate the progression-free survival and prognosis in the TNBC patients.
2021-10-18 | E-MTAB-10886 | biostudies-arrayexpress
Project description:BackgroundFormin-binding protein 1/17 (FNBP1/FBP17), as a membrane-bound protein, is wildly expressed in eukaryotic cells and performs a critical role in tumor tumorigenesis and progression. However, the relationship between FNBP1 and immune infiltrating cells, prognostic value in patients still require comprehensive understanding. We purposed to explore the correlations of FNBP1 expression, prognosis and immune infiltration levels in various cancers.MethodThe expression and survival data of FNBP1 were collected from Oncomine, TIMER, GEPIA, Kaplan-Meier Plotter and PrognoScan databases. Correlations between FNBP1 and immune infiltrates were analyzed in TIMER and GEPIA databases.ResultsCompared with normal tissues, FNBP1 is significantly differentially expressed in a variety of tumor tissues. FNBP1 has significant and complex effects on the prognosis of kinds of cancers. High-expression was obviously correlated with better prognosis in breast carcinoma and lung adenocarcinoma, while worse prognosis in stomach adenocarcinoma. Besides, FNBP1 had a correlation with various immune infiltrating cells and diverse immune gene markers in breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), and stomach adenocarcinoma (STAD). FNBP1 was also positively correlated with the adjustment of CD8+ cells, T cells, M2 macrophage, neutrophils, monocyte, Th1 cells, T regulatory cells (Treg) and Tumor-associated macrophages (TAMs). The expression level of FNBP1 is closely positively correlated with the expression level of multiple immune checkpoints in the three cancers. In addition, FNBP1 is significantly positively correlated with the expression levels of a variety of immunosuppressive molecules.ConclusionOur findings reveal FNBP1 can serve as a significant biomarker to influence the prognosis and the immune infiltrating levels in different cancers. The differential expression of FNBP1 might not only contribute to the judgment of metastatic and non-metastatic tumors but also in the immune escape by upregulating the expression of immune checkpoints.
| S-EPMC8742955 | biostudies-literature
Project description:Pancreatic adenocarcinoma (PAAD) is a malignancy with the highest mortality rate worldwide. There is a pressing need for novel biomarkers that can facilitate early detection and serve as targets for therapeutic interventions beyond the commonly utilized CA199 marker. This study utilized microarray datasets (GSE15471, GSE62165, and GSE28735) from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) and construct a protein-protein interaction network using STRING and Cytoscape. Hub genes were selected using BiNGO. Expression profiles and clinical data from the Cancer Genome Atlas (TCGA) were then used to compare the expression levels of CTSK and PLAU in pancreatic cancer and healthy pancreatic tissues via the Wilcoxon rank-sum test, with further validation using qPCR. Functional enrichment analysis was conducted to explore potential signaling pathways and biological functions. Prognostic values were assessed by the Kaplan-Meier and Cox regression analyses, and an overall survival (OS) nomogram was created to predict 1-, 2-, and 3-year survival after cancer diagnosis. The infiltration of immune cells was evaluated by single-sample gene set enrichment analysis. The methylation status of both genes was analyzed using the UALCAN and MethSurv databases. The results demonstrated that CTSK and PLAU were overexpressed in pancreatic cancer and that the hypomethylation status of both genes was associated with a poor prognosis. The overexpression of both genes was positively correlated with various immune cells, and functional enrichment analysis revealed that they were associated with immune cell infiltration. Besides, the effects of PLAU on the migration and invasion of pancreatic cancer cells were also verified by scratch and transwell experiments. Consequently, CTSK and PLAU have potential as prognostic biomarkers for pancreatic cancer.
| S-EPMC10701022 | biostudies-literature
Project description:Bladder cancer is one of the most common malignant tumors of the urinary system that seriously threatens the health of a population. In recent years, the application of immunotherapy has significantly changed the treatment of bladder cancer, but only some patients can benefit from the treatment with immune-checkpoint inhibitors. Many problems are unsolved in the field of bladder cancer immunotherapy, especially in the search for genes that are critical to the level of immune cell infiltration and new effective therapeutic targets. We attempted to use bioinformatics analysis to identify immune gene markers related to the prognosis of bladder cancer and to establish a prognostic signature for bladder cancer patients based on their immune gene expression profiles. We used univariate Cox proportional hazards regression analysis, the least absolute shrinkage and selection operator (LASSO) Cox regression, and multivariate Cox proportional hazards regression analysis from The Cancer Genome Atlas bladder cancer cohort (TCGA-BLCA). Fifteen genes related to prognosis were screened using the survival analysis, correlation analysis, cancer and adjacent cancer differential expression analysis, and mutation analysis. The potential biological role of these genes was determined using survival analysis and principal component analysis (PCA). The receiver operating characteristic (ROC) curve assesses the prognostic value of the predictive signature. The gene ontology (GO), Kyoto Encyclopedia of Gene and Genome (KEGG), Gene set enrichment analysis (GSEA), and other methods were used to reveal the differential gene enrichment in the signaling pathways and cellular processes of high- and low-risk groups. The single-sample GSEA (ssGSEA) method was used to quantify the infiltration levels of 24 immune cells in the tumor immune microenvironment and these immune genes were found to be closely related to the tumor immune microenvironment. In summary, we screened 15 immune genes that were closely related to bladder cancer overall survival (OS) and may be potential prognostic indicators of bladder cancer. They may have research and clinical application value in bladder cancer immunotherapy. We used 15 immune genes to construct a new immune-related gene signature that was verified and could be helpful in improving individualized prognosis of patients with bladder cancer.
| S-EPMC7324668 | biostudies-literature
Project description:Compelling evidence indicates that immune function is correlated with the prognosis of bladder cancer (BC). Here, we aimed to develop a clinically translatable immune-related gene pairs (IRGPs) prognostic signature to estimate the overall survival (OS) of bladder cancer. From the 251 prognostic-related IRGPs, 37 prognostic-related IRGPs were identified using LASSO regression. We identified IRGPs with the potential to be prognostic markers. The established risk scores divided BC patients into high and low risk score groups, and the survival analysis showed that risk score was related to OS in the TCGA-training set (p < 0.001; HR = 7.5 [5.3, 10]). ROC curve analysis showed that the AUC for the 1-year, 3-year, and 5-year follow-up was 0.820, 0.883, and 0.879, respectively. The model was verified in the TCGA-testing set and external dataset GSE13507. Multivariate analysis showed that risk score was an independent prognostic predictor in patients with BC. In addition, significant differences were found in gene mutations, copy number variations, and gene expression levels in patients with BC between the high and low risk score groups. Gene set enrichment analysis showed that, in the high-risk score group, multiple immune-related pathways were inhibited, and multiple mesenchymal phenotype-related pathways were activated. Immune infiltration analysis revealed that immune cells associated with poor prognosis of BC were upregulated in the high-risk score group, whereas immune cells associated with a better prognosis of BC were downregulated in the high-risk score group. Other immunoregulatory genes were also differentially expressed between high and low risk score groups. A 37 IRGPs-based risk score signature is presented in this study. This signature can efficiently classify BC patients into high and low risk score groups. This signature can be exploited to select high-risk BC patients for more targeted treatment.
| S-EPMC8331311 | biostudies-literature
Project description:Hypoxia is a common feature of tumor microenvironment (TME). This study aims to establish the genetic features related to hypoxia in Bladder urothelial carcinoma (BLCA) and investigate the potential correlation with hypoxia in the TME and immune cells. We established a BLCA outcome model using the hypoxia-related genes from The Cancer Genome Atlas using regression analysis and verified the model using the Gene Expression Omnibus GSE32894 cohort. We measured the effect of each gene in the hypoxia-related risk model using the Human Protein Atlas website. The predictive abilities were compared using the area under the receiver operating characteristic curves. Gene Set Enrichment Analysis was utilized for indicating enrichment pathways. We analyzed immune cell infiltration between risk groups using the CIBERSORT method. The indicators related to immune status between the two groups were also analyzed. The findings indicated that the high-risk group had better outcomes than the low-risk group in the training and validation sets. Each gene in the model affected the survival of BLCA patients. Our hypoxia-related risk model had better performance compared to other hypoxia-related markers (HIF-1α and GLUT-1). The high-risk group was enriched in immune-related pathways. The expression of chemokines and immune cell markers differed significantly between risk groups. Immune checkpoints were more highly expressed in the high-risk group. These findings suggest that the hypoxia-related risk model predicts patients' outcomes and immune status in BLCA risk groups. Our findings may contribute to the treatment of BLCA.
| S-EPMC8569353 | biostudies-literature