Dataset Information

Development of prognostic signature based on immune-related genes in muscle-invasive bladder cancer: bioinformatics analysis of TCGA database.

ABSTRACT:

Background

Muscle-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.

Results

There 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.

Conclusion

This 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.

Methods

Bioinformatics 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.

SUBMITTER: Jin K

PROVIDER: S-EPMC7880322 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of prognostic signature based on immune-related genes in muscle-invasive bladder cancer: bioinformatics analysis of TCGA database.

Jin Kun K Qiu Shi S Jin Di D Zhou Xianghong X Zheng Xiaonan X Li Jiakun J Liao Xinyang X Yang Lu L Wei Qiang Q

Aging 20210119 2

<h4>Background</h4>Muscle-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.<h4>Results</h4>There 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 ...[more]

PMID: 33465047

Similar Datasets

Project description:BackgroundMuscle-invasive bladder cancer (MIBC) patients are subject to unfavorable treatment options and a high recurrence rate. The status of TP53 mutations played an essential role in the progression and the prognosis of MIBC. The present study proposed to investigate the association between TP53 mutations and immunophenotype in MIBC.ResultsWe established an immune prognostic model (IPM) ground on the immune-associated genes derived from variation analysis between wild-type TP53 and mutated TP53 TCGA-MIBC patients, and validated in another cohort from GEO database. Based on IPM, we divided MIBC patients into low and high risk subgroups. The high risk MIBC patients had higher proportions of macrophages M1, and lower proportions of T cells regulatory (Tregs) and activated dendritic cells than the low risk MIBC patients. Moreover, the expression of immune checkpoints genes (PD1, CTLA4, LAG3, HAVCR2 and TIGIT) was higher in the high risk patients than the low risk patients. In clinical application, IPM exhibited better survival prediction than conventional clinical characteristics.ConclusionsOur investigation presented practical prognostic significance for MIBC patients and displayed the overarching landscape of the immune response in the MIBC microenvironment.MethodsData were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) analysis between the TP53 mutated and wild-type MIBC patients was conducted. The CIBERSORT algorithm was performed to evaluate the proportion of immune cell types. Gene expression profiles from the TCGA and GEO were used as training and testing cohorts to build and validate an immune-related prognostic model (IPM). Genes in the IPM model were first screened by univariate Cox analysis, then filtered by the least absolute shrinkage and selection operator (LASSO) Cox regression. A nomogram was finally established and evaluated by combining both the IPM and other clinical factors.

Project description:Breast cancer is one of the most common malignancies in women worldwide. Many studies have shown that tumor microenvironment cells, immune cells, and stromal cell infiltration have an important impact on prognosis, so it is important to identify biomarkers for achieving better treatment and prognosis.To better understand the relationship between immune and stromal cell-related genes and prognosis, we screened patients with breast cancer in The Cancer Genome Atlas (TCGA) database and divided them into high and low groups based on immune/stromal scores. We next identified differentially expressed immune-related genes that are significantly associated with the prognosis of patients with breast cancer for functional enrichment analysis and protein-protein interaction networks, respectively. Finally, we selected a separate breast cancer cohort in gene expression synthesis (GEO) for validation.Both immune scores and stromal scores are meaningful in the correlation of subtype classification. Disease-free survival of cases with the high score group of immune scores is statistically longer than the cases in the low score group. Differentially expressed immune-related genes extracted from the comparison can effectively evaluate the prognosis of patients with breast cancer and these genes are primarily involved in immune responses, extracellular matrix, and chemokine activity. At last, we obtained a series of verified tumor immune-related genes that predict the prognosis of patients with breast cancer.Combining the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression database and the TCGA database to extract the list of tumor microenvironment related genes which may help to outline the prognosis of patients with breast cancer. Some previously overlooked genes have the potential to become additional biomarkers for breast cancer. Further research on these genes can reveal a new understanding of the potential relationship between tumor microenvironment and breast cancer prognosis.

Project description:Background:Bladder cancer (BCa) is a common urothelial malignancy. The Cancer Genome Atlas (TCGA) database allows for an opportunity to analyze the relationship between gene expression and clinical outcomes in bladder cancer patients. This study is aimed at identifying prognosis-related genes in the bladder cancer microenvironment. Methods:Immune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We divided bladder cancer patients into high and low groups based on their immune/stromal scores. Then, differentially expressed genes (DEGs) were identified in bladder cancer patients based on the TCGA database. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. Finally, we validated identified genes associated with bladder cancer prognosis through a cohort study in the Gene Expression Omnibus (GEO) database. Results:A higher stromal score was associated with female (vs. malep = 0.037), age > 65 (vs.age ? 65?p = 0.015), T3/4 (vs. T1/2,p < 0.001), N status(p = 0.016), and pathological high grade (vs. low gradeP < 0.001). By analyzing DEGs, there were 1125 genes commonly upregulated, and 209 genes were commonly downregulated. Protein-protein interaction networks further showed the important protein that may be involved in the biological behavior and prognosis of BCa, such as FN1, CXCL12, CD3E, LCK, and ZAP70. A total of 14 DEGs were found to be associated with overall survival of bladder cancer. After validation by a cohort of 165 BCa cases with detailed follow-up information from GSE13507, 10 immune-associated DEGs were demonstrated to be predictive of prognosis in BCa. Among them, 5 genes have not been reported previously associated with the prognosis of BCa, including BTBD16, OLFML2B, PRRX1, SPINK4, and SPON2. Conclusions:Our study elucidated tight associations between stromal score and clinical characteristics as well as prognosis in BCa. Moreover, we obtained a group of genes closely related to the prognosis of BCa in the tumor microenvironment.

Dataset Information

Development of prognostic signature based on immune-related genes in muscle-invasive bladder cancer: bioinformatics analysis of TCGA database.

Background

Results

Conclusion

Methods

Publications

Development of prognostic signature based on immune-related genes in muscle-invasive bladder cancer: bioinformatics analysis of TCGA database.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets