Hypoxia-related prognostic model in bladder urothelial reflects immune cell infiltration
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ABSTRACT:
SUBMITTER: Li L
PROVIDER: S-EPMC8569353 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Colon adenocarcinoma (COAD) is a common tumor of the gastrointestinal tract with a high mortality rate. Current research has identified many genes associated with immune infiltration that play a vital role in the development of COAD. In this study, we analysed the prognostic and diagnostic features of such immune-related genes in the context of colonic adenocarcinoma (COAD). We analysed 17 overlapping gene expression profiles of COAD and healthy samples obtained from TCGA-COAD and public single-cell sequencing resources, to identify potential therapeutic COAD targets. We evaluated the abundance of immune infiltration with those genes using the TIMER (Tumor Immune Estimation Resource) deconvolution method. Subsequently, we developed predictive and survival models to assess the prognostic value of these genes. The LGALS4 (Galectin-4) gene was found to be significantly (P<0.05) downregulated in COAD and bladder urothelial carcinoma (BLCA) compared to healthy samples. We identified LGALS4 as a prognostic and diagnostic marker for multiple cancer types, including COAD and BLCA. Our analysis reveals a series of novel candidate drug targets, as well as candidate molecular markers, that may explain the pathogenesis of COAD and BLCA. LGALS4 gene is associated with multiple cancer types and is a possible prognostic, as well as diagnostic, marker of COAD and BLCA.
| S-EPMC8581917 | 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:Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. CARS1, CD44, FANCD2, HMGCR, NCOA4, SLC7A11, and ACACA were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan-Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank P = 7.815 × 10-11). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies.
| S-EPMC8220217 | biostudies-literature
Project description:Background:The aim of this study is to identify possible prognostic-related immune genes in bladder urothelial carcinoma and to try to predict the prognosis of bladder urothelial carcinoma based on these genes. Methods:The Cancer Genome Atlas (TCGA) expression profile data and corresponding clinical traits were obtained. Differential gene analysis was performed using R software. Reactome was used to analyze the pathway of immune gene participation. The differentially expressed transcription factors and differentially expressed immune-related genes were extracted from the obtained list of differentially expressed genes, and the transcription factor-immune gene network was constructed. To analyze the relationship between immune genes and clinical traits of bladder urothelial carcinoma, a multifactor Cox proportional hazards regression model based on the expression of immune genes was established and validated. Results:Fifty-eight immune genes were identified to be associated with the prognosis of bladder urothelial carcinoma. These genes were enriched in Cytokine Signaling in Immune System, Signaling by Receptor Tyrosine Kinases, Interferon alpha/beta signaling, and other immune related pathways. Transcription factor-immune gene regulatory network was established, and EBF1, IRF4, SOX17, MEF2C, NFATC1, STAT1, ANXA6, SLIT2, and IGF1 were screened as hub genes in the network. The model calculated by the expression of 16 immune genes showed a good survival prediction ability (p < 0.05 and AUC?=?0.778). Conclusion:A transcription factor-immune gene regulatory network related to the prognosis of bladder urothelial carcinoma was established. EBF1, IRF4, SOX17, MEF2C, NFATC1, STAT1, ANXA6, SLIT2, and IGF1 were identified as hub genes in the network. The proportional hazards regression model constructed by 16 immune genes shows a good predictive ability for the prognosis of bladder urothelial carcinoma.
| S-EPMC7201587 | biostudies-literature
Project description:To explore novel therapeutic targets, develop a gene signature and construct a prognostic nomogram of bladder cancer (BCa). Transcriptome data and clinical traits of BCa were downloaded from UCSC Xena database and Gene Expression Omnibus (GEO) database. We then used the method of Single sample Gene Set Enrichment analysis (ssGSEA) to calculate the infiltration abundances of 24 immune cells in eligible BCa samples. By weighted correlation network analysis (WGCNA), we identified turquoise module with strong and significant association with the infiltration abundance of immune cells which were associated with overall survival of BCa patients. Subsequently, we developed an immune cell infiltration-related gene signature based on the module genes (MGs) and immune-related genes (IRGs) from the Immunology Database and Analysis Portal (ImmPort). Then, we tested the prognostic power and performance of the signature in both discovery and external validation datasets. A nomogram integrated with signature and clinical features were ultimately constructed and tested. Five prognostic immune cell infiltration-related module genes (PIRMGs), namely FPR1, CIITA, KLRC1, TNFRSF6B, and WFIKKN1, were identified and used for gene signature development. And the signature showed independent and stable prognosis predictive power. Ultimately, a nomogram consisting of signature, age and tumor stage was constructed, and it showed good and stable predictive ability on prognosis. Our prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for BCa. Further researches are needed to verify the clinical effectiveness of this nomogram and these biomarkers.
| S-EPMC8370985 | biostudies-literature
Project description:BackgroundIncreasing evidence has shown that hypoxia microenvironment relates to tumor initiation and progression. However, no studies focus on the application of hypoxia-associated genes in predicting osteosarcoma patients' prognosis. This research aims to identify the hypoxia-associated genes related to osteosarcoma metastasis and construct a gene signature to predict osteosarcoma prognosis.MethodsThe differentially expressed messenger RNAs (DEmRNAs) related to osteosarcoma metastasis were identified from Therapeutically Applicable Research to Generate Effective Treatments (Target) database. Univariate and multivariate cox regression analyses were performed to develop the hypoxia-associated prognostic signature. The Kaplan-Meier (KM) survival analyses of patients with high and low hypoxia risk scores were conducted. The nomogram was constructed and the gene signature was validated in the external Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate the relationships between immune infiltration and gene signature.ResultsTwo genes, including decorin (DCN) and prolyl 4-hydroxylase subunit alpha 1 (P4HA1), were involved in the hypoxia-associated gene signature. In training and testing datasets, patients with high-risk scores showed lower survival rates and the gene signature was identified as the independent prognostic factor. Receiver operating characteristic (ROC) curves demonstrated the robustness of signature. Functional analyses of DEmRNAs among high- and low-risk groups revealed that immune-associated functions and pathways were significantly enriched. Furthermore, ssGSEA showed that five immune cells (DCs, macrophages, neutrophils, pDCs, and TIL) and three immune features (CCR, APC co inhibition, and Check-point) were down-regulated in the high-risk group.ConclusionThe current study established and validated a novel hypoxia-associated gene signature in osteosarcoma. It could act as a prognostic biomarker and serve as therapeutic guidance in clinical applications.
| S-EPMC8012854 | biostudies-literature
Project description:Pentatricopeptide repeat domain 1 (PTCD1) was reported to regulate mitochondrial metabolism and oxidative phosphorylation. However, the effect and mechanism of PTCD1 in the development of bladder urothelial carcinoma (BLCA) remain unclear. The databases from The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) were used to analyze the expression changes, clinical features, and prognostic values of PTCD1. A nomogram was built to predict the prognostic outcomes of BLCA cases. The potential genes interacting with PTCD1 were explored by Weighted Gene Coexpression Network Analysis (WGCNA). The estimation of associations between PTCD1 and tumor mutations, tumor immunities, and m6A methylations was performed. The study found that the gradual decrease of PTCD1 expression was observed with the increase of stage and grade. Low PTCD1 expression was greatly correlated with higher pathological stage, N stage, and poor prognosis in TCGA cohorts; interestingly, low-grade BLCA cases all exhibited high expression of PTCD1. HPA database analysis implied that the expression of PTCD1 protein in BLCA was lower than that in normal bladder tissue, and the protein expression of PTCD1 in high-grade BLCA was lower than that in low-grade BLCA. Multivariate Cox regression analysis indicated that PTCD1 may serve as an independent factor influencing prognosis of BLCA. Mechanistically, PTCD1 played a regulatory role in BLCA progression through multiple tumor-related pathways containing PI3K-Akt signaling, ECM-receptor interaction, oxidative phosphorylation, and extracellular matrix organization. WGCNA reported that PTCD1 had a strong positive correlation with POLR2J, ZNHT1, ATP5MF, PDAP1, BUD31, and COPS6. Besides, the mRNA expression of PTCD1 was negatively associated with immune cells' infiltrations, immune functions, and checkpoints, especially with some m6A methylation regulators in BLCA. In sum, downregulation of PTCD1 expression may be involved in the development of BLCA and remarkably correlated with poor prognosis. Meantime, it showed an influence in immune cell infiltration and may serve as an agreeable prognostic indicator in BLCA.
| S-EPMC9256401 | biostudies-literature
Project description:Due to the rarity and heterogeneity, it is challenging to explore and develop new therapeutic targets for patients with sarcoma. Recently, immune cell infiltration in the tumor microenvironment (TME) was widely studied, which provided a novel potential approach for cancer treatment. The competing endogenous RNA (ceRNA) regulatory network has been reported as a critical molecular mechanism of tumor development. However, the role of the ceRNA regulatory network in the TME of sarcoma remains unclear. In this study, gene expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) sarcoma datasets, and an immune infiltration-related ceRNA network was constructed, which comprised 14 lncRNAs, 13 miRNAs, and 23 mRNAs. Afterward, we constructed an immune infiltration-related risk score model based on the expression of IRF1, MFNG, hsa-miR-940, and hsa-miR-378a-5p, presenting a promising performance in predicting the prognosis of patients with sarcoma.
| S-EPMC8281254 | biostudies-literature
Project description:BackgroundImmune cells in the tumor microenvironment are an important prognostic indicator in diffuse large B-cell lymphoma (DLBCL). However, information on the heterogeneity and risk stratification of these cells is limited. We sought to develop a novel immune model to evaluate the prognostic intra-tumoral immune landscape of patients with DLBCL.MethodsThe ESTIMATE and CIBERSORT algorithms were used to estimate the numbers of 22 infiltrating immune cells based on the gene expression profiles of 229 patients with DLBCL who were recruited from a public database. The least absolute shrinkage and selection operator (Lasso) penalized regression analyses and nomogram model were used to construct and evaluate the prognostic immunoscore (PIS) model for overall survival prediction. An immune gene prognostic score (IGPS) was generated by Gene Set Enrichment Analysis (GSEA) and Cox regression analysis was and validated in an independent NCBI GEO dataset (GSE10846).ResultsA higher proportion of activated natural killer cells was associated with a poor outcome. A total of five immune cells were selected in the Lasso model and DLBCL patients with high PIS showed a poor prognosis (hazard ratio (HR) 2.16; 95% CI [1.33-3.50]; P = 0.002). Differences in immunoscores and their related outcomes were attributed to eight specific immune genes involved in the cytokine-cytokine receptor interaction and chemokine signaling pathways. The IGPS based on a weighted formula of eight genes is an independent prognostic factor (HR: 2.14, 95% CI [1.40-3.28]), with high specificity and sensitivity in the validation dataset.ConclusionsOur findings showed that a PIS model based on immune cells is associated with the prognosis of DLBCL. We developed a novel immune-related gene-signature model associated with the PIS model and enhanced the prognostic functionality for the prediction of overall survival in patients with DLBCL.
| S-EPMC7414766 | biostudies-literature
Project description:Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive solid tumor. Because most studies have focused on the intrinsic carcinogenic pathways of tumors, we focused on the relationship between N6-methyladenosine (m6A) and the prognosis of HNSCC in the tumor immune microenvironment. We downloaded RNA-seq data from the TCGA dataset and used univariate Cox regression to screen m6A-related lncRNAs. The expression value of LASSO-screened genes was the sum of LASSO regression coefficients. We then evaluated relationships between the risk score and cellular components or cellular immune response. Differences in immune response under various algorithms were visualized with heat maps. The GSVA package in R was used to analyze GO, BP, KEGG, and hallmark gene sets of immune checkpoint clusters and immune checkpoint scores. The GSEA analysis was performed with the cluster profile package, yielding 21 m6A genes. Related lncRNAs were screened with Pearson's correlations, and the resulting 442 lncRNAs were screened using single-factor analysis. Eight lncRNAs closely related to prognosis were identified through survival random forest. Survival analysis showed that patients with a high risk score had a poor prognosis. Low- and high-risk-score groups differed significantly in m6A gene expression. Prognostic scores from different algorithms were significantly correlated with B cells, T cells, and memory cells in the immune microenvironment. Expression of immune checkpoints and signal pathways differed significantly across risk-score groups, suggesting that m6A could mediate lncRNA-induced immune system dysfunction and affect HNSCC development. A comprehensive study of tumor-cell immune characteristics should provide more insight into the complex immune microenvironment, thus contributing to the development of new immunotherapeutic agents.
| S-EPMC8397575 | biostudies-literature