Construction and validation of a gene signature related to bladder urothelial carcinoma based on immune gene analysis

Xing, Peng; Jiang, Zhengming; Liu, Yang

doi:10.1186/s12885-022-09794-9

Cited by 3 publications

(1 citation statement)

References 84 publications

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“…to propose biomarkers 65 , 66 . Some research work proposed signature models with good performance but they all followed a different approach to create such gene sets and did not consider hallmark pathways along with other gentic fators 67 , 68 . The study aimed to refine the prognostic accuracy and therapeutic strategies for BLCA patients through the integration of multi-omics data and the application of advanced bioinformatics methodologies.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis

Chatterjee,

Mou,

Sultana

et al. 2024

Sci Rep

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Being a frequent malignant tumor of the genitourinary system, Bladder Urothelial Carcinoma (BLCA) has a poor prognosis. This study focused on identifying and validating prognostic biomarkers utilizing methylation, transcriptomics, and clinical data from The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA BLCA) cohort. The impact of altered differentially methylated hallmark pathway genes was subjected to clustering analysis to observe changes in the transcriptional landscape on BLCA patients and identify two subtypes of patients from the TCGA BLCA population where Subtype 2 was associated with the worst prognosis with a p-value of 0.00032. Differential expression and enrichment analysis showed that subtype 2 was enriched in immune-responsive and cancer-progressive pathways, whereas subtype 1 was enriched in biosynthetic pathways. Following, regression and network analyses revealed Epidermal Growth Factor Receptor (EGFR), Fos-related antigen 1 (FOSL1), Nuclear Factor Erythroid 2 (NFE2), ADP-ribosylation factor-like protein 4D (ARL4D), SH3 domain containing ring finger 2 (SH3RF2), and Cadherin 3 (CDH3) genes to be the most significant prognostic gene markers. These genes were used to construct a risk model that separated the BLCA patients into high and low-risk groups. The risk model was also validated in an external dataset by performing survival analysis between high and low-risk groups with a p-value < 0.001 and the result showed the high group was significantly associated with poor prognosis compared to the low group. Single-cell analyses revealed the elevated level of these genes in the tumor microenvironment and associated with immune response. High-grade patients also tend to have a high expression of these genes compared to low-grade patients. In conclusion, this research developed a six-gene signature that is pertinent to the prediction of overall survival (OS) and might contribute to the advancement of precision medicine in the management of bladder cancer.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis

Chatterjee,

Mou,

Sultana

et al. 2024

Sci Rep

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Lipid metabolism subtypes reflects the prognosis and immune profiles of bladder cancer patients by NMF clustering and building related signature

Zhou,

Chai,

et al. 2024

Discov Onc

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Background Lipid metabolism is crucial in tumor formation and progression. However, the role of lipid metabolism genes (LMGs) in bladder cancer (BLCA) are unknown. The purpose of this study was to construct a LMGs-related subtypes that predicted the treatment and prognosis of BLCA patients. Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used for this study. The gene set enrichment analysis (GSEA) was utilized to distinguish functional differences between high-risk (HR) and low-risk (LR) groups. Single-sample GSEA (ssGSEA) was employed to determine potential associations between prognostic outcomes and immune status. Results First, BLCA patients were divided into two subtypes by non-negative matrix factorization (NMF) clustering, and there were substantial variations in survival status, immune cell infiltration and immune classification between the two subtypes. Next, a prognostic signature involving 8 LMGs was identified (AKR1B1, SCD, CYP27B1, UGCG, SGPL1, FASN, TNFAIP8L3, PLA2G2A). HR patients exhibited worse outcome than LR patients. Multivariate Cox regression analysis confirmed that LMGs-related signature was an independent prognostic indicator of BLCA patients’ survival. Compared with clinicopathological variables, LMGs-related signature showed higher prognostic predictive ability, with an area under curve of 0.720 at 5 years of follow-up. Through immunotherapy analysis, drug sensitivity analysis, TIDE score and immune cell infiltration characteristics, LMGs-related signature was confirmed to accurately predict the prognosis and treatment response of BLCA patients. Conclusion Our newly established prognostic signature, which involved eight LMGs, can give prognostic distinction for BLCA and may eventually lead to novel targets for treatment. Supplementary Information The online version contains supplementary material available at 10.1007/s12672-024-01631-8.

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The development of the integration of red gene and curriculum thinking based on BP neural network mathematical algorithm

Yang¹,

Guan

2023

Applied Mathematics and Nonlinear Sciences

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In the background of the era of digitalization of big data information, the traditional teaching of ideological and political courses faces huge challenges. The innovation of ideological and political courses education is imperative, and the topic of ideological and political education combined with big data technology has attracted the attention and research of ideological educators. This paper focuses on the development of the integration of red genes and curriculum thinking and politics based on the BP neural network mathematical algorithm. Firstly, we briefly understand the concept and principle of the BP neural network mathematical algorithm and algorithm by finding literature, constructing BP neural network model, analyzing the specific strategy of the BP neural network teaching algorithm for the integration of red gene and curriculum Civic and Political development, and data analysis take the ideological and political achievement of a university between 2006 and 2015 as the learning sample and establishes the BP neural network system by conducting the actual achievement and predicted grades data analysis. Then the established model was used to predict the ideological and political grades of the university from 2016 to 2020. The comparison study and discussion were conducted with the actual grades. The results showed that the actual grades of students and the predicted grades both showed a positive linear correlation. The study had a promotional effect on the improvement of students’ grades. The maximum error between the actual and predicted grades was 3, which maximally ensured that the BP neural network-based model predicted the accuracy and stability of the ideological and political grades. This study provides a substantial reference for ideological and political curriculum research and helps students establish correct socialist core values, thus having great historical significance for developing ideological and political education in China

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Construction and validation of a gene signature related to bladder urothelial carcinoma based on immune gene analysis

Cited by 3 publications

References 84 publications

Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis

Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis

Lipid metabolism subtypes reflects the prognosis and immune profiles of bladder cancer patients by NMF clustering and building related signature

The development of the integration of red gene and curriculum thinking based on BP neural network mathematical algorithm

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