ArticleBMC genomic data2022
Identification of immune-related and autophagy-related genes for the prediction of survival in bladder cancer.
Article in BMC genomic data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Orosomucoid 1 interacts with S100A12 and activates ERK signalling to expedite the advancement of bladder cancer.Cell adhesion & migration · 2025Article
- Prognostic value and immunotherapy analysis of immune cell-related genes in laryngeal cancer.PeerJ · 2025Article
- Identification of bladder cancer subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes.Scientific reports · 2024Article
- Serum protein profiling reveals an inflammation signature as a predictor of early breast cancer survival.Breast cancer research : BCR · 2024Article
- Investigating the impact of regulatory B cells and regulatory B cell-related genes on bladder cancer progression and immunotherapeutic sensitivity.Journal of experimental & clinical cancer research : CR · 2024Article
- Comprehensive analysis of CPSF4-related alternative splice genes in hepatocellular carcinoma.Journal of cancer research and clinical oncology · 2023Article
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4 authors.
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Abstract
backgroundBladder cancer has the characteristics of high morbidity and mortality, and the prevalence of bladder cancer has been increasing in recent years. Immune and autophagy related genes play important roles in cancer, but there are few studies on their effects on the prognosis of bladder cancer patients.
methodsUsing gene expression data from the TCGA-BLCA database, we clustered bladder cancer samples into 6 immune-related and autophagy-related molecular subtypes with different prognostic outcomes based on 2208 immune-related and autophagy-related genes. Six subtypes were divided into two groups which had significantly different prognosis. Differential expression analysis was used to explore genes closely related to the progression of bladder cancer. Then we used Cox stepwise regression to define a combination of gene expression levels and immune infiltration indexes to construct the risk model. Finally, we built a Nomogram which consist of risk score and several other prognosis-related clinical indicators.
resultsThe risk model suggested that high expression of C5AR2, CSF3R, FBXW10, FCAR, GHR, OLR1, PGLYRP3, RASGRP4, S100A12 was associated with poor prognosis, while high expression level of CD96, IL10, MEFV pointed to a better prognosis. Validation by internal and external dataset suggested that our risk model had a high ability to discriminate between the outcomes of patients with bladder cancer. The immunohistochemical results basically confirmed our results. The C-Index value and Calibration curves verified the robustness of Nomogram.
conclusionsOur study constructed a model that included a risk score for patients with bladder cancer, which provided a lot of helps to predict the prognosis of patients with bladder cancer.
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