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pmid:34268106
Construction and Validation of a Novel Eight-Gene Risk Signature to Predict the Progression and Prognosis of Bladder Cancer.
Ruiliang Wang · Zongtai Zheng · Shiyu Mao · Wentao Zhang · Ji Liu · Cheng Li · Shenghua Liu · Xudong Yao
Frontiers in oncology · 2021 doi:10.3389/fonc.2021.632459
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Experiment
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Data
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Complete file
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It is therefore important to find new relevant molecular models that will allow for effective prediction of the progression and prognosis of bladder cancer (BC). Using RNA-Sequence data of 49 BC patients in Shanghai tenth people's hospital (STPH) and weighted gene co-expression network analysis methods, a co-expression network of genes was developed and three key modules associated with malignant progression were selected. Based on the genes in three key modules, an eight-gene risk signature was established using univariate Cox regression and the Least absolute shrinkage and selection operator Cox model in The Cancer Genome Atlas Program (TCGA) and validated in validation sets. Subsequently, a nomogram based on the risk signature was constructed for prognostic prediction. The mRNA and protein expression levels of eight genes in cell lines and tissues were further investigated. The novel eight-gene risk signature was closely related to the malignant clinical features of BC and could predict the prognosis of patients in the training dataset (TCGA) and four validation sets (GSE32894, GSE13507, IMvigor210 trial, and STPH). The nomogram showed good prognostic prediction and calibration. The mRNA and protein expression levels of the eight genes were differentially expressed in cell lines and tissues. In our study, we established a novel eight-gene risk signature that could predict the progression and prognoses of BC patients.", "claims": [{"id": "claim-data-access", "statement": "Public data that support the findings of this study are openly available in TCGA (http://cancergenome.nih.gov/), NCBI GEO dataset GSE32894 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32894) and GSE13507 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507). 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