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  • Frontiers Media SA  (2)
  • Gui, Chengpeng  (2)
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  • Frontiers Media SA  (2)
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  • 1
    Online Resource
    Online Resource
    Table_1.XLS
    Frontiers Media SA ; 2021
    In:  Frontiers in Cell and Developmental Biology Vol. 9 ( 2021-3-18)
    Details
    In: Frontiers in Cell and Developmental Biology, Frontiers Media SA, Vol. 9 ( 2021-3-18)
    Abstract: Immune checkpoint inhibitor (ICI) treatment has been used to treat advanced urothelial cancer. Molecular markers might improve risk stratification and prediction of ICI benefit for urothelial cancer patients. We analyzed 406 cases of bladder urothelial cancer from The Cancer Genome Atlas (TCGA) data set and identified 161 messenger RNAs (mRNAs) as differentially expressed immunity genes (DEIGs). Using the LASSO Cox regression model, an eight-mRNA-based risk signature was built. We validated the prognostic and predictive accuracy of this immune-related risk signature in 348 metastatic urothelial cancer (mUC) samples treated with anti-PD-L1 (atezolizumab) from IMvigor210. We built an immune-related risk signature based on the eight mRNAs: ANXA1, IL22, IL9R, KLRK1, LRP1, NRG3, SEMA6D, and STAP2. The eight-mRNA-based risk signature successfully categorizes patients into high-risk and low-risk groups. Overall survival was significantly different between these groups, regardless if the initial TCGA training set, the internal TCGA testing set, all TCGA set, or the ICI treatment set. The hazard ratio (HR) of the high-risk group to the low-risk group was 3.65 ( p & lt; 0.0001), 2.56 ( p & lt; 0.0001), 3.36 ( p & lt; 0.0001), and 2.42 ( p = 0.0009). The risk signature was an independent prognostic factor for prediction survival. Moreover, the risk signature was related to immunity characteristics. In different tumor mutational burden (TMB) subgroups, it successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome. Our eight-mRNA-based risk signature is a stable biomarker for urothelial cancer and might be able to predict which patients benefit from ICI treatment. It might play a role in precision individualized immunotherapy.
    Type of Medium: Online Resource
    ISSN: 2296-634X
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fcell.2021.646982
    DOI: 10.3389/fcell.2021.646982.s001
    DOI: 10.3389/fcell.2021.646982.s002
    DOI: 10.3389/fcell.2021.646982.s003
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2737824-X
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  • 2
    Online Resource
    Online Resource
    Image2.TIF
    Frontiers Media SA ; 2022
    In:  Frontiers in Genetics Vol. 13 ( 2022-5-13)
    Details
    In: Frontiers in Genetics, Frontiers Media SA, Vol. 13 ( 2022-5-13)
    Abstract: Background: Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion in the kidney. This study aims to establish an aging and senescence-related mRNA model for risk assessment and prognosis prediction in ccRCC patients. Methods: ccRCC data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. By applying univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, a new prognostic model based on aging and senescence-related genes (ASRGs) was established. Depending on the prognostic model, high- and low-risk groups were identified for further study. The reliability of the prediction was evaluated in the validation cohort. Pan-cancer analysis was conducted to explore the role of GNRH1 in tumors. Results: A novel prognostic model was established based on eight ASRGs. This model was an independent risk factor and significantly correlated with the prognosis and clinicopathological features of ccRCC patients. The high- and low-risk groups exhibited distinct modes in the principal component analysis and different patterns in immune infiltration. Moreover, the nomogram combining risk score and other clinical factors showed excellent predictive ability, with AUC values for predicting 1-, 3-, and 5-year overall survival in the TCGA cohort equal to 0.88, 0.82, and 0.81, respectively. Conclusion: The model and nomogram based on the eight ASRGs had a significant value for survival prediction and risk assessment for ccRCC patients, providing new insights into the roles of aging and senescence in ccRCC.
    Type of Medium: Online Resource
    ISSN: 1664-8021
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fgene.2022.871088
    DOI: 10.3389/fgene.2022.871088.s001
    DOI: 10.3389/fgene.2022.871088.s002
    DOI: 10.3389/fgene.2022.871088.s003
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606823-0
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