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Identification of an immune gene-associated prognostic signature in patients with bladder cancer

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Abstract

A deeper understanding of the interaction between tumor cell and the immune microenvironment in bladder cancer may help select predictive and prognostic biomarkers. The current study aims to construct a prognostic signature for bladder cancer by analysis of molecular characteristics, as well as tumor-immune interactions. RNA-sequencing and clinical information from bladder cancer patients were downloaded from the TCGA database. The single sample Gene Sets Enrichment Analysis (ssGSEA) and Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) were employed to separate the samples into two clusters. Lasso Cox regression was performed to construct an immune gene signature for bladder cancer. The correlation between key target genes of immune checkpoint blockade and the prognostic signature was also analyzed. Dataset from Gene Expression Omnibus (GEO) was retrieved for validation. Two immunophenotypes and immunological characteristics were identified, and a 17-immune gene signature was constructed to provide an independent prognostic signature for bladder cancer. The signature was verified through external validation and correlated with genomic characteristics and clinicopathologic features. Finally, a nomogram was generated from the clinical characteristics and immune signature. Our study reveals a tumor-immune microenvironment signature useful for prognosis in bladder cancer. The results provide information on the potential development of treatment strategies for bladder cancer patients. Prospective studies are warranted to validate the prognostic capability of this model, but these data highlight the role of the microenvironment in the clinical outcome of patients.

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Fig. 1: Study flowchart.
Fig. 2: Hierarchical clustering.
Fig. 3: DEGs, DEIGs, and PIGs expression.
Fig. 4: Identification of subtype-specific pathways and networks.
Fig. 5: Construction and validation of IGPM-based risk signature.
Fig. 6: Relationship of IGPM-based risk signature with clinical characteristics, kIGs, immune microenvironment and TMB.
Fig. 7: Analysis by proportional hazards model.

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Data availability

Publicly available datasets were analyzed in this study.

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Author notes

  1. These authors contributed equally: Zhiqiang Wang, Liping Zhu.

Authors and Affiliations

  1. Department of Urology, Shouguang Hospital of Traditional Chinese Medicine, Shouguang, Shandong Province, China

    Zhiqiang Wang

  2. Department of Medical Oncology, Shouguang Hospital of Traditional Chinese Medicine, Shouguang, Shandong Province, China

    Liping Zhu

  3. Outpatient Surgery Center, Shouguang Hospital of Traditional Chinese Medicine, Shouguang, Shandong Province, China

    Li Li

  4. Division of Cancer, Department of Surgery and Cancer, Imperial College London, London, United Kingdom

    Justin Stebbing

  5. Department of Immunotherapy, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan Province, China

    Zibing Wang

  6. Department of Respiratory Disease, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China

    Ling Peng

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  1. Zhiqiang Wang
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Contributions

ZW, LP, and LZ designed and supervised the study. ZW, LP, ZW, LL, and LZ analyzed the data and wrote the original draft. ZW, LP, and JS edited the draft. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Ling Peng.

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JS’ conflicts can be found at https://www.nature.com/onc/editors. None are relevant here. The author declares no competing interests.

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Wang, Z., Zhu, L., Li, L. et al. Identification of an immune gene-associated prognostic signature in patients with bladder cancer. Cancer Gene Ther 29, 494–504 (2022). https://doi.org/10.1038/s41417-022-00438-5

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