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Cited by 24Bladder and Urothelial Cancer TreatmentsRadiomics and Machine Learning in Medical ImagingBreast Cancer Treatment Studies

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Robust Prognostic Subtyping of Muscle-Invasive Bladder Cancer Revealed by Deep Learning-Based Multi-Omics Data Integration

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Zhang, Xiaolong, Wang, Jiayin, Lu, Jiabin, Su, Lili, Wang, Changxi, Huang, Yuhua, Zhang, Xuanping, Zhu, Xiaoyan·2021·Frontiers in Oncology
DOI 10.3389/fonc.2021.689626PMC PMC8378227
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Abstract

Muscle-invasive bladder cancer (MIBC) is the most common urinary system carcinoma associated with poor outcomes. It is necessary to develop a robust classification system for prognostic prediction of MIBC. Recently, increasing omics data at different levels of MIBC were produced, but few integration methods were used to classify MIBC that reflects the patient’s prognosis. In this study, we constructed an autoencoder based deep learning framework to integrate multi-omics data of MIBC and clustered samples into two different subgroups with significant overall survival difference (P= 8.11 × 10-5). As an independent prognostic factor relative to clinical information, these two subtypes have some significant genomic differences. Remarkably, the subtype of poor prognosis had significant higher frequency of chromosome 3p deletion. Immune decomposition analysis results showed that these two MIBC subtypes had different immune components including macrophages M1, resting NK cells, regulatory T cells, plasma cells, and naïve B cells. Hallmark gene set enrichment analysis was performed to investigate the functional character difference between these two MIBC subtypes, which revealed that activated IL-6/JAK/STAT3 signaling, interferon-alpha response, reactive oxygen species pathway, and unfolded protein response were significantly enriched in upregulated genes of high-risk subtype. We constructed MIBC subtyping models based on multi-omics data and single omics data, respectively, and internal and external validation datasets showed the robustness of the prediction model as well as its ability of prognosis (P< 0.05 in all datasets). Finally, through bioinformatics analysis and immunohistochemistry experiments, we found that KRT7 can be used as a biomarker reflecting MIBC risk.

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  • Particularly in cancer patient survival subtyping with multi-omics modalities, the sequence of joint representation learning through an AE followed by feature selection with univariate Cox proportional hazard modeling and unsupervised label inference has become popular.

    “Particularly in cancer patient survival subtyping with multi-omics modalities, this sequence of steps has become popular [25–31, 34].”

    From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [30]

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Metadata

Authors
Zhang, X., Wang, J., Lu, J., Su, L., Wang, C., Huang, Y., Zhang, X., & Zhu, X.
Affiliations
  • Shenzhen University (CN)
  • Xi'an Jiaotong University (CN)
  • Sun Yat-sen University Cancer Center (CN)
Corresponding author
Wang, Jiayin
ORCID
Venue
Frontiers in Oncology
Publisher
Frontiers Media SA
Published
2021-08-06
Volume
11
Pages
689626-689626
ISSN
2234-943X (ISSN-L)
DOI
10.3389/fonc.2021.689626
PMC
PMC8378227
OpenAlex
W3192984034
Cited by
24
References
2
Citations by year
2026: 2 · 2025: 5 · 2024: 6 · 2023: 7 · 2022: 3 · 2021: 1
FWCI
3.99
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Oncology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (81702791)
  • National Key Research and Development Program of China (2017YFA0105900)
Topics
  • Health Sciences › Medicine › Surgery › Bladder and Urothelial Cancer Treatments
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
muscle-invasive bladder cancer, multi-omics integration, autoencoder, chromosome 3p deletion, tumor immune microenvironment, IL-6/JAK/STAT3 signaling, interferon-alpha response, unfolded protein response, overall survival

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