ArticleBioscience reports2020
Survival prediction in patients with colon adenocarcinoma via multi-omics data integration using a deep learning algorithm.
Article in Bioscience reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- Transforming multi-omics data into images for disease classification: A review of techniques and tools.Journal of pathology informatics · 2026Review
- Adaptive multi-omics integration framework for breast cancer survival analysis.Scientific reports · 2025Article
- NEDD4L inhibits epithelial-mesenchymal transition in gastric cancer by mediating BICC1 ubiquitination.The Kaohsiung journal of medical sciences · 2025Article
- Mechanisms of GPM6A in Malignant Tumors.Cancer reports (Hoboken, N.J.) · 2025Review
- Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.Frontiers in genetics · 2025Article
- Key processes in tumor metastasis and therapeutic strategies with nanocarriers: a review.Molecular biology reports · 2024Review
- A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.Journal of cancer research and clinical oncology · 2023Article
- Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- The Glycoprotein M6a Is Associated with Invasiveness and Radioresistance of Glioblastoma Stem Cells.Cells · 2022Article
- The role of EphA7 in different tumors.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2022Review
- Multimodal deep learning for biomedical data fusion: a review.Briefings in bioinformatics · 2022Review
- A roadmap for multi-omics data integration using deep learning.Briefings in bioinformatics · 2022Review
- An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.Computational and mathematical methods in medicine · 2022Article
- Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- RUNX1T1, a potential prognostic marker in breast cancer, is co-ordinately expressed with ERα, and regulated by estrogen receptor signalling in breast cancer cells.Molecular biology reports · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposed a deep learning (DL) algorithm to predict survival in patients with colon adenocarcinoma (COAD) based on multi-omics integration. The survival-sensitive model was constructed using an autoencoder for DL implementation based on The Cancer Genome Atlas (TCGA) data of patients with COAD. The autoencoder framework was compared to PCA, NMF, t-SNE, and univariable Cox-PH model for identifying survival-related features. The prognostic robustness of the inferred survival risk groups was validated using three independent confirmation cohorts. Differential expression analysis, Pearson's correlation analysis, construction of miRNA-target gene network, and function enrichment analysis were performed. Two risk groups with significant survival differences were identified in TCGA set using the autoencoder-based model (log-rank p-value = 5.51e-07). The autoencoder framework showed superior performance compared to PCA, NMF, t-SNE, and the univariable Cox-PH model based on the C-index, log-rank p-value, and Brier score. The robustness of the classification model was successfully verified in three independent validation sets. There were 1271 differentially expressed genes, 10 differentially expressed miRNAs, and 12 hypermethylated genes between the survival risk groups. Among these, miR-133b and its target genes (GNB4, PTPRZ1, RUNX1T1, EPHA7, GPM6A, BICC1, and ADAMTS5) were used to construct a network. These genes were significantly enriched in ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and glucose metabolism-related pathways. The risk subgroups obtained through a multi-omics data integration pipeline using the DL algorithm had good robustness. miR-133b and its target genes could be potential diagnostic markers. The results would assist in elucidating the possible pathogenesis of COAD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.