ArticleBMC medical informatics and decision making2020
Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 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
- Development of a prognostic prediction model incorporatingJournal of gastrointestinal oncology · 2026Article
- An Interpretable Omics-to-Image Transformer Framework for Cancer Prognosis Prediction.Computational and structural biotechnology journal · 2026Article
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Identification of clinically relevant profiles in colorectal cancer through integrated analysis of bacterial DNA and metabolome in serum.Frontiers in immunology · 2025Article
- Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review.Journal of diabetes and metabolic disorders · 2024Article
- Membrane Trafficking-Related Genes Predict Tumor Immune Microenvironment and Prognosis in Colorectal Cancer.Biochemical genetics · 2024Article
- A pyroptosis-related lncRNA-based prognostic index for hepatocellular carcinoma by relative expression orderings.Translational cancer research · 2024Article
- Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data.Frontiers in oncology · 2024Article
- Multiomic Investigations into Lung Health and Disease.Microorganisms · 2023Review
- Implementation of Public Health Genomics in Africa: Lessons from the COVID-19 pandemic, challenges, and recommendations.Journal of medical virology · 2023Review
- Development and validation of a survival prediction model for 113,239 patients with colon cancer: a retrospective cohort study.Journal of gastrointestinal oncology · 2022Article
- A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways.Journal of oncology · 2022Article
- Analysis of the tumor microenvironment and mutation burden identifies prognostic features in thymic epithelial tumors.American journal of cancer research · 2022Article
- Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer.International journal of molecular sciences · 2021Article
- Proteomic profiling reveals a signature for optimizing prognostic prediction in Colon Cancer.Journal of Cancer · 2021Article
- Cancer Omics in Africa: Present and Prospects.Frontiers in oncology · 2020Review
- Integrative clustering methods for multi-omics data.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data.
methodsIn total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell's concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno's concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates.
resultsCombinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell's concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno's concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively.
conclusionIn conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.