ArticleBMC bioinformatics2022
Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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
26 citing papers in PubMed, 36 citations in OpenAlex.
- A machine learning-based basement membrane gene signature model for predicting ovarian cancer survival.Translational cancer research · 2026Article
- High FCRL5 expression predicts poor treatment response and survival in newly diagnosed multiple myeloma: a retrospective study.Frontiers in oncology · 2026Article
- Integrative multiomic and immune profiling of lung adenocarcinoma: molecular landscapes, gene expression, and treatment response insights.The oncologist · 2025Article
- Expression of amine oxidase-related proteins in breast phyllodes tumor.Histology and histopathology · 2025Article
- The role of MYEOV gene: a review and future directions.Frontiers in oncology · 2025Review
- A multi-cohort validated OXPHOS signature predicts survival and immune profiles in grade II/III glioma patients.Frontiers in immunology · 2025Article
- Monoamine Oxidase B in Cancers: Implications for Therapeutics and Prognosis.Journal of inflammation research · 2025Review
- Article
- Identifying cancer prognosis genes through causal learning.Briefings in bioinformatics · 2024Article
- Effect of colorectal cancer stem cells on the development and metastasis of colorectal cancer.World journal of gastrointestinal oncology · 2024Review
- Artificial intelligence methods available for cancer research.Frontiers of medicine · 2024Review
- A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection.Heliyon · 2024Article
- Review
- Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis.Heliyon · 2024Article
- Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation.JAMIA open · 2024Article
- Identification of basement membrane-related signatures for estimating prognosis, immune infiltration landscape and drug candidates in pancreatic adenocarcinoma.Journal of Cancer · 2024Article
- The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.Frontiers in medicine · 2024Article
- Molecular Complexity of Colorectal Cancer: Pathways, Biomarkers, and Therapeutic Strategies.Cancer management and research · 2024Review
- Gut microbiome and nutrition-related predictors of response to immunotherapy in cancer: making sense of the puzzle.BJC reports · 2023Review
- Subcellular Expression Patterns of FKBP Prolyl Isomerase 10 (FKBP10) in Colorectal Cancer and Its Clinical Significance.International journal of molecular sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesImmune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.
methodsThe current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model.
resultsTwenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis.
conclusionsThe current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/ , which was valuable for individualized treatment decision before surgery.
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.