ArticleJournal of gastrointestinal oncology2021
Identification and validation of xenobiotic metabolism-associated prognostic signature based on five genes to evaluate immune microenvironment in colon cancer.
Article in Journal of gastrointestinal oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 14 citations in OpenAlex.
- A nine-gene nicotine-metabolism signature predicts prognosis and characterizes the immune landscape in colon adenocarcinoma.Journal of gastrointestinal oncology · 2026Article
- HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients.NPJ precision oncology · 2025Article
- Integrative multi-omics study identifies sex-specific molecular signatures and immune modulation in bladder cancer.Frontiers in bioinformatics · 2025Article
- Breast Cancer Exposomics.Life (Basel, Switzerland) · 2024Review
- Reprogramming of palmitic acid induced by dephosphorylation of ACOX1 promotes β-catenin palmitoylation to drive colorectal cancer progression.Cell discovery · 2023Article
- Increased Kremen2 predicts worse prognosis in colon cancer.Pathology oncology research : POR · 2023Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundXenobiotic metabolism plays an important role in the progression of colon cancer; however, little is known about its related biomarkers. This study sought to construct a prognostic model related to xenobiotic metabolism in colon cancer, and further reveal the characteristics of tumor immune microenvironment based on the prognostic model.
methodsTranscriptome data of 41 normal colon tissues and 473 colon tumor tissues and the clinical features of 452 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Data on xenobiotic metabolism genes (XMGs) were obtained from the hallmark xenobiotic metabolism set of the Molecular Signatures Database (MSigDB) and articles. Additionally, data on differential XMGs in colon cancer were acquired for a functional enrichment analysis by R software. An XMG prognostic model was constructed by a Cox regression analysis, and evaluated using Kaplan-Meier survival curves, risk curves, receiver operating characteristic (ROC) curves, and an independent prognostic analysis in a training cohort and validation cohort. Moreover, tumor immune infiltration and negative regulatory immune genes of cancer-immunity cycle (CIC), including immune checkpoints and immune cytokines, were further analyzed between low- and high-risk groups in both the training and validation cohorts. Differences with P value <0.05 were interpreted as statistically significant.
resultsA total of 126 differential XMGs were distinguished in the colon cancer data set, which were mainly enriched in the metabolism pathways of drugs and nutrients. There were 5 optimized genes (i.e.,
conclusionsThe XMGs in the prognostic model have a good prediction effect on the prognosis of colon cancer patients. The T cells CD4 memory resting, and Tregs, immune checkpoints
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