ArticleOncoTargets and therapy2021
Development and Validation of an Autophagy-Stroma-Based Microenvironment Gene Signature for Risk Stratification in Colorectal Cancer.
Article in OncoTargets and therapy, 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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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Tensins in Cancer: Integration of Their Domain Functions, Context-Dependent Regulation and Biomarker Potential.Biology · 2025Review
- Experimental prognostic model integrating N6-methyladenosine-related programmed cell death genes in colorectal cancer.iScience · 2024Article
- Integration of bulk RNA sequencing data and single-cell RNA sequencing analysis on the heterogeneity in patients with colorectal cancer.Functional & integrative genomics · 2023Article
- A Novel Gene List Identifies Tumors with a Stromal-Mesenchymal Phenotype and Worse Prognosis in Gastric Cancer.Cancers · 2023Article
- Construction and validation of a bladder cancer risk model based on autophagy-related genes.Functional & integrative genomics · 2023Article
- Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma.Frontiers in pharmacology · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer is the fourth most common cancer and the second leading cause of cancer-related death in the USA. The aim of this study was to establish a tumor gene signature based on tumor stromal cell and autophagy for predicting the risk of recurrence in patients with colorectal cancer.
methodsWe used "Rtsne" and "xCell" R packages to estimate autophagy and stroma status, respectively. The discovery cohort used microarray gene expression data retrieved from the GSE39582 dataset. The Cox regression model and Least Absolute Shrinkage and Selection Operator (LASSO) were used to identify prognostic genes and to construct an autophagy-stroma-based gene signature. Moreover, external validation was conducted using GSE17538, GSE38832, TCGA database, and patient data obtained from the First Hospital of China Medical University (CMU).
resultsThe LASSO model identified three genes (
conclusionWe have established and verified a feasible prognostic model of colorectal cancer based on autophagy and stromal cell characteristics of patients. The model can be used to evaluate recurrence risk of cancer patients, and the hub genes in the model provide potential targets for targeted colorectal cancer treatment.
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