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Identification of a basement membrane-related gene signature for predicting prognosis, immune infiltration, and drug sensitivity in colorectal cancer. | LitMetric

AI Article Synopsis

  • - Colorectal cancer (CRC) is a common gastrointestinal malignancy, and research suggests that basement membranes (BMs) may significantly influence its development and progression.
  • - A nine-gene risk signature was created to predict overall survival in CRC patients by analyzing RNA expression data and patient information from major databases, and its effectiveness was validated through various methods.
  • - This risk signature allowed for the classification of patients into different risk groups based on their clinicopathological features, tumor environment, and treatment responses, ultimately aiding in the identification of high-risk patients.

Article Abstract

Background: Colorectal cancer (CRC) is the most common malignancy affecting the gastrointestinal tract. Extensive research indicates that basement membranes (BMs) may play a crucial role in the initiation and progression of the disease.

Methods: Data on the RNA expression patterns and clinicopathological information of patients with CRC were sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A BM-linked risk signature for the prediction of overall survival (OS) was formulated using univariate Cox regression and combined machine learning techniques. Survival outcomes, functional pathways, the tumor microenvironment (TME), and responses to both immunotherapy and chemotherapy within varying risk classifications were also investigated. The expression trends of the model genes were evaluated by reverse transcription polymerase chain reaction (RT-PCR) and the Human Protein Atlas (HPA) database.

Results: A nine-gene risk signature containing UNC5C, TINAG, TIMP1, SPOCK3, MMP1, AGRN, UNC5A, ADAMTS4, and ITGA7 was constructed for the prediction of outcomes in patients with CRC. The expression profiles of these candidate genes were verified using RT-PCR and the HPA database and were found to be consistent with the findings on differential gene expression in the TCGA dataset. The validity of the signature was confirmed using the GEO cohort. The patients were stratified into different risk groups according to differences in clinicopathological characteristics, TME features, enrichment functions, and drug sensitivities. Lastly, the prognostic nomogram model based on the risk score was found to be effective in identifying high-risk patients and predicting OS.

Conclusion: A basement membrane-related risk signature was constructed and found to be effective for predicting the prognosis of patients with CRC.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11246870PMC
http://dx.doi.org/10.3389/fonc.2024.1428176DOI Listing

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