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pmid:38311637

Construction and validation of a colon cancer prognostic model based on tumor mutation burden-related genes.

Daoyang Zou · Tianwen Xu

Scientific reports · 2024 doi:10.1038/s41598-024-53257-z

Paper

complete

Experiment

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Data

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Analysis

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Execution

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pmid:38311637
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2026-11-01T19:20:15Z
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Construction and validation of a colon cancer prognostic model based on tumor mutation burden-related genes.
doi
10.1038/s41598-024-53257-z
pmid
38311637
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[0]
Daoyang Zou
[1]
Tianwen Xu
journal
Scientific reports
year
2024
abstract
Currently, immunotherapy has entered the clinical diagnosis and treatment guidelines for colon cancer, but existing immunotherapy markers cannot predict the effectiveness of immunotherapy well. This study utilized the TCGA-COAD queue to perform differential gene analysis on high and low-mutation burden samples, and screen differentially expressed genes (DEGs). To explore new molecular markers or predictive models of immunotherapy by using DEGs for NMF classification and prognostic model construction. Through systematic bioinformatics analysis, the TCGA-COAD cohort was successfully divided into high mutation burden subtypes and low mutation burden subtypes by NMF typing using DEGs. The proportion of MSI-H between high mutation burden subtypes was significantly higher than that of low mutation burden subtypes, but there was no significant difference in immunotherapy efficacy between the two subtypes. Drug sensitivity analysis showed significant differences in drug sensitivity between the two subtypes. Subsequently, we constructed a prognostic model using DEGs, which can effectively predict patient survival and immunotherapy outcomes. The prognosis and immunotherapy outcomes of the low-risk group were significantly better than those of the high-risk group. The external dataset validation of the constructed prognostic model using the GSE39582 dataset from the GEO database yielded consistent results. At the same time, we also analyzed the TMB and MSI situation between the high and low-risk groups, and the results showed that there was no significant difference in TMB between the high and low-risk groups, but the proportion of MSI-H in the high-risk group was significantly higher than that in the low-risk group. Finally, we conclude that TMB is not a suitable molecular marker for predicting the efficacy of immunotherapy in colon cancer. The newly constructed prognostic model can effectively differentiate the prognosis of colon cancer patients and predict their immunotherapy efficacy.
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Finally, use TIDE scoring (http://tide.dfci.harvard.edu/) to predict the effectiveness of immunotherapy in different subtypes.
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sections[7] "Survival analysis, drug sensitivity analysis, and immunotherapy sensitivity analysis of two subtypes" paragraph 2
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Finally, use TIDE scoring (http://tide.dfci.harvard.edu/) to predict the effectiveness of immunotherapy in different subtypes.
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The data that support the findings of this study are openly available in the TCGA database (https://portal.gdc.cancer.gov/repository) and GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582).
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sections[34] "notes" paragraph 1
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The data that support the findings of this study are openly available in the TCGA database (https://portal.gdc.cancer.gov/repository) and GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582).
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Finally, we conclude that TMB is not a suitable molecular marker for predicting the efficacy of immunotherapy in colon cancer.
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Finally, we conclude that TMB is not a suitable molecular marker for predicting the efficacy of immunotherapy in colon cancer.
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Finally, we conclude that TMB is not a suitable molecular marker for predicting the efficacy of immunotherapy in colon cancer.
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The data that support the findings of this study are openly available in the TCGA database (https://portal.gdc.cancer.gov/repository) and GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582).
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title
Gene expression Classification of Colon Cancer defines six molecular subtypes with distinct clinical, molecular and survival characteristics [Expression]
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Finally, use TIDE scoring (http://tide.dfci.harvard.edu/) to predict the effectiveness of immunotherapy in different subtypes.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38311637", "kind": "science-record", "revision": 2, "content_hash": "sha256:0f101e6f420f11212c7bf24560d7d351353a6b2f1b93b36b8ce6df2804bde3ab", "created_at": "2026-11-01T19:20:15Z", "updated_at": "2026-11-01T21:32:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a colon cancer prognostic model based on tumor mutation burden-related genes.", "doi": "10.1038/s41598-024-53257-z", "pmid": "38311637", "authors": ["Daoyang Zou", "Tianwen Xu"], "journal": "Scientific reports", "year": 2024, "abstract": "Currently, immunotherapy has entered the clinical diagnosis and treatment guidelines for colon cancer, but existing immunotherapy markers cannot predict the effectiveness of immunotherapy well. This study utilized the TCGA-COAD queue to perform differential gene analysis on high and low-mutation burden samples, and screen differentially expressed genes (DEGs). To explore new molecular markers or predictive models of immunotherapy by using DEGs for NMF classification and prognostic model construction. Through systematic bioinformatics analysis, the TCGA-COAD cohort was successfully divided into high mutation burden subtypes and low mutation burden subtypes by NMF typing using DEGs. The proportion of MSI-H between high mutation burden subtypes was significantly higher than that of low mutation burden subtypes, but there was no significant difference in immunotherapy efficacy between the two subtypes. Drug sensitivity analysis showed significant differences in drug sensitivity between the two subtypes. Subsequently, we constructed a prognostic model using DEGs, which can effectively predict patient survival and immunotherapy outcomes. The prognosis and immunotherapy outcomes of the low-risk group were significantly better than those of the high-risk group. The external dataset validation of the constructed prognostic model using the GSE39582 dataset from the GEO database yielded consistent results. At the same time, we also analyzed the TMB and MSI situation between the high and low-risk groups, and the results showed that there was no significant difference in TMB between the high and low-risk groups, but the proportion of MSI-H in the high-risk group was significantly higher than that in the low-risk group. Finally, we conclude that TMB is not a suitable molecular marker for predicting the efficacy of immunotherapy in colon cancer. 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