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Research Article
Prognostic Biomarker MUC4 Mutations in Colon Adenocarcinoma Correlating with Tumor Microenvironment
Feng Gao
Xinjiang Medical University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Immunotherapy is a new strategy for Colorectal cancer (CRC) treatment. Tumor mutation burden (TMB) may act as an emerging biomarker for predicting responses to immunotherapy. Nevertheless, no studies investigate if these gene mutations correlate to TMB and tumor-infiltrating immune cells.
Methods: Somatic mutation data for CRC samples were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) datasets. Then, we investigated the relationship between these mutant genes, TMB and overall survival outcomes. GSEA analysis was performed to explore the underlying mechanism of mutant gene. Finally, we further verified the connection between gene mutations and immune response.
Results: We identified 17 common mutant genes shared by both two cohorts. Further analysis found that MUC4 mutation was strongly related to higher TMB and predicted a poorer prognosis. Moreover, functional enrichment analysis of samples with MUC4 mutation revealed that they were involved in regulating the natural killer cell mediated cytotoxicity signaling pathway. Significant changes in the proportion of the immune cells of CD8+ T cells, activated NK cells, M1 macrophages and resting memory CD4+ T cells were observed using the CIBERSORT algorithm.
Conclusions: Our research revealed that MUC4 mutation significantly correlated with increased TMB, a worse prognosis and modulating the immune microenvironment, which may be considered a biomarker to predict the outcome of the immune response in colorectal cancer.
Keywords
colon adenocarcinoma， MUC4，tumor mutation burden, tumor microenvironment, prognosis
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This is a preprint. It has not completed peer review.
Research Article
Prognostic Biomarker MUC4 Mutations in Colon Adenocarcinoma Correlating with Tumor Microenvironment
Feng Gao
Xinjiang Medical University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 07 Jan, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Immunotherapy is a new strategy for Colorectal cancer (CRC) treatment. Tumor mutation burden (TMB) may act as an emerging biomarker for predicting responses to immunotherapy. Nevertheless, no studies investigate if these gene mutations correlate to TMB and tumor-infiltrating immune cells.
Methods: Somatic mutation data for CRC samples were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) datasets. Then, we investigated the relationship between these mutant genes, TMB and overall survival outcomes. GSEA analysis was performed to explore the underlying mechanism of mutant gene. Finally, we further verified the connection between gene mutations and immune response.
Results: We identified 17 common mutant genes shared by both two cohorts. Further analysis found that MUC4 mutation was strongly related to higher TMB and predicted a poorer prognosis. Moreover, functional enrichment analysis of samples with MUC4 mutation revealed that they were involved in regulating the natural killer cell mediated cytotoxicity signaling pathway. Significant changes in the proportion of the immune cells of CD8+ T cells, activated NK cells, M1 macrophages and resting memory CD4+ T cells were observed using the CIBERSORT algorithm.
Conclusions: Our research revealed that MUC4 mutation significantly correlated with increased TMB, a worse prognosis and modulating the immune microenvironment, which may be considered a biomarker to predict the outcome of the immune response in colorectal cancer.
Figure 1
Figure 2
Figure 3
Figure 4