This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Yi Zhao
China Medical University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Purpose: To identify CD8+ T lymphocyte related co-expressed genes that increase CD8+ T lymphocyte proportions in breast cancer, and to elucidate the underling mechanisms among relevant genes in the tumor microenvironment.
Method: We obtained breast cancer expression matrix data, patient phenotype following information from TCGA–BRCA FPKM. Tumor purity, immune score, stromal score, and estimate score were calculated using the ‘estimate’ package in R. The CD8+ T lymphocyte proportions in each breast carcinoma sample were estimated using the CIBERSORT algorithm. The samples with P < 0.05 were considered to be significant and were taken into the weighted gene co-expression network analysis. Based on the CD8+ T lymphocyte proportion and tumor purity, we generated CD8+ T lymphocyte co-expression networks and selected the most CD8+ T lymphocyte related module as our interested co-expression modules. We constructed a Cox regression risk model based on CD8+ T cell-related factors and explored their correlations with microenvironmental indicators.
Results: A breast carcinoma CD8+ T lymphocyte proportion co-expression yellow module was determined. Four CD8+ T lymphocyte proportion co-expressed genes (CD74, HLA-DMA, HCST and GIMAP4) were determined to increase the CD8+ T lymphocyte proportion levels in breast cancer patients. The yellow module was significantly enriched in the antigen presentation process, cellular response to interferon−gamma, and leukocyte proliferation. Subsequently, we generated risk scores based on CD74, HLA-DMA, HCST, and GIMAP4. The AUC of the CD8+ T lymphocyte related risk score was 0.66. The risk score showed significant prognostic ability in various subgroups. Expression levels of proteins encoded by CD74, HLA-DMA, HCST and GIMAP4 were lower in the breast carcinoma samples than in normal tissue, suggesting that expression differences both at the mRNA and the protein levels.
Conclusion: These four CD8+ T lymphocyte proportion co-expression genes increase CD8+ T lymphocyte in breast cancer by an antigen presentation process. The mechanism might suggest new pathways to improve outcomes in patients who do not benefit from immune therapy.
Keywords
CD8+ T lymphocyte, Antigen presentation, Response to interferon−gamma, Weighted gene co-expression network analysis (WGCNA), Tumor microenvironment, Breast cancer
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Yi Zhao
China 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 22 Jan, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Purpose: To identify CD8+ T lymphocyte related co-expressed genes that increase CD8+ T lymphocyte proportions in breast cancer, and to elucidate the underling mechanisms among relevant genes in the tumor microenvironment.
Method: We obtained breast cancer expression matrix data, patient phenotype following information from TCGA–BRCA FPKM. Tumor purity, immune score, stromal score, and estimate score were calculated using the ‘estimate’ package in R. The CD8+ T lymphocyte proportions in each breast carcinoma sample were estimated using the CIBERSORT algorithm. The samples with P < 0.05 were considered to be significant and were taken into the weighted gene co-expression network analysis. Based on the CD8+ T lymphocyte proportion and tumor purity, we generated CD8+ T lymphocyte co-expression networks and selected the most CD8+ T lymphocyte related module as our interested co-expression modules. We constructed a Cox regression risk model based on CD8+ T cell-related factors and explored their correlations with microenvironmental indicators.
Results: A breast carcinoma CD8+ T lymphocyte proportion co-expression yellow module was determined. Four CD8+ T lymphocyte proportion co-expressed genes (CD74, HLA-DMA, HCST and GIMAP4) were determined to increase the CD8+ T lymphocyte proportion levels in breast cancer patients. The yellow module was significantly enriched in the antigen presentation process, cellular response to interferon−gamma, and leukocyte proliferation. Subsequently, we generated risk scores based on CD74, HLA-DMA, HCST, and GIMAP4. The AUC of the CD8+ T lymphocyte related risk score was 0.66. The risk score showed significant prognostic ability in various subgroups. Expression levels of proteins encoded by CD74, HLA-DMA, HCST and GIMAP4 were lower in the breast carcinoma samples than in normal tissue, suggesting that expression differences both at the mRNA and the protein levels.
Conclusion: These four CD8+ T lymphocyte proportion co-expression genes increase CD8+ T lymphocyte in breast cancer by an antigen presentation process. The mechanism might suggest new pathways to improve outcomes in patients who do not benefit from immune therapy.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
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