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Research Article
Zengren Zhao
Department of General Surgery, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
The tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC).
Methods
Transcriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database.
Results
Gene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C‐index of 0.769 (95%CI, 0.717–0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen‐IRGs signature. The AUC for 1‐, 3‐, and 5‐year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates.
Conclusions
In this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.
Keywords
colorectal cancer, immune-related gene signature, tumor microenvironment, TCGA, GEO, prognosis
Figure 1
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Figure 9
This is a list of supplementary files associated with this preprint. Click to download.
- FigureS1.tif
Validation of the nomogram predicting overall survival for CRC patients in the GSE39582 cohort. A, Nomogram predicting 1-year, 3-year and 5-year OS for CRC patients. B, Calibration plot for nomogram predicted and observed 3-year overall survival rate. C, Decision curve analysis for 1-year, 3-year and 5-year overall survival predictions. D, Decision curve analysis comparing nomogram with the TNM stage and the thirteen-IRGs signature. E, Time-dependent ROC curves for overall survival prediction by the nomogram in GSE39582 cohort. F, Kaplan–Meier curves for overall survival prediction by the nomogram in GSE39582 cohort.
- FigureS2.tif
Validation of correlation of IRGs extracted from TCGA database with overall survival in GEO cohort. Kaplan‐Meier survival curves were generated for selected immune-related genes extracted from the comparison of groups of high (red line) and low (blue line) gene expression. p<0.05 in Log‐rank test.
- TableS1.docx
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View the published article at BMC Cancer.
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Reviewers agreed
On 04 Mar, 2021
Reviewers invited
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Cancer.
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.
Learn more about In Review
Research Article
Zengren Zhao
Department of General Surgery, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Cancer.
Version 1
Posted 28 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 06 May, 2021
Reviews received
Received 13 Apr, 2021
Reviews received
Received 20 Mar, 2021
Reviewers agreed
On 15 Mar, 2021
Reviewers agreed
On 04 Mar, 2021
Reviewers invited
Invitations sent on 27 Dec, 2020
Editor assigned
On 27 Dec, 2020
Editor invited
On 22 Dec, 2020
Submission checks complete
On 22 Dec, 2020
First submitted
On 09 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Cancer.
Background
The tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC).
Methods
Transcriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database.
Results
Gene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C‐index of 0.769 (95%CI, 0.717–0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen‐IRGs signature. The AUC for 1‐, 3‐, and 5‐year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates.
Conclusions
In this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
This is a list of supplementary files associated with this preprint. Click to download.
- FigureS1.tif
Validation of the nomogram predicting overall survival for CRC patients in the GSE39582 cohort. A, Nomogram predicting 1-year, 3-year and 5-year OS for CRC patients. B, Calibration plot for nomogram predicted and observed 3-year overall survival rate. C, Decision curve analysis for 1-year, 3-year and 5-year overall survival predictions. D, Decision curve analysis comparing nomogram with the TNM stage and the thirteen-IRGs signature. E, Time-dependent ROC curves for overall survival prediction by the nomogram in GSE39582 cohort. F, Kaplan–Meier curves for overall survival prediction by the nomogram in GSE39582 cohort.
- FigureS2.tif
Validation of correlation of IRGs extracted from TCGA database with overall survival in GEO cohort. Kaplan‐Meier survival curves were generated for selected immune-related genes extracted from the comparison of groups of high (red line) and low (blue line) gene expression. p<0.05 in Log‐rank test.
- TableS1.docx
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