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Comprehensive assessments of germline deletion structural variants reveal the association between prognostic MUC4 and CEP72 deletions and immune response gene expression in colorectal cancer patients
Jung-Hsien Chiang
National Cheng Kung University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Human Genomics.
Background Functional disruptions by large germline genomic structural variants in susceptible genes are known risks for cancer. Few studies have used deletion structural variants (DSVs) to predict cancer risk with neural networks or studied the relationship between DSVs and immune gene expression to stratify prognosis.
Methods Whole-genome sequencing (WGS) data was analyzed with the blood samples of 192 cancer and 499 noncancer subjects with or without family cancer history (FCH). Ninety-nine colorectal cancer (CRC) patients had immune response gene expression data. To build the cancer risk predictive model and identify DSVs in familial cancer, we used joint calling tools and attention-weighted model. The survival support vector machine (survival-SVM) was used to select prognostic DSVs.
Results We identified 671 DSVs that could predict cancer risk. The area under the curve (AUC) of receiver operating characteristic curve (ROC) of attention-weighted model was 0.71. The 3 most frequent DSV genes observed in cancer patients were identified as ADCY9, AURKAPS1, and RAB3GAP2 (p < 0.05). We identified 65 immune-associated DSV markers for assessing cancer prognosis (P < 0.05). The functional protein of MUC4 DSV gene interacted with MAGE1expresssion, according to the STRING database. The causal inference model showed that deleting the CEP72 DSV gene could affect the recurrence-free survival (RFS) of IFIT1 expression.
Conclusions We established an explainable attention-weighted model for cancer risk prediction and used the survival-SVM for prognostic stratification by using DSV and immune gene expression datasets. It can provide the genetic landscape of cancer patients and help predict the clinical outcome.
Keywords
Whole genome sequencing, Cancer risk, Deletion structural variants, MUC4, CEP72
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This is a list of supplementary files associated with this preprint. Click to download.
- Supplementarytable6.CRCcharacteristics.docx
- Supplementarytable5.57Sixfunctionalcategories.xlsx
- Supplementarytable4.Coxproportional.xlsx
- Supplementarytable3.57Thespecificgenesinsubjects.xlsx
- Supplementarytable2.20ThefrequencyspectrumofDSVs.xlsx
- Supplementarytable1.DSVinformation.xlsx
- Supplementaryfigure6.SV20200129.pdf
- Supplementaryfigure5.SV20200129.pdf
- Supplementaryfigure420200818.pdf
- Supplementaryfigure3.SV20200129.pdf
- Supplementaryfigure2.SV20200129.pdf
- Supplementaryfigure1.SV20200820.pdf
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CURRENT STATUS: Published
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Reviewer #2 agreed
On 15 Nov, 2020
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Received 09 Oct, 2020
Reviewers invited
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Subject Areas
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This preprint is under consideration at Human Genomics. A preprint is 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
Primary research
Comprehensive assessments of germline deletion structural variants reveal the association between prognostic MUC4 and CEP72 deletions and immune response gene expression in colorectal cancer patients
Jung-Hsien Chiang
National Cheng Kung 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
Version 1
Posted 21 Sep, 2020
Published
On 11 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 26 Nov, 2020
Review #2 received
Received 22 Nov, 2020
Reviewer #2 agreed
On 15 Nov, 2020
Review #1 received
Received 09 Oct, 2020
Reviewers invited
Invitations sent on 05 Oct, 2020
Reviewer #1 agreed
On 05 Oct, 2020
First submitted
On 17 Sep, 2020
Editor assigned
On 17 Sep, 2020
Submission checks complete
On 16 Sep, 2020
Editor invited
On 16 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Human Genomics.
Background Functional disruptions by large germline genomic structural variants in susceptible genes are known risks for cancer. Few studies have used deletion structural variants (DSVs) to predict cancer risk with neural networks or studied the relationship between DSVs and immune gene expression to stratify prognosis.
Methods Whole-genome sequencing (WGS) data was analyzed with the blood samples of 192 cancer and 499 noncancer subjects with or without family cancer history (FCH). Ninety-nine colorectal cancer (CRC) patients had immune response gene expression data. To build the cancer risk predictive model and identify DSVs in familial cancer, we used joint calling tools and attention-weighted model. The survival support vector machine (survival-SVM) was used to select prognostic DSVs.
Results We identified 671 DSVs that could predict cancer risk. The area under the curve (AUC) of receiver operating characteristic curve (ROC) of attention-weighted model was 0.71. The 3 most frequent DSV genes observed in cancer patients were identified as ADCY9, AURKAPS1, and RAB3GAP2 (p < 0.05). We identified 65 immune-associated DSV markers for assessing cancer prognosis (P < 0.05). The functional protein of MUC4 DSV gene interacted with MAGE1expresssion, according to the STRING database. The causal inference model showed that deleting the CEP72 DSV gene could affect the recurrence-free survival (RFS) of IFIT1 expression.
Conclusions We established an explainable attention-weighted model for cancer risk prediction and used the survival-SVM for prognostic stratification by using DSV and immune gene expression datasets. It can provide the genetic landscape of cancer patients and help predict the clinical outcome.
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Figure 2
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Figure 6