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

Functional disruptions by large germline genomic structural variants in susceptible genes are known risks for cancer. We used deletion structural variants (DSVs) generated from germline whole-genome sequencing (WGS) and DSV immune-related association tumor microenvironment (TME) to predict cancer ri...

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Published in:	Human genomics 2021-01, Vol.15 (1), p.3-3, Article 3
Main Authors:	Lin, Peng-Chan, Chen, Hui-O, Lee, Chih-Jung, Yeh, Yu-Min, Shen, Meng-Ru, Chiang, Jung-Hsien
Format:	Article
Language:	eng
Subjects:	Adult Aged Breast cancer Cancer risk CEP72 Classification Colon cancer Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - genetics Colorectal Neoplasms - immunology Colorectal Neoplasms - pathology Deletion structural variants Female Gene expression Gene Expression Regulation, Neoplastic Genetic Predisposition to Disease Genomes Genomics Germ-Line Mutation - genetics Humans Immune response Immune system Immunity - genetics Immunity - immunology Machine learning Male Medical prognosis Microtubule-Associated Proteins - genetics Middle Aged MUC4 Mucin-4 - genetics Patients Prediction models Primary Research Principal components analysis Prognosis Sequence Deletion - genetics Support vector machines Survival Survival analysis Tumor microenvironment Tumor Microenvironment - genetics Tumor Microenvironment - immunology Whole genome sequencing
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Summary:	Functional disruptions by large germline genomic structural variants in susceptible genes are known risks for cancer. We used deletion structural variants (DSVs) generated from germline whole-genome sequencing (WGS) and DSV immune-related association tumor microenvironment (TME) to predict cancer risk and prognosis. We investigated the contribution of germline DSVs to cancer susceptibility and prognosis by silicon and causal inference models. DSVs in germline WGS data were generated from the blood samples of 192 cancer and 499 non-cancer subjects. Clinical information, including family cancer history (FCH), was obtained from the National Cheng Kung University Hospital and Taiwan Biobank. Ninety-nine colorectal cancer (CRC) patients had immune response gene expression data. We used joint calling tools and an attention-weighted model to build the cancer risk predictive model and identify DSVs in familial cancer. The survival support vector machine (survival-SVM) was used to select prognostic DSVs. We identified 671 DSVs that could predict cancer risk. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of the 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). The DSVs in SGSM2 and LHFPL3 were relevant to colorectal cancer. We found a higher incidence of FCH in cancer patients than in non-cancer subjects (p < 0.05). SMYD3 and NKD2DSV genes were associated with cancer patients with FCH (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 MAGE1 expression, according to the STRING database. The causal inference model showed that deleting the CEP72 DSV gene affect the recurrence-free survival (RFS) of IFIT1 expression. We established an explainable attention-weighted model for cancer risk prediction and used the survival-SVM for prognostic stratification by using germline DSVs and immune gene expression datasets. Comprehensive assessments of germline DSVs can predict the cancer risk and clinical outcome of colon cancer patients.
ISSN:	1479-7364 1473-9542 1479-7364