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

Abstract

Background 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. Methods 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. Results 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. Conclusions 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.

Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics

“…In the breast cancer scenario, IFNGR1 expression is abridged or wholly lost and the immunoreactivity of heterogeneous IFNGR1 is linked to the morphological heterogeneity in breast cancer cells . One of the essential biomarkers shortlisted in our study is long intergenic nonprotein-coding RNA 2370 (LINC02370), an RNA gene that has a significant role in cancers. , …”

“…50 One of the essential biomarkers shortlisted in our study is long intergenic nonprotein-coding RNA 2370 (LINC02370), an RNA gene that has a significant role in cancers. 51,52 To summarize, out of these nine drug-sensitive biomarkers, we consider TNFSF15, DCAF6, KDM6A, PHETA2, IFNGR1, and LINC02370 as potential biomarkers that are drugsensitive. These are further selected for their radiosensitivity or radioresistance.…”

Identification of differentially expressed HERV-K(HML-2) loci in colorectal cancer

“…It has been shown that the downregulation of LHFPL3 expression was associated with reduced viability, proliferation, and invasiveness of human glioma cells ( Li et al, 2019 ). When the deletion occurs in LHFP3, its gene function is impaired, resulting in a worse prognosis for colorectal cancer patients ( Lin et al, 2021 ). KYAT1 and CR1 are protein-coding genes intersecting with the upregulated differential loci of HERV-K (HML-2) pol .…”

Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care

“…We demonstrated that germline susceptibility and deletion structural variants can have an impact on the survival and therapeutic strategies for stage III colorectal cancer. For example, patients with germline DNA repair genetic variants and CEP72 deletion structural variants have better survival in CRCs [ 84 ]. Using AI model analysis, we could stratify the risk of cancer recurrence.…”