OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method. METHODS: WES was performed to identify potential single nucleotide polymorphism (SNP) or mutation sites in a DR pedigree comprising 10 members. A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features. The contribution of each feature was assessed using Shapley Additive explanation analysis. The efficacies of the models with and without SNP were compared. RESULTS: WES revealed that seven SNPs/mutations ( rs116911833 in TRIM7, 1997T>C in LRBA, 1643T>C in PRMT10, rs117858678 in C9orf152, rs201922794 in CLDN25, rs146694895 in SH3GLB2, and rs201407189 in FANCC) were associated with DR. Notably, the model including rs146694895 and rs201407189 achieved better performance in predicting DR (accuracy: 80.2%; sensitivity: 83.3%; specificity: 76.7%; area under the receiver operating characteristic curve [AUC]: 80.0%) than the model without these SNPs (accuracy: 79.4%; sensitivity: 80.3%; specificity: 78.3%; AUC: 79.3%). CONCLUSION Novel SNP sites associated with DR were identified in the DR pedigree. Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.
Diabetic Retinopathy/diagnosis* ; Humans ; Machine Learning ; Male ; Female ; Polymorphism, Single Nucleotide ; Middle Aged ; Exome Sequencing ; Aged ; Adult ; Pedigree ; Diabetes Mellitus, Type 2/complications* ; Genetic Predisposition to Disease ; Mutation

OBJECTIVETo explore the association between XAGE-1b gene expression and the clinical characteristics of non-small cell lung cancer (NSCLC).

METHODSTumor tissue and adjacent normal lung tissue specimens were obtained surgically from 30 patients with resectable NSCLC, from which the total RNA was extracted for RT-PCR to amplify full-length XAGE-1b gene. The products of RT-PCR were identified by electrophoresis and sequencing. The expression of XAGE-1b gene and its association with the clinical characteristics of the patients were analyzed.

RESULTSIn the 30 tumor tissue specimens, the expression rate of XAGE-1b gene was 40%, but none of the normal lung tissues expressed this gene. The gene expression was not related to the patients' age, gender, tumor differentiation or clinical stages, but showed significant correlation to their pathological classification. The expression rate of XAGE-1b gene in adenocarcinoma was much higher than that in tumors of other pathological types (61.1% vs 8.3%, P=0.015). XAGE-1b gene expression tended to increase with the TNM stages, which, however, failed to find statistical data support (P>0.05).

CONCLUSIONSXAGE-1b gene is highly expressed in lung adenocarcinoma, and can be an ideal target for tumor immunotherapy.

Antigens, Neoplasm ; genetics ; Carcinoma, Non-Small-Cell Lung ; genetics ; pathology ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Lung Neoplasms ; genetics ; pathology ; Male ; Middle Aged ; RNA, Messenger ; genetics ; metabolism ; Reverse Transcriptase Polymerase Chain Reaction