Aim: Kinship analysis in trace amounts and degraded biological samples has consistently posed a challenge in forensic practice. With shorter amplicons and no stutter peak, Insertion/Deletion polymorphisms (InDels) significantly improve kinship analyses of deceased individuals and their potential living relatives. However, room for improvement remains in identifying 2nd-degree and more distant kinships. To address this issue, a kinship analysis workflow based on machine learning (ML) models was proposed.
Methods: Based on multiple kinship parameters including identity-by-state (IBS) scores, k coefficients, proportion identity-by-descent (IBD), and likelihood ratio (LR) values, this pilot study applied a recently validated InDel locus to preliminarily develop an ML workflow for forensic kinship multi-classification.
Results: In the binary classification of 2nd-degree relatives and unrelated pairs, the LR cutoff threshold workflow and the ML workflow achieved a similar accuracy of 0.9194. However, the ML method had a conclusiveness rate (CR) of 1.0, compared to 0.7066 for the LR workflow. In the multiclass task, the LR-based workflow had a macro F1 score of 0.6955/0.5212 and a CR of 0.7375/0.7046 for single and dual thresholds methods, respectively. However, the ML-based workflow showed that the optimal model - feature combination (XGBoost-IBD+LR) could classify all samples conclusively, with a macro F1 score of 0.9020.
Conclusion: In summary, the ML workflow enhanced the kinship analysis efficiency based on the InDel genotyping system by combining multiple parameters, aiming to provide a more flexible and efficient solution for large-scale database screening.
Aim: A disintegrin and metalloproteinase domain 9 (ADAM9) is involved in various human diseases, including cone-rod dystrophy, Alzheimer’s disease, cancer, and viral infections. However, its comprehensive expression profile and therapeutic potential across cancers remain poorly understood.
Methods: Pan-cancer analyses of ADAM9 expression, mutation status, and prognostic value were conducted using The Cancer Genome Atlas (TCGA) and cBioPortal datasets. The differences between cancer and normal tissues, as well as survival associations, were examined. Mutation landscapes were characterized, and in vitro assays were performed in prostate, breast, and lung cancer cell lines treated with increasing concentrations of Thymoquinone Derivative FL12 (TQFL12). ADAM9 messenger RNA (mRNA) and protein levels were determined by quantitative polymerase chain reaction and Western blotting, respectively.
Results:ADAM9 expression was elevated in multiple cancers, but decreased in Kidney chromophobe (KICH) and Thyroid carcinoma (THCA). High ADAM9 expression correlated with longer overall survival (OS) in Colon adenocarcinoma (COAD), while predicting poorer OS in Breast Invasive Carcinoma (BRCA), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC), KICH, Liver hepatocellular carcinoma (LIHC), Brain Lower Grade Glioma (LGG), Mesothelioma (MESO), Pancreatic Adenocarcinoma (PAAD), and Uveal Melanoma (UVM), suggesting its role as an unfavorable prognostic biomarker in cancers. ADAM9 also exhibited frequent mutations, with mutated cases showing improved progression-free and disease-specific survival, implying favorable prognostic relevance. Notably, TQFL12 - a novel compound synthesized in our laboratory - significantly suppressed ADAM9 protein expression in a dose-dependent manner in 22RV1, MDA-MB-231, and H1975 cells without altering mRNA levels, suggesting that the regulatory effect may occur at the post-translational or translational level.
Conclusions: These results highlight ADAM9 as a potential prognostic marker and therapeutic target, while identifying TQFL12 as a promising inhibitor across multiple cancers.