AIMC Topic: Carcinogenesis

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Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.

Ecotoxicology and environmental safety
Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic R...

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions.

Signal transduction and targeted therapy
Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria ...

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders.

Cell death & disease
Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular bioma...

DiaDeL: An Accurate Deep Learning-Based Model With Mutational Signatures for Predicting Metastasis Stage and Cancer Types.

IEEE/ACM transactions on computational biology and bioinformatics
Mutational signatures help identify cancer-associated genes that are being involved in tumorigenesis pathways. Hence, these pathways guide precision medicine approaches to find appropriate drugs and treatments. The pattern of mutations varies in diff...

Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.

BMC biology
BACKGROUND: Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenes...

A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.

Cancer research
Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samples on a large scale. These cohort-based approaches cannot directly identify individual pathogenic n...

HCV nonstructural protein 4 is associated with aggressiveness features of breast cancer.

Breast cancer (Tokyo, Japan)
BACKGROUND: Hepatitis C virus (HCV) has the lymphotropic feature that is supposed to be the reason of related extrahepatic manifestation. HCV viral oncoproteins may participate in the regulation of some gene expression that has been implicated in tum...

Mutagenic Potential ofBos taurus Papillomavirus Type 1 E6 Recombinant Protein: First Description.

BioMed research international
Bovine papillomavirus (BPV) is considered a useful model to study HPV oncogenic process. BPV interacts with the host chromatin, resulting in DNA damage, which is attributed to E5, E6, and E7 viral oncoproteins activity. However, the oncogenic mechani...

Mathematical Modeling Quantifies "Just-Right" APC Inactivation for Colorectal Cancer Initiation.

Cancer research
UNLABELLED: Dysregulation of the tumor suppressor gene adenomatous polyposis coli (APC) is a canonical step in colorectal cancer development by promoting activation of the WNT/β-catenin pathway. Curiously, most colorectal tumors carry biallelic mutat...

Dissecting cross-lineage tumourigenesis under p53 inactivation through single-cell multi-omics and spatial transcriptomics.

Clinical and translational medicine
BACKGROUND: Tumour suppressor genes, exemplified by TP53 (encoding the human p53), function as critical guardians against tumourigenesis. Germline TP53-inactivating mutations underlie Li-Fraumeni syndrome, a hereditary cancer predisposition disorder ...