Welcome to NADCdb

NADCdb is the first integrated and innovative database designed to elucidate the key regulatory factors could involved in non-AIDS defining cancer (NADC) development and their functional mechanisms. It employs a joint analysis strategy to integrate and mine 957 transcriptomic datasets from people living with HIV (PLWH) and 9,543 from cancer patients, and systematically identifies a set of key factors with potential regulatory effects across 16 NADC types. Importantly, it offered exhaustive functional annotations, phenotypic correlation analyses, PPI network mappings and cMAP analyses for these key factors to delve deeper into their potential functions in NADC development. NADCdb comprises three core models: "dNADC", "rNADC", and "iPredict", along with an interactive "rNADC" tool, and two user-friendly query webpages, labeled as "Cancer" and "Gene" for convenient querying and guidance, which aims to provide new insights and impetus for the mechanisms, diagnosis and treatment of NADC.





Workflow of NADCdb


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NADCdb Features

NADCdb was released in June, 2024.

  • HIV samples: 151 ART samples, 205 non-ART samples, 601 healthy control samples.
  • Cancer samples: 8,643 tumor tissue samples and 900 paracancerous tissue of 16 cancers.
  • Features: immune indicators, inflammation indicators and the common pathogenic pathway.
  • Machine learning: Random forest (RF), Conditional inference tree (CIT), LASSO
  • Functions: GO, KEGG, Reactome, WikiPathways, Hallmark Gene Sets, BioCarta Gene Sets, Kinase Classe, Protein Functions, Subcellular Localization, Secretory Protein, PathogenicLoF, dbGap, GWAS, Human Phenotype Ontology, Drug, Disease and Gene Associations (DisGeNET), and Disease Ontology (DO), Tissue Specificity and Expression Patterns in Normal Tissues or Cancers.
  • Associated analysis: PPI, WGCNA, cMAP