# NADCdb: A Joint Transcriptomic Database for Non-AIDS-Defining Cancer Research in HIV-Positive Individuals

## TL;DR

NADCdb is a new database combining transcriptomic data to study non-AIDS-defining cancers in HIV-positive individuals, offering insights into risk factors and immune biomarkers.

## Contribution

NADCdb introduces a joint transcriptomic database with novel analytical modules for risk assessment and pathogenesis of non-AIDS-defining cancers in HIV-positive individuals.

## Key findings

- NADCdb identified 1905 key immune biomarkers for 16 non-AIDS-defining cancers.
- The database achieved over 75% accuracy in identifying pathogenic drivers of NADCs.
- The HIV-associated clear cell renal cell carcinoma model showed over 90% prediction accuracy in external validation.

## Abstract

Non-AIDS-defining cancers (NADCs) have emerged as an increasingly prominent cause of non-AIDS-related morbidity and mortality among people living with HIV (PLWH). However, the scarcity of NADC clinical samples, compounded by privacy and security constraints, continues to present formidable obstacles to advancing pathological and clinical investigations. In this study, we adopted a joint analysis strategy and deeply integrated and analyzed transcriptomic data from 12,486 PLWH and cancer patients to systematically identify potential key regulators for 23 NADCs. This effort culminated in NADCdb—a database specifically engineered for NADC pathological exploration, structured around three mechanistic frameworks rooted in the interplay of immunosuppression, chronic inflammation, carcinogenic viral infections, and HIV-derived oncogenic pathways. The “rNADC” module performed risk assessment by prioritizing genes with aberrant expression trajectories, deploying bidirectional stepwise regression coupled with logistic modeling to stratify the risks for 21 NADCs. The “dNADC” module, synergized patients’ dysregulated genes with their regulatory networks, using Random Forest (RF) and Conditional Inference Trees (CITs) to identify pathogenic drivers of NADCs, with an accuracy exceeding 75% (in the external validation cohort, the prediction accuracy of the HIV-associated clear cell renal cell carcinoma model exceeded 90%). Meanwhile, “iPredict” identified 1905 key immune biomarkers for 16 NADCs based on the distinct immune statuses of patients. Importantly, we conducted multi-dimensional profiling of these key determinants, including in-depth functional annotations, phenotype correlations, protein–protein interaction (PPI) networks, TF-miRNA-target regulatory networks, and drug prediction, to deeply dissect their mechanistic roles in NADC pathogenesis. In summary, NADCdb serves as a novel, centralized resource that integrates data and provides analytical frameworks, offering fresh perspectives and a valuable platform for the scientific exploration of NADCs.