Integrative Functional Genomics Analysis of Kaposi Sarcoma Cohorts

TL;DR

This study compiles and analyzes RNA-seq data from Kaposi sarcoma patients across multiple regions to identify distinct disease clusters and provide accessible resources for further research.

Contribution

The paper introduces a harmonized, integrated dataset of Kaposi sarcoma samples with host and viral gene expression profiles and immune data.

Findings

Four distinct KS clusters were identified based on gene expression and immune profiles.

The dataset is publicly available via the UCSC Xena browser for exploration and analysis.

The study enables non-bioinformatic researchers to correlate host and viral transcriptomes in KS.

Abstract

Kaposi sarcoma (KS) is an AIDS-defining cancer and a significant global health challenge caused by KS-associated herpesvirus (KSHV). NGS-based approaches have profiled KS lesions in a minimal number of studies compared with other neoplastic diseases. Here we present a compiled and harmonized dataset of 131 KS and non-tumor cutaneous samples in the context of their predicted pathway activities, immune infiltrate, KSHV and HIV gene expression profiles, and their associated clinical data representing patient populations from Argentina, United States (USA), and Sub-Saharan Africa cohorts. RNA-seq data from 9 Argentinian KS lesions were generated and integrated with previously published datasets derived from the USA and sub-Saharan African cohorts from Tanzania, Zambia, and Uganda. An unsupervised analysis of 131 KS-related samples allowed us to identify four KS clusters based on their host and KSHV gene expression profiles, immune infiltrate, and the activity of specific signaling pathways. The compiled RNA-seq profile is shared with the research community through the UCSC Xena browser for further visualization, download, and analysis (https://kaposi.xenahubs.net/). These resources will allow biologists without bioinformatics knowledge to explore and correlate the host and viral transcriptome in a curated dataset of different KS RNA-seq-based cohorts, which can lead to novel biological insights and biomarker discovery.

Figures