# ZBED6–IGF2–PIK3C3 autophagy axis drives ccRCC progression: A multi-omics integration study

## TL;DR

This study identifies a new regulatory pathway involving ZBED6, IGF2, and PIK3C3 that drives kidney cancer progression and offers a six-gene model for predicting patient outcomes.

## Contribution

The discovery of the ZBED6–IGF2–PIK3C3 regulatory axis and its role in autophagy-driven ccRCC progression.

## Key findings

- ZBED6 activates PIK3C3 via repression of IGF2 to promote pro-tumorigenic autophagy.
- A six-gene prognostic signature stratifies ccRCC patients with distinct survival outcomes.
- ZBED6 promotes ccRCC cell proliferation, migration, and invasion in functional assays.

## Abstract

Autophagy supports clear cell renal cell carcinoma (ccRCC) progression, yet its upstream regulatory mechanisms remain to be fully defined. Integrating bulk, single-cell, and spatial transcriptomics, we identify a regulatory axis wherein the transcription factor ZBED6 activates the expression of the autophagy-initiating kinase PIK3C3 via the repression of IGF2, thereby driving pro-tumorigenic autophagy. Spatial analysis confirms the co-localization of ZBED6 and PIK3C3 in tumor tissues. Using genes associated with this axis, we develop a six-gene prognostic signature that stratifies patients with distinct survival outcomes and differential responses to immunotherapy and targeted therapy. Functional assays show that ZBED6 promotes ccRCC cell proliferation, migration, and invasion. This work elucidates a pathway governing autophagy in ccRCC and provides a framework for prognostic assessment and precision therapy.

•Identification of the ZBED6-PIK3C3 regulatory axis in ccRCC•Discovery of the IGF2-mediated ZBED6-PIK3C3 regulatory axis•Analysis based on multi-omics data•Construction of a 6-gene prognostic model based on PIK3C3

Identification of the ZBED6-PIK3C3 regulatory axis in ccRCC

Discovery of the IGF2-mediated ZBED6-PIK3C3 regulatory axis

Analysis based on multi-omics data

Construction of a 6-gene prognostic model based on PIK3C3

Renal cancer; Omics; Transcriptomics; Bioinformatics; Machine learning