In many applications, covariates are naturally organized into groups, yet conventional group-selection methods often retain all variables within selected groups, failing to achieve within-group sparsity. To address this limitation, we propose an adaptive two-stage sparse-group selection procedure for high-dimensional quantile regression. The proposed method integrates convolution-smoothed quantile loss with $\ell_{2,0}$ and $\ell_0$ penalties to identify important groups and relevant variables within them, achieving sparsity at both levels. We establish theoretical guarantees for sure screening and exact support recovery at both stages, as well as consistent model-size selection using information criteria, with explicit bounds on failure probabilities. The proposed procedure is computationally efficient by restricting optimization to selected groups and active variables. Simulation studies and a breast cancer gene-expression analysis demonstrate its competitive performance in variable selection, estimation accuracy, and computational efficiency, while identifying biologically relevant genes and delivering accurate quantile predictions.

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