Quantile estimation with apriori known expectation

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I have the following problem:

If we have a typical random sample $\{X_1, X_2, ...\}$ from some unknown distribution and we want to estimate a quantile we just need to sort our observations and take a specific observation from this sorted sequence.

Let us know assume that we know additionally that $\mathbb{E}[X_i] = 0$ (or any other number).

My question is:

If we have our observations $\{x_1, x_2, ..., x_n\}$, can we substract from them its sample mean (that should be close to $0$ as $\mathbb{E}[X_i] = 0$) and then sort them and take a specific quantile.

Is it mathematically correct?

Can I say sth about my new quantile estimator? Does it have better or worse properties than the standard one?