Shap or LGBM gain for feature selection?

Which one do you use during recursive feature elimination or forward/backward selection? I’ve always used gain and only used shap for analytics on model predictions, but came across some shap values recommendations.

Bonus question: have you used “null importance” / permutation method? Fitting models with shuffled targets to remove features that look predictive by chance