The phase ordering problem, which aims to find suitable pass sequences for a given program on a target architecture, is critical in compiler optimization. One key challenge of this problem lies in the complex interplay among different passes within the vast optimization space of possible pass sequences. To better explore the interplay among passes, this paper proposes a new concept called {\em booster pass chain} (BPC), and presents a novel approach that identifies and leverages the BPCs to optimize the code size. Specifically, a BPC is a sequence of passes with positive interplay that, when presented as a whole, may exhibit significant optimization effects for certain programs. We then propose an iterative algorithm to extract BPCs, based on which we build a candidate set of pass sequences. For a given program, we also train a neural network to predict the suitable pass sequences from the candidate set. Experimental evaluations on 16 datasets containing 6,186 programs demonstrate the effectiveness of the proposed approach. That is, the candidate set achieves an average of 9.9% improvement compared to the LLVM -Oz flag in code size reduction, and selecting the top-3 pass sequences using the neural network predictor achieves 6.9% improvement. Our code and results are available at \url{https://anonymous.4open.science/r/Anoymous_EBPC4CPO}.