  Prognostic signatures play an important role in clinical research, offering
insights into the potential health outcomes of patients and guiding therapeutic
decisions. Although single-gene prognostic biomarkers are valuable, multi-gene
prognostic signatures offer deeper insights into disease progression. In this
paper, we propose MulMarker, a comprehensive framework for identifying
multi-gene prognostic signatures across various diseases. MulMarker comprises
three core modules: a chatbot for addressing user queries, a module for
identifying multi-gene prognostic signatures, and a module for generating
tailored reports. Employing MulMarker, we identified a cell cycle-related
prognostic signature that consists of CCNA1/2, CCNB1/2/3, CCNC, CCND1/2/3,
CCNE1/2, CCNF, CCNG1/2, and CCNH. Based on the prognostic signature, we
successfully stratified patients into high-risk and low-risk groups. Notably,
our analysis revealed that patients in the low-risk group demonstrated a
significantly higher survival rate compared to those in the high-risk group.
Overall, MulMarker represents an efficient approach for the identification of
multi-gene prognostic signatures. We release the code of MulMarker at
https://github.com/Tina9/MulMarker.
