Chemotherapy is still the most fundamental treatment for advanced
cancers so far. Previous studies have indicated that immune cell infiltration (ICI) index could serve as a
biomarker to predict
chemotherapy benefit in
breast cancer and
colorectal cancer. However, due to different responses of
tumor infiltrating immune cells (TIICs) to
chemotherapy, the prediction efficiency of ICI index is not fully confirmed by now. In our study, we first extended this conclusion in 7
cancers that high ICI index could certainly indicate
chemotherapy benefit (P<0.05). But we also found the fraction of different TIICs and the interaction of TIICs were varies greatly from
cancer to
cancer. Therefore, we executed correlation and causal network analysis to identify
chemotherapy associated immune feature genes, and fortunately identified six co-owned immune feature genes (CD48, GPR65, C3AR1, CD2, CD3E and ARHGAP9) in 10
cancers (BLCA, BRCA,
COAD, LUAD, LUSC, OV, PAAD, SKCM, STAD and UCEC). Base on this, we developed a
chemotherapy benefit prediction model within six co-owned immune feature genes through random forest classifying (AUC =0.83) in
cancers mentioned above, and validated its efficiency in external datasets. In short, our work offers a novel model with a shrinking panel which has the potential to guide optimal
chemotherapy in
cancer.