Immunotherapy prediction and chemosensitivity analysis

We collected three GEO immunotherapy cohorts ({"type":"entrez-geo","attrs":{"text":"GSE78220","term_id":"78220"}}GSE78220³⁰ {"type":"entrez-geo","attrs":{"text":"GSE35640","term_id":"35640"}}GSE35640³¹ and {"type":"entrez-geo","attrs":{"text":"GSE100797","term_id":"100797"}}GSE100797³²), along with the IMvigor210 cohort, to investigate the relationship between TEX characteristics and immunotherapy. Data processing was carried out using the "IMvigor210CoreBiologies" R package from the IMvigor210 cohort ³³. We used the "IMvigor210CoreBiologies" R package from the IMvigor210 cohort to process data. Furthermore, to establish the immunogenicity based on immunomodulators, immunosuppressive cells, MHC molecules, and effector cells, we utilised the Immunophenoscore (IPS) algorithm. This algorithm calculates the IPS score using the impartial gene expression of a representative cell type through a machine-learning methodology. A higher IPS score indicates an improved response to immunotherapy. IPS scores for patient samples of TCGA-LUAD were acquired from The Cancer Immunome Atlas (TCIA) database (https://tcia.at/home).