Fig. 1

The computational framework for constructing the MLIIC signature. The top 15% expressed RNAs were adopted for candidate immune-related RNAs for each immune cell line. TSI was a widely used index to assess gene expression level relationships in tissue specification. TSI was applied to calculate the expression specificity of candidate immune-related RNAs for each cell type. The highly expressed RNAs in all immune cell types were identified as igRNA. igRNAs were believed to have high specificity in all immune cell types. igRNAs significantly upregulated in immune cell lines and downregulated in TNBC cell lines were identified as IIC-RNAs. IIC-RNAs were believed to be specific for immune cell lines and unspecific for TNBC cell lines, which were used as the input for ML-based classification and dimensionality reduction. Six ML algorithms for classification were utilized to determine potentially valuable IIC-RNAs. Univariate Cox regression analysis was further performed to screen out IIC-RNAs with prognostic features. Three ML algorithms for survival were taken to identify more valuable IIC-RNAs that were used as the input for signature construction. The MLIIC signature was eventually constructed according to RSF scoring with the best performance among 19 ML algorithms for scoring. The relationship between MLIIC signature, prognosis, biological function, tumor immune microenvironment, genome alternations, chemotherapeutic drug, and the immunotherapeutic response was thoroughly explored in the subsequent validation session. Finally, the MLIIC signature was verified using the LUAD tissue chip