Background: Bladder cancer (BLCA) is highly heterogeneous with distinct molecular subtypes. This research aimed to investigate the heterogeneity of different molecular subtypes from a tumor microenvironment perspective and develop a molecular-subtype-associated immune prognostic signature that can be recognized by MRI radiomics features. Methods: Individuals with BLCA in The Cancer Genome Atlas (TCGA) and IMvigor210 were classified into luminal and basal subtypes according to the UNC classification. The proportions of tumor-infiltrating immune cells (TIICs) were examined using The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm. Immune-linked genes that were expressed differentially between luminal and basal subtypes and associated with prognosis were selected to develop the immune prognostic signature (IPS) and utilized for the classification of the selected individuals into low- and high-risk groups. Functional enrichment analysis (GSEA) was performed on the IPS. The data from RNA-sequencing and MRI images of 111 BLCA samples in our center were utilized to construct a least absolute shrinkage and selection operator (LASSO) model for the prediction of patients’ IPSs. Results: Half of the TIICs showed differential distributions between the luminal and basal subtypes. IPS was highly associated with molecular subtypes, critical immune checkpoint gene expression, prognoses, and immunotherapy response. The prognostic value of the IPS was further verified through several validation data sets (GSE32894, GSE31684, GSE13507, and GSE48277) and meta-analysis. GSEA revealed that some oncogenic pathways were co-enriched in the group at high risk. A novel performance of a LASSO model developed as per ten radiomics features was achieved in terms of IPS prediction in both the validation (area under the curve (AUC): 0.810) and the training (AUC: 0.839) sets. Conclusions: Dysregulation of TIICs contributed to the heterogeneity between the luminal and basal subtypes. The IPS can facilitate molecular subtyping, prognostic evaluation, and personalized immunotherapy. A LASSO model developed as per the MRI radiomics features can predict the IPSs of affected individuals.

When a patient has two or more primary tumors, excluding the possibility of diffuse, recurrent, or metastatic, they can be defined as having multiple primary malignant neoplasms (MPMNs). Moreover, cases of three primary urinary tract tumors are very rare. Here, we reported a patient of MPMNs with four primary tumors, including three urinary tract cancers (renal cancer, prostate cancer, and bladder cancer) and lung cancer. The four tumors appeared over 13 years, and pathological results confirmed that they were all primary tumors after different surgeries. In addition, we established patient-derived organoids (PDOs) by collecting tumor specimens. Hematoxylin-eosin (H&E) staining of PDOs showed that the organoids were histopathological consistent with parental tumor. Immunohistochemistry showed that PDOs can also reflect the expression of pathological markers in patients. At the same time, PDOs may also serve as “avatars” of patients to predict sensitivity to different drugs. In summary, we reported a case of MPMNs with four primary tumors and established PDOs from its tumor specimens. A personalized treatment strategy was established based on the histopathological characteristics of the organoids.