The investigation of correlations between radiomic and genomic profiling in breast cancer (BC) molecular subtypes is crucial for understanding disease mechanisms and providing personalized treatment. We present a well-designed radiogenomic framework—image-gene-gene set (IMAGGS), which detects multi-way associations in BC subtypes by integrating radiomic and genomic features.Our dataset consists of 721 patients, each of whom has 12 ultrasound (US) images captured from different angles and gene mutation data. To better characterize tumor traits, 12 multi-angle US images are fused using two distinct strategies. Then, we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm, deviating from the prevalent one-to-one relationship pattern observed in previous studies. Key radiomic and genomic features are screened using these associations. In addition, gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes. We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases. Taken together, IMAGGS provides a comprehensive characterization for diseases by associating imaging, genes, and gene sets, paving the way for biological interpretation of radiomics and development of targeted therapy.

T-box transcription factor T (TBXT; T) is required for mesodermal formation and axial skeletal development. Although it has been extensively studied in various model organisms, human congenital vertebral malformations (CVMs) involving T are not well established. Here, we report a family with 15 CVM patients distributed across four generations. All affected individuals carry a heterozygous mutation, T c.596A>G (p.Q199R), which is not found in unaffected family members, indicating co-segregation of the genotype and phenotype. In vitro assays show that T p.Q199R increases the nucleocytoplasmic ratio and enhances its DNA-binding affinity, but reduces its transcriptional activity compared to the wild-type. To determine the pathogenicity of this mutation in vivo, we generated a Q199R knock-in mouse model that recapitulates the human CVM phenotype. The heterozygous Q199R mice show subtle kinked or shortened tails, while the homozygous mice exhibit tail filaments and severe vertebral deformities. Overall, we show that the Q199R mutation in T causes CVM in humans and mice, providing new evidence supporting the function of T in the genetic etiology of human CVM.

Messenger RNA (mRNA) translation consists of initiation, elongation, termination, and ribosome recycling, carried out by the translation machinery, primarily including tRNAs, ribosomes, and translation factors (TrFs). Translational regulators transduce signals of growth and development, as well as biotic and abiotic stresses, to the translation machinery, where global or selective translational control occurs to modulate mRNA translation efficiency (TrE). As the basis of translational control, the translation machinery directly determines the quality and quantity of newly synthesized peptides and, ultimately, the cellular adaption. Thus, regulating the availability of diverse machinery components is reviewed as the central strategy of translational control. We provide classical signaling pathways (e.g., integrated stress responses) and cellular behaviors (e.g., liquid-liquid phase separation) to exemplify this strategy within different physiological contexts, particularly during host-microbe interactions. With new technologies developed, further understanding this strategy will speed up translational medicine and translational agriculture.