Some bispecific radiotracers have been developed to overcome the limitations of monospecific tracers and improve detection sensitivity for heterogeneous tumor lesions. Here we aim to synthesize two bispecific tracers targeting prostate-specific membrane antigen (PSMA) and fibroblast activation protein (FAP), which are key markers expressed in prostate cancer. A pyridine-based FAP-targeted ligand was synthesized through multi-step organic synthesis, and then connected to the 2-Nal-containing PSMA-targeted motif. Ki(PSMA) values of Ga-complexed bispecific ligands, Ga-AV01084 and Ga-AV01088, were 11.6 ± 3.25 and 28.7 ± 6.05 nM, respectively, and the IC50(FAP) values of Ga-AV01084 and Ga-AV01088 were 10.9 ± 0.67 and 16.7 ± 1.53 nM, respectively. The uptake values for PSMA-expressing LNCaP tumor xenografts were 9.05 ± 1.54 and 8.85 ± 1.25 %ID/g for [68Ga]Ga-AV010184 and [68Ga]Ga-AV01088, respectively, which were lower than the monospecific PSMA-targeted tracer [68Ga]Ga-PSMA-617 (16.7 ± 2.30 %ID/g). The uptake values for FAP-expressing HEK293T:hFAP tumor xenografts were 1.90 ± 0.41 and 1.20 ± 0.25 %ID/g for [68Ga]Ga-AV01084 and [68Ga]Ga-AV01088, respectively, which were also lower than the monospecific FAP-targeted tracer, [68Ga]Ga-AV02070 (7.93 ± 1.88 %ID/g). Further investigations are needed to optimize the selection of linkers and targeted pharmacophores to improve the tumor uptake of PSMA/FAP bispecific tracers for prostate cancer imaging.

Low-grade glioma (LGG) is a highly aggressive disease in the skull. On the other hand, anoikis, a specific form of cell death induced by the loss of cell contact with the extracellular matrix, plays a key role in cancer metastasis. In this study，anoikis-related genes (ANRGs) were used to identify LGG subtypes and to construct a prognostic model for LGG patients. In addition, we explored the immune microenvironment and enrichment pathways between different subtypes. We constructed an anoikis-related gene signature using the TCGA cohort and investigated the differences in clinical features, mutational landscape, immune cell infiltration, etc. between different risk groups. Kaplan-Meier analysis showed that the characteristics of ANRGs in the high-risk group were associated with poor prognosis in LGG patients. The risk score was identified as an independent prognostic factor. The high-risk group had higher immune cell infiltration, tumor mutation load, immune checkpoint gene expression, and ICB treatment response. Functional analysis showed that these high- and low-risk groups had different immune statuses and drug sensitivity. Risk scores were used together with LGG clinicopathological features to construct a nomogram, and DCA analysis showed that the model could enable patients to benefit from clinical treatment strategies.