The ALBI grade predicted PHLF and overall survival in patients with HCC undergoing liver resection with curative intent more accurately than the CP grade.

Most of the biomedical materials printed using 3D bioprinting are static and are unable to alter/transform with dynamic changes in the internal environment of the body. The emergence of four-dimensional (4D) printing addresses this problem. By preprogramming dynamic polymer materials and their nanocomposites, 4D printing is able to produce the desired shapes or transform functions under specific conditions or stimuli to better adapt to the surrounding environment. In this review, the current and potential applications of 4D-printed materials are introduced in different aspects of the biomedical field, e.g., tissue engineering, drug delivery, and sensors. In addition, the existing limitations and possible solutions are discussed. Finally, the current limitations of 4D-printed materials along with their future perspective are presented to provide a basis for future research.

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Background. The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME-related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer. Methods. We calculated immune and stromal scores for 385 colon cancer samples from The Cancer Genome Atlas (TCGA) database using the ESTIMATE algorithm. We identified nine TME-related prognostic genes using Cox regression analysis. We evaluated associations between protein expression, extent of immune cell infiltrate, and patient survival. We calculated risk scores and built a clinical predictive model for the TME-related gene signature. Receiver operating characteristic (ROC) curves were generated to assess the predictive power of the signature. We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients using the pRRophetic algorithm. The expression of immune checkpoint genes was evaluated. Results. High immune and stromal scores are significantly associated with poor overall survival ( p < 0.05 ). We identified 773 differential TME-related prognostic genes associated with survival; these genes were enriched in immune-related pathways. Nine key prognostic genes were identified and were used to construct a TME-related prognostic signature: CADM3, LEP, CD1B, PDE1B, CCL22, ABI3BP, IGLON5, SELE, and TGFB1. This signature identified a high-risk group with worse survival outcomes, based on Kaplan-Meier analysis. A nomogram composed of clinicopathological factors and risk score exhibited good accuracy. Drug sensitivity analysis identified no difference in sensitivity between the high-risk and low-risk groups. High-risk patients had higher expression of PD-1, PDL-1, and CTLA-4 and lower expression of LAG-3 and VSIR. Infiltration of dendritic cells was higher in the high-risk group. Conclusions. We identified a novel prognostic TME-related gene expression signature in colon cancer. Stratification of patients based on this gene signature could be used to improve outcomes and guide better therapy for colon cancer patients.

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