Switchable surface and loading/release of target molecules in hierarchically porous PLA nonwovens based on shape memory effect.

In this work, hierarchically porous PLA (polylactic acid) shape memory nonwovens were prepared by electrospinning its blend solution with PEO (polyethylene oxide) and subsequent water etching. Based on shape memory effect resulting from tiny crystals and the amorphous matrix of PLA, the switch between compact and porous surfaces has been achieved via cyclical hot-pressing and recovery in a hot water bath. After hot-pressing, the disappearance of hierarchical pores contributes to compact surface, enabling embedding of the target molecule in PLA nonwoven (i.e., CLOSE state). Upon exposure to heat, PLA nonwoven recovers to its permanent shape and exhibits a porous surface, providing a penetrative diffusion pathway for small molecules (i.e., OPEN state). The hierarchically porous structure and shape memory effect endow PLA nonwoven with the capability of rapid release. Our results provide a good candidate for some potential applications, such as temperature-controlled quick-release of catalysts and drugs.

The LncRNA signature associated with cuproptosis as a novel biomarker of prognosis in immunotherapy and drug screening for clear cell renal cell carcinoma.

Cuproptosis is a new form of cell death, the second form of metal ion-induced cell death defined after ferroptosis. Recently, cuproptosis has been suggested to be associated with tumorigenesis. However, the relationship between cuproptosis and patient prognosis in clear cell renal cell carcinoma (ccRCC) in the context of immunotherapy remains unknown. The aim of this study was to investigate the correlation between cuproptosis-related long non-coding RNA (lncRNA) and ccRCC in terms of immunity as well as prognosis. Clinical information on lncRNAs associated with differences in cuproptosis genes in ccRCC and normal tissues was collected from The Cancer Genome Atlas (TCGA) dataset. Univariate Cox regression was used to screen lncRNAs. A total of 11 lncRNAs closely associated with cuproptosis were further screened and established using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression, and the samples were randomly divided into training and test groups. A risk prognostic model was constructed using the training group, and the model was validated using the test group. We investigated the predictive ability of the prognostic risk model in terms of clinical prognosis, tumor mutation, immune escape, immunotherapy, tumor microenvironment, immune infiltration levels, and tumor drug treatment of ccRCC. Using the median risk score, patients were divided into low and high-risk groups. Kaplan-Meier curves showed that the overall survival (OS) of patients in the high-risk group was significantly worse than low-risk group (p < 0.001). Receiver operating characteristic (ROC) curves further validated the reliability of our model. The model consistently and accurately predicted prognosis at 1, 3, and 5 years, with an AUC above 0.7. Tumor cell genes generally precede morphological abnormalities; therefore, the model we constructed can effectively compensate for the traditional method of evaluating the prognosis of patients with renal cancer, and our model was also clinically meaningful in predicting ccRCC staging. In addition, lower model risk scores determined by mutational load indicated a good chance of survival. The high-risk group had greater recruitment of immune cells, while the anti-immune checkpoint immunotherapy was less efficacious overall than that of the low-risk group. Tumor and immune-related pathways were enriched, and anti-tumor agents were selected to improve the survival of ccRCC. This prognostic risk model is based on the levels of cuproptosis-associated lncRNAs and provides a new perspective in the clinical assessment and precise treatment of ccRCC.

A Predictive Model Based on Pyroptosis-Related Gene Features Can Effectively Predict Clear Cell Renal Cell Carcinoma Prognosis and May Be an Underlying Target for Immunotherapy.

Methods: The clinical information and RNA-seq data of ccRCC patients were collected from the TCGA dataset to first explore differential pyroptosis-related genes (PRGs). Univariate Cox regression and consensus clustering were applied to identify ccRCC subtypes. The prognostic PRGs were subjected to LASSO regression analysis to establish a prognostic model and to investigate its value and function. Finally, the relationship of the model immunity checkpoints and immunity infiltration was assessed. Results: The receiver operating characteristic (ROC) showed that the 1-year, 3-year, and 5-year prediction rates of the prognostic model were 0.715, 0.693, and 0.732, respectively. The high-risk group had lower overall survival and higher stage than the low-risk group. Functional enrichment analysis showed that PRGs were significantly enriched mainly in the PPAR pathway, inflammatory pathway, and immune activity. ccRCC patient prognosis correlates with immune components in the microenvironment, and immune checkpoint molecules are significantly expressed in the high-risk group. Immunotherapy may be effective in the high-risk group. Conclusion: Pyroptosis-related gene has an important impact on the progression of ccRCC and can be used as an independent predictor of patient prognosis. In addition, immune checkpoint molecules are significantly upregulated in high-risk populations, which may be a potential target for immunotherapy.

SNAP25 is a potential prognostic biomarker for prostate cancer.

BACKGROUND: Prostate cancer (PCa) is one of the most lethal cancers in male individuals. The synaptosome associated protein 25 (SNAP25) gene is a key mediator of multiple biological functions in tumors. However, its significant impact on the prognosis in PCa remains to be elucidated. METHODS: We performed a comprehensive analysis of the Cancer Genome Atlas dataset (TCGA) to identify the differentially expressed genes between PCa and normal prostate tissue. We subjected the differentially expressed genes to gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes functional analysis, and constructed a protein-protein interaction network. We then screened for pivotal genes to identify the hub genes of prognostic significance by performing Cox regression analysis. We identified SNAP25 as one such gene and analyzed the relationship between its expression in PCa to poor prognosis using GEPIA interactive web server. RESULTS: TCGA database demonstrated that SNAP25 was significantly downregulated in PCa. The progressive decrease in SNAP25 expression with the increase in the clinical staging and grading of PCa demonstrates that reduced SNAP25 expression considerably exacerbates the clinical presentation. Our findings confirm that SNAP25 expression strongly correlates with overall survival, which was determined using the Gleason score. We also validated the role of SNAP25 expression in the prognosis of patients with PCa. We used Gene Set Enrichment and Gene Ontology analyses to evaluate the function of SNAP25 and further explored the association between SNAP25 expression and tumor-infiltrating immune cells using the Tumor Immune Assessment Resource database. We found for the first time that SNAP25 is involved in the activation, differentiation, and migration of immune cells in PCa. Its expression was positively correlated with immune cell infiltration, including B cells, CD8+ T cells, CD4+ T cells, neutrophils, dendritic cells, macrophages, and natural killer cells. SNAP25 expression also positively correlated with chemokines/chemokine receptors, suggesting that SNAP25 may regulate the migration of immune cells. In addition, our experimental results verified the low expression of SNAP25 in PCa cells. CONCLUSION: Our findings indicate a relationship between SNAP25 expression and PCa, demonstrating that SNAP25 is a potential prognostic biomarker due to its vital role in immune infiltration.