Background: Targeted therapeutic strategies for advanced colorectal cancer (CRC) have been limited.STING is crucial to the antitumor immunotherapy, for it stimulates IFN signaling to mediate the crosstalk between innate and adaptive immune responses. Emerging evidence suggests that STING also contributes to the prognosis of CRC. However, prognostic models relating to STING have not yet been explored.Methods: A total of 431 CRC samples from the TCGA database were analyzed to explore the prognostic value of STING-related genes. We trained prognostic models using the multivariate Cox regression. A STING-related prognostic score (SPS) was calculated as the gene expression multiplied by the corresponding coefficients of the final model. A backward stepAIC strategy was adopted to select the optimal model. A nomogram was used to personalize medical decisions for CRC.Results: The expression level of STING was upregulated in the CMS1 subtype (P=0.036). Among STING-related genes, DHX9 (HR =0.72, P=0.01), IRF2 (HR =1.34, P=0.022), and POLR1D (HR =1.23, P=0.038) showed significant prognostic value. The SPS was proven to be an independent risk factor (training: HR =2.9, P=0.00013; validation: HR =3.02, P=0.01), and outperformed random classifiers in identifying high-risk CRC. The high SPS group was characterized by less genomic aberrations, upregulated IL6-JAK-STAT3 and IL2-STAT5 signaling pathways, increased expression of TIM-3, increased infiltration of regulatory T (Treg) cells and T helper 17 (Th17) cells, and decreased infiltration of M0 macrophages.Finally, the nomogram based on the SPS and clinical factors showed good performance in CRC.Conclusions: SPS is an independent risk factor that could identify high-risk CRC. While ICBs may benefit patients of the CMS1 subtype, for the CMS2, CMS3, and CMS4 subtypes in the high SPS group, STING agonists and immunotherapies targeting the Th17 axis may be beneficial. Finally, the SPS-based nomogram could help advance personalized medical decisions for CRC.
Metal oxide gas sensors have long faced the challenge of low response and poor selectivity, especially at room temperature (RT). Herein, a synergistic effect of electron scattering and space charge transfer is proposed to comprehensively improve gas sensing performance of n‐type metal oxides toward oxidizing NO2 (electron acceptor) at RT. To this end, the porous SnO2 nanoparticles (NPs) assembled from grains of about 4 nm with rich oxygen vacancies are developed through an acetylacetone‐assisted solvent evaporation approach combined with precise N2 and air calcinations. The results show that the as‐fabricated porous SnO2 NPs sensor exhibits an unprecedented NO2‐sensing performance, including outstanding response (Rg/Ra = 772.33 @ 5 ppm), fast recovery (<2 s), an extremely low detection limit (10 ppb), and exceptional selectivity (response ratio >30) at RT. Theoretical calculation and experimental tests confirm that the excellent NO2 sensing performance is mainly attributed to the unique synergistic effect of electron scattering and space charge transfer. This work proposes a useful strategy for developing high‐performance RT NO2 sensors using metal oxides, and provides an in‐depth understanding for the basic characteristics of the synergistic effect on gas sensing, paving the way for efficient and low power consumption gas detection at RT.
Chronic liver diseases usually developed through stepwise pathological transitions under the persistent risk factors. The molecular changes during liver transitions are pivotal to improve liver diagnostics and therapeutics yet still remain elusive. Cumulative large-scale liver transcriptomic studies have been revealing molecular landscape of various liver conditions at bulk and single-cell resolution, however, neither single experiment nor databases enabled thorough investigations of transcriptomic dynamics along the progression of liver diseases. Here we establish GepLiver, a longitudinal and multidimensional liver expression atlas integrating expression profiles of 2469 human bulk tissues, 492 mouse samples, 409,775 single cells from 347 human samples and 27 liver cell lines spanning 16 liver phenotypes with uniformed processing and annotating methods. Using GepLiver, we have demonstrated dynamic changes of gene expression, cell abundance and crosstalk harboring meaningful biological associations. GepLiver can be applied to explore the evolving expression patterns and transcriptomic features for genes and cell types respectively among liver phenotypes, assisting the investigation of liver transcriptomic dynamics and informing biomarkers and targets for liver diseases.
BackgroundGastric cancer (GC) is a highly molecular heterogeneous tumor with poor prognosis. Epithelial-mesenchymal transition (EMT) process and cancer stem cells (CSCs) are reported to share common signaling pathways and cause poor prognosis in GC. Considering about the close relationship between these two processes, we aimed to establish a gene signature based on both processes to achieve better prognostic prediction in GC.MethodsThe gene signature was constructed by univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses by using The Cancer Genome Atlas (TCGA) GC cohort. We performed enrichment analyses to explore the potential mechanisms of the gene signature. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves were implemented to assess its prognostic value in TCGA cohort. The prognostic value of gene signature on overall survival (OS), disease-free survival (DFS), and drug sensitivity was validated in different cohorts. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of the prognostic value of gene signature for OS and DFS prediction was performed in the Fudan cohort.ResultsA prognostic signature including SERPINE1, EDIL3, RGS4, and MATN3 (SERM signature) was constructed to predict OS, DFS, and drug sensitivity in GC. Enrichment analyses illustrated that the gene signature has tight connection with the CSC and EMT processes in GC. Patients were divided into two groups based on the risk score obtained from the formula. The Kaplan-Meier analyses indicated high-risk group yielded significantly poor prognosis compared with low-risk group. Pearson’s correlation analysis indicated that the risk score was positively correlated with carboplatin and 5-fluorouracil IC50 of GC cell lines. Multivariate Cox regression analyses showed that the gene signature was an independent prognostic factor for predicting GC patients’ OS, DFS, and susceptibility to adjuvant chemotherapy.ConclusionsOur SERM prognostic signature is of great value for OS, DFS, and drug sensitivity prediction in GC, which may give guidance to the development of targeted therapy for CSC- and EMT-related gene in the future.
In recent years, substantial advancements have been made in the development of enzyme-free glucose sensors utilizing pristine metal-organic frameworks (MOFs) and their combinations. This paper provides a comprehensive exploration of various MOF-based glucose sensors, encompassing monometallic MOF sensors as well as multi-metal MOF combinations. These approaches demonstrate improved glucose detection capabilities, facilitated by the augmented surface area and availability of active sites within the MOF structures. Furthermore, the paper delves into the application of MOF complexes and derivatives in enzyme-free glucose sensing. Derivatives incorporating carbon or metal components, such as carbon cloth synthesis, rGO-MOF composites, and core–shell structures incorporating noble metals, exhibit enhanced electrochemical performance. Additionally, the integration of MOFs with foams or biomolecules, such as porphyrins, enhances the electrocatalytic properties for glucose detection. Finally, this paper concludes with an outlook on the future development prospects of enzyme-free glucose MOF sensors.
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