Universal inter-molecular radical transfer reactions on metal surfaces.

On-surface synthesis provides tools to prepare low-dimensional supramolecular structures. Traditionally, reactive radicals are a class of single-electron species, serving as exceptional electron-withdrawing groups. On metal surfaces, however, such species are affected by conduction band screening effects that may even quench their unpaired electron characteristics. As a result, radicals are expected to be less active, and reactions catalyzed by surface-stabilized radicals are rarely reported. Herein, we describe a class of inter-molecular radical transfer reactions on metal surfaces. With the assistance of aryl halide precursors, the coupling of terminal alkynes is steered from non-dehydrogenated to dehydrogenated products, resulting in alkynyl-Ag-alkynyl bonds. Dehalogenated molecules are fully passivated by detached hydrogen atoms. The reaction mechanism is unraveled by various surface-sensitive technologies and density functional theory calculations. Moreover, we reveal the universality of this mechanism on metal surfaces. Our studies enrich the on-surface synthesis toolbox and develop a pathway for producing low-dimensional organic materials.

In vivo immunological activity of chitosan-derived nanoparticles.

Chitosan has been studied as an immunomodulator, but few studies have used chitosan derivatives as adjuvants alone. After a preliminary study, we found that nanoparticles prepared from chitosan derivatives had better cellular immune activity when used as an adjuvant. Therefore, animal experiments were conducted to further investigate the performance and mechanism of these nanoparticles as immune adjuvants. We injected mice with the chitosan nanoparticle vaccine and measured the expression levels of immunoglobulins, immune factors, and immune genes in tissues and tissue sections. The results showed that C236-HACC-OVA (C2,3,6-chitosan sulfate-chitosan quaternary ammonium salt-ovalbumin) and NO-HACC-OVA (NO-carboxymethyl chitosan-chitosan quaternary ammonium salt-ovalbumin) nanoparticles can significantly improve the secretion of the immune factors IL-6, TNF, and IL-1ß. The level of IgG1 was highly significant after administering both nanoparticles, but IgG2 was not significant in mice. Three immune factors (IL-4, IL-6, and IL-17) were secreted at high levels in mouse serum at a nanoparticle dose of 0.3 mg/mouse. These nanoparticles also have high safety in the liver, kidney, and spleen of mice. This study proves the possibility of using chitosan derivative nanoparticles as vaccine adjuvants. These data further indicate that chitosan derivative nanoparticles have potential for use as vaccine adjuvants and demonstrate that polysaccharides have a unique position in green vaccine research.

Prognostic model development and molecular subtypes identification in bladder urothelial cancer by oxidative stress signatures.

BACKGROUND: Mounting studies indicate that oxidative stress (OS) significantly contributes to tumor progression. Our study focused on bladder urothelial cancer (BLCA), an escalating malignancy worldwide that is growing rapidly. Our objective was to verify the predictive precision of genes associated with overall survival (OS) by constructing a model that forecasts outcomes for bladder cancer and evaluates the prognostic importance of these genetic markers. METHODS: Transcriptomic data were obtained from TCGA-BLCA and GSE31684, which are components of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. To delineate distinct molecular subtypes, we employed the non-negative matrix factorization (NMF)method. The significance of OS-associated genes in predicting outcomes was assessed using lasso regression, multivariate Cox analysis, and univariate Cox regression analysis. For external validation, we employed the GSE31684 dataset. CIBERSORT was utilized to examine the tumor immune microenvironment (TIME). A nomogram was created and verified using calibration and receiver operating characteristic (ROC) curves, which are based on risk signatures. We examined variations in clinical characteristics and tumor mutational burden (TMB) among groups classified as high-risk and low-risk. To evaluate the potential of immunotherapy, the immune phenomenon score (IPS) was computed based on the risk score. In the end, the pRRophetic algorithm was employed to forecast the IC50 values of chemotherapy medications. RESULTS: In our research, we examined the expression of 275 genes associated with OS in 19 healthy and 414 cancerous tissues of the bladder obtained from the TCGA database. As a result, a new risk signature was created that includes 4 genes associated with OS (RBPMS, CRYAB, P4HB, and PDGFRA). We found two separate groups, C1 and C2, that showed notable variations in immune cells and stromal score. According to the Kaplan-Meier analysis, patients classified as high-risk experienced a considerably reduced overall survival in comparison to those categorized as low-risk (P<0.001). The predictive capability of the model was indicated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve surpassing 0.6. Our model showed consistent distribution of samples from both the GEO database and TCGA data. Both the univariate and multivariate Cox regression analyses validated the importance of the risk score in relation to overall survival (P < 0.001). According to our research, patients with a lower risk profile may experience greater advantages from using a CTLA4 inhibitor, whereas patients with a higher risk profile demonstrated a higher level of responsiveness to Paclitaxel and Cisplatin. In addition, methotrexate exhibited a more positive outcome in patients with low risk compared to those with high risk. CONCLUSIONS: Our research introduces a novel model associated with OS gene signature in bladder cancer, which uncovers unique survival results. This model can assist in tailoring personalized treatment approaches and enhancing patient therapeutic effect in the management of bladder cancer.

Precisely constructing hybrid nanogap arrays via wet-transfer of dielectric metasurfaces onto a plasmonic mirror.

We propose a new method for fabricating hybrid metasurfaces by combining Mie and plasmonic resonances. Our approach involves obtaining an ultrasmooth gold film and separately structuring monocrystalline silicon (c-Si) nanoantenna arrays, which are then wet-transferred and finally immobilized onto the gold film. The experimental and simulation analysis reveals the importance of the native oxide layer of Si and demonstrates fascinating dispersion curves with nanogap resonances and bound states in the continuum. The localized field enhancements in the nanogap cavities result from the coupling between multipolar Mie resonances and their mirror images in the gold film. This effective method improves our understanding of hybrid modes and offers opportunities for developing active metasurfaces, such as depositing c-Si nanoantenna arrays onto stretchable polydimethylsiloxane substrates or electro-optic and piezoelectric sensitive lithium niobate films for potential applications in MEMS, LiDAR, and beyond.

Constructing Two-Dimensional Distorted Kagome Lattices on Ag(111).

Two-dimensional (2D) tessellation of organic species acquired increased interest recently because of their potential applications in physics, biology, and chemistry. Herein, we successfully synthesized the chiral distorted Kagome lattice p3 (333) with bicomponent precursors on Ag(111). Scanning tunneling microscopy and density functional calculation studies reveal that the networks are formed by multiple intermolecular hydrogen bonds. The network structures can be rationally tuned by adjusting the stoichiometric ratio of the reaction precursors. Our study provides new strategies to synthesize complex low-dimensional nanostructures on metal surfaces.

Dual Control of Enhanced Quasi-Bound States in the Continuum Emission from Resonant c-Si Metasurfaces.

Optical bound states in the continuum (BICs) offer strong interactions with quantum emitters and have been extensively studied for manipulating spontaneous emission, lasing, and polariton Bose-Einstein condensation. However, the out-coupling efficiency of quasi-BIC emission, crucial for practical light-emitting devices, has received less attention. Here, we report an adaptable approach for enhancing quasi-BIC emission from a resonant monocrystalline silicon (c-Si) metasurface through lattice and multipolar engineering. We identify dual-BICs originating from electric quadrupoles (EQ) and out-of-plane magnetic dipoles, with EQ quasi-BICs exhibiting concentrated near-fields near the c-Si nanodisks. The enhanced fractional radiative local density of states of EQ quasi-BICs overlaps spatially with the emitters, promoting efficient out-coupling. Furthermore, coupling the EQ quasi-BICs with Rayleigh anomalies enhances directional emission intensity, and we observe inherent opposite topological charges in the multipolarly controlled dual-BICs. These findings provide valuable insights for developing efficient nanophotonic devices based on quasi-BICs.

Internode Length Is Correlated with GA3 Content and Is Crucial to the Harvesting Performance of Tea-Picking Machines.

High labor costs and labor shortages are limiting factors affecting the tea industry in Anhui Province. Thus, exploiting the full mechanization of shoot harvesting is an urgent task in the tea industry. Tea quality is greatly influenced by the integrity rate of tea leaves; therefore, it is important to choose tea cultivars suitable for machine picking. In this study, seven tea cultivars were used to investigate the relationship between internode length and blade angle with respect to newly formed tea shoots and machine harvesting in field experiments (Xuanchen City, Kuiling village) conducted throughout the year (in the autumn of 2021, in the early spring of 2022, and in the summer of 2022). Our results showed that the internode length (L2 or L4) had a significant and positive correlation with the integrity rate of tea buds and leaves in seven tea cultivars over three seasons. However, no significant correlation was found between the blade angle and the integrity rate of tea buds and leaves. In addition, a strong and positive correlation was found between the levels of GA1 (R2 > 0.7), GA3 (R2 > 0.85), and IAA (R2 > 0.6) regarding the internodes and internode lengths of the seven tea cultivars. Moreover, the relative expression levels of CsGA20ox, CsGA3ox1, and CsGA3ox2 in Echa1 (the longer internode) were significantly higher compared with those in Zhenong113 (the shorter internode). Overall, our results show that the internode length is an important factor for the machine harvesting of tea leaves and that the level of GA3 is strongly associated with internode length.

Clinical value of anoikis-related genes and molecular subtypes identification in bladder urothelial carcinoma and in vitro validation.

Background: Anoikis is a programmed cell death process that was proven to be associated with cancer. Uroepithelial carcinoma of the bladder (BLCA) is a malignant disease of the urinary tract and has a strong metastatic potential. To determine whether anoikis-associated genes can predict the prognosis of BLCA accurately, we evaluated the prognostic value of anoikis-associated genes in BLCA and constructed the best model to predict prognosis. Method: The BLCA transcriptome data were downloaded from TCGA and GEO databases, and genes with differential expression were selected and then clustered using non-negative matrix factorization (NMF). The genes with the most correlation with anoikis were screened and identified using univariate Cox regression, lasso regression, and multivariate Cox regression. The GEO dataset was used for external validation. Nomograms were created based on risk characteristics in combination with clinical variants and the performance of the model was validated with receiver operating characteristic (ROC) curves. The immunotherapeutic significance of this risk score was assessed using the immune phenomenon score (IPS). IC50 values of predictive chemotherapeutic agents were calculated. Finally, we used RT-qPCR to determine the mRNA expression of four genes, CALR, FASN, CASP6, and RAD9A. Result: We screened 406 tumor samples and 19 normal tissue samples from the TCGA database. Based on anoikis-associated genes, we classified patients into two subtypes (C1 and C2) using NMF method. Subsequently, nine core genes were screened by multiple methods after analysis, which were used to construct risk profiles. The design of nomograms based on risk profiles and clinical variables, ROC, and calibration curves confirmed that the model could well have the ability to predict the survival of BLCA patients at 1, 3, and 5 years. By predicting the IC50 values of chemotherapeutic drugs, it was learned that the high-risk group (HRG) was more susceptible to paclitaxel, gemcitabine, and cisplatin, and the low-risk group (LRG) was more susceptible to veriparib and afatinib. Conclusion: In summary, the risk score of anoikis-associated genes can be applied as a predictor to predict the prognosis of BLCA in clinical practice.

Clinical characteristics and prostate-cancer-specific mortality of competitive risk nomogram in the second primary prostate cancer.

Background: With the development of early diagnosis and treatment, the second primary malignancy (SPM) attracts increasing attention. The second primary prostate cancer (spPCa) is an important class of SPM, but remains poorly understood. Methods: We retrospectively analyzed 3,322 patients with spPCa diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Chi-square test was applied to compare demographic and clinical variables and analyze causes of death. Multivariate competitive risk regression model was used to identify risk factors associated with prostate-cancer-specific mortality (PCSM), and these factors were enrolled to build a nomogram of competitive risk. The C-index, calibration curve, and decision curve analysis (DCA) were employed to evaluate the discrimination ability of our nomogram. Results: The median follow-up (interquartile range, IQR) time was 47 (24-75) months, and the median (IQR) diagnosis interval between the first primary cancer (FPC) and spPCa was 32 (16-57) months. We found that the three most common sites of SPM were the urinary system, digestive system, and skin. Through multivariate competitive risk analysis, we enrolled race (p < 0.05), tumor-node-metastasis (TNM) stage (p < 0.001), Gleason score (p < 0.05), surgery (p = 0.002), and radiotherapy (p = 0.032) to construct the model to predict the outcomes of spPCa. The C-index was 0.856 (95% CI, 0.813-0.899) and 0.905 (95% CI, 0.941-0.868) in the training and validation set, respectively. Moreover, both the calibration curve and DCA illustrated that our nomogram performed well in predicting PCSM. Conclusion: In conclusion, we identified four risk factors associated with the prognosis of spPCa and construct a competing risk nomogram, which performed well in predicting the 3-, 5-, and 10-year PCSM.

Macrophages Phenotype Regulated by IL-6 Are Associated with the Prognosis of Platinum-Resistant Serous Ovarian Cancer: Integrated Analysis of Clinical Trial and Omics.

Background: The treatment of platinum-resistant recurrent ovarian cancer (PROC) is a clinical challenge and a hot topic. Tumor microenvironment (TME) as a key factor promoting ovarian cancer progression. Macrophage is a component of TME, and it has been reported that macrophage phenotype is related to the development of PROC. However, the mechanism underlying macrophage polarization and whether macrophage phenotype can be used as a prognostic indicator of PROC remains unclear. Methods: We used ESTIMATE to calculate the number of immune and stromal components in high-grade serous ovarian cancer (HGSOC) cases from The Cancer Genome Atlas database. The differential expression genes (DEGs) were analyzed via protein-protein interaction network, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analysis to reveal major pathways of DEGs. CD80 was selected for survival analysis. IL-6 was selected for gene set enrichment analysis (GSEA). A subsequent cohort study was performed to confirm the correlation of IL-6 expression with macrophage phenotype in peripheral blood and to explore the clinical utility of macrophage phenotype for the prognosis of PROC patients. Results: A total of 993 intersecting genes were identified as candidates for further survival analysis. Further analysis revealed that CD80 expression was positively correlated with the survival of HGSOC patients. The results of GO and KEGG analysis suggested that macrophage polarization could be regulated via chemokine pathway and cytokine-cytokine receptor interaction. GSEA showed that the genes were mainly enriched in IL-6-STAT-3. Correlation analysis for the proportion of tumor infiltration macrophages revealed that M2 was correlated with IL-6. The results of a cohort study demonstrated that the regulation of macrophage phenotype by IL-6 is bidirectional. The high M1% was a protective factor for progression-free survival. Conclusion: Thus, the macrophage phenotype is a prognostic indicator in PROC patients, possibly via a hyperactive IL-6-related pathway, providing an additional clue for the therapeutic intervention of PROC.

PTGIS May Be a Predictive Marker for Ovarian Cancer by Regulating Fatty Acid Metabolism.

Background: Ovarian cancer tends to metastasize to the omentum, which is an organ mainly composed of adipose tissue. Many studies have found that fatty acid metabolism is related to the occurrence and metastasis of cancers. Therefore, it is possible that fatty acid metabolism-related genes (FAMRG) affect the prognosis of ovarian cancer patients. Methods: First, profiles of ovarian cancer and normal ovarian tissue transcriptomes were acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. A LASSO regression predictive model was developed via the "glmnet" R package. The nomogram was created via the "regplot." Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses were conducted to determine the FAMRGs' roles. The percentage of immunocyte infiltration was calculated via CIBERSORT. Using "pRRophetic," the sensitivity of eight regularly used medications and immunotherapy was anticipated. Results: 125 genes were determined as different expression genes (DEGs). Based on RXRA, ECI2, PTGIS, and ACACB, a prognostic model is created and the risk score is calculated. Analyses of univariate and multivariate regressions revealed that the risk score was a distinct prognostic factor (univariate: HR: 2.855, 95% CI: 1.756-4.739, P < 0.001; multivariate: HR: 2.943, 95% CI: 1.800-4.812, P < 0.001). The nomogram demonstrated that it properly predicted the 1-year survival rate. The expression of memory B molecular units, follicular helper T molecular units, regulatory T molecular units, and M1 macrophages differed remarkably between the groups at high and low risk (P < 0.05). Adipocytokine signaling pathways, cancer pathways, and degradation of valine, leucine, and isoleucine vary between high- and low-risk populations. The findings of the GO enrichment revealed that the extracellular matrix and cellular structure were the two most enriched pathways. PTGIS, which is an important gene in fatty acid metabolism, was identified as the hub gene. This result was verified in ovarian cancer and ovarian tissues. The connection between the gene and survival was statistically remarkable (P = 0.015). The pRRophetic algorithm revealed that the low-risk group was more adaptable to cisplatin, doxorubicin, 5-fluorouracil, and etoposide (P < 0.001). Conclusion: PTGIS may be an indicator of prognosis and a possible therapeutic target for the therapy of ovarian cancer patients. The fatty acid metabolism of immune cells may be controlled, which has an indirect effect on cancer cell growth.

Highly Selective On-Surface [2 + 2] Cycloaddition Induced by Hierarchical Metal-Organic Hybrids.

On-surface synthesis of phenylenes is a promising strategy to form extended π-conjugated frameworks but normally lacks selectivity in achieving uniform products. Herein we demonstrate that the debromination reaction of 2,3-dibromophenazine (DBPZ) on Au(111) and Ag(111) surfaces can vary significantly considering the involvement of metal-organic hybrids (MOHs). On Au(111), [2 + 2] and [2 + 2 + 2] cycloadditions facilitate instantaneously upon the debromination occurring, while on Ag(111), several MOHs have been observed under sequential thermal annealing, leading to finally the uniform [2 + 2] cycloaddition product exclusively. By means of scanning tunneling microscopy (STM) and bond-resolved atomic force microscopy (BR-AFM), we have unambiguously depicted the chemical structure of related reaction intermediates and unraveled the undocumented role of hierarchical evolution of MOHs in steering the chemical selectivity.

Enhancement of protein translation by CRISPR/dCasRx coupled with SINEB2 repeat of noncoding RNAs.

The use of new long noncoding RNAs (lncRNAs) as biotechnological or therapeutic tools is still in its infancy, despite recent efforts to uncover their involvement in various biological processes including mRNA translation. An important question is whether lncRNA functional elements can be used to target translation of mRNAs of interest by incorporating the RNA-targeting CRISPR tools. The CRISPR/dCasRx-SINEB2 technology was developed in this research by coupling the sgRNA of a catalytically inactive Type VI-D Cas13 enzyme (CasRx) to an integrated SINEB2 domain of uchl1 lncRNA that promotes the translation of targeted mRNA. It has been demonstrated to be effective and adaptable in selectively increasing the expression of a variety of exogenous and endogenous proteins with a variety of functions with minimal off-target effects. dCasRx-SINEB2 is currently the sole CRISPR-related technique for translational control of gene expression, and works just as well or even better than the traditional RNAe tool under comparable conditions. Additionally, human cancer cells can be prevented from proliferating and migrating both in vitro and in vivo by dCasRx-SINEB2-targeted mRNA translation of transcripts encoding for antitumor proteins, including PTEN and P53. The present study provides an innovative protein enhancement method that will have several applications in biopharmaceuticals production and cancer research.

Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation.

Background: Tumor microenvironment (TME) takes a non-negligible role in the progression and metastasis of bladder urothelial carcinoma (BLCA) and tumor development could be inhibited by macrophage M1 in TME. The role of macrophage M1-related genes in BLCA adjuvant therapy has not been studied well. Methods: CIBERSOR algorithm was applied for identification tumor-infiltrating immune cells (TICs) subtypes of subjects from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. We identified potential modules of M1 macrophages by weighted gene co-expression network analysis (WGCNA). Nomogram was determined by one-way Cox regression and lasso regression analysis for M1 macrophage genes. The data from GEO are taken to verify the models externally. Kaplan-Meier and receiver operating characteristic (ROC) curves validated prognostic value of M1 macrophage genes. Finally, we divided patients into the low-risk group (LRG) and the high-risk group (HRG) based on the median risk score (RS), and the predictive value of RS in patients with BLCA immunotherapy and chemotherapy was investigated. Bladder cancer (T24, 5637, and BIU-87) and bladder uroepithelial cell line (SV-HUC-1) were used for in vitro validation. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was employed to validate the associated genes mRNA level. Results: 111 macrophage M1-related genes were identified using WGCNA. RS model containing three prognostically significant M1 macrophage-associated genes (FBXO6, OAS1, and TMEM229B) was formed by multiple Cox analysis, and a polygenic risk model and a comprehensive prognostic line plot was developed. The calibration curve clarified RS was a good predictor of prognosis. Patients in the LRG were more suitable for programmed cell death protein 1 (PD1) and cytotoxic T lymphocyte associate protein-4 (CTLA4) combination immunotherapy. Finally, chemotherapeutic drug models showed patients in the LRG were more sensitive to gemcitabine and mitomycin. RT-qPCR result elucidated the upregulation of FBXO6, TMEM229B, and downregulation of OAS1 in BLCA cell lines. Conclusion: A predictive model based on M1 macrophage-related genes can help guide us in the treatment of BLCA.

Influence of Molecular Configurations on the Desulfonylation Reactions on Metal Surfaces.

On-surface synthesis is a powerful methodology for the fabrication of low-dimensional functional materials. The precursor molecules usually anchor on different metal surfaces via similar configurations. The activation energies are therefore solely determined by the chemical activity of the respective metal surfaces. Here, we studied the influence of the detailed adsorption configuration on the activation energy on different metal surfaces. We systematically studied the desulfonylation homocoupling for a molecular precursor on Au(111) and Ag(111) and found that the activation energy is lower on inert Au(111) than on Ag(111). Combining scanning tunneling microscopy observations, synchrotron radiation photoemission spectroscopy measurements, and density functional theory calculations, we elucidate that the phenomenon arises from different molecule-substrate interactions. The molecular precursors anchor on Au(111) via Au-S interactions, which lead to weakening of the phenyl-S bonds. On the other hand, the molecular precursors anchor on Ag(111) via Ag-O interactions, resulting in the lifting of the S atoms. As a consequence, the activation barrier of the desulfonylation reactions is higher on Ag(111), although silver is generally more chemically active than gold. Our study not only reports a new type of on-surface chemical reaction but also clarifies the influence of detailed adsorption configurations on specific on-surface chemical reactions.

Clinical neutrophil-associated genes as reliable predictors of hepatocellular carcinoma.

Background: Growing evidence suggests that infiltrating neutrophils are key players in hepatocellular carcinoma (HCC) tumor progression. However, a comprehensive analysis of the biological roles of neutrophil infiltration and related genes in clinical outcomes and immunotherapy is lacking. Methods: HCC samples were obtained from the TCGA and GEO databases. The CIBERSORT algorithm was used to reveal the TIME landscape. Gene modules significantly associated with neutrophils were found using weighted gene co-expression network analysis (WGCNA), a "dynamic tree-cut" algorithm, and Pearson correlation analysis. Genes were screened using Cox regression analysis and LASSO and prognostic value validation was performed using Kaplan-Meier curves and receiver operating characteristic (ROC) curves. Risk scores (RS) were calculated and nomograms were constructed incorporating clinical variables. Gene set variation analysis (GSVA) was used to calculate signaling pathway activity. Immunophenoscore (IPS) was used to analyze differences in immunotherapy among samples with different risk scores. Finally, the relationship between RS and drug sensitivity was explored using the pRRophetic algorithm. Results: 10530 genes in 424 samples (50 normal samples, 374 tumor samples) were obtained from the TCGA database. Using WGCNA, the "MEbrown" gene module was most associated with neutrophils. Nine genes with prognostic value in HCC (PDLIM3, KLF2, ROR2, PGF, EFNB1, PDZD4, PLN, PCDH17, DOK5) were finally screened. Prognostic nomograms based on RS, gender, tumor grade, clinical stage, T, N, and M stages were constructed. The nomogram performed well after calibration curve validation. There is an intrinsic link between risk score and TMB and TIME. Samples with different risk scores differed in different signaling pathway activity, immunopharmaceutical treatment and chemotherapy sensitivity. Conclusion: In conclusion, a comprehensive analysis of neutrophil-related prognostic features will help in prognostic prediction and advance individualized treatment.

Development and validation of a competing risk model for second primary pancreatic ductal adenocarcinoma: A population-based study.

Background: With advances in early diagnosis and treatment, the number of cancer survivors continues to grow, and more and more cancer survivors face the threat of second primary cancer (SPM). Second primary pancreatic ductal adenocarcinoma (spPDAC) is an important subclass of SPM, but its prognostic characteristics are poorly understood. Methods: A total of 5,439 spPDAC samples and 67,262 primary pancreatic ductal adenocarcinoma (pPDAC) samples were extracted from the SEER database for this study. Survival differences between spPDAC and pPDAC samples were compared using Kaplan-Meier curves and log-rank tests. The Fine and Gray proportional subdistributed hazard method was used to analyze potential associations between clinical variables and pancreatic ductal adenocarcinoma-specific death (PDACSD) and death from other causes. After that, the clinical variables significantly related to PDACSD were screened out to construct a competing risk nomogram, which was used to evaluate the probability of the occurrence of PDACSD. The C-index was used to evaluate the discriminative ability of the model. The area under the curve (AUC) was used to verify the discrimination of the model. The calibration curve was used to verify the calibration of the model. Decision curve analysis (DCA) was used to validate the clinical utility of the model. Results: Compared with patients with spPDAC, the pPDAC sample had a better prognosis (p = 0.0017). Across all spPDAC samples, the three most common sites of first-present cancer were the prostate, breast, and digestive system. Age (p < 0.001), race (p = 0.006), interval (p = 0.016), location (p < 0.001), T stage (p = 0.003), M stage (p < 0.001), chemotherapy (p < 0.001), and radiotherapy (p = 0.006) were the clinical variables associated with PDACSD screened by multivariate competing risks analysis. The concordance index values for the training and validation sets were 0.665 (95% CI, 0.655, 0.675) and 0.666 (95% CI, 0.650, 0.682), respectively. AUC, calibration curve, and DCA indicated that the model we constructed had good discrimination, calibration, and clinical utility. Conclusions: In conclusion, we first analyzed the impact of previous cancer history on prognosis. We then constructed a competing risk model that can predict the probability of developing PDACSD in spPDAC. This model has good discriminative ability, calibration, and clinical practicability and has certain guiding value for clinical decision-making.

Clinical M2 Macrophage-Related Genes Can Serve as a Reliable Predictor of Lung Adenocarcinoma.

Background: Numerous studies have found that infiltrating M2 macrophages play an important role in the tumor progression of lung adenocarcinoma (LUAD). However, the roles of M2 macrophage infiltration and M2 macrophage-related genes in immunotherapy and clinical outcomes remain obscure. Methods: Sample information was extracted from TCGA and GEO databases. The TIME landscape was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to find M2 macrophage-related gene modules. Through univariate Cox regression, lasso regression analysis, and multivariate Cox regression, the genes strongly associated with the prognosis of LUAD were screened out. Risk score (RS) was calculated, and all samples were divided into high-risk group (HRG) and low-risk group (LRG) according to the median RS. External validation of RS was performed using GSE68571 data information. Prognostic nomogram based on risk signatures and other clinical information were constructed and validated with calibration curves. Potential associations of tumor mutational burden (TMB) and risk signatures were analyzed. Finally, the potential association of risk signatures with chemotherapy efficacy was investigated using the pRRophetic algorithm. Results: Based on 504 samples extracted from TCGA database, 183 core genes were identified using WGCNA. Through a series of screening, two M2 macrophage-related genes (GRIA1 and CLEC3B) strongly correlated with LUAD prognosis were finally selected. RS was calculated, and prognostic risk nomogram including gender, age, T, N, M stage, clinical stage, and RS were constructed. The calibration curve shows that our constructed model has good performance. HRG patients were suitable for new ICI immunotherapy, while LRG was more suitable for CTLA4-immunosuppressive therapy alone. The half-maximal inhibitory concentrations (IC50) of the four chemotherapeutic drugs (metformin, cisplatin, paclitaxel, and gemcitabine) showed significant differences in HRG/LRG. Conclusions: In conclusion, a comprehensive analysis of the role of M2 macrophages in tumor progression will help predict prognosis and facilitate the advancement of therapeutic techniques.

Clinical Eosinophil-Associated Genes can Serve as a Reliable Predictor of Bladder Urothelial Cancer.

Background: Numerous studies have shown that infiltrating eosinophils play a key role in the tumor progression of bladder urothelial carcinoma (BLCA). However, the roles of eosinophils and associated hub genes in clinical outcomes and immunotherapy are not well known. Methods: BLCA patient data were extracted from the TCGA database. The tumor immune microenvironment (TIME) was revealed by the CIBERSORT algorithm. Candidate modules and hub genes associated with eosinophils were identified by weighted gene co-expression network analysis (WGCNA). The external GEO database was applied to validate the above results. TIME-related genes with prognostic significance were screened by univariate Cox regression analysis, lasso regression, and multivariate Cox regression analysis. The patient's risk score (RS) was calculated and divided subjects into high-risk group (HRG) and low-risk group (LRG). The nomogram was developed based on the risk signature. Models were validated via receiver operating characteristic (ROC) curves and calibration curves. Differences between HRG and LRG in clinical features and tumor mutational burden (TMB) were compared. The Immune Phenomenon Score (IPS) was calculated to estimate the immunotherapeutic significance of RS. Half-maximal inhibitory concentrations (IC50s) of chemotherapeutic drugs were predicted by the pRRophetic algorithm. Results: 313 eosinophil-related genes were identified by WGCNA. Subsequently, a risk signature containing 9 eosinophil-related genes (AGXT, B3GALT2, CCDC62, CLEC1B, CLEC2D, CYP19A1, DNM3, SLC5A9, SLC26A8) was finally developed via multiplex analysis and screening. Age (p < 0.001), grade (p < 0.001), and RS (p < 0.001) were independent predictors of survival in BLCA patients. Based on the calibration curve, our risk signature nomogram was confirmed as a good predictor of BLCA patients' prognosis at 1, 3, and 5 years. The association analysis of RS and immunotherapy indicated that low-risk patients were more credible for novel immune checkpoint inhibitors (ICI) immunotherapy. The chemotherapeutic drug model suggests that RS has an effect on the drug sensitivity of patients. Conclusions: In conclusion, the eosinophil-based RS can be used as a reliable clinical predictor and provide insights into the precise treatment of BLCA.

The synthesis, characterization and immunological activity of mucopolysaccharide-quaternized chitosan nanoparticles.

In this study, nanoparticles were prepared by using positively charged quaternized chitosan and negatively charged mucopolysaccharide such as chondroitin sulfate, heparin and hyaluronic acid. The nanoparticles have a stable nanostructure with particle size in 336.2-424.5 nm, potential in 18.5-31.1 mV and polydispersity index PDI of 0.172-0.335. Moreover, their encapsulation efficiency was 68.77 % and 64.89 %, and they have low endotoxin and good stability. It can significantly promote the expression of IL-6, TNF-α, and IL-1ß of DCS cells. Moreover, the in vivo immune activity of heparin-quaternized chitosan-OVA nanoparticles against BALB/C mice was showed that, the nanoparticles could significantly promote the secretion of immunoglobulins in mice including IgG1 and IgG2. And nanoparticle also can promote the production of immune factors. Meanwhile, the expression of immune factor genes was also elevated. Furthermore, the results of tissue section experiments showed that the nanoparticles are safety of the body.