Preparation of Protein-like Silver–Cysteine Hybrid Nanowires and Application in Ultrasensitive Immunoassay of Cancer Biomarker

Novel protein-like silver–cysteine hybrid nanowires (<i>p</i>-SCNWs) have been synthesized by a green, simple, nontemplate, seedless, and one-step aqueous-phase approach. AgNO₃ and l-cysteine were dissolved in distilled water, forming Ag–cysteine precipitates and HNO₃. Under vigorous stirring, the pH of the solution was rapidly adjusted to 9.0 by addition of concentrated sodium hydroxide solution, leading to quick dissolution of the Ag-cysteine precipitates and sudden appearance of white precipitates of <i>p</i>-SCNWs. The <i>p</i>-SCNWs are monodispersed nanowires with diameter of 100 nm and length of tens of micrometers, and have abundant carboxyl (−COOH) and amine (−NH₂) groups at their surfaces, large amounts of peptide-linkages and S-bonding silver ions (Ag⁺) inside, making them look and act like Ag-hybrid protein nanostructures. The abundant −COOH and −NH₂ groups at the surfaces of <i>p</i>-SCNWs have been found to facilitate the reactions between the <i>p</i>-SCNWs and proteins including antibodies. Furthermore, the fact that the <i>p</i>-SCNWs contain large amounts of silver ions enables biofunctionalized <i>p</i>-SCNWs to be excellent signal amplifying chemiluminescence labels for ultrasensitive and highly selective detection of important antigens, such as cancer biomarkers. In this work, the immunoassay of carcinoembryonic antigen (CEA) in human serum was taken as an example to demonstrate the immunoassay applications of antibody-functionalized <i>p</i>-SCNWs. By the novel <i>p</i>-SCNW labels, CEA can be detected in the linear range from 5 to 400 fg/mL with a limit of detection (LOD) of 2.2 fg/mL (at signal-to-noise ratio of 3), which is much lower than that obtained by commercially available enzyme-linked immunosorbent assay (ELISA). Therefore, the synthesized <i>p</i>-SCNWs are envisioned to be an excellent carrier for proteins and related immunoassay strategy would have promising applications in ultrasensitive clinical screening of cancer biomarkers for early diagnostics of cancers

Table_2_Identification and validation of an immune signature associated with EMT and metabolic reprogramming for predicting prognosis and drug response in bladder cancer.docx

BackgroundEpithelial-mesenchymal transition (EMT), one leading reason of the dismal prognosis of bladder cancer (BLCA), is closely associated with tumor invasion and metastasis. We aimed to develop a novel immune−related gene signature based on different EMT and metabolic status to predict the prognosis of BLCA.MethodsGene expression and clinical data were obtained from TCGA and GEO databases. Patients were clustered based on EMT and metabolism scores calculated by ssGSEA. The immune-related differentially expressed genes (DEGs) between the two clusters with the most obvious differences were used to construct the signature by LASSO and Cox analysis. Time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves were utilized to evaluate the gene signature in training and validation cohorts. Finally, the function of the signature genes AHNAK and NFATC1 in BLCA cell lines were explored by cytological experiments.ResultsBased on the results of ssGSEA, TCGA patients were divided into three clusters, among which cluster 1 and cluster 3 had completely opposite EMT and metabolic status. Patients in cluster 3 had a significantly worse clinical prognosis than cluster 1. Immune-related DEGs were selected between the two clusters to construct the predictive signature based on 14 genes. High-risk patients had poorer prognosis, lower proportions of CD8+ T cells, higher EMT and carbohydrate metabolism, and less sensitivity to chemotherapy and immunotherapy. Overexpression of AHNAK or NFATC1 promoted the proliferation, migration and invasion of T24 and UMUC3 cells. Silencing ANHAK or NFATC1 could effectively inhibit EMT and metabolism in T24 and UMUC3 cells.ConclusionThe established immune signature may act as a promising model for generating accurate prognosis for patients and predicting their EMT and metabolic status, thus guiding the treatment of BLCA patients.</p

Table_1_Identification and validation of an immune signature associated with EMT and metabolic reprogramming for predicting prognosis and drug response in bladder cancer.docx

BackgroundEpithelial-mesenchymal transition (EMT), one leading reason of the dismal prognosis of bladder cancer (BLCA), is closely associated with tumor invasion and metastasis. We aimed to develop a novel immune−related gene signature based on different EMT and metabolic status to predict the prognosis of BLCA.MethodsGene expression and clinical data were obtained from TCGA and GEO databases. Patients were clustered based on EMT and metabolism scores calculated by ssGSEA. The immune-related differentially expressed genes (DEGs) between the two clusters with the most obvious differences were used to construct the signature by LASSO and Cox analysis. Time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves were utilized to evaluate the gene signature in training and validation cohorts. Finally, the function of the signature genes AHNAK and NFATC1 in BLCA cell lines were explored by cytological experiments.ResultsBased on the results of ssGSEA, TCGA patients were divided into three clusters, among which cluster 1 and cluster 3 had completely opposite EMT and metabolic status. Patients in cluster 3 had a significantly worse clinical prognosis than cluster 1. Immune-related DEGs were selected between the two clusters to construct the predictive signature based on 14 genes. High-risk patients had poorer prognosis, lower proportions of CD8+ T cells, higher EMT and carbohydrate metabolism, and less sensitivity to chemotherapy and immunotherapy. Overexpression of AHNAK or NFATC1 promoted the proliferation, migration and invasion of T24 and UMUC3 cells. Silencing ANHAK or NFATC1 could effectively inhibit EMT and metabolism in T24 and UMUC3 cells.ConclusionThe established immune signature may act as a promising model for generating accurate prognosis for patients and predicting their EMT and metabolic status, thus guiding the treatment of BLCA patients.</p

Image_1_Identification and validation of an immune signature associated with EMT and metabolic reprogramming for predicting prognosis and drug response in bladder cancer.tif

BackgroundEpithelial-mesenchymal transition (EMT), one leading reason of the dismal prognosis of bladder cancer (BLCA), is closely associated with tumor invasion and metastasis. We aimed to develop a novel immune−related gene signature based on different EMT and metabolic status to predict the prognosis of BLCA.MethodsGene expression and clinical data were obtained from TCGA and GEO databases. Patients were clustered based on EMT and metabolism scores calculated by ssGSEA. The immune-related differentially expressed genes (DEGs) between the two clusters with the most obvious differences were used to construct the signature by LASSO and Cox analysis. Time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves were utilized to evaluate the gene signature in training and validation cohorts. Finally, the function of the signature genes AHNAK and NFATC1 in BLCA cell lines were explored by cytological experiments.ResultsBased on the results of ssGSEA, TCGA patients were divided into three clusters, among which cluster 1 and cluster 3 had completely opposite EMT and metabolic status. Patients in cluster 3 had a significantly worse clinical prognosis than cluster 1. Immune-related DEGs were selected between the two clusters to construct the predictive signature based on 14 genes. High-risk patients had poorer prognosis, lower proportions of CD8+ T cells, higher EMT and carbohydrate metabolism, and less sensitivity to chemotherapy and immunotherapy. Overexpression of AHNAK or NFATC1 promoted the proliferation, migration and invasion of T24 and UMUC3 cells. Silencing ANHAK or NFATC1 could effectively inhibit EMT and metabolism in T24 and UMUC3 cells.ConclusionThe established immune signature may act as a promising model for generating accurate prognosis for patients and predicting their EMT and metabolic status, thus guiding the treatment of BLCA patients.</p

Table_4_Identification and validation of an immune signature associated with EMT and metabolic reprogramming for predicting prognosis and drug response in bladder cancer.xlsx

BackgroundEpithelial-mesenchymal transition (EMT), one leading reason of the dismal prognosis of bladder cancer (BLCA), is closely associated with tumor invasion and metastasis. We aimed to develop a novel immune−related gene signature based on different EMT and metabolic status to predict the prognosis of BLCA.MethodsGene expression and clinical data were obtained from TCGA and GEO databases. Patients were clustered based on EMT and metabolism scores calculated by ssGSEA. The immune-related differentially expressed genes (DEGs) between the two clusters with the most obvious differences were used to construct the signature by LASSO and Cox analysis. Time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves were utilized to evaluate the gene signature in training and validation cohorts. Finally, the function of the signature genes AHNAK and NFATC1 in BLCA cell lines were explored by cytological experiments.ResultsBased on the results of ssGSEA, TCGA patients were divided into three clusters, among which cluster 1 and cluster 3 had completely opposite EMT and metabolic status. Patients in cluster 3 had a significantly worse clinical prognosis than cluster 1. Immune-related DEGs were selected between the two clusters to construct the predictive signature based on 14 genes. High-risk patients had poorer prognosis, lower proportions of CD8+ T cells, higher EMT and carbohydrate metabolism, and less sensitivity to chemotherapy and immunotherapy. Overexpression of AHNAK or NFATC1 promoted the proliferation, migration and invasion of T24 and UMUC3 cells. Silencing ANHAK or NFATC1 could effectively inhibit EMT and metabolism in T24 and UMUC3 cells.ConclusionThe established immune signature may act as a promising model for generating accurate prognosis for patients and predicting their EMT and metabolic status, thus guiding the treatment of BLCA patients.</p

Image_2_Identification and validation of an immune signature associated with EMT and metabolic reprogramming for predicting prognosis and drug response in bladder cancer.tif

BackgroundEpithelial-mesenchymal transition (EMT), one leading reason of the dismal prognosis of bladder cancer (BLCA), is closely associated with tumor invasion and metastasis. We aimed to develop a novel immune−related gene signature based on different EMT and metabolic status to predict the prognosis of BLCA.MethodsGene expression and clinical data were obtained from TCGA and GEO databases. Patients were clustered based on EMT and metabolism scores calculated by ssGSEA. The immune-related differentially expressed genes (DEGs) between the two clusters with the most obvious differences were used to construct the signature by LASSO and Cox analysis. Time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves were utilized to evaluate the gene signature in training and validation cohorts. Finally, the function of the signature genes AHNAK and NFATC1 in BLCA cell lines were explored by cytological experiments.ResultsBased on the results of ssGSEA, TCGA patients were divided into three clusters, among which cluster 1 and cluster 3 had completely opposite EMT and metabolic status. Patients in cluster 3 had a significantly worse clinical prognosis than cluster 1. Immune-related DEGs were selected between the two clusters to construct the predictive signature based on 14 genes. High-risk patients had poorer prognosis, lower proportions of CD8+ T cells, higher EMT and carbohydrate metabolism, and less sensitivity to chemotherapy and immunotherapy. Overexpression of AHNAK or NFATC1 promoted the proliferation, migration and invasion of T24 and UMUC3 cells. Silencing ANHAK or NFATC1 could effectively inhibit EMT and metabolism in T24 and UMUC3 cells.ConclusionThe established immune signature may act as a promising model for generating accurate prognosis for patients and predicting their EMT and metabolic status, thus guiding the treatment of BLCA patients.</p

Table_3_Identification and validation of an immune signature associated with EMT and metabolic reprogramming for predicting prognosis and drug response in bladder cancer.xlsx

BackgroundEpithelial-mesenchymal transition (EMT), one leading reason of the dismal prognosis of bladder cancer (BLCA), is closely associated with tumor invasion and metastasis. We aimed to develop a novel immune−related gene signature based on different EMT and metabolic status to predict the prognosis of BLCA.MethodsGene expression and clinical data were obtained from TCGA and GEO databases. Patients were clustered based on EMT and metabolism scores calculated by ssGSEA. The immune-related differentially expressed genes (DEGs) between the two clusters with the most obvious differences were used to construct the signature by LASSO and Cox analysis. Time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves were utilized to evaluate the gene signature in training and validation cohorts. Finally, the function of the signature genes AHNAK and NFATC1 in BLCA cell lines were explored by cytological experiments.ResultsBased on the results of ssGSEA, TCGA patients were divided into three clusters, among which cluster 1 and cluster 3 had completely opposite EMT and metabolic status. Patients in cluster 3 had a significantly worse clinical prognosis than cluster 1. Immune-related DEGs were selected between the two clusters to construct the predictive signature based on 14 genes. High-risk patients had poorer prognosis, lower proportions of CD8+ T cells, higher EMT and carbohydrate metabolism, and less sensitivity to chemotherapy and immunotherapy. Overexpression of AHNAK or NFATC1 promoted the proliferation, migration and invasion of T24 and UMUC3 cells. Silencing ANHAK or NFATC1 could effectively inhibit EMT and metabolism in T24 and UMUC3 cells.ConclusionThe established immune signature may act as a promising model for generating accurate prognosis for patients and predicting their EMT and metabolic status, thus guiding the treatment of BLCA patients.</p