Size- and Voltage-Dependent Electron Transport of C2N-Rings-Based Molecular Chains.

C2N-ring-based molecular chains were designed at the molecular level and theoretically demonstrated to show distinctive and valuable electron transport properties that were superior to the parent carbonaceous system and other similar nanoribbon-based molecular chains. This new -type molecular chain presented an exponential attenuation of the conductance and electron transmission with the length. Essentially, the molecular chain retained the electron-resonant tunneling within 7 nm and the dominant transport orbital was the LUMO. Shorter molecular chains with stronger conductance anomalously possessed a larger tunnel barrier energy, attributing to the compensation of a much smaller HOMO-LUMO gap, and these two internal factors codetermined the transport capacity. Some influencing factors were also studied. In contrast to the common O impurity with a tiny effect on electron transmission of the C2N rings chain, the common H impurity clearly improved it. When the temperature was less than 400 K, the electron transmission varied with temperature within a narrow range, and the structural disorder deriving from proper heating did not greatly modify the transmission possibility and the exponentially decreasing tendency with the length. In a non-equilibrium condition, the current increased overall with the bias but the growth rate varied with size. A valuable negative differential resistance (NDR) effect appeared in longer molecular chains with an even number of big carbon-nitrogen rings and strengthened with size. The emergence of such an effect originated from the reduction in transmission peaks. The conductance of longer molecular chains was enhanced with the voltage but the two shortest ones presented completely different trends. Applying the bias was demonstrated to be an effective way for C2N-ring-based molecular chains to slow down the conductance decay constant and affect the transport regime. C2N-ring-based molecular chains show a perfect application in tunneling diodes and controllable molecular devices.

Electron Transport Properties of Graphene/WS2 Van Der Waals Heterojunctions.

Van der Waals heterojunctions of two-dimensional atomic crystals are widely used to build functional devices due to their excellent optoelectronic properties, which are attracting more and more attention, and various methods have been developed to study their structure and properties. Here, density functional theory combined with the nonequilibrium Green's function technique has been used to calculate the transport properties of graphene/WS2 heterojunctions. It is observed that the formation of heterojunctions does not lead to the opening of the Dirac point of graphene. Instead, the respective band structures of both graphene and WS2 are preserved. Therefore, the heterojunction follows a unique Ohm's law at low bias voltages, despite the presence of a certain rotation angle between the two surfaces within the heterojunction. The transmission spectra, the density of states, and the transmission eigenstate are used to investigate the origin and mechanism of unique linear I-V characteristics. This study provides a theoretical framework for designing mixed-dimensional heterojunction nanoelectronic devices.

Metabolic fluorine labeling and hotspot imaging of dynamic gut microbiota in mice.

Real-time localization and microbial activity information of indigenous gut microbiota over an extended period of time remains a challenge with existing visualizing methods. Here, we report a metabolic fluorine labeling (MEFLA)-based strategy for monitoring the dynamic gut microbiota via 19F magnetic resonance imaging (19F MRI). In situ labeling of different microbiota subgroups is achieved by using a panel of peptidoglycan-targeting MEFLA probes containing 19F atoms of different chemical shifts, and subsequent real-time in vivo imaging is accomplished by multiplexed hotspot 19F MRI with high sensitivity and unlimited penetration. Using this method, we realize extended visualization (>24 hours) of native gut microbes located at different intestinal sections and semiquantitative analysis of their metabolic dynamics modulated by various conditions, such as the host death and different ß-lactam antibiotics. Our strategy holds great potential for noninvasive and real-time assessing of the metabolic activities and locations of the highly dynamic gut microbiota.

Exploring biomarkers for prognosis and neoadjuvant chemosensitivity in rectal cancer: Multi-omics and ctDNA sequencing collaboration.

Introduction: This study aimed to identified the key genes and sequencing metrics for predicting prognosis and efficacy of neoadjuvant chemotherapy (nCT) in rectal cancer (RC) based on genomic DNA sequencing in samples with different origin and multi-omics association database. Methods: We collected 16 RC patients and obtained DNA sequencing data from cancer tissues and plasma cell-free DNA before and after nCT. Various gene variations were analyzed, including single nucleotide variants (SNV), copy number variation (CNV), tumor mutation burden (TMB), copy number instability (CNI) and mutant-allele tumor heterogeneity (MATH). We also identified genes by which CNV level can differentiate the response to nCT. The Cancer Genome Atlas database and the Clinical Proteomic Tumor Analysis Consortium database were used to further evaluate the specific role of therapeutic relevant genes and screen out the key genes in multi-omics levels. After the intersection of the screened genes from differential expression analysis, survival analysis and principal components analysis dimensionality reduction cluster analysis, the key genes were finally identified. Results: The genes CNV level of principal component genes in baseline blood and cancer tissues could significantly distinguish the two groups of patients. The CNV of HSP90AA1, EGFR, SRC, MTOR, etc. were relatively gained in the better group compared with the poor group in baseline blood. The CNI and TMB was significantly different between the two groups. The increased expression of HSP90AA1, EGFR, and SRC was associated with increased sensitivity to multiple chemotherapeutic drugs. The nCT predictive score obtained by therapeutic relevant genes could be a potential prognostic indicator, and the combination with TMB could further refine prognostic prediction for patients. After a series of analysis in multi-omics association database, EGFR and HSP90AA1 with significant differences in multiple aspects were identified as the key predictive genes related to prognosis and the sensitivity of nCT. Discussion: This work revealed that effective combined application and analysis in multi-omics data are critical to search for predictive biomarkers. The key genes EGFR and HSP90AA1 could serve as an effective biomarker to predict prognose and neoadjuvant chemosensitivity.

Fluorescent metabolic labeling-based quick antibiotic susceptibility test for anaerobic bacteria.

Because of the advancements in medicine and science, the numbers of patients surviving complicated diseases are continuously increasing, which in turn leads to elevated chances of anaerobic infections by endogenous bacteria. Traditional growth yield-based antibiotic susceptibility tests (ASTs) against anaerobic bacteria are very time-consuming (≥48 h) and labor intensive, which delays the timely guidance of antibiotic prescription and increases the mortality of patients. Inspired by a fluorescent d-amino acid (FDAA) labeling-based AST (FaAST) that we recently developed for quick determination of aerobic bacteria's susceptibilities, here we report an accurate and fast AST method for anaerobic pathogens. Based on flow cytometry analysis of anaerobes that have been treated with various doses of antibiotics and metabolically labeled with FDAA, the intensities of which can reflect their affected metabolic status by the drugs, the MICs of each drug can then be determined. The whole process can be completed in 5 h. After testing 40 combinations of the representative anaerobic bacteria and antibiotics, our method demonstrates a high susceptibility category accuracy of 95.0%. This FaAST-based protocol is helpful in accurately and quickly guiding antibiotic decisions when treating critical infections caused by anaerobic bacteria.

Correction: Fluorescent metabolic labeling-based quick antibiotic susceptibility test for anaerobic bacteria.

[This corrects the article DOI: 10.1039/D2CB00163B.].

Bisphenol A Promotes the Progression of Colon Cancer Through Dual-Targeting of NADPH Oxidase and Mitochondrial Electron-Transport Chain to Produce ROS and Activating HIF-1α/VEGF/PI3K/AKT Axis.

Bisphenol A (BPA) is a high-production-volume industrial chemical. Despite recent research conducted on its carcinogenicity, its role in the development of colon cancer (CC) has been rarely studied. This study aims to evaluate the effects of BPA on the migration and invasion of CC cells. First, we clinically verified that patients with CC exhibit higher serum BPA level than healthy donors. Subsequently, different CC cell lines were exposed to a series of BPA concentrations, and the migration and invasion of cells were detected by the wound healing test and transwell assay. Finally, N-acetyl-L-cysteine (NAC) and siHIF-1α intervention was used to explore the effects of ROS and HIF-1α on cell migration and invasion, respectively. The results demonstrated that the occurrence of BPA-induced migration and invasion were dependent on the dose and time and was most pronounced in DLD1 cells. ROS production was jointly driven by NADPH oxidase (NOX) and mitochondrial electron-transport chain (ETC). Furthermore, the intervention of NAC and siHIF-1α blocked the HIF-1α/VEGF/PI3K/AKT axis and inhibited cell migration and invasion. In conclusion, our results suggest that BPA exposure promotes the excessive production of ROS induced by NOX and ETC, which in turn activates the HIF-1α/VEGF/PI3K/AKT axis to promote the migration and invasion of CC cells. This study provides new insights into the carcinogenic effects of BPA on CC and warns people to pay attention to environmental pollution and the harm caused to human health by low-dose BPA.

Identification and Validation in a Novel Quantification System of Ferroptosis Patterns for the Prediction of Prognosis and Immunotherapy Response in Left- and Right-Sided Colon Cancer.

Background: This study aimed to establish a novel quantification system of ferroptosis patterns and comprehensively analyze the relationship between ferroptosis score (FS) and the immune cell infiltration (ICI) characterization, tumor mutation burden (TMB), prognosis, and therapeutic sensitivity in left-sided and right-sided colon cancers (LCCs and RCCs, respectively). Methods: We comprehensively evaluated the ferroptosis patterns in 444 LCCs and RCCs based on 59 ferroptosis-related genes (FRGs). The FS was constructed to quantify ferroptosis patterns by using principal component analysis algorithms. Next, the prognostic value and therapeutic sensitivities were evaluated using multiple methods. Finally, we performed weighted gene co-expression network analysis (WGCNA) to identify the key FRGs. The IMvigor210 cohort, TCGA-COAD proteomics cohort, and Immunophenoscores were used to verify the predictive abilities of FS and the key FRGs. Results: Two ferroptosis clusters were determined. Ferroptosis cluster B demonstrated a high degree of congenital ICI and stromal-related signal enrichment with a poor prognosis. The prognosis, response of targeted inhibitors, and immunotherapy were significantly different between high and low FS groups (HSG and LSG, respectively). HSG was characterized by high TMB and microsatellite instability-high subtype with poor prognosis. Meanwhile, LSG was more likely to benefit from immunotherapy. ALOX5 was identified as a key FRG based on FS. Patients with high protein levels of ALOX5 had poorer prognoses. Conclusion: This work revealed that the evaluation of ferroptosis subtypes will contribute to gaining insight into the heterogeneity in LCCs and RCCs. The quantification for ferroptosis patterns played a non-negligible role in predicting ICI characterization, prognosis, and individualized immunotherapy strategies.

A Novel Quantification System Combining iTRAQ Technology and Multi-Omics Assessment to Predict Prognosis and Immunotherapy Efficacy in Colon Cancer.

Background: Colon cancer is one of the most common cancer types, although it has certain unique genetic features. This study aimed to develop a unique score for assessing prognosis and immunotherapy efficacy using integrated multi-omics analysis. Methods: Isobaric tagging for relative and absolute quantification (iTRAQ) based proteomic analysis was used to screen differentially expressed proteins (DEP) between tumor and normal samples. DEP mRNA obtained from TCGA were clustered into different categories to show landscape-related prognosis and function. Following that, DEG was extracted from DEP mRNA, and the DEP-related score (DEPRS) was constructed to investigate the difference in immunotherapy prognosis and sensitivity. Finally, WCGNA, random forest, and artificial neural networks were used to screen for key genes. The prognostic value and protein level of these genes were validated. Results: A total of 243 DEPs were identified through iTRAQ analysis, and the corresponding DEP mRNA was clustered into three. Following a series of tests, 1,577 DEGs were identified from overlapped DEP mRNA clusters and were classified into three gene clusters. The two types of clusters described above shared comparable characteristics in terms of prognosis and function. Then, it was established that a high DEPRS indicated a poor prognosis and DEPRS had significant associations with TMB, MSI status, and immunotherapeutic response. Finally, the key genes HART3 and FBLN2 were identified and were found to be implicated in immunotherapy and prognosis. Conclusion: The development of a DEPRS based on multi-omics analysis will aid in improving our understanding of colon cancer and guiding a more effective immunotherapy strategy. DEPRS and key genes are used as biomarkers in the clinical evaluation of patients.

Identification and quantification of immune infiltration landscape on therapy and prognosis in left- and right-sided colon cancer.

BACKGROUND: The left-sided and right-sided colon cancer (LCCs and RCCs, respectively) have unique molecular features and clinical heterogeneity. This study aimed to identify the characteristics of immune cell infiltration (ICI) subtypes for evaluating prognosis and therapeutic benefits. METHODS: The independent gene datasets, corresponding somatic mutation and clinical information were collected from The Cancer Genome Atlas and Gene Expression Omnibus. The ICI contents were evaluated by "ESTIMATE" and "CIBERSORT." We performed two computational algorithms to identify the ICI landscape related to prognosis and found the unique infiltration characteristics. Next, principal component analysis was conducted to construct ICI score based on three ICI patterns. We analyzed the correlation between ICI score and tumor mutation burden (TMB), and stratified patients into prognostic-related high- and low- ICI score groups (HSG and LSG, respectively). The role of ICI scores in the prediction of therapeutic benefits was investigated by "pRRophetic" and verified by Immunophenoscores (IPS) (TCIA database) and an independent immunotherapy cohort (IMvigor210). The key genes were preliminary screened by weighted gene co-expression network analysis based on ICI scores. And they were further identified at various levels, including single cell, protein and immunotherapy response. The predictive ability of ICI score for prognosis was also verified in IMvigor210 cohort. RESULTS: The ICI features with a better prognosis were marked by high plasma cells, dendritic cells and mast cells, low memory CD4+ T cells, M0 macrophages, M1 macrophages, as well as M2 macrophages. A high ICI score was characterized by an increased TMB and genomic instability related signaling pathways. The prognosis, sensitivities of targeted inhibitors and immunotherapy, IPS and expression of immune checkpoints were significantly different in HSG and LSG. The genes identified by ICI scores and various levels included CA2 and TSPAN1. CONCLUSION: The identification of ICI subtypes and ICI scores will help gain insights into the heterogeneity in LCC and RCC, and identify patients probably benefiting from treatments. ICI scores and the key genes could serve as an effective biomarker to predict prognosis and the sensitivity of immunotherapy.

d-Amino Acid-Based Metabolic Labeling Enables a Fast Antibiotic Susceptibility Test of Both Isolated Bacteria and Bronchoalveolar Lavage Fluid.

The threat of multidrug-resistant bacteria has escalated rapidly, increasing the demand for accurate antibiotic susceptibility tests (ASTs). Traditional bacterial growth yield-based ASTs often take overnight to report, delaying the timely guidance of antibiotic use. Here, a fluorescent d-amino acid (FDAA) labeling-based AST (FaAST) is reported, which can quickly provide accurate minimum inhibitory concentrations (MICs). The FDAA-labeling signals that reflect the bacterial metabolic status underlie the flow cytometry-based strategy for MIC determination. Resistant bacteria show a reluctant decline in FDAA-labeling (inhibited metabolism) after treatment with the corresponding antibiotics, whereas susceptible bacteria demonstrate quick responses to low doses of drugs. The MICs are determined based on the changing trends in labeling. After testing 23 clinical isolates and laboratory strains of the most critical drug-resistant bacteria against a panel of representative antibiotics, FaAST shows a high susceptibility category with an accuracy of 98.13%. Moreover, FaAST can also make quick and accurate diagnosis against bronchoalveolar lavage fluids collected from hospital-acquired pneumonia patients, saving 2-4 days in guiding antibiotic use for this life-threatening infection. Thus, the speed, accuracy, and broad applicability of FaAST will be valuable in informing antibiotic decisions when treating critical infections caused by drug-resistant bacteria.

Assessment of immune status of laryngeal squamous cell carcinoma can predict prognosis and guide treatment.

BACKGROUND: In the past few years, immunotherapy has changed the way we treat solid tumors. People pay more and more attention to the immune microenvironment of laryngeal squamous cell carcinoma (LSCC). In this study, our immunotherapy research took advantage of the clinical database and focused our in-depth analysis on the tumor microenvironment (TME). METHODS: This study evaluated the relationship between the clinical outcome and the local tissue and overall immune status in 412 patients with primary LSCC. We constructed and validated a risk model that could predict prognosis, assess immune status, identify high-risk patients, and develop personalized treatment plans through bioinformatics. In addition, through immunohistochemical analysis, we verified the differential expression of CTSL and KDM5D genes with the largest weight coefficients in the model in LSCC tissues and their influence on the prognosis and tumor-infiltrating lymphocytes (TILs). RESULTS: We found that interstitial tumor-infiltrating lymphocytes, tumor parenchymal-infiltrating lymphocyte volume, tumor infiltrates lymphocytes of frontier invasion, and the platelet-to-lymphocyte ratio (PLR) were independent factors affecting the prognosis of patients with LSCC. A novel risk model can guide clinicians to accurately predict prognosis, identify high-risk patients, and formulate personalized treatment plans. The differential expression of genes such as CTSL and KDM5D has a significant correlation with the TILs of LSCC and the prognosis of patients. CONCLUSION: Local and systemic inflammatory markers in patients with laryngeal squamous cell carcinoma are reliable prognostic factors. The risk model and CTSL, KDM5D gene have important potential research value.

Identification and Validation of Immune-Related Gene for Predicting Prognosis and Therapeutic Response in Ovarian Cancer.

Ovarian cancer (OC) is a devastating malignancy with a poor prognosis. The complex tumor immune microenvironment results in only a small number of patients benefiting from immunotherapy. To explore the different factors that lead to immune invasion and determine prognosis and response to immune checkpoint inhibitors (ICIs), we established a prognostic risk scoring model (PRSM) with differential expression of immune-related genes (IRGs) to identify key prognostic IRGs. Patients were divided into high-risk and low-risk groups according to their immune and stromal scores. We used a bioinformatics method to identify four key IRGs that had differences in expression between the two groups and affected prognosis. We evaluated the sensitivity of treatment from three aspects, namely chemotherapy, targeted inhibitors (TIs), and immunotherapy, to evaluate the value of prediction models and key prognostic IRGs in the clinical treatment of OC. Univariate and multivariate Cox regression analyses revealed that these four key IRGs were independent prognostic factors of overall survival in OC patients. In the high-risk group comprising four genes, macrophage M0 cells, macrophage M2 cells, and regulatory T cells, observed to be associated with poor overall survival in our study, were higher. The high-risk group had a high immunophenoscore, indicating a better response to ICIs. Taken together, we constructed a PRSM and identified four key prognostic IRGs for predicting survival and response to ICIs. Finally, the expression of these key genes in OC was evaluated using RT-qPCR. Thus, these genes provide a novel predictive biomarker for immunotherapy and immunomodulation.

Comprehensive analysis of prognostic immune-related genes and drug sensitivity in cervical cancer.

BACKGROUND: Cervical cancer (CC) is the leading cause of cancer-related death in women. A limited number of studies have investigated whether immune-prognostic features can be used to predict the prognosis of CC. This study aimed to develop an improved prognostic risk scoring model (PRSM) for CC based on immune-related genes (IRGs) to predict survival and determine the key prognostic IRGs. METHODS: We downloaded the gene expression profiles and clinical data of CC patients from the TCGA and GEO databases. The ESTIMATE algorithm was used to calculate the score for both immune and stromal cells. Differentially expressed genes (DEGs) in different subpopulations were analyzed by "Limma". A weighted gene co-expression network analysis (WGCNA) was used to establish a DEG co-expression module related to the immune score. Immune-related gene pairs (IRGPs) were constructed, and univariate- and Lasso-Cox regression analyses were used to analyze prognosis and establish a PRSM. A log-rank test was used to verify the accuracy and consistency of the scoring model. Identification of the predicted key IRG was ensured by the application of functional enrichment, DisNor, protein-protein interactions (PPIs) and heatmap. Finally, we extracted the key prognostic immune-related genes from the gene expression data, validated the key genes by immunohistochemistry and analyzed the correlation between their expression and drug sensitivity. RESULTS: A new PRSM was developed based on 22 IRGPs. The prognosis of the low-risk group in the model group (P < 0.001) and validation group (P = 0.039) was significantly better than that in the high-risk group. Furthermore, M1 and M2 macrophages were highly expressed in the low-risk group. Retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) and the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway were significantly enriched in the low-risk group. Three representative genes (CD80, CD28, and LCP2) were markers of CC prognosis. CD80 and CD28 may more prominent represent important indicators to improve patient prognosis. These key genes was positively correlated with drug sensitivity. Finally, we found that differences in the sensitivity to JNK inhibitors could be distinguished based on the use and risk grouping of this PRSM. CONCLUSIONS: The prognostic model based on the IRGs and key genes have potential clinical significance for predicting the prognosis of CC patients, providing a foundation for clinical prognosis judgment and individualized treatment.

Prognostic Immunity and Therapeutic Sensitivity Analyses Based on Differential Genomic Instability-Associated LncRNAs in Left- and Right-Sided Colon Adenocarcinoma.

Background: The purpose of our study was to develop a prognostic risk model based on differential genomic instability-associated (DGIA) long non-coding RNAs (lncRNAs) of left-sided and right-sided colon cancers (LCCs and RCCs); therefore, the prognostic key lncRNAs could be identified. Methods: We adopted two independent gene datasets, corresponding somatic mutation and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Identification of differential DGIA lncRNAs from LCCs and RCCs was conducted with the appliance of "Limma" analysis. Then, we screened out key lncRNAs based on univariate and multivariate Cox proportional hazard regression analysis. Meanwhile, DGIA lncRNAs related prognostic model (DRPM) was established. We employed the DRPM in the model group and internal verification group from TCGA for the purpose of risk grouping and accuracy verification of DRPM. We also verified the accuracy of key lncRNAs with GEO data. Finally, the differences of immune infiltration, functional pathways, and therapeutic sensitivities were analyzed within different risk groups. Results: A total of 123 DGIA lncRNAs were screened out by differential expression analysis. We obtained six DGIA lncRNAs by the construction of DRPM, including AC004009.1, AP003555.2, BOLA3-AS1, NKILA, LINC00543, and UCA1. After the risk grouping by these DGIA lncRNAs, we found the prognosis of the high-risk group (HRG) was significantly worse than that in the low-risk group (LRG) (all p < 0.05). In all TCGA samples and model group, the expression of CD8+ T cells in HRG was lower than that in LRG (all p < 0.05). The functional analysis indicated that there was significant upregulation with regard to pathways related to both genetic instability and immunity in LRG, including cytosolic DNA sensing pathway, response to double-strand RNA, RIG-Ⅰ like receptor signaling pathway, and Toll-like receptor signaling pathway. Finally, we analyzed the difference and significance of key DGIA lncRNAs and risk groups in multiple therapeutic sensitivities. Conclusion: Through the analysis of the DGIA lncRNAs between LCCs and RCCs, we identified six key DGIA lncRNAs. They can not only predict the prognostic risk of patients but also serve as biomarkers for evaluating the differences of genetic instability, immune infiltration, and therapeutic sensitivity.

Identification of genetic variants in a pedigree associated with epilepsy by using whole exome sequencing and whole genome sequencing.

Epilepsy is a common heterogeneous group of neurological disorders including electroencephalographic and brain imaging. We used whole exome sequencing and whole genome sequencing to identify variants in a pedigree associated with epilepsy. Cranium CT scan showed that the lateral right parietal lobe was hyperdense, and there were no clear boundaries with brain tissue in affected cases. Using WES, one exclusive nonsynonymous mutant in gene TSC2 (Chr16:2138307; c.5240 T > G; p.Ile1747Ser) was involved in this disease. Further analysis showed that de novo variant in TSC2 was high conserved across different species. Moreover, the two affected sisters and their father had the same compound heterozygous variants in TSC2, while the father had no epilepsy but depigmentation. These variants demonstrated that variant in TSC2 may result in epilepsy with incomplete penetrance in humans, and the CNV and SV variants we identified probably be involved in this disease.

Deubiquitinating Enzymes Orchestrate the Cancer Stem Cell-Immunosuppressive Niche Dialogue: New Perspectives and Therapeutic Potential.

Cancer stem cells (CSCs) are sparks for igniting tumor recurrence and the instigators of low response to immunotherapy and drug resistance. As one of the important components of tumor microenvironment, the tumor associated immune microenvironment (TAIM) is driving force for the heterogeneity, plasticity and evolution of CSCs. CSCs create the inhibitory TAIM (ITAIM) mainly through four stemness-related signals (SRSs), including Notch-nuclear factor-κB axis, Hedgehog, Wnt and signal transducer and activator of transcription. Ubiquitination and deubiquitination in proteins related to the specific stemness of the CSCs have a profound impact on the regulation of ITAIM. In regulating the balance between ubiquitination and deubiquitination, it is crucial for deubiquitinating enzymes (DUBs) to cleave ubiquitin chains from substrates. Ubiquitin-specific peptidases (USPs) comprise the largest family of DUBs. Growing evidence suggests that they play novel functions in contribution of ITAIM, including regulating tumor immunogenicity, activating stem cell factors, upregulating the SRSs, stabilizing anti-inflammatory receptors, and regulating anti-inflammatory cytokines. These overactive or abnormal signaling may dampen antitumor immune responses. The inhibition of USPs could play a regulatory role in SRSs and reversing ITAIM, and also have great potential in improving immune killing ability against tumor cells, including CSCs. In this review, we focus on the USPs involved in CSCs signaling pathways and regulating ITAIM, which are promising therapeutic targets in antitumor therapy.

Identification of Immune-Related LncRNA Pairs for Predicting Prognosis and Immunotherapeutic Response in Head and Neck Squamous Cell Carcinoma.

Long noncoding RNAs (lncRNAs) have multiple functions with regard to the cancer immunity response and the tumor microenvironment. The prognosis of head and neck squamous cell carcinoma (HNSCC) is still poor currently, and it may be effective to predict the clinical outcome and immunotherapeutic response of HNSCC by immunogenic analysis. Therefore, by using univariate COX analysis and Lasso Cox regression, we identified a signature consisting of 21 immune-related lncRNA pairs (IRLPs) that predicted clinical outcome and Immunotherapeutic response in HNSCC. Specifically, it was associated with immune cell infiltration (i.e., T cells CD4 memory resting, CD8 T cells, macrophages M0, M2, and NK cells), and more importantly this signature was strongly related with immune checkpoint inhibitors (ICIs) [such as PDCD1 (r = -0.35, P < 0.001), CTLA4 (r = -0.26, P < 0.001), LAG3 (r = -0.22, P < 0.001) and HAVCR2 (r = -0.2, P < 0.001)] and immunotherapy-related biomarkers (MMR and HLA). The present study highlighted the value of the 21 IRLPs signature as a predictor of prognosis and immunotherapeutic response in HNSCC.

Prognostic Immune-Related Analysis Based on Differentially Expressed Genes in Left- and Right-Sided Colon Adenocarcinoma.

BACKGROUND: Colon adenocarcinoma (COAD) can be divided into left-sided and right-sided COAD (LCCs and RCCs, respectively). They have unique characteristics in various biological aspects, particularly immune invasion and prognosis. The purpose of our study was to develop a prognostic risk scoring model (PRSM) based on differentially expressed immune-related genes (IRGs) between LCCs and RCCs, therefore the prognostic key IRGs could be identified. METHODS: The gene sets and clinical information of COAD patients were derived from TCGA and GEO databases. The comparison of differentially expressed genes (DEGs) of LCCs and RCCs were conducted with appliance of "Limma" analysis. The establishment about co-expression modules of DEGs related with immune score was conducted by weighted gene co-expression network analysis (WGCNA). Furthermore, we screened the module genes and completed construction of gene pairs. The analysis of the prognosis and the establishment of PRSM were performed with univariate- and lasso-Cox regression. We employed the PRSM in the model group and verification group for the purpose of risk group assignment and PRSM accuracy verification. Finally, the identification of the prognostic key IRGs was guaranteed by the adoption of functional enrichment, "DisNor" and protein-protein interaction (PPI). RESULTS: A total of 215 genes were screened out by differential expression analysis and WGCNA. A PRSM with 16 immune-related gene pairs (IRGPs) was established upon the genes pairing. Furthermore, we confirmed that the risk score was an independent factor for survival by univariate- and multivariate-Cox regression. The prognosis of high-risk group in model group (P < 0.001) and validation group (P = 0.014) was significantly worse than that in low-risk group. Treg cells (P < 0.001) and macrophage M0 (P = 0.015) were highly expressed in the high-risk group. The functional analysis indicated that there was significant up-regulation with regard of lymphocyte and cytokine related terms in low-risk group. Finally, we identified five prognostic key IRGs associated with better prognosis through PPI and prognostic analysis, including IL2RB, TRIM22, CIITA, CXCL13, and CXCR6. CONCLUSION: Through the analysis and screening of the DEGs between LCCs and RCCs, we constructed a PRSM which could predicate prognosis of LCCs and RCCs, and five prognostic key IRGs were identified as well. Therefore, the basis for identifying the benefits of immunotherapy and immunomodulatory was built.

Ultrathin PdCu alloy nanosheet-assembled 3D nanoflowers with high peroxidase-like activity toward colorimetric glucose detection.

Enzyme-mimetic properties of nanomaterials can be efficiently tuned by controlling their size, composition, and structure. Here, ultrathin PdCu alloy nanosheet-assembled three-dimensional (3D) nanoflowers (Pd1Cux NAFs) with tunable surface composition are obtained via a generalized strategy. In presence of H2O2, the as-synthesized Pd1Cux NAFs can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to the oxidized form of TMB (oxTMB) with a characteristic absorption peak at 652 nm. Interestingly, Pd1Cux NAFs show obviously composition-dependent peroxidase-like catalytic activities because of the synergistic interaction of nanoalloy. Additionally, different from 2D Pd nanosheets, the distinctive 3D superstructures are featured with rich approachable sites and proper layer spacing, which are in favor of fast mass transport and electron transfers during the catalytic process. Among the studied Pd1Cux NAFs, the Pd1Cu1.7 NAFs show the highest enzyme-like activities and can be successfully applied for the colorimetric detection of glucose with a low detection limit of 2.93 ± 0.53 µM. This work provides an efficient avenue to fabricate PdCu NAF nanozymes in biosensing toward glucose detection. Two-dimensional (2D) PdCu ultrathin nanosheet-assembled 3D nanoflowers (Pd1Cux NAFs) with tunable surface composition exhibit substantially enhanced intrinsic peroxidase-like catalytic activities. The Pd1Cu1.7 NAFs are successfully used as peroxidase mimic catalyst for the colorimetric detection of glucose with low detection limit of 2.93 µM.