Single Effective Complex Loading into Zero-Mode Waveguides Optimized with Fluorescence Evaluation at Quenching and Accumulation Checkpoints.

Single-molecule detection with high accuracy and specialty plays an important role in biomedical diagnosis and screening. Zero-mode waveguides (ZMWs) enable the possibility of single biological molecule detection in real time. Nevertheless, the absence of a reliable assessment for single effective complex loading has constrained further applications of ZMWs in complex interaction. Both the quantity and activity of the complex loaded into ZMWs have a critical effect on the efficiency of detection. Herein, a fluorescence evaluation at quenching and accumulation checkpoints was established to assess and optimize single effective complex loading into ZMWs. A primer-template-enzyme ternary complex was designed, and then an evaluation for quantity statistics at the quenching checkpoint and functional activity at the accumulation checkpoint was used to validate the effectiveness of complexes loaded into ZMWs. By optimizing the parameters such as loading time, procedures, and enzyme amount, the single-molecule effective occupancy was increased to 25.48%, achieving 68.86% of the theoretical maximum value (37%) according to Poisson statistics. It is of great significance to provide effective complex-loading validation for improving the sample-loading efficiency of single-molecule assays or sequencing in the future.

Self-correction of cycle threshold values by a normal distribution-based process to improve accuracy of quantification in real-time digital PCR.

The digital polymerase chain reaction (dPCR) is a new and developing nucleic acid detection technology with high sensitivity that can realize the absolute quantitative analysis of samples. In order to improve the accuracy of quantitative results, real-time digital PCR emphasizes the kinetic information during amplification to identify prominent abnormal data. However, it is challenging to use a unified standard to accurately classify the amplification curve of each well as negative and positive, due to the interference caused by various factors in the experiment. In this work, a normal distribution-based cycle threshold value self-correcting model (NCSM) was established, which focused on the feature of the cycle threshold values in amplification curves and conducted continuous detection and correction on the whole. The cycle threshold value distribution was closer to the ideal normal distribution to avoid the influence of interference. Thus, the model achieves a more accurate classification between positive and negative results. The corrective process was applied to plasmid samples and resulted in an accuracy improvement from 92 to 99%. The coefficient of variation was below 5% when considering the quantitation of a range between 100 and 10,000 copies. At the same time, by utilizing this model, the distribution of cycle threshold values at the endpoint can be predicted with fewer thermal cycles, which can reduce the cycling time by around 25% while maintaining a consistency of more than 98%. Therefore, using the NCSM can effectively enhance the quantitative accuracy and increase the detection efficiency based on the real-time dPCR platform.

Efficient Simultaneous Detection of Metabolites Based on Electroenzymatic Assembly Strategy.

Objective and Impact Statement: We describe an electroenzymatic mediator (EM) sensor based on an electroenzymatic assembly peak separation strategy, which can efficiently realize the simultaneous detection of 3 typical cardiovascular disease (CVD) metabolites in 5 µl of plasma under one test. This work has substantial implications toward improving the efficiency of chronic CVD assessment. Introduction: Monitoring CVD of metabolites is strongly associated with disease risk. Independent and time-consuming detection in hospitals is unfavorable for chronic CVD management. Methods: The EM was flexibly designed by the cross-linking of electron mediators and enzymes, and 3 EM layers with different characteristics were assembled on one electrode. Electrons were transferred under tunable potential; 3 metabolites were quantitatively detected by 3 peak currents that correlated with metabolite concentrations. Results: In this study, the EM sensor showed high sensitivity for the simultaneous detection of 3 metabolites with a lower limit of 0.01 mM. The linear correlation between the sensor and clinical was greater than 0.980 for 242 patients, and the consistency of risk assessment was 94.6%. Conclusion: Metabolites could be expanded by the EM, and the sensor could be a promising candidate as a home healthcare tool for CVD risk assessment.

Revealing the Binding Events of Single Proteins on Exosomes Using Nanocavity Antennas beyond Zero-Mode Waveguides.

Exosomes (EXOs) play a crucial role in biological action mechanisms. Understanding the biological process of single-molecule interactions on the surface of the EXO membrane is essential for elucidating the precise function of the EXO receptor. However, due to dimensional incompatibility, monitoring the binding events between EXOs of tens to hundreds of nanometers and biomolecules of nanometers using existing nanostructure antennas is difficult. Unlike the typical zero-mode waveguides (ZMWs), this work presents a nanocavity antenna (λvNAs) formed by nanocavities with diameters close to the visible light wavelength dimensions. Effective excitation volumes suitable for observing single-molecule fluorescence were generated in nanocavities of larger diameters than typical ZMWs; the optimal signal-to-noise ratio obtained was 19.5 when the diameter was 300 nm and the incident angle was ∼50°. EXOs with a size of 50-150 nm were loaded into λvNAs with an optimized diameter of 300-500 nm, resulting in appreciable occupancy rates that overcame the nanocavity size limitation for large-volume biomaterial loading. Additionally, this method identified the binding events between the single transmembrane CD9 proteins on the EXO surface and their monoclonal antibody anti-CD9, demonstrating that λvNAs expanded the application range beyond subwavelength ZMWs. Furthermore, the λvNAs provide a platform for obtaining in-depth knowledge of the interactions of single molecules with biomaterials ranging in size from tens to hundreds of nanometers.

How Oxidative Stress Induces Depression?

Depression increasingly affects a wide range and a large number of people worldwide, both physically and psychologically, which makes it a social problem requiring prompt attention and management. Accumulating clinical and animal studies have provided us with substantial insights of disease pathogenesis, especially central monoamine deficiency, which considerably promotes antidepressant research and clinical treatment. The first-line antidepressants mainly target the monoamine system, whose drawbacks mainly include slow action and treatment resistant. The novel antidepressant esketamine, targeting on central glutamatergic system, rapidly and robustly alleviates depression (including treatment-resistant depression), whose efficiency is shadowed by potential addictive and psychotomimetic side effects. Thus, exploring novel depression pathogenesis is necessary, for seeking more safe and effective therapeutic methods. Emerging evidence has revealed vital involvement of oxidative stress (OS) in depression, which inspires us to pursue antioxidant pathway for depression prevention and treatment. Fully uncovering the underlying mechanisms of OS-induced depression is the first step towards the avenue, thus we summarize and expound possible downstream pathways of OS, including mitochondrial impairment and related ATP deficiency, neuroinflammation, central glutamate excitotoxicity, brain-derived neurotrophic factor/tyrosine receptor kinase B dysfunction and serotonin deficiency, the microbiota-gut-brain axis disturbance and hypothalamic-pituitary-adrenocortical axis dysregulation. We also elaborate on the intricate interactions between the multiple aspects, and molecular mechanisms mediating the interplay. Through reviewing the related research progress in the field, we hope to depict an integral overview of how OS induces depression, in order to provide fresh ideas and novel targets for the final goal of efficient treatment of the disease.

Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients' prognosis and immunotherapy response.

BACKGROUND: Epigenetic regulations of immune responses are essential for cancer development and growth. As a critical step, comprehensive and rigorous explorations of m6A methylation are important to determine its prognostic significance, tumor microenvironment (TME) infiltration characteristics and underlying relationship with glioblastoma (GBM). METHODS: To evaluate m6A modification patterns in GBM, we conducted unsupervised clustering to determine the expression levels of GBM-related m6A regulatory factors and performed differential analysis to obtain m6A-related genes. Consistent clustering was used to generate m6A regulators cluster A and B. Machine learning algorithms were implemented for identifying TME features and predicting the response of GBM patients receiving immunotherapy. RESULTS: It is found that the m6A regulatory factor significantly regulates the mutation of GBM and TME. Based on Europe, America, and China data, we established m6Ascore through the m6A model. The model accurately predicted the results of 1206 GBM patients from the discovery cohort. Additionally, a high m6A score was associated with poor prognoses. Significant TME features were found among the different m6A score groups, which demonstrated positive correlations with biological functions (i.e., EMT2) and immune checkpoints. CONCLUSIONS: m6A modification was important to characterize the tumorigenesis and TME infiltration in GBM. The m6Ascore provided GBM patients with valuable and accurate prognosis and prediction of clinical response to various treatment modalities, which could be useful to guide patient treatments.

Automatic Seizure Detection Based on Stockwell Transform and Transformer.

Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.

Multi-omics profiles refine L-dopa decarboxylase (DDC) as a reliable biomarker for prognosis and immune microenvironment of clear cell renal cell carcinoma.

Background: Increasing evidence indicates that L-dopa decarboxylase (DDC), which mediates aberrant amino acid metabolism, is significantly associated with tumor progression. However, the impacts of DDC are not elucidated clearly in clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate DDC prognostic value and potential mechanisms for ccRCC patients. Methods: Transcriptomic and proteomic expressions of and clinical data including 532 patients with ccRCC (The Cancer Genome Atlas RNA-seq data), 226 ccRCC samples (Gene Expression Omnibus), 101 ccRCC patients from the E-MTAB-1980 cohort, and 232 patients with ccRCC with proteogenomic data (Fudan University Shanghai Cancer Center) were downloaded and analyzed to investigate the prognostic implications of DDC expression. Cox regression analyses were implemented to explore the effect of DDC expression on the prognosis of pan-cancer. The "limma" package identified the differentially expressed genes (DEGs) between high DDC subgroups and low DDC groups. Functional enrichments were performed based DEGs between DDC subgroups. The differences of immune cell infiltrations and immune checkpoint genes between DDC subgroups were analyzed to identify potential influence on immune microenvironment. Results: We found significantly decreased DDC expression in ccRCC tissues compared with normal tissues from multiple independent cohorts based on multi-omics data. We also found that DDC expression was correlated with tumor grades and stages.The following findings revealed that lower DDC expression levels significantly correlated with shorter overall survival (P <0.001) of patients with ccRCC. Moreover, we found that DDC expression significantly correlated with an immunosuppressive tumor microenvironment, higher intra-tumoral heterogeneity, elevated expression of immune checkpoint CD274, and possibly mediated malignant behaviors of ccRCC cells via the PI3k/Akt signaling pathway. Conclusion: The present study is the first to our knowledge to indicate that decreased DDC expression is significantly associated with poor survival and an immune-suppressive tumor microenvironment in ccRCC. These findings suggest that DDC could serve as a biomarker for guiding molecular diagnosis and facilitating the development of novel individual therapeutic strategies for patients with advanced ccRCC.

The possible pathogenesis of macular caldera in patients with North Carolina macular dystrophy.

BACKGROUND: This study provides a detailed description of a Chinese family with North Carolina macular dystrophy (NCMD) and explores its possible pathogenesis. METHODS: Five individuals from a three-generation family underwent general ophthalmic examination, multi-imaging examinations and visual electrophysiology examinations when possible. Genetic characterization was carried out by target region sequencing and high-throughput sequencing in affected patients. RESULTS: Despite severe fundus changes, patients had relatively good visual acuity. Genetic analysis showed that affected patients had PRDM13 gene duplication and heterozygous mutations of the ABCA4 gene. Optical coherence tomography (OCT) showed an abnormal retinal pigment epithelium (RPE) layer in patients with grade 2 lesions, while the neurosensory retina was relatively normal. In grade 3 patients, RPE and choroid atrophy were greater than that of the neurosensory retina, showing concentric atrophy. CONCLUSIONS: RPE and choroidal atrophy were found to play an important role in the development of macular caldera.

Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma.

Introduction: In hepatocellular carcinoma (HCC), alternative splicing (AS) is related to tumor invasion and progression. Methods: We used HCC data from a public database to identify AS subtypes by unsupervised clustering. Through feature analysis of different splicing subtypes and acquisition of the differential alternative splicing events (DASEs) combined with enrichment analysis, the differences in several subtypes were explored, cell function studies have also demonstrated that it plays an important role in HCC. Results: Finally, in keeping with the differences between these subtypes, DASEs identified survival-related AS times, and were used to construct risk proportional regression models. AS was found to be useful for the classification of HCC subtypes, which changed the activity of tumor-related pathways through differential splicing effects, affected the tumor microenvironment, and participated in immune reprogramming. Conclusion: In this study, we described the clinical and molecular characteristics providing a new approach for the personalized treatment of HCC patients.

A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma.

BACKGROUND: Glioblastoma is one of the most common brain cancers in adults, and is characterized by recurrence and little curative effect. An effective treatment for glioblastoma patients remains elusive worldwide. 7-methylguanosine (m7G) is a common RNA modification, and its role in tumors has become a research hotspot. METHODS: By searching for differentially expressed genes related to m7G, we generated a prognostic signature via cluster analysis and established classification criteria of high and low risk scores. The effectiveness of classification was validated using the Non-negative matrix factorization (NMF) algorithm, and repeatedly verified using training and test groups. The dimension reduction method was used to clearly show the difference and clinical significance of the data. All analyses were performed via R (version 4.1.2). RESULTS: According to the signature that included four genes (TMOD2, CACNG2, PLOD3, and TMSB10), glioblastoma patients were divided into high and low risk score groups. The survival rates between the two groups were significantly different, and the predictive abilities for 1-, 3-, and 5-year survivals were effective. We further established a Nomogram model to further examine the signature,as well as other clinical factors, with remaining significant results. Our signature can act as an independent prognostic factor related to immune-related processes in glioblastoma. CONCLUSIONS: Our research addresses the gap in knowledge in the m7G and glioblastoma research fields. The establishment of a prognostic signature and the extended analysis of the tumor microenvironment, immune correlation, and tumor mutation burden further suggest the important role of m7G in the development and development of this disease. This work will provide support for future research.

Integrated microdevice with a windmill-like hole array for the clog-free, efficient, and self-mixing enrichment of circulating tumor cells.

Circulating tumor cells (CTCs) have tremendous potential to indicate disease progression and monitor therapeutic response using minimally invasive approaches. Considering the limitations of affinity strategies based on their cost, effectiveness, and simplicity, size-based enrichment methods that involve low-cost, label-free, and relatively simple protocols have been further promoted. Nevertheless, the key challenges of these methods are clogging issues and cell aggregation, which reduce the recovery rates and purity. Inspired by the natural phenomenon that the airflow around a windmill is disturbed, in this study, a windmill-like hole array on the SU-8 membrane was designed to perturb the fluid such that cells in a fluid would be able to self-mix and that the pressure acting on cells or the membrane would be dispersed to allow a greater velocity. In addition, based on the advantages of fluid coatings, a lipid coating was used to modify the membrane surface to prevent cell aggregation and clogging of the holes. Under the optimal conditions, recovery rates of 93% and 90% were found for A549 and HeLa cells in a clinical simulation test of our platform with a CTC concentration of 20-100 cells per milliliter of blood. The white blood cell (WBC) depletion rate was 98.7% (n = 15), and the CTC detection limit was less than 10 cells per milliliter of blood (n = 6). Moreover, compared with conventional membrane filtration, the advantages of the proposed device for the rapid (2 mL/min) and efficient enrichment of CTCs without clogging were shown both experimentally and theoretically. Due to its advantages in the efficient, rapid, uniform, and clog-free enrichment of CTCs, our platform offers great potential for metastatic detection and therapy analyses.

The heparinase-linked differential time method allows detection of heparin potency in whole blood with high sensitivity and dynamic range.

Anticoagulation therapy with heparin is an effective treatment against thrombosis. Heparin tends to cause spontaneous bleeding and requires regular monitoring during therapy. Most high-sensitivity heparin sensors have focused on the concentration detection in clarified buffer solution. However, the pharmacodynamics of heparin vary depending on individual patient or disease, while potency detection with high sensitivity and dynamic range outperforms concentration detection in clinical diagnosis. In this study, a novel heparinase-linked differential time (HLDT) method was established with a two-zone of Graphene modified Carbon (GR-C) sensor, which was utilized to evaluate heparin potency in whole blood. It was based on electrochemical measurement of clotting time shifting associated with presence or absence of heparinase. Heparinase inhibits the anticoagulant ability of heparin by forming a heparin-antithrombin-thrombin complex during coagulation. And the intensity and peak time of electrochemical current were associated with thrombin activity and clotting on the electrode. The results demonstrated that the sensor had high selectivity for heparin potency in 10 µL of whole blood with a detection limit of 0.1 U/mL, and the linear detection range was 0.1-5 U/mL. The coefficient of variation (CV) of the peak time was less than 5%, and linear correlation between the GR-C sensor and the TEG-5000 instrument was 0.987. Thus, the HLDT method has better clinical application due to its good repeatability, high sensitivity and wide range in heparin potency evaluation.

The development of real-time digital PCR technology using an improved data classification method.

For digital polymerase chain reaction (PCR), data classification is always a crucial task. The dynamic real-time amplification process information of each partition is always ignored in typical digital PCR analysis, which can easily lead to inaccurate outcomes. In this work, an integrated device that offers real-time chip-based digital PCR analysis was established. In addition, an enhanced process-based classification model (PAM) was built and trained. And then the device and the analytical model were employed in classification tasks for different concentrations of Epstein-Barr Virus (EBV) plasmid quantification assays. The results indicated that the real-time analysis device achieved a linearity of 0.97, the classification method was able to distinguish the false-positive curves, and the recognition error of positive wells was decreased by 64.4% compared with typical static analysis techniques when low concentrations of samples were tested. With these advantages, it is supposed that the real-time digital PCR analysis apparatus and the improved classification method can be employed to enhance the performance of digital PCR technology.

Integrating m6A Regulators-Mediated Methylation Modification Models and Tumor Immune Microenvironment Characterization in Caucasian and Chinese Low-Grade Gliomas.

Background: As an important epigenetic modification, m6A methylation plays an essential role in post-transcriptional regulation and tumor development. It is urgently needed to comprehensively and rigorously explore the prognostic value of m6A regulators and its association with tumor microenvironment (TME) infiltration characterization of low-grade glioma (LGG). Methods: Based on the expression of 20 m6A regulatory factors, we comprehensively evaluated the m6A modification patterns of LGG after unsupervised clustering. Subsequent analysis of the differences between these groups was performed to obtain m6A-related genes, then consistent clustering was conducted to generate m6AgeneclusterA and m6AgeneclusterB. A Random Forest and machining learning algorithms were used to reduce dimensionality, identify TME characteristics and predict responses for LGG patients receiving immunotherapies. Results: Evident differential m6A regulators were found in mutation, CNV and TME characteristics of LGG. Based on TCGA and CGGA databases, we identified that m6A regulators clusterA could significantly predict better prognosis (p = 0.00016) which enriched in mTOR signaling pathway, basal transcription factors, accompanied by elevated immune cells infiltration, and decreased IDH and TP53 mutations. We also investigated the distribution of differential genes in m6A regulators clusters which was closely associated with tumor immune microenvironment through three independent cohort comparisons. Next, we established m6Ascore based on previous m6A model, which accurately predicts outcomes in 1089 LGG patients (p < 0.0001) from discovering cohort and 497 LGG patients from testing cohort. Significant TME characteristics, including genome heterogeneity, abidance of immune cells, and clinicopathologic parameters have been found between m6Ascore groups. Importantly, LGG patients with high m6Ascore are confronted with significantly decreased responses to chemotherapies, but benefit more from immunotherapies. Conclusion: In conclusion, this study first demonstrates that m6A modification is crucial participant in tumorigenesis and TME infiltration characterization of LGG based on large-scale cohorts. The m6Ascore provides useful and accurately predict of prognosis and clinical responses to chemotherapy, immunotherapy and therapeutic strategy development for LGG patients.

circ_PTN contributes to -cisplatin resistance in glioblastoma via PI3K/AKT signaling through the miR-542-3p/PIK3R3 pathway.

Glioblastoma has been identified as the most common and aggressive primary brain tumor in adults. Recently, it has been found that cisplatin (DDP) treatment is a common chemotherapeutic method for GBM patients. circ_PTN (ID number: hsa_circ_0003949) is a newly found circular (circRNA) which has been proved to be highly expressed in GBM cells, while its role in GBM remains unclear. Therefore, our study focused on investigating the role of circ_PTN in the DDP resistance of GBM cells. The expression of circ_PTN in DDP-sensitive and DDP-resistant GBM cells was detected in our assay. Functional experiments were utilized to unveil the effects of circ_PTN on the DDP resistance of GBM cells. Moreover, mechanism assays were conducted to confirm the mechanism of how circ_PTN affected the DDP resistance of GBM cells. According to the results, we found that circ_PTN promoted the DDP resistance of GBM cells through activation of the PI3K/AKT pathway. Moreover, circ_PTN silencing inhibited the DDP resistance of GBM tumors in vivo. To conclude, our study unveiled the influence of circ_PTN on the DDP resistance of GBM cells, which might provide a therapeutic target for GBM treatment via DDP.

NAP1L1 promotes proliferation and chemoresistance in glioma by inducing CCND1/CDK4/CDK6 expression through its interaction with HDGF and activation of c-Jun.

The prognosis of glioma is poor as its pathogenesis and mechanisms underlying cisplatin chemoresistance remain unclear. Nucleosome assembly protein 1 like 1 (NAP1L1) is regarded as a hallmark of malignant tumors. However, the role of NAP1L1 in glioma remains unknown. In this study, we aimed to investigate the molecular functions of NAP1L1 in glioma and its involvement in cisplatin chemoresistance, if any. NAP1L1 was found to be upregulated in samples from The Cancer Genome Atlas (TCGA) database. Immunohistochemistry indicated that NAP1L1 and hepatoma-derived growth factor (HDGF) were enhanced in glioma as compared to the para-tumor tissues. High expressions of NAP1L1 and HDGF were positively correlated with the WHO grade, KPS, Ki-67 index, and recurrence. Moreover, NAP1L1 expression was also positively correlated with the HDGF expression in glioma tissues. Functional studies suggested that knocking down NAP1L1 could significantly inhibit glioma cell proliferation both in vitro and in vivo, as well as enhance the sensitivity of glioma cells to cisplatin (cDDP) in vitro. Mechanistically, NAP1L1 could interact with HDGF at the protein level and they co-localize in the cytoplasm. HDGF knockdown in NAP1L1-overexpressing glioma cells significantly inhibited cell proliferation. Furthermore, HDGF could interact with c-Jun, an oncogenic transcription factor, which eventually induced the expressions of cell cycle promoters, CCND1/CDK4/CDK6. This finding suggested that NAP1L1 could interact with HDGF, and the latter recruited c-Jun, a key oncogenic transcription factor, that further induced CCND1/CDK4/CDK6 expression, thereby promoting proliferation and chemoresistance in glioma cells. High expression of NAP1L1 in glioma tissues indicated shorter overall survival in glioma patients.

Comprehensive Multi-Omics Identification of Interferon-γ Response Characteristics Reveals That RBCK1 Regulates the Immunosuppressive Microenvironment of Renal Cell Carcinoma.

Interferon-gamma (IFN-γ) has a complex role in modulating the tumor microenvironment (TME) during renal cell carcinoma (RCC) development. To define the role of IFN-γ response genes in RCC progression, we characterized the differential gene expression, prognostic implications, and DNA variation profiles of selected IFN-γ response signatures, which exhibited a significant hazard ratio for the overall survival (OS) and progression-free survival (PFS) of papillary, chromophobia, and clear cell RCC (ccRCC) patients (n = 944). Prognostic nomograms were constructed to predict the outcomes for ccRCC patients, highlighting the prognostic implications of RANBP2-type and C3HC4-type zinc finger containing 1 (RBCK1). Interestingly, large-scale pan-cancer samples (n = 12,521) and three single-cell RNA datasets revealed that RBCK1 showed markedly differential expression between cancer and normal tissues and significantly correlated with tumor-infiltrating immune cells, tumor purity, and immune checkpoint molecules, such as PD-L1, CTLA-4, LAG-3, and TIGIT in pan-cancer samples. Notably, the TIDE score was significantly higher in the RBCK1high group compared with the RBCK1low group in both ccRCC and RCC cohorts. Besides, immunohistochemistry staining showed significantly elevated RBCK1 expression in tumors (n = 50) compared with kidney samples (n = 40) from a real-world cohort, Fudan University Shanghai Cancer Center (FUSCC, Shanghai). After RBCK1 expression was confirmed in ccRCC, we found a significantly decreased number of infiltrating CD4+ T cells, CD4+ FOXP3+ Treg cells, M1 macrophages, and CD56bight/dim NK cells in the immune-cold RBCK1high group. In addition to the distinct heterogeneous immune microenvironment, the increased expression of RBCK1 predicted a prominently worse prognosis than the RBCK1low group for 232 ccRCC patients in the FUSCC proteomic cohort. Furthermore, after transfected with siRNA in human ccRCC cells, extraordinarily decreased cell proliferation, migration capacities, and prominently elevated apoptosis tumor cell proportion were found in the siRNA groups compared with the negative control group. In conclusion, this study identified IFN-γ response clusters, which might be used to improve the prognostic accuracy of immune contexture in the ccRCC microenvironment. Immune-cold RBCK1high patients have pro-tumorigenic immune infiltration and significantly worse outcomes than RBCK1low patients based on results from multi-omics to real-world data. Our discovery of novel independent prognostic indicators for RCC highlights the association between tumor alterations and immune phenotype.

Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment.

The tumor microenvironment (TME) contributes to the initiation and progression of many neoplasms. However, the impact of low-grade glioma (LGG) purity on carcinogenesis remains to be elucidated. We selected 509 LGG patients with available genomic and clinical information from the TCGA database. The percentage of tumor infiltrating immune cells and the tumor purity of LGG were evaluated using the ESTIMATE and CIBERSORT algorithms. Stromal-related genes were screened through Cox regression, and protein-protein interaction analyses and survival-related genes were selected in 487 LGG patients from GEO database. Hub genes involved in LGG purity were then identified and functionally annotated using bioinformatics analyses. Prognostic implications were validated in 100 patients from an Asian real-world cohort. Elevated tumor purity burden, immune scores, and stromal scores were significantly associated with poor outcomes and increased grade in LGG patients from the TCGA cohort. In addition, CD3E was selected with the most significant prognostic value (Hazard Ratio=1.552, P<0.001). Differentially expressed genes screened according to CD3E expression were mainly involved in stromal related activities. Additionally, significantly increased CD3E expression was found in 100 LGG samples from the validation cohort compared with adjacent normal brain tissues. High CD3E expression could serve as an independent prognostic indicator for survival of LGG patients and promotes malignant cellular biological behaviors of LGG. In conclusion, tumor purity has a considerable impact on the clinical, genomic, and biological status of LGG. CD3E, the gene for novel membrane immune biomarker deeply affecting tumor purity, may help to evaluate the prognosis and develop individual immunotherapy strategies for LGG patients. Evaluating the ratio of differential tumor purity and CD3E expression levels may provide novel insights into the complex structure of the LGG microenvironment and targeted drug development.

The effects of aging and dopaminergic inhibition on large scale maze learning in rhesus monkeys.

Studies have shown that both aging and dopaminergic dysfunction affected spatial learning and memory. Systematic dopaminergic inhibition, by dopamine receptor (DR) antagonist treatment, impaired spatial delayed-response (SDR) performance, which mostly requires self/body centered egocentric reference frame, in rhesus monkeys. However, the influence of DR blocking on large scale maze learning, which mainly involves world centered allocentric reference frame, remains unclear. Moreover, the effects of aging on the process also remain unknown. Present study investigated the issues, using large scale mazes composed of 8 maze units. Maze No. 1 was used for adaptation and training. Mazes No. 2-4 were used to investigate influence of aging, by comparing learning performance between young and aged rhesus monkeys. Mazes No. 5-8 were used to investigate the effects of DR antagonist treatment, SKF-83566 (0.02, 0.2 mg/kg) and haloperidol (0.001, 0.01 mg/kg). The result showed similar learning performance between young and aged monkeys in mazes No. 2-4. In mazes No. 5-8, we also found similar learning performance after acute DR antagonist injection, compared with pre-treatment baseline performance in mazes No. 2-4, in both young and aged groups. The result showed similar maze learning performance between young and aged monkeys in mazes (No. 2-4), suggesting no significant influence of aging on allocentric spatial learning. We also found similar maze performance in both groups, after dopamine receptor antagonist treatment in mazes (No. 5-8) compared with pre-treatment baseline performance in mazes (No. 2-4), suggesting no significant influence of dopaminergic inhibition on allocentric spatial learning. Together, the present study potentially suggested insensitivity of allocentric spatial learning to cognitive aging and acute systematic dopaminergic inhibition.