Spatial-temporal differentiation pattern and influencing factors of land economic density at the township scale in Zhejiang Province.

Based on the land economic density of 892 town units, the spatial pattern of the land economic density in Zhejiang Province is analyzed using the coefficient of variation, spatial classification, and spatial correlation methods, and the influencing factors are analyzed using a spatial regression model. The results are as follows: (1) The coefficients of variation were 2.6 and 3.1 in 2014 and 2019, respectively, indicating that the degree of imbalance of the town's industrial economy at the county level increased. (2) The distribution of the high-level agglomeration areas was characterized by one core area and two sub-core areas. The main core area was located at the junction of Hangzhou City, Shaoxing City, and Jiaxing City, and the two sub-core areas were located in Yuyao City and the main urban area of Ningbo City. In addition, several small-scale agglomeration areas composed of medium and high-level units were distributed in Wenzhou City. (3) The high-value agglomeration and low-value agglomeration distribution in the spatial correlation patterns was identified using the spatial auto-correlation method. The hot spots and sub-hot spots were distributed in Northern Zhejiang, and the cold spots formed a large-scale agglomeration in Quzhou City, Lishui City, Taizhou City, and several other cities in Southern Zhejiang. (4) Compared with the county scale, the spatial scope of the high-level areas in Northern Zhejiang shrunk significantly at the township scale, and the high-level agglomeration areas along the southeast coast changed into a cluster of several townships. (5) According to the geographically weighted regression (GWR) model, the importance of influencing factors is as follows: population density > regional area > industrial output value per capita > total population > proportion of secondary and tertiary personnel > total employees.

Analysis of network patterns and its influencing factors in Chengdu-Chongqing urban agglomeration based on multi-flow.

With the strengthening of the cross-regional flows of the economy, information, innovation, and population, this paper constructs a network model of multi-flow integration and analyzes the spatial pattern and influencing factors of urban networks in Chengdu-Chongqing Urban Agglomeration using social network analysis and spatial analysis technology. The main conclusions are as follows. (1) The density and efficiency are in the transition stage from the primary level to the medium level in the comprehensive network. (2) The overall pattern keeps a polyhedral pyramid structure with Chengdu ↔ Chongqing as the core axis, and the grade of each axis has been significantly raised. (3) Four groups are formed using the social network method and show a geographic proximity effect. In addition, the connections within each group are relatively close, but the connections between the groups are significantly different. (4) Location conditions, economic development level, enterprise development level, scientific research investment, scientific and technological development level, and government support have a greater impact on the formation of the comprehensive network of Chengdu-Chongqing urban agglomeration. Information application level and transportation accessibility show a small impact and human capital level has not yet produced a significant impact.

Most discriminative stimuli for functional cell type clustering.

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron.

The Association Between Tinnitus Sensation-Level Loudness and Sleep Quality in Patients With Subjective Consecutive Tinnitus: A Mediation Analysis.

PURPOSE: So far, there have been no in-depth analyses of the connection between tinnitus sensation-level loudness and sleep quality. Accordingly, the present study was formulated as a mediation analysis focused on exploring this relationship. METHOD: Overall, 1,255 adults with consecutive subjective tinnitus who had sought outpatient treatment were enrolled in the present study. RESULTS: Direct effects of tinnitus sensation-level loudness on sleep quality were not statistically significant (95% confidence intervals [CI] include zero), as measured by the point estimate, -0.016. However, the 95% CI for indirect effects did not include zero when assessing the Self-Rating Anxiety Scale (SAS) scores, the Self-Rating Depression Scale (SDS) scores, the visual analogue scale (VAS) scores, and self-reported tinnitus annoyance. CONCLUSIONS: These results suggest that tinnitus sensation-level loudness does not directly have an effect on sleep quality. However, it indirectly impacts sleep quality, mediated by SAS scores, SDS scores, the impact of tinnitus on life measured using the VAS, and self-reported tinnitus annoyance. As such, alleviating anxiety and depression in patients with tinnitus may result in reductions in their insomnia even if there is no reduction in tinnitus loudness. Importantly, otolaryngologists and other clinicians treating tinnitus should refer patients with tinnitus suffering from insomnia with comorbid depression or anxiety for appropriate psychological and/or psychiatric treatment.

Transcriptional-profile changes in the medial geniculate body after noise-induced tinnitus.

Tinnitus is a disturbing condition defined as the occurrence of acoustic hallucinations with no actual sound. Although the mechanisms underlying tinnitus have been explored extensively, the pathophysiology of the disease is not completely understood. Moreover, genes and potential treatment targets related to auditory hallucinations remain unknown. In this study, we examined transcriptional-profile changes in the medial geniculate body after noise-induced tinnitus in rats by performing RNA sequencing and validated differentially expressed genes via quantitative polymerase chain reaction analysis. The rat model of tinnitus was established by analyzing startle behavior based on gap-pre-pulse inhibition of acoustic startles. We identified 87 differently expressed genes, of which 40 were upregulated and 47 were downregulated. Pathway-enrichment analysis revealed that the differentially enriched genes in the tinnitus group were associated with pathway terms, such as coronavirus disease COVID-19, neuroactive ligand-receptor interaction. Protein-protein-interaction networks were established, and two hub genes (Rpl7a and AC136661.1) were identified among the selected genes. Further studies focusing on targeting and modulating these genes are required for developing potential treatments for noise-induced tinnitus in patients.

Spatial pattern of China's rural digital economy based on subjective-Objective evaluation: Evidence from 2085 counties.

The rural digital economy plays an essential role in China's industrial upgrading, transformation, and urban-rural integration. To determine the state of China's rural digital economy, we constructed a county-level evaluation system using the subjective-objective evaluation method and calculated the digital economic levels of 2085 counties. Then, we analyzed the spatial distribution characteristics, spatial autocorrelation pattern, spatial disequilibrium degree, and spatial driving force of the rural digital economy at the county level using spatial analysis technology and a self-organizing feature mapping model. The results are as follows: 1) Compared with the real economy, the agglomeration effect of the digital economy was more obvious, and the economic gradient was more significant. Specifically, the dense high-value regions formed a continuous belt on the eastern coast from the Beijing-Tianjin area to the Pearl River Delta, opposite the dense low-value regions in the west. 2) There were significant differences in the rural digital economy within cities or provinces. Intraregional differences were not necessarily linked to the overall digital economy level because central and northeastern China presented a more balanced rural digital economy. 3) Digital network performance, e-commerce level, and economic vitality were identified as the core factors influencing the rural digital economy.

Pain relief effect of angiopuncture therapy on patients with postoperative pain: A clinical trial.

BACKGROUND: The objective of this study is to study the pain relief effects of angiopuncture therapy in patients with postoperative pain. METHODS: Forty-one patients were randomly selected based on the inclusion and exclusion criteria. Doppler imaging was performed to locate the cutaneous perforator. Angiopuncture was performed on the first postoperative day. A Numerical Rating Scale was used to evaluate the degree of pain before and after angiopuncture. Utilizing the paired t test or Wilcoxon signed rank test, all pre- and post-data were examined, and further subgroup analysis based on time was performed. RESULTS: Variance analysis revealed a significant difference before and after angiopuncture (P < .05). The results of the subgroup analysis showed the pain-relieving effect of angiopuncture for postoperative pain patients at the time points of 6 hours, 12 hours, 24 hours, 48 hours, and 72 hours was apparent (P < .05). CONCLUSION: The angiopuncture therapy approach may assist in pain relief in patients with postoperative pain.

Perforating cutaneous vessels: A key feature of acupoints - Anatomical evidence from five-Shu acupoints in the upper limbs.

Acupuncture has been proven an effective clinical treatment for numerous pathological conditions and malfunctions. However, substantial anatomical evidence for acupuncture points (APs) and meridians is still lacking, so the location of APs is relatively subjective and understanding of the biological mechanisms of acupuncture is limited. All these problems hinder the clinical applications and worldwide acceptance of acupuncture. Our long-term microsurgery experience has indicated that Perforating Cutaneous Vessels (PCVs) are highly relevant to APs but the anatomical evidence is insufficient. To address this lack, two specimens of fresh adult human upper limbs were dissected using an advanced vascular perfusion-fixation method and then examined. The results show that all 30 five-Shu APs in the upper limbs have corresponding PCVs. Both specimens showed a 100% coincidence rate between APs and PCVs, indicating that PCVs could be critical anatomical features of APs. This study also provides an anatomical basis for locating APs objectively via preliminary detection of PCVs. The findings could lead to a better theoretical understanding of mechanisms of acupuncture and the essence of meridians.

Outcomes of cochlear implantation in 75 patients with auditory neuropathy.

Background: Cochlear implantation (CI) outcomes in patients with auditory neuropathy (AN) are variable, which hampers patients' decisions on CI. Objective: This study aims to assess the outcomes of CI in individuals diagnosed with AN and to examine the various factors that may influence the effectiveness of this intervention. Methods: A total of 75 patients diagnosed with AN were included in the study. The hearing threshold, the score of categories of auditory performance (CAP), speech intelligibility rating (SIR), and speech audiometry were tested. Genetic testing was conducted by medical exome sequencing in 46 patients. Results: After CI, the average aided hearing threshold for patients with prelingual and post-lingual onset was 38.25 ± 6.63 dB and 32.58 ± 9.26 dB, respectively; CAP score improved to 5.52 ± 1.64 (p < 0.001) and 6.00 ± 0.96 (p < 0.001), respectively; SIR score increased to 3.57 ± 1.22 (p < 0.001) and 4.15 ± 0.95 (p < 0.001), respectively. Maximum speech recognition ranged from 58 to 93% for prelingual onset patients and 43 to 98% for those with post-lingual onset. Speech outcomes of CI in cases with cochlear nerve (CN) deficiency were significantly poorer (p = 0.008). Molecular etiologies, including TWIST1, ACTG1, m.A7445G, and a copy-number variant (CNV) carrying ACTB, were related to AN here. Conclusion: CI is a viable therapy option for patients with AN; CN deficiency might impact outcomes of CI.

Accuracy analysis of traditional acupoint location and the coincidence of cutaneous arterial perforators and acupoints.

Several reports have shown a coincidence relationship between perforators and acupoints. However, there have been few previous reports of objective experimental methods to verify the reliability of the accuracy of acupoint location (APL) with nearby perforators. This research aimed to determine the internal agreement of the APL of five acupuncturists and to analyze the coincidence rate of acupoints with nearby perforators. Three two healthy volunteers were recruited with the inclusion and exclusion criteria. Three TCM clinical physicians determined acupoints in areas of the lower limb of participants. Two microsurgeons sketched corresponding regions based on the most common skin flap operation sites, located bone markers, and drew the skin flap axis. Doppler ultrasound was used to mark the perforator point and the distances measured for both points. There is no significant difference in the distance between the acupoints and perforators localization in different groups, and there are significant differences between the angle formed by acupoints and penetrators in all groups. All the points located by the traditional Chinese medicine (TCM) therapists are distributed around the dot. The distance between the coordinate point (A-B) of Wenliu (LI7) localization is the largest, reaching 16.6 mm. The accuracy of the acupoint location of each physician is limited by the clinical experience of physicians, and the difference among them is significant. There is a certain correspondence between the location of acupoints and perforators, which needs further studies to confirm.

The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://www.sensorium-competition.net/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of five mice, containing responses from over 38,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.

Prognostic factor analysis in patients with early-stage nasopharyngeal carcinoma in the USA.

Aim: To evaluate independent risk factors specific for early-stage nasopharyngeal carcinoma (NPC). Methods: A total of 566 patients with early-stage NPC from 2004 to 2019 were identified using the Surveillance, Epidemiology and End Results database. Results: Older ages (70-79 and >80 years) were independent risk factors, with hazard ratios of 1.961 and 5.011, respectively. The hazard ratio for early-stage NPC in Asian and Pacific Islander residents (0.475) was lower than that for White residents. A tumor size <3 cm was a protective factor for overall and cancer-specific survival in the current study. Conclusion: In patients with early-stage NPC, age >70 years, race and tumor size were independent prognosticators for cancer-specific survival.

Transcriptional Profile Changes after Noise-Induced Tinnitus in Rats.

Tinnitus is an unpleasant symptom characterized by detective hearing without the actual sound input. Despite numerous studies elucidating a variety of pathomechanisms inducing tinnitus, the pathophysiology of tinnitus is not fully understood. The genes that are closely associated with this subtype of the auditory hallucination that could be utilized as potential treatment targets are still unknown. In this study, we explored the transcriptional profile changes of the auditory cortex after noise-induced tinnitus in rats using high throughput sequencing and verification of the detected genes using quantitative PCR (qPCR). Tinnitus models were established by analyzing startle behaviors through gap pre-pulse inhibition (PPI) of the acoustic startle. Two hundred and fifty-nine differential genes were identified, of which 162 genes were up-regulated and 97 genes were down-regulated. Analysis of the pathway enrichment indicated that the tinnitus group exhibited increased gene expression related to neurodegenerative disorders such as Huntington's disease and Amyotrophic lateral sclerosis. Based on the identified genes, networks of protein-protein interaction were established and five hub genes were identified through degree rank, including Fos, Nr4a1, Nr4a3, Egr2, and Egr3. Therein, the Fos gene ranked first with the highest degree after noise exposure, and may be a potential target for the modulation of noise-induced tinnitus.

Study on tinnitus-related electroencephalogram microstates in patients with vestibular schwannomas.

Tinnitus is closely associated with cognition functioning. In order to clarify the central reorganization of tinnitus in patients with vestibular schwannoma (VS), this study explored the aberrant dynamics of electroencephalogram (EEG) microstates and their correlations with tinnitus features in VS patients. Clinical and EEG data were collected from 98 VS patients, including 76 with tinnitus and 22 without tinnitus. Microstates were clustered into four categories. Our EEG microstate analysis revealed that VS patients with tinnitus exhibited an increased frequency of microstate C compared to those without tinnitus. Furthermore, correlation analysis demonstrated that the Tinnitus Handicap Inventory (THI) score was negatively associated with the duration of microstate A and positively associated with the frequency of microstate C. These findings suggest that the time series and syntax characteristics of EEG microstates differ significantly between VS patients with and without tinnitus, potentially reflecting abnormal allocation of neural resources and transition of functional brain activity. Our results provide a foundation for developing diverse treatments for tinnitus in VS patients.

Biology-Informed Recurrent Neural Network for Pandemic Prediction Using Multimodal Data.

In the biomedical field, the time interval from infection to medical diagnosis is a random variable that obeys the log-normal distribution in general. Inspired by this biological law, we propose a novel back-projection infected-susceptible-infected-based long short-term memory (BPISI-LSTM) neural network for pandemic prediction. The multimodal data, including disease-related data and migration information, are used to model the impact of social contact on disease transmission. The proposed model not only predicts the number of confirmed cases, but also estimates the number of infected cases. We evaluate the proposed model on the COVID-19 datasets from India, Austria, and Indonesia. In terms of predicting the number of confirmed cases, our model outperforms the latest epidemiological modeling methods, such as vSIR, and intelligent algorithms, such as LSTM, for both short-term and long-term predictions, which shows the superiority of bio-inspired intelligent algorithms. In general, the use of mobility information improves the prediction accuracy of the model. Moreover, the number of infected cases in these three countries is also estimated, which is an unobservable but crucial indicator for the control of the pandemic.

Bipartite invariance in mouse primary visual cortex.

A defining characteristic of intelligent systems, whether natural or artificial, is the ability to generalize and infer behaviorally relevant latent causes from high-dimensional sensory input, despite significant variations in the environment. To understand how brains achieve generalization, it is crucial to identify the features to which neurons respond selectively and invariantly. However, the high-dimensional nature of visual inputs, the non-linearity of information processing in the brain, and limited experimental time make it challenging to systematically characterize neuronal tuning and invariances, especially for natural stimuli. Here, we extended "inception loops" - a paradigm that iterates between large-scale recordings, neural predictive models, and in silico experiments followed by in vivo verification - to systematically characterize single neuron invariances in the mouse primary visual cortex. Using the predictive model we synthesized Diverse Exciting Inputs (DEIs), a set of inputs that differ substantially from each other while each driving a target neuron strongly, and verified these DEIs' efficacy in vivo. We discovered a novel bipartite invariance: one portion of the receptive field encoded phase-invariant texture-like patterns, while the other portion encoded a fixed spatial pattern. Our analysis revealed that the division between the fixed and invariant portions of the receptive fields aligns with object boundaries defined by spatial frequency differences present in highly activating natural images. These findings suggest that bipartite invariance might play a role in segmentation by detecting texture-defined object boundaries, independent of the phase of the texture. We also replicated these bipartite DEIs in the functional connectomics MICrONs data set, which opens the way towards a circuit-level mechanistic understanding of this novel type of invariance. Our study demonstrates the power of using a data-driven deep learning approach to systematically characterize neuronal invariances. By applying this method across the visual hierarchy, cell types, and sensory modalities, we can decipher how latent variables are robustly extracted from natural scenes, leading to a deeper understanding of generalization.

Pattern completion and disruption characterize contextual modulation in mouse visual cortex.

A key role of sensory processing is integrating information across space. Neuronal responses in the visual system are influenced by both local features in the receptive field center and contextual information from the surround. While center-surround interactions have been extensively studied using simple stimuli like gratings, investigating these interactions with more complex, ecologically-relevant stimuli is challenging due to the high dimensionality of the stimulus space. We used large-scale neuronal recordings in mouse primary visual cortex to train convolutional neural network (CNN) models that accurately predicted center-surround interactions for natural stimuli. These models enabled us to synthesize surround stimuli that strongly suppressed or enhanced neuronal responses to the optimal center stimulus, as confirmed by in vivo experiments. In contrast to the common notion that congruent center and surround stimuli are suppressive, we found that excitatory surrounds appeared to complete spatial patterns in the center, while inhibitory surrounds disrupted them. We quantified this effect by demonstrating that CNN-optimized excitatory surround images have strong similarity in neuronal response space with surround images generated by extrapolating the statistical properties of the center, and with patches of natural scenes, which are known to exhibit high spatial correlations. Our findings cannot be explained by theories like redundancy reduction or predictive coding previously linked to contextual modulation in visual cortex. Instead, we demonstrated that a hierarchical probabilistic model incorporating Bayesian inference, and modulating neuronal responses based on prior knowledge of natural scene statistics, can explain our empirical results. We replicated these center-surround effects in the multi-area functional connectomics MICrONS dataset using natural movies as visual stimuli, which opens the way towards understanding circuit level mechanism, such as the contributions of lateral and feedback recurrent connections. Our data-driven modeling approach provides a new understanding of the role of contextual interactions in sensory processing and can be adapted across brain areas, sensory modalities, and species.

Functional connectomics reveals general wiring rule in mouse visual cortex.

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Towards a Foundation Model of the Mouse Visual Cortex.

Understanding the brain's perception algorithm is a highly intricate problem, as the inherent complexity of sensory inputs and the brain's nonlinear processing make characterizing sensory representations difficult. Recent studies have shown that functional models-capable of predicting large-scale neuronal activity in response to arbitrary sensory input-can be powerful tools for characterizing neuronal representations by enabling high-throughput in silico experiments. However, accurately modeling responses to dynamic and ecologically relevant inputs like videos remains challenging, particularly when generalizing to new stimulus domains outside the training distribution. Inspired by recent breakthroughs in artificial intelligence, where foundation models-trained on vast quantities of data-have demonstrated remarkable capabilities and generalization, we developed a "foundation model" of the mouse visual cortex: a deep neural network trained on large amounts of neuronal responses to ecological videos from multiple visual cortical areas and mice. The model accurately predicted neuronal responses not only to natural videos but also to various new stimulus domains, such as coherent moving dots and noise patterns, underscoring its generalization abilities. The foundation model could also be adapted to new mice with minimal natural movie training data. We applied the foundation model to the MICrONS dataset: a study of the brain that integrates structure with function at unprecedented scale, containing nanometer-scale morphology, connectivity with >500,000,000 synapses, and function of >70,000 neurons within a ~1mm3 volume spanning multiple areas of the mouse visual cortex. This accurate functional model of the MICrONS data opens the possibility for a systematic characterization of the relationship between circuit structure and function. By precisely capturing the response properties of the visual cortex and generalizing to new stimulus domains and mice, foundation models can pave the way for a deeper understanding of visual computation.

Pan-cancer analysis identifies NT5E as a novel prognostic biomarker on cancer-associated fibroblasts associated with unique tumor microenvironment.

Background: Ecto-5'-nucleotidase (NT5E) encodes the cluster of differentiation 73 (CD73), whose overexpression contributes to the formation of immunosuppressive tumor microenvironment and is related to exacerbated prognosis, increased risk of metastasis and resistance to immunotherapy of various tumors. However, the prognostic significance of NT5E in pan-cancer is obscure so far. Methods: We explored the expression level of NT5E in cancers and adjacent tissues and revealed the relationship between the NT5E expression level and clinical outcomes in pan-cancer by utilizing the UCSC Xena database. Then, correlation analyses were performed to evaluate the relationship between NT5E expression and immune infiltration level via EPIC, MCP-counter and CIBERSORT methods, and the enrichment analysis were employed to identify NT5E-interacting molecules and functional pathways. Furthermore, we conducted single-cell analysis to explore the potential role of NT5E on single-cell level based on the CancerSEA database. Meanwhile, gene set enrichment analysis (GSEA) in single-cell level was also conducted in TISCH database and single-cell signature explorer was utilized to evaluate the epithelial-mesenchymal transition (EMT) level in each cell type. Results: The expression level of NT5E was aberrant in almost all cancer types, and was correlated with worse prognosis in several cancers. Notably, NT5E overexpression was related to worse overall survival (OS) in pancreatic adenocarcinoma (PAAD), head and neck squamous cell carcinoma (HNSC), mesothelioma (MESO), stomach adenocarcinoma (STAD), uveal melanoma (UVM) and cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (p < 0.01). NT5E-related immune microenvironment analysis revealed that NT5E is associated positively with the degree of infiltration of cancer-associated fibroblasts (CAFs) and endothelial cells in most cancers. Enrichment analysis of cellular component (CC) demonstrated the critical part of NT5E played in cell-substrate junction, cell-substrate adherens junction, focal adhesion and external side of plasma membrane. Finally, single-cell analysis of NT5E illuminated that EMT function of CAFs was elevated in basal cell carcinoma (BCC), skin cutaneous melanoma (SKCM), HNSC and PAAD. Conclusion: NT5E could serve as a potential prognostic biomarker for cancers. The potential mechanism may be related to the upregulated EMT function of CAFs, which provides novel inspiration for immunotherapy by targeting CAFs with high NT5E expression.