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Abstract Background Canine circovirus (CanineCV), a non-enveloped virus with a circular DNA genome, has been identified in various avian and mammalian species, including domestic and wild canids. This study aimed to comprehensively analyze the prevalence of CanineCV across diverse animal species in 11 provinces of China. Results A total of 1,666 serum samples were collected, revealing a 5.82% prevalence of CanineCV in dogs, with the highest rates being observed in southern and eastern China. Phylogenetic analysis of 266 global CanineCV genomes sourced from the NCBI identified six distinct genotypes, elucidating the complex dynamics of their evolution. Evidence suggested a potential bat origin for CanineCV, with positive selection and high rates of evolution being observed. Recombination analysis revealed dynamic genetic exchange, highlighting the intricate nature of CanineCV evolution. Mutational analysis identified key amino acid substitutions likely to influence the virus’s adaptation. Additionally, glycosylation, palmitoylation, and SUMOylation sites were predicted, shedding light on crucial functional properties of the virus. Conclusions This study provides a global perspective on the origin, genetic diversity, and evolutionary dynamics of CanineCV. Understanding these factors is crucial for elucidating its epidemiology and potential health risks.

Abstract The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.

Abstract Background Previous studies have observed a link between immunophenotypes and lung cancer, both of which are closely associated with genetic factors. However, the causal relationship between them remains unclear. Methods Bidirectional Mendelian randomization (MR) was performed on publicly available genome-wide association study (GWAS) summary statistics to analyze the causal relationships between 731 immunophenotypes and lung cancer. Sensitivity analyses were conducted to verify the robustness, heterogeneity, and potential horizontal pleiotropy of our findings. Results Following Bonferroni adjustment, CD14− CD16+ monocyte (OR = 0.930, 95%CI 0.900–0.960, P = 8.648 × 10− 6, P Bonferroni = 0.006) and CD27 on CD24+ CD27+ B cells (OR = 1.036, 95%CI 1.020–1.053, P = 1.595 × 10 − 5, P Bonferroni = 0.012) were identified as having a causal role in lung cancer via the inverse variance weighted (IVW) method. At a more relaxed threshold, CD27 on IgD+ CD24+ B cell (OR = 1.035, 95%CI 1.017–1.053, P = 8.666 × 10− 5, P Bonferroni = 0.063) and CD27 on switched memory B cell (OR = 1.037, 95%CI 1.018–1.056, P = 1.154 × 10− 4, P Bonferroni = 0.084) were further identified. No statistically significant effects of lung cancer on immunophenotypes were found. Conclusions The elevated level of CD14− CD16+ monocytes was a protective factor against lung cancer. Conversely, CD27 on CD24+ CD27+ B cell was a risk factor. CD27 on class-switched memory B cells and IgD+ CD24+ B cells were potential risk factors for lung cancer. This research enhanced our comprehension of the interplay between immune responses and lung cancer risk. Additionally, these findings offer valuable perspectives for the development of immunologically oriented therapeutic strategies.

Canine circovirus (CanineCV), which is a new mammalian circovirus first reported in the United States in 2012, mainly causes diarrhea and vomiting in dogs. As CanineCV evolves and new subtypes emerge, there is an urgent need for new detection technologies to improve the sensitivity and detection rates of viruses in complex scenarios. A chip digital PCR(cdPCR) assay was established for the detection of CanineCV in this study. The results showed good reproducibility, specificity and a linear relationship; the minimum detection limit of CanineCV by cdPCR was 6.62 copies/μL, which is 10 times more sensitive than quantitative real-time PCR (qPCR). The qPCR-positive detection rate was 1 %, while CanineCV cdPCR (2.1 %) exhibited a greater positive detection rate. Fifteen complete genomes were sequenced and subdivided into CanineCV-1 and CanineCV-3. In conclusion, we developed a rapid, reliable, and specific cdPCR method for screening and monitoring canine CV.

Abstract The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Due to the continuous emergence of new virus variants, even if vaccines have been widely used, the detection of asymptomatic infected persons is still important in the epidemic control. Based on the unique characteristics of COVID-19 spreading dynamics, here we propose a theoretical framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy, even with incomplete information of the contract-tracing networks. Furthermore, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading.

Abstract Background Various pathogenic bacterial infections caused by acupuncture have raised widespread concern, but paravertebral abscesses and bloodstream infections of Burkholderia pseudomallei (B.pseudomallei) after acupuncture have not been reported. Case presentation A 49-year-old man was admitted to hospital with recurrent back pain and fever for 1 month, along with the finding of undiagnosed diabetes. He was considered to have tuberculosis because of unrelieved high fever and pulmonary nodules. Bilateral blood culture suggested B.pseudomallei infection, MRI of the lumbar spine suggested paravertebral abscess, and the final diagnosis was paravertebral abscess and bloodstream infection after acupuncture combined with migrating lung infection. He was discharged after abscess debridement and intensive anti-infective therapy, but no further oral antibiotics were administered because of his poor adherence. More than 5 months later, he was readmitted with the urine culture findings of B.pseudomallei. No other abscess formation was observed and he received oral antibiotics for more than 3 months without recurrence. Conclusions Acupuncture may lead to B.pseudomallei infection in high-risk groups, and inadequate treatment can lead to recurrent infections.

Complex interactions among a large number of individuals lead to multiple patterns of collective human behavior. However, the theoretical prediction of pattern transitions has not been empirically confirmed. This is because in previous empirical studies, different patterns were observed in different systems, and the coexistence of multiple patterns in the same system was rarely found. By investigating nearly 10 million messages in 252 QQ groups, we find rich distribution patterns of interevent time for human collective behavior, including the transitions between bimodal distribution, double-power-law distribution, and single-power-law distribution. The model developed in this paper suggests that human physiological rhythms and the high collective activity play key roles in the presentation of the single-power-law distribution. These results enhance the empirical research in the field of human dynamics, and are helpful for understanding many complex socioeconomic phenomena.

The likelihood of linking within a complex network is of importance to solve real-world problems, but it is challenging to predict. Sun et al. show that the link predictability limit can be well estimated by measuring the shortest compression length of a network without a need of prediction algorithm.

Given the complexity of the application scenarios of rolling bearing and the severe scarcity of fault samples, a solution to the issue of fault diagnosis under varying working conditions along with the absence of fault samples is required. A numerical model-driven cross-domain fault diagnosis method targeting variable working conditions is proposed based on the cross-Domain Nuisance Attribute Projection (cDNAP). Firstly, the simulation datasets consisting of multiple fault types under variable working conditions are constructed to solve the problem of incomplete fault samples. Secondly, the simulation datasets are expanded by means of generating adversarial network to ensure sufficient samples for subsequent model training. Finally, cDNAP is used to obtain the cross-domain simulation projection matrix, which eliminates the variance in the distribution of measured and simulated sample features under varying working conditions. The experimental results of cross-domain for variable working conditions show that the diagnostic accuracy reaches up to 99%. Compared with DANN, DSAN, and DAAN domain adversarial neural networks, the proposed method performs better in bearing fault diagnosis.

This work presents a highly secure anticounterfeiting strategy based on upconversion/afterglow hybrids with tricolor emissions tuned by a single 975 nm laser. The hybrids are composed of NaYF4:Yb/Tm and NaYF4:Yb/Er microrods and CaS:Eu2+ afterglow phosphors. Under 975 nm excitation, the hybrids exhibit multicolor emissions from green to white by adjusting laser power and then emit red afterglow light when the 975 nm laser is off. Under synergistic excitation of the blue-green light emitted by Tm/Er microrods, the red afterglow emission not only has a strong initial intensity but also lasts for 3 s. Obvious trichromatic changes from green to white to red can be observed by the naked eye. A pattern printed by the hybrid ink exhibits tricolor emissions by laser adjustment and switch. This proves that upconversion/afterglow hybrids are an excellent candidate for anticounterfeiting applications with high-level security but a simple recognition method.

For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation, namely IACMDSR, is developed in this paper. Firstly, the IACMD is employed to simultaneously separate the distinct fault types and extract multiple resonance frequencies induced by them. Next, an adaptive bilateral wavelet hyper-dictionary that digs deeper into the periodicity and waveform characteristics exhibited by the real fault impulse response is constructed to identify and reconstruct each type of fault-induced feature with the help of the orthogonal matching pursuit (OMP) algorithm. Finally, the fault characteristic frequency can be detected via an envelope demodulation analysis of the reconstructed signal. A simulation and two sets of experimental results confirm that the developed IACMDSR model is a powerful and versatile tool and consistently outperforms the leading MCKDSR and MCKDMWF models. Furthermore, the developed model has satisfactory capability in practical applications because the IACMD has no requirement for the input number of the signal components and the adaptive bilateral wavelet is powerfully matched to the real fault-induced impulse response.

Preferences or dislikes for specific numbers are ubiquitous in human society. In traditional Chinese culture, people show special preference for some numbers, such as 6, 8, 10, 100, 200, etc. By analyzing the data of 6.8 million users of Sina Weibo, one of the largest online social media platforms in China, we discover that users exhibit a distinct preference for the number 200, i.e., a significant fraction of users prefer to follow 200 friends. This number, which is very close to the Dunbar number that predicts the cognitive limit on the number of stable social relationships, motivates us to investigate how the preference for numbers in traditional Chinese culture is reflected on social media. We systematically portray users who prefer 200 friends and analyze their several important social features, including activity, popularity, attention tendency, regional distribution, economic level, and education level. We find that the activity and popularity of users with the preference for the number 200 are relatively lower than others. They are more inclined to follow popular users, and their social portraits change relatively slowly. Besides, users who have a stronger preference for the number 200 are more likely to be located in regions with underdeveloped economies and education. That indicates users with the preference for the number 200 are likely to be vulnerable groups in society and are easily affected by opinion leaders.

BACKGROUND:Chromoblastomycosis is a chronic skin and subcutaneous fungal infection caused by dematiaceous fungi and is associated with low cure and high relapse rates. In southern China, Fonsecaea monophora and Fonsecaea pedrosoi are the main causative agents. PRINCIPAL FINDINGS:We treated 5 refractory and complex cases of chromoblastomycosis with 5-aminolevulinic acid photodynamic therapy (ALA-PDT) combined with oral antifungal drugs. The lesions improved after 4 to 9 sessions of ALA-PDT treatment at an interval of one or two weeks, and in some cases, mycological testing results became negative. The isolates were assayed for susceptibility to antifungal drugs and ALA-PDT in vitro, revealing sensitivity to terbinafine, itraconazole and voriconazole, with ALA-PDT altering the cell wall and increasing reactive oxygen species production. CONCLUSIONS:These results provide the basis for the development of a new therapeutic approach, and ALA-PDT combined with oral antifungal drugs constitutes a promising alternative method for the treatment of refractory and complex cases of chromoblastomycosis.

Background Self-renewal and pluripotency are considered as unwavering features of embryonic stem cells (ESCs). How ESCs regulate the self-renewal and differentiation is a central question in development and regenerative medicine research. Epidermal growth factor receptor (EGFR) was identified as a critical regulator in embryonic development, but its role in the maintenance of ESCs is poorly understood. Methods Here, EGFR was disrupted by its specific inhibitor AG1478 in mouse ESCs (mESCs), and its self-renewal and pluripotency were characterized according to their proliferation, expression of pluripotency markers, embryoid body (EB) formation, and mRNA expression patterns. We also used another EGFR inhibitor (gefitinib) and RNA interference assay to rule out the possibility of non-specific effects of AG1478. Results EGFR inhibition by AG1478 treatment in mESCs markedly reduced cell proliferation, caused cell cycle arrest at G0/G1 phase, and altered protein expressions of the cell cycle regulatory genes (CDK2 (decreased 11.3%) and proliferating cell nuclear antigen (decreased 25.2%)). The immunoreactivities and protein expression of pluripotency factors (OCT4 (decreased 26.9%)) also dramatically decreased, while the differentiation related genes (GATA4 (increased 1.6-fold)) were up-regulated in mESCs after EGFR inhibition. Meanwhile, EGFR inhibition in mESCs disrupted EB formation, indicating its impaired pluripotency. Additionally, the effects observed by EGFR inhibition with another inhibitor gefitinib and siRNA were consistent with those observed by AG1478 treatment in mESCs. These effects were manifested in the decreased expression of proliferative and pluripotency-related genes and the increased expression of genes involved in differentiation. Moreover, RNA-seq analysis displayed that transcript profiling was changed significantly after EGFR inhibition by AG1478. A large number of differentially expressed genes were involved in cell cycle, apoptotic process, epigenetic modification, and metabolic process, which were related to self-renewal and pluripotency, confirming that EGFR deficiency impaired self-renewal and pluripotency in mESCs. Conclusions Taken together, our results demonstrated the importance of EGFR in guarding the stemness of mESCs.

To learn more about redundancy phenomenon,a measurement to quantify this redundancy was proposed and the probability of individual redundancy in the process of information diffusion was deduced.Using simulated networks,how information propagation rate,network density,clustering coefficient affect information redundancy was investigated.The diffusion characteristics of redundant information through the actual data of Sina Weibo was observed and the perspective of information redundancy in advertising promotion and product marketing was discussed from marketing.The findings provide a new perspective for further understanding of the information diffusion characteristics of online social media.

Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.

Social networks constitute a new platform for information propagation, but its success is crucially dependent on the choice of spreaders who initiate the spreading of information. In this paper, we remove edges in a network at random and the network segments into isolated clusters. The most important nodes in each cluster then form a set of influential spreaders, such that news propagating from them would lead to extensive coverage and minimal redundancy. The method utilizes the similarities between the segmented networks before percolation and the coverage of information propagation in each social cluster to obtain a set of distributed and coordinated spreaders. Our tests of implementing the susceptible-infected-recovered model on Facebook and Enron email networks show that this method outperforms conventional centrality-based methods in terms of spreadability and coverage redundancy. The suggested way of identifying influential spreaders thus sheds light on a new paradigm of information propagation in social networks.

In many clinical trials, it is important to balance treatment allocation over covariates. Although a great many papers have been published on balancing over discrete covariates, the procedures for continuous covariates have been less well studied. Traditionally, a continuous covariate usually needs to be transformed to a discrete one by splitting its range into several categories. Such practice may lead to loss of information and is susceptible to misspecification of covariate distribution. The more recent papers seek to define an imbalance measure that preserves the nature of continuous covariates and set the allocation rule in order to minimize that measure. We propose a new design, which defines the imbalance measure by the maximum assignment difference when all possible divisions of the covariate range are considered. This measure depends only on ranks of the covariate values and is therefore free of covariate distribution. In addition, we developed an efficient algorithm to implement the new procedure. By simulation studies we show that the new procedure is able to keep good balance properties in comparison with other popular designs.

Interdependent networks in areas ranging from infrastructure to economics are ubiquitous in our society, and the study of their cascading behaviors using percolation theory has attracted much attention in recent years. To analyze the percolation phenomena of these systems, different mathematical frameworks have been proposed, including generating functions and eigenvalues, and others. These different frameworks approach phase transition behaviors from different angles and have been very successful in shaping the different quantities of interest, including critical threshold, size of the giant component, order of phase transition, and the dynamics of cascading. These methods also vary in their mathematical complexity in dealing with interdependent networks that have additional complexity in terms of the correlation among different layers of networks or links. In this work, we review a particular approach of simple, self-consistent probability equations, and we illustrate that this approach can greatly simplify the mathematical analysis for systems ranging from single-layer network to various different interdependent networks. We give an overview of the detailed framework to study the nature of the critical phase transition, the value of the critical threshold, and the size of the giant component for these different systems.

A fundamental problem in network science is to predict how certain individuals are able to initiate new networks to spring up “new ideas.” Frequently, these changes in trends are triggered by a few innovators who rapidly impose their ideas through “viral” influence spreading, producing cascades of followers and fragmenting an old network to create a new one. Typical examples include the rise of scientific ideas or abrupt changes in social media, like the rise of Facebook to the detriment of Myspace. How this process arises in practice has not been conclusively demonstrated. Here, we show that a condition for sustaining a viral spreading process is the existence of a multiplex-correlated graph with hidden “influence links.” Analytical solutions predict percolation-phase transitions, either abrupt or continuous, where networks are disintegrated through viral cascades of followers, as in empirical data. Our modeling predicts the strict conditions to sustain a large viral spreading via a scaling form of the local correlation function between multilayers, which we also confirm empirically. Ultimately, the theory predicts the conditions for viral cascading in a large class of multiplex networks ranging from social to financial systems and markets.

Member 5 of the Upper Triassic Xujiahe Formation (T3X5) in central Sichuan Basin has made a breakthrough in exploration recently. However, this new stratum has not been investigated sufficiently with respect to basic geology, making its types and distribution of sedimentary facies unclear, which severely restricts its subsequent exploration evaluation. In this study, types of sedimentary microfacies in the first sand group of T3X5 (T3X51) are clarified through core observation and logging interpretation using core, log and seismic data, and then distribution of sedimentary microfacies in T3X51 is determined according to seismic waveform features and seismic prediction. The results show that T3X51 in the Dongfengchang area is mainly composed of deltaic deposits of several microfacies, such as delta front underwater distributary channel, sheet sand, and interdistributary bay. On seismic sections, different microfacies are significantly different in waveform features, the underwater distributary channel is characterized by one trough between two peaks, while diversion bay exhibits chaotic reflections between T6 and T51. The sedimentary microfacies varied greatly during the depositional period of T3X51 in the Dongfengchang area, this is because that the sediment supply was mainly controlled by the southwest and southeast provenance regions. Three superimposed underwater distributary channels are developed in the Dongfengchang area. The phase-1 superimposed underwater distributary channel in the northwest transition to sheet sand in the northeast, the phase-2 superimposed underwater distributary channel in the south extends shortly, the phase-3 superimposed underwater distributary channel in the northeast has a large development scale. These research findings are helpful to guide the subsequent exploration of T3X5 gas reservoir and also theoretically significant for investigating the depositional evolution of the Xujiahe Formation in central Sichuan Basin.

Abstract The complexity of brain activity reflects its ability to process information, adapt to environmental changes, and transition between states. However, it remains unclear how schizophrenia (SZ) affects brain activity complexity, particularly its dynamic changes. This study aimed to investigate the abnormal patterns of brain activity complexity in SZ, their relationship with cognitive deficits, and the impact of antipsychotic medication. Forty-four drug-naive first-episode (DNFE) SZ patients and thirty demographically matched healthy controls (HC) were included. Functional MRI-based sliding window analysis was utilized for the first time to calculate weighted permutation entropy to characterize complex patterns of brain activity in SZ patients before and after 12 weeks of risperidone treatment. Results revealed reduced complexity in the caudate, putamen, and pallidum at baseline in SZ patients compared to HC, with reduced complexity in the left caudate positively correlated with Continuous Performance Test (CPT) and Category Fluency Test scores. After treatment, the complexity of the left caudate increased. Regions with abnormal complexity showed decreased functional connectivity, with complexity positively correlated with connectivity strength. We observed that the dynamic complexity of the brain exhibited the characteristic of spontaneous, recurring “complexity drop”, potentially reflecting transient state transitions in the resting brain. Compared to HC, patients exhibited reduced scope, intensity, and duration of complexity drop, all of which improved after treatment. Reduced duration was negatively correlated with CPT scores and positively with clinical symptoms. The results suggest that abnormalities in brain activity complexity and its dynamic changes may underlie cognitive deficits and clinical symptoms in SZ patients. Antipsychotic treatment partially restores these abnormalities, highlighting their potential as indicators of treatment efficacy and biomarkers for personalized therapy.

Abstract Microbial communities are ubiquitous in nature and play an important role in ecology and human health. Cross-feeding is thought to be core to microbial communities, though it remains unclear precisely why it emerges. Why have multi-species microbial communities evolved in many contexts and what protects microbial consortia from invasion? Here, we review recent insights into the emergence and stability of coexistence in microbial communities. A particular focus is the long-term evolutionary stability of coexistence, as observed for microbial communities that spontaneously evolved in the E. coli long-term evolution experiment (LTEE). We analyze these findings in the context of recent work on trade-offs between competing microbial objectives, which can constitute a mechanistic basis for the emergence of coexistence. Coexisting communities, rather than monocultures of the ‘fittest’ single strain, can form stable endpoints of evolutionary trajectories. Hence, the emergence of coexistence might be an obligatory outcome in the evolution of microbial communities. This implies that rather than embodying fragile metastable configurations, some microbial communities can constitute formidable ecosystems that are difficult to disrupt.

Abstract Background Asians and Asian Americans have the lowest rate of mental health service utilization (25%) in the US compared to other racial/ethnic groups (39 − 52%), despite high rates of depression, anxiety, and suicidal ideation. The lack of culturally-responsive mental health trainings hinders access to mental health services for these populations. We assessed the mental health priorities of Asian communities in Greater Boston and evaluated cultural responsiveness of the Mental Health First Aid (MHFA), a first-responder training teaching participants skills to recognize signs of mental health and substance use challenges, and how to appropriately respond. Methods This is community-based participatory research with the Boston Chinatown Neighborhood Center (BCNC), Asian Women For Health (AWFH), and the Addressing Disparities in Asian Populations through Translational Research (ADAPT) Coalition. We conducted focus groups with community-based organization staff and community members to assess mental health priorities of Asian populations in Boston, MA. We then evaluated the utility and cultural-responsiveness of the English-language MHFA for Asian populations through pre- and post-training questionnaires and focus groups with community participants. Paired t-tests were used to evaluate questionnaire responses. Thematic analysis was used to analyze interviews. Results In total, ten staff and eight community members participated in focus groups, and 24 community members completed the MHFA and pre- and post-training questionnaires. Common mental health challenges in the Asian communities reported by participants were loneliness, high stigma around mental illnesses, academic pressure, and acculturation stress. Compared to pre-training, MHFA participants demonstrated lower personal mental health stigma (p

Abstract Background Gastric cancer (GC) is a malignant digestive tract tumor with a high recurrence rate and poor prognosis. Fucosylation is important in tumor glycosylation, in which the key enzyme is fucosyltransferase (FUT). FUT11 is a member of the fucosyltransferase family and has been closely associated with the development of multiple cancers. However, the specific relationship between FUT11 and GC prognosis and its molecular mechanism has not been fully studied. This study explored FUT11 expression, clinical correlation, and its role in GC occurrence and development to deepen understanding of its function. Methods FUT11 expression in 33 cancers was preliminarily analyzed using the Tumor Immunoassay Resource (TIMER2.0) database. FUT11 expression in GC was evaluated using The Cancer Genome Atlas stomach adenocarcinoma (TCGA-STAD) and Gene Expression Profiling Interactive Analysis (GEPIA2) data and verified using the Gene Expression Omnibus (GEO) GSE65801 dataset. Furthermore, we studied the survival prognosis of FUT11 in GC and analyzed its effect on the survival rate of patients with GC using the KM-plotter. We also performed COX regression analysis on TCGA GC clinical data and analyzed FUT11 expression in the pathway using the STRING and LinkedOmics databases. Moreover, the relationship between FUT11 and GC immune infiltration level was examined, and the Kaplan–Meier survival analysis diagram was constructed. The FUT11 genetic variation information was retrieved using cBioPortal, and its drug sensitivity was analyzed using CellMiner. Finally, differential FUT11 expression in GC tissues was verified using immunohistochemistry. Results The data mining and analysis demonstrated that FUT11 expression was abnormally elevated in GC tissues and correlated with poor patient prognosis. The FUT11 expression level was an independent prognostic factor for GC. The difference in FUT11 expression level resulted in different degrees of immune cell infiltration in the patients with GC, which might regulate the tumor microenvironment. FUT11 affected GC development by participating in cancer pathways such as PI3K–AKT, neuroactive ligand–receptor, and MAPK. Immunohistochemical staining revealed that FUT11 was highly expressed in GC. Conclusions This study revealed that FUT11 expression is significantly increased in GC tissues. This increase is associated with poor prognosis and might affect immune regulation. FUT11 might have immunological and targeted therapeutic value, providing a new approach to GC treatment.