Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification.

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.

CE-GAN: Community Evolutionary Generative Adversarial Network for Alzheimer's Disease Risk Prediction.

In the studies of neurodegenerative diseases such as Alzheimer's Disease (AD), researchers often focus on the associations among multi-omics pathogeny based on imaging genetics data. However, current studies overlook the communities in brain networks, leading to inaccurate models of disease development. This paper explores the developmental patterns of AD from the perspective of community evolution. We first establish a mathematical model to describe functional degeneration in the brain as the community evolution driven by entropy information propagation. Next, we propose an interpretable Community Evolutionary Generative Adversarial Network (CE-GAN) to predict disease risk. In the generator of CE-GAN, community evolutionary convolutions are designed to capture the evolutionary patterns of AD. The experiments are conducted using functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data. CE-GAN achieves 91.67% accuracy and 91.83% area under curve (AUC) in AD risk prediction tasks, surpassing advanced methods on the same dataset. In addition, we validated the effectiveness of CE-GAN for pathogeny extraction. The source code of this work is available at https://github.com/fmri123456/CE-GAN.

Structure Mapping Generative Adversarial Network for Multi-View Information Mapping Pattern Mining.

Multi-view learning is dedicated to integrating information from different views and improving the generalization performance of models. However, in most current works, learning under different views has significant independency, overlooking common information mapping patterns that exist between these views. This paper proposes a Structure Mapping Generative adversarial network (SM-GAN) framework, which utilizes the consistency and complementarity of multi-view data from the innovative perspective of information mapping. Specifically, based on network-structured multi-view data, a structural information mapping model is proposed to capture hierarchical interaction patterns among views. Subsequently, three different types of graph convolutional operations are designed in SM-GAN based on the model. Compared with regular GAN, we add a structural information mapping module between the encoder and decoder wthin the generator, completing the structural information mapping from the micro-view to the macro-view. This paper conducted sufficient validation experiments using public imaging genetics data in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. It is shown that SM-GAN outperforms baseline and advanced methods in multi-label classification and evolution prediction tasks.

Community Graph Convolution Neural Network for Alzheimer's Disease Classification and Pathogenetic Factors Identification.

As a complex neural network system, the brain regions and genes collaborate to effectively store and transmit information. We abstract the collaboration correlations as the brain region gene community network (BG-CN) and present a new deep learning approach, such as the community graph convolutional neural network (Com-GCN), for investigating the transmission of information within and between communities. The results can be used for diagnosing and extracting causal factors for Alzheimer's disease (AD). First, an affinity aggregation model for BG-CN is developed to describe intercommunity and intracommunity information transmission. Second, we design the Com-GCN architecture with intercommunity convolution and intracommunity convolution operations based on the affinity aggregation model. Through sufficient experimental validation on the AD neuroimaging initiative (ADNI) dataset, the design of Com-GCN matches the physiological mechanism better and improves the interpretability and classification performance. Furthermore, Com-GCN can identify lesioned brain regions and disease-causing genes, which may assist precision medicine and drug design in AD and serve as a valuable reference for other neurological disorders.

The extraction and identification of active components of the sex pheromones of Asian citrus psyllid, Diaphorina citri.

In this study, we extracted and identified the active components of the Asian citrus psyllid, Diaphorina citri sex pheromones to provide a basis for further development of sex attractants. Under laboratory conditions, mating activity in D. citri started 3 d after emergence, which peaked at 6-7 d, and mating activity had no obvious peak during the observed period 7:00-21:00 h. Additionally, D. citri males were attracted to the emanations from conspecific females, especially to the n-hexane extracts of the pheromone. A total of 17 compounds were identified from the n-hexane extracts of female and male D. citri by gas chromatography-mass spectrometer (GC-MS). Among them, 13 compounds were identified from the female D. citri n-hexane extracts, of which 7 (dichloromethane, acetic acid, toluene, butyl acetate, ethyl carbamoylacetate, α-pinene, and 1-nonanal) were not found in the male D. citri n-hexane extracts. In addition, a total of 33 compounds were identified from the solid phase microextraction (SPME) volatiles of the male and female D. citri adults. Among these, 17 compounds were identified from the female D. citri volatiles, of which 6 (cycloheptatriene, 5-methyl-2-phenylindole, 1-dodecanol, cis-11-hexadecena, dodecyl aldehyde, and nerylacetone) were not identified in the volatiles of the D. citri males. It was found that males were significantly attracted to 0.1-10 µL/mL acetic acid and 1-nonanal with the selection rates ranging from 62.04%-70.56% and 62.22%-67.22%, respectively. Therefore, the results of this study suggest that acetic acid and 1-nonanal might be the active compounds of the female D. citri sex pheromones.

Molecular Mechanisms of the Melatonin Receptor Pathway Linking Circadian Rhythm to Type 2 Diabetes Mellitus.

Night-shift work and sleep disorders are associated with type 2 diabetes (T2DM), and circadian rhythm disruption is intrinsically involved. Studies have identified several signaling pathways that separately link two melatonin receptors (MT1 and MT2) to insulin secretion and T2DM occurrence, but a comprehensive explanation of the molecular mechanism to elucidate the association between these receptors to T2DM, reasonably and precisely, has been lacking. This review thoroughly explicates the signaling system, which consists of four important pathways, linking melatonin receptors MT1 or MT2 to insulin secretion. Then, the association of the circadian rhythm with MTNR1B transcription is extensively expounded. Finally, a concrete molecular and evolutionary mechanism underlying the macroscopic association between the circadian rhythm and T2DM is established. This review provides new insights into the pathology, treatment, and prevention of T2DM.

Analysis on the status quo of the awareness rate of core knowledge of cancer prevention and treatment and its influencing factors among residents in Liaoning Province in 2021 / 中华预防医学杂志

Objective: To analyze the status quo of the knowledge and related factors of cancer prevention and treatment among residents in Liaoning Province in 2021. Methods: From August to November 2021, through network sampling method, 17 474 permanent residents aged 15-69 years in Liaoning Province were surveyed. The WeChat public account was used to collect information such as demographic characteristics and core knowledge of cancer prevention and treatment. The Chi-square test was used to compare the difference of the level of the cancer prevention and treatment knowledge among different groups. The multivariate logistic regression model was used to analyze the related factors. Results: Among the 17 474 subjects, 43.1% (7 528) were male and 58.7% (10 262) were urban residents. The overall awareness rate was 72.3%, and the awareness rate of cancer cognition, prevention, early diagnosis and treatment, cancer management and rehabilitation were 71.4%, 67.6%, 72.7%, 83.4% and 63.5%, respectively. The multivariate logistic regression model showed that the residents who were man (OR: 0.850, 95%CI: 0.781-0.925), in rural areas (OR: 0.753, 95%CI: 0.694-0.817), 55-59 years old (OR: 0.851, 95%CI: 0.751-0.963), quitters (OR: 0.721, 95%CI: 0.640-0.813) and smoker (OR: 0.724, 95%CI: 0.654-0.801) had lower awareness rates, while the residents who were 35-54 years old (OR: 1.312, 95%CI: 1.202-1.432), with an educational level of junior high school/senior high school/college degree or above (OR: 1.834-5.130, 95%CI: 1.575-6.047), technical personnel (OR: 1.592, 95%CI: 1.367-1.854), civil servant/institution staff (OR: 1.282, 95%CI: 1.094-1.503), enterprise/business/service staff (OR: 1.218, 95%CI: 1.071-1.385), retired (OR: 1.324, 95%CI: 1.114-1.573) and with family history of cancer (OR: 1.369, 95%CI: 1.266-1.481) had higher awareness rates. Conclusion: The level of the awareness of core knowledge of cancer prevention and treatment among residents in Liaoning Province has met the requirements of the Healthy China Action. Region, gender, education level, age, family history of cancer and smoking are relevant factors.

Discovery of highly potent and selective 7-ethyl-10-hydroxycamptothecin-glucose conjugates as potential anti-colorectal cancer agents.

7-Ethyl-10-hydroxycamptothecin (SN38), a highly potent metabolite of irinotecan, has an anticancer efficacy 100-1000 folds more than irinotecan in vitro. However, the clinical application of SN38 has been limited due to the very narrow therapeutic window and poor water solubility. Herein, we report the SN38-glucose conjugates (Glu-SN38) that can target cancer cells due to their selective uptake via glucose transporters, which are overexpressed in most cancers. The in vitro antiproliferative activities against human cancer cell lines and normal cells of Glu-SN38 were investigated. One of the conjugates named 5b showed high potency and selectivity against human colorectal cancer cell line HCT116. Furthermore, 5b remarkably inhibited the growth of HCT116 in vivo. These results suggested that 5b could be a promising drug candidate for treating colorectal cancer.

A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection.

Imaging genetics provides unique insights into the pathological studies of complex brain diseases by integrating the characteristics of multi-level medical data. However, most current imaging genetics research performs incomplete data fusion. Also, there is a lack of effective deep learning methods to analyze neuroimaging and genetic data jointly. Therefore, this paper first constructs the brain region-gene networks to intuitively represent the association pattern of pathogenetic factors. Second, a novel feature information aggregation model is constructed to accurately describe the information aggregation process among brain region nodes and gene nodes. Finally, a deep learning method called feature information aggregation and diffusion generative adversarial network (FIAD-GAN) is proposed to efficiently classify samples and select features. We focus on improving the generator with the proposed convolution and deconvolution operations, with which the interpretability of the deep learning framework has been dramatically improved. The experimental results indicate that FIAD-GAN can not only achieve superior results in various disease classification tasks but also extract brain regions and genes closely related to AD. This work provides a novel method for intelligent clinical decisions. The relevant biomedical discoveries provide a reliable reference and technical basis for the clinical diagnosis, treatment and pathological analysis of disease.

Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer's Disease With Imaging Genetic Data.

Alzheimer's disease (AD) is a neurodegenerative disease with profound pathogenetic causes. Imaging genetic data analysis can provide comprehensive insights into its causes. To fully utilize the multi-level information in the data, this article proposes a hypergraph structural information aggregation model, and constructs a novel deep learning method named hypergraph structural information aggregation generative adversarial networks (HSIA-GANs) for the automatic sample classification and accurate feature extraction. Specifically, HSIA-GAN is composed of generator and discriminator. The generator has three main functions. First, vertex graph and edge graph are constructed based on the input hypergraph to present the low-order relations. Second, the low-order structural information of hypergraph is extracted by the designed vertex convolution layers and edge convolution layers. Finally, the synthetic hypergraph is generated as the input of the discriminator. The discriminator can extract the high-order structural information directly from hypergraph through vertex-edge convolution, fuse the high and low-order structural information, and finalize the results through the full connection (FC) layers. Based on the data acquired from AD neuroimaging initiative, HSIA-GAN shows significant advantages in three classification tasks, and extracts discriminant features conducive to better disease classification.

Giant nontraumatic myositis ossificans in a child: A case report.

BACKGROUND: Nontraumatic myositis ossificans is a rare disease whose specific pathogenesis is unclear. Early diagnosis of this disease is very difficult in children because of difficulties in determining medical history and nonspecific early clinical manifestations, which may lead to the failure of timely and effective diagnosis and treatment in some patients. We report the diagnosis and treatment of a child with nontraumatic myositis ossificans and summarize the clinical characteristics and diagnosis and treatment of the disease. CASE SUMMARY: An 8-year-old girl first came to our hospital for more than a week with pain in the right lower limb. There was no history of trauma or strenuous activities. On physical examination, no mass on the right thigh was found, and the movement of the right lower extremity was limited. Ultrasonography showed synovitis of the hip, and bed rest was recommended. Three days later, the child's pain persisted and worsened, accompanied by fever and other discomforts. She came to our hospital again and a mass was found on the right thigh with redness and swelling on the surface. The images showed a soft tissue tumor on the right thigh with calcification. Routine blood tests revealed that the inflammation index was significantly increased. In case of infection, the patient was given antibiotics, and the pain was relieved soon after, without fever. However, the right thigh mass persisted and hardened. The patient underwent incision biopsy more than 1 mo later, and the postoperative pathology showed nontraumatic myositis ossificans. After approximately 9 mo of observation, the tumor still persisted, which affected the life of the child, and then resection was performed. Since follow-up, there has been no recurrence. CONCLUSION: Due to the difficulty in discerning a child's medical history and the diverse early manifestations, it is difficult to diagnose nonossifying muscle disease in children in its early stage. Measures such as timely follow-up and periodic image monitoring are conducive to early diagnosis of the disease. The disease has a certain degree of self-limitation, and it can be observed and treated first. If the tumor persists in the later stage or affects functioning, then surgery is considered.

Feature aggregation graph convolutional network based on imaging genetic data for diagnosis and pathogeny identification of Alzheimer's disease.

The roles of brain regions activities and gene expressions in the development of Alzheimer's disease (AD) remain unclear. Existing imaging genetic studies usually has the problem of inefficiency and inadequate fusion of data. This study proposes a novel deep learning method to efficiently capture the development pattern of AD. First, we model the interaction between brain regions and genes as node-to-node feature aggregation in a brain region-gene network. Second, we propose a feature aggregation graph convolutional network (FAGCN) to transmit and update the node feature. Compared with the trivial graph convolutional procedure, we replace the input from the adjacency matrix with a weight matrix based on correlation analysis and consider common neighbor similarity to discover broader associations of nodes. Finally, we use a full-gradient saliency graph mechanism to score and extract the pathogenetic brain regions and risk genes. According to the results, FAGCN achieved the best performance among both traditional and cutting-edge methods and extracted AD-related brain regions and genes, providing theoretical and methodological support for the research of related diseases.

An orally available Mpro inhibitor is effective against wild-type SARS-CoV-2 and variants including Omicron.

Emerging SARS-CoV-2 variants continue to cause waves of new infections globally. Developing effective antivirals against SARS-CoV-2 and its variants is an urgent task. The main protease (Mpro) of SARS-CoV-2 is an attractive drug target because of its central role in viral replication and its conservation among variants. We herein report a series of potent α-ketoamide-containing Mpro inhibitors obtained using the Ugi four-component reaction. The prioritized compound, Y180, showed an IC50 of 8.1 nM against SARS-CoV-2 Mpro and had oral bioavailability of 92.9%, 31.9% and 85.7% in mice, rats and dogs, respectively. Y180 protected against wild-type SARS-CoV-2, B.1.1.7 (Alpha), B.1.617.1 (Kappa) and P.3 (Theta), with EC50 of 11.4, 20.3, 34.4 and 23.7 nM, respectively. Oral treatment with Y180 displayed a remarkable antiviral potency and substantially ameliorated the virus-induced tissue damage in both nasal turbinate and lung of B.1.1.7-infected K18-human ACE2 (K18-hACE2) transgenic mice. Therapeutic treatment with Y180 improved the survival of mice from 0 to 44.4% (P = 0.0086) upon B.1.617.1 infection in the lethal infection model. Importantly, Y180 was also highly effective against the B.1.1.529 (Omicron) variant both in vitro and in vivo. Overall, our study provides a promising lead compound for oral drug development against SARS-CoV-2.

IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data.

Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.

Pathogeny Detection for Mild Cognitive Impairment via Weighted Evolutionary Random Forest With Brain Imaging and Genetic Data.

Medical imaging technology and gene sequencing technology have long been widely used to analyze the pathogenesis and make precise diagnoses of mild cognitive impairment (MCI). However, few studies involve the fusion of radiomics data with genomics data to make full use of the complementarity between different omics to detect pathogenic factors of MCI. This paper performs multimodal fusion analysis based on functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data of MCI patients. In specific, first, using correlation analysis methods on sequence information of regions of interests (ROIs) and digitalized gene sequences, the fusion features of samples are constructed. Then, introducing weighted evolution strategy into ensemble learning, a novel weighted evolutionary random forest (WERF) model is built to eliminate the inefficient features. Consequently, with the help of WERF, an overall multimodal data analysis framework is established to effectively identify MCI patients and extract pathogenic factors. Based on the data of MCI patients from the ADNI database and compared with some existing popular methods, the superiority in performance of the framework is verified. Our study has great potential to be an effective tool for pathogenic factors detection of MCI.

Rational Design of Dot-on-Rod Nano-Heterostructure for Photocatalytic CO2 Reduction: Pivotal Role of Hole Transfer and Utilization.

Inspired by green plants, artificial photosynthesis has become one of the most attractive approaches toward carbon dioxide (CO2 ) valorization. Semiconductor quantum dots (QDs) or dot-in-rod (DIR) nano-heterostructures have gained substantial research interest in multielectron photoredox reactions. However, fast electron-hole recombination or sluggish hole transfer and utilization remains unsatisfactory for their potential applications. Here, the first application of a well-designed ZnSe/CdS dot-on-rods (DORs) nano-heterostructure for efficient and selective CO2 photoreduction with H2 O as an electron donor is presented. In-depth spectroscopic studies reveal that surface-anchored ZnSe QDs not only assist ultrafast (≈2 ps) electron and hole separation, but also promote interfacial hole transfer participating in oxidative half-reactions. Surface photovoltage (SPV) spectroscopy provides a direct image of spatially separated electrons in CdS and holes in ZnSe. Therefore, ZnSe/CdS DORs photocatalyze CO2 to CO with a rate of ≈11.3 µmol g-1 h-1 and ≥85% selectivity, much higher than that of ZnSe/CdS DIRs or pristine CdS nanorods under identical conditions. Obviously, favored energy-level alignment and unique morphology balance the utilization of electrons and holes in this nano-heterostructure, thus enhancing the performance of artificial photosynthetic solar-to-chemical conversion.

Application of 16 G Biopsy Needle in Transperineal Template-Guided Prostate Biopsy.

BACKGROUND: The aim of this study was to evaluate the clinical value of 16 G biopsy needle in transperineal template-guided prostate biopsy (TTPB), compared with 18 G biopsy needle. METHODS: The patients who underwent TTPB from August 2020 to February 2021 were randomized into 2 groups using a random number table. The control group (n = 65) and the observation group (n = 58) performed biopsy with 18 G (Bard MC l820) and 16 G (Bard MC l616) biopsy needles, respectively. Positive rate of biopsy, Gleason score, complications, and pain score were statistically analyzed. RESULTS: The age, prostate volume, PSA, and the number of cores were comparable between the 2 groups. The positive rate of biopsy in the observation group was 68.9% (40/58), meanwhile the control group was 46.2% (30/65). There was statistical difference between the 2 groups (p = 0.011). Gleason score of the observation group (8 [7-9]) was higher than that of the control group (8 [6-9]) (p = 0.038). There was no significant difference in pain score and complications including hematuria, hematospermia, perineal hematoma, infection, and urinary retention between the 2 groups (p > 0.05). CONCLUSIONS: 16 G biopsy needle significantly improved the positive rates and accurately evaluate the nature of lesions, meanwhile did not increase the incidence of complications compared with 18 G biopsy needle.

Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer's disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological mechanism of LMCI, a method called genetic evolved random forest (GERF) is applied. Resting functional magnetic resonance imaging (rfMRI) and gene data are obtained from 62 subjects (36 LMCI and 26 normal controls), and Pearson correlation analysis is adopted to perform the multimodal fusion of two types of data to construct fusion features. We identified pathogenic brain regions and genes that are highly related to LMCI using GERF and achieves a good effect. Compared with the normal control (NC) group, the abnormal brain regions of LMCI are PUT.L, PreCG.L, IFGtriang.R, REC.R, DCG.R, PoCG.L, and HES.L, and the pathogenic genes are FHIT, RF00019, FRMD4A, PTPRD, and RBFOX1. More importantly, most of these risk genes and abnormal brain regions have been confirmed to be related to AD and MCI in previous studies. In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI.

Up-Regulating ERIC by CRISPR-dCas9-VPR Inhibits Cell Proliferation and Invasion and Promotes Apoptosis in Human Bladder Cancer.

LncRNAs are defined as non-coding RNAs that are longer than 200 nucleotides in length. The previous studys has shown that lncRNAs played important roles in the regulation of gene expression and were essential in mammalian development and disease processes. Inspired by the observation that lncRNAs are aberrantly expressed in tumors, we extracted RNA from Bladder urothelial carcinoma and matched histologically normal urothelium from each patient and bladder carcinoma cell lines. Then, we reversed transcribed them into cDNA.Last, we investigated the expression patterns of ERIC by the fluorescence quantitative PCR in bladder cancer tissues and cell lines. CRISPR-dCas9-VPR targeting ERIC plasmid was transfected into T24 and 5637 cells, and cells were classified into two groups: negative control (NC) and ERIC overexpression group. MTT assay, transwell assay, and flow cytometry were performed to examine changes in cell proliferation, invasiveness, and apoptosis. We found that the expression of ERIC was down-regulated in bladder urothelial carcinoma compared to matched histologically normal urotheliam. The differences of the expression of this gene were large in the bladder cancer lines. Compared with the negative control group, the ERIC overexpression group showed significantly decreased cell proliferation rate (t = 7.583, p = 0.002; t = 3.283, p = 0.03) and invasiveness (t = 11.538, p < 0.001; t = 8.205, p = 0.01); and increased apoptotic rate (t = -34.083, p < 0.001; t = -14.316, p < 0.001). Our study lays a foundation for further study of its pathogenic mechanism in bladder cancer.