Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies.

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Hsa_circ_0003098 promotes bladder cancer progression via miR-377-5p/ACAT2 axis.

Accumulating evidence has proven that circRNAs play vital roles in tumor progression. Nevertheless, the mechanisms underlying circRNAs in bladder cancer (BCa) remain largely unknown. The purpose of this study was to identify the role and investigate the potential molecular mechanisms of hsa_circ_0003098 in BCa. We confirmed that hsa_circ_0003098 expression was significantly upregulated in BCa tissues, of which expression was remarkably associated with poor prognosis. Functionally, overexpression of hsa_circ_0003098 promoted BCa cell proliferation, migration, and invasion in vitro as well as tumor growth in vivo. Mechanistically, hsa_circ_0003098 promoted upregulation of ACAT2 expression and induced cholesteryl ester accumulation via acting as a sponge for miR-377-5p. Thus, hsa_circ_0003098 plays an oncogenic role in BCa and may serve as a potential biomarker and therapeutic target for BCa.

CircZNF609 promotes bladder cancer progression and inhibits cisplatin sensitivity via miR-1200/CDC25B pathway.

Circular RNAs (circRNAs) have been extensively studied in tumor development and treatment. CircZNF609 (hsa_circ_0000615) has been shown to serve as an oncogene in all kinds of solid tumors and may act as the novel biomarker in tumor diagnosis and therapy in tumor early diagnosis and therapy. However, the underlying character and mechanism of circZNF609 in cisplatin chemosensitivity and bladder cancer (BCa) development were unknown. The expression level of cell division cycle 25B (CDC25B), microRNA 1200 (miR-1200), and circZNF609 in BCa cells and tissues depended on quantitative real-time PCR (qRT-PCR). CDC25B protein level was assayed with Western blot. Functional assays in vitro and in vivo had been conducted to inspect the important role of circZNF609 on BCa progression and cisplatin chemosensitivity in BCa. RNA sequencing and online databases were used to predict the interactions among circZNF609, miR-1200, and CDC25B. Mechanistic exploration was confirmed by RNA pull-down assay, RNA fluorescence in situ hybridization (FISH) and Dual luciferase reporter assay. CircZNF609 expression was increased significantly in BCa cell lines and tissues. For BCa patients, increased expression of circZNF609 was correlated with a worse survival. In vitro and in vivo, enforced expression of circZNF609 enhanced BCa cells proliferation, migration, and cisplatin chemoresistance. Mechanistically, circZNF609 alleviated the inhibition effect on target CDC25B expression by sponging miR-1200. CircZNF609 promoted tumor growth through novel circZNF609/miR-1200/CDC25B axis, implying that circZNF609 has significant potential to act as a new diagnostic biomarker and therapeutic target in BCa. Enhancing cisplatin sensitivity is an important direction for bladder cancer management. 1. This research reveals that circZNF609 improves bladder cancer progression and inhibits cisplatin sensitivity by inducing G1/S cell cycle arrest via a novel miR-1200/CDC25B cascades. 2. CircZNF609 was confirmed associated with worse survival of bladder cancer patients. 3. CircZNF609 act as a prognostic biomarker for bladder cancer treatment.

N6-methyladenosine-related single-nucleotide polymorphism analyses identify oncogene RNFT2 in bladder cancer.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) in N6-methyladenosine (m6A) related genetic locus play significant roles in tumorigenesis and development. The expression level of many oncogenes and tumour suppressor genes changed because of m6A-associated SNPs. In addition, the relationship between m6A-SNP and bladder cancer (BCa) has not been well studied. METHODS: We screened m6A-SNPs in BCa by combining m6A-SNPs data and GWAS-SNPs data. Expression quantitative trait loci (eQTL) and differential expression gene (DEGs) analyses were performed. In ring finger protein, transmembrane 2 (RNFT2), rs3088107 (C > G) was found to have significant eQTL signals and make RNFT2 gene differentially-regulated mostly in BCa. We validated the expression level of RNFT2 in 32 pairs of BCa tissues and eight BCa cell lines by quantitative real-time PCR (qRT-PCR). Functional assays were performed to investigate the role of rs3088107 and RNFT2 in BCa in vitro. RESULTS: We identified 673 m6A-SNPs, which were associated with BCa. Of these m6A-SNPs, 221 showed eQTL signals, amongst which, rs3088107 in RNFT2 showed significant eQTL signals. Results of bioinformatic analyses showed that 11 genes with m6A-SNPs had a differential expression level in BCa. RNFT2 was predicted to be significantly up-regulated in BCa. The qRT-PCR results validated that RNFT2 was highly expressed in our own BCa tissues and cell lines. High expression of RNFT2 also indicated a worse overall survival. We also revealed that rs3088107 (C > G) could inhibit the expression and m6A modification of RNFT2 by qRT-PCR, western-blot and m6A-RIP assays. Moreover, the results of functional assays indicated that RNFT2 promoted BCa cell proliferation and migration. CONCLUSION: This research found that m6A-SNPs were associated with oncogene RNFT2 in BCa. Furthermore, m6A-SNPs showed great application potential as a new BCa diagnostic biomarker and prognostic indicator.

Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted.

BACKGROUND: Contact patterns play a key role in the spread of respiratory infectious diseases in human populations. During the COVID-19 pandemic, the regular contact patterns of the population have been disrupted due to social distancing both imposed by the authorities and individual choices. Many studies have focused on age-mixing patterns before the COVID-19 pandemic, but they provide very little information about the mixing patterns in the COVID-19 era. In this study, we aim at quantifying human heterogeneous mixing patterns immediately after lockdowns implemented to contain COVID-19 spread in China were lifted. We also provide an illustrative example of how the collected mixing patterns can be used in a simulation study of SARS-CoV-2 transmission. METHODS AND RESULTS: In this work, a contact survey was conducted in Chinese provinces outside Hubei in March 2020, right after lockdowns were lifted. We then leveraged the estimated mixing patterns to calibrate a mathematical model of SARS-CoV-2 transmission. Study participants reported 2.3 contacts per day (IQR: 1.0-3.0) and the mean per-contact duration was 7.0 h (IQR: 1.0-10.0). No significant differences in average contact number and contact duration were observed between provinces, the number of recorded contacts did not show a clear trend by age, and most of the recorded contacts occurred with family members (about 78%). The simulation study highlights the importance of considering age-specific contact patterns to estimate the COVID-19 burden. CONCLUSIONS: Our findings suggest that, despite lockdowns were no longer in place at the time of the survey, people were still heavily limiting their contacts as compared to the pre-pandemic situation.

The COVID-19 outbreak in Sichuan, China: Epidemiology and impact of interventions.

In January 2020, a COVID-19 outbreak was detected in Sichuan Province of China. Six weeks later, the outbreak was successfully contained. The aim of this work is to characterize the epidemiology of the Sichuan outbreak and estimate the impact of interventions in limiting SARS-CoV-2 transmission. We analyzed patient records for all laboratory-confirmed cases reported in the province for the period of January 21 to March 16, 2020. To estimate the basic and daily reproduction numbers, we used a Bayesian framework. In addition, we estimated the number of cases averted by the implemented control strategies. The outbreak resulted in 539 confirmed cases, lasted less than two months, and no further local transmission was detected after February 27. The median age of local cases was 8 years older than that of imported cases. We estimated R0 at 2.4 (95% CI: 1.6-3.7). The epidemic was self-sustained for about 3 weeks before going below the epidemic threshold 3 days after the declaration of a public health emergency by Sichuan authorities. Our findings indicate that, were the control measures be adopted four weeks later, the epidemic could have lasted 49 days longer (95% CI: 31-68 days), causing 9,216 more cases (95% CI: 1,317-25,545).

Mechanism of RNA modification N6-methyladenosine in human cancer.

Since the breakthrough discoveries of DNA and histone modifications, the field of RNA modifications has gained increasing interest in the scientific community. The discovery of N6-methyladenosine (m6A), a predominantly internal epigenetic modification in eukaryotes mRNA, heralded the creation of the field of epi-transcriptomics. This post-transcriptional RNA modification is dynamic and reversible, and is regulated by methylases, demethylases and proteins that preferentially recognize m6A modifications. Altered m6A levels affect RNA processing, degradation and translation, thereby disrupting gene expression and key cellular processes, ultimately resulting in tumor initiation and progression. Furthermore, inhibitors and regulators of m6A-related factors have been explored as therapeutic approaches for treating cancer. In the present review, the mechanisms of m6A RNA modification, the clinicopathological relevance of m6A alterations, the type and frequency of alterations and the multiple functions it regulates in different types of cancer are discussed.

A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition.

Recent research on hand detection and gesture recognition has attracted increasing interest due to its broad range of potential applications, such as human-computer interaction, sign language recognition, hand action analysis, driver hand behavior monitoring, and virtual reality. In recent years, several approaches have been proposed with the aim of developing a robust algorithm which functions in complex and cluttered environments. Although several researchers have addressed this challenging problem, a robust system is still elusive. Therefore, we propose a deep learning-based architecture to jointly detect and classify hand gestures. In the proposed architecture, the whole image is passed through a one-stage dense object detector to extract hand regions, which, in turn, pass through a lightweight convolutional neural network (CNN) for hand gesture recognition. To evaluate our approach, we conducted extensive experiments on four publicly available datasets for hand detection, including the Oxford, 5-signers, EgoHands, and Indian classical dance (ICD) datasets, along with two hand gesture datasets with different gesture vocabularies for hand gesture recognition, namely, the LaRED and TinyHands datasets. Here, experimental results demonstrate that the proposed architecture is efficient and robust. In addition, it outperforms other approaches in both the hand detection and gesture classification tasks.

CGFTrans: Cross-Modal Global Feature Fusion Transformer for Medical Report Generation.

Medical report generation, as a cross-modal automatic text generation task, can be highly significant both in research and clinical fields. The core is to generate diagnosis reports in clinical language from medical images. However, several limitations persist, including a lack of global information, inadequate cross-modal fusion capabilities, and high computational demands. To address these issues, we propose cross-modal global feature fusion Transformer (CGFTrans) to extract global information meanwhile reduce computational strain. Firstly, we introduce mesh recurrent network to capture inter-layer information at different levels to address the absence of global features. Then, we design feature fusion decoder and define 'mid-fusion' strategy to separately fuse visual and global features with medical report embeddings, which enhances the ability of the cross-modal joint learning. Finally, we integrate shifted window attention into Transformer encoder to alleviate computational pressure and capture pathological information at multiple scales. Extensive experiments conducted on three datasets demonstrate that the proposed method achieves average increments of 2.9%, 1.5%, and 0.7% in terms of the BLEU-1, METEOR and ROUGE-L metrics, respectively. Besides, it achieves average increments -22.4% and 17.3% training time and images throughput, respectively.

Cross-Modal Retrieval With Noisy Correspondence via Consistency Refining and Mining.

The success of existing cross-modal retrieval (CMR) methods heavily rely on the assumption that the annotated cross-modal correspondence is faultless. In practice, however, the correspondence of some pairs would be inevitably contaminated during data collection or annotation, thus leading to the so-called Noisy Correspondence (NC) problem. To alleviate the influence of NC, we propose a novel method termed Consistency REfining And Mining (CREAM) by revealing and exploiting the difference between correspondence and consistency. Specifically, the correspondence and the consistency only be coincident for true positive and true negative pairs, while being distinct for false positive and false negative pairs. Based on the observation, CREAM employs a collaborative learning paradigm to detect and rectify the correspondence of positives, and a negative mining approach to explore and utilize the consistency. Thanks to the consistency refining and mining strategy of CREAM, the overfitting on the false positives could be prevented and the consistency rooted in the false negatives could be exploited, thus leading to a robust CMR method. Extensive experiments verify the effectiveness of our method on three image-text benchmarks including Flickr30K, MS-COCO, and Conceptual Captions. Furthermore, we adopt our method into the graph matching task and the results demonstrate the robustness of our method against fine-grained NC problem. The code is available on https://github.com/XLearning-SCU/2024-TIP-CREAM.

CircZNF609 inhibited bladder cancer immunotherapy sensitivity via enhancing fatty acid uptake through IGF2BP2/CD36 pathway.

Circular RNAs (circRNAs) are gaining attention for their involvement in immune escape and immunotherapy sensitivity regulation. CircZNF609 is a well-known oncogene in various solid tumours. Our previous research revealed its role in reducing the chemosensitivity of bladder cancer (BCa) to cisplatin. However, the underlying role of circZNF609 in BCa immune escape and immunotherapy sensitivity remains unknown. We conducted BCa cells-CD8 + T cells co-culture assays, cell line-derived xenograft and patient-derived xenograft mouse models with human immune reconstitution to further confirm the role of circZNF609 in BCa immune escape and immunotherapy sensitivity. Overexpression of circZNF609 promoted BCa immune escape in vitro and in vivo. Mechanistically, circZNF609 was bound to IGF2BP2, enhancing its interaction with the 3'-untranslated region of CD36. This increased the stability of the CD36 mRNA, leading to enhanced fatty acid uptake by BCa cells and fatty acid depletion within the tumour microenvironment. Additionally, the nuclear export of circZNF609 was regulated by DDX39B. CircZNF609 promoted immune escape and suppressed BCa immunotherapy sensitivity by regulating the newly identified circZNF609/IGF2BP2/CD36 cascade. Therefore, circZNF609 holds potential as both a biomarker and therapeutic target in BCa immunotherapy.

METTL16 suppressed the proliferation and cisplatin-chemoresistance of bladder cancer by degrading PMEPA1 mRNA in a m6A manner through autophagy pathway.

N6-methyladenosine (m6A) is important in the physiological processes of many species. Methyltransferase-like 16 (METTL16) is a novel discovered m6A methylase, regulating various tumors in an m6A-dependent manner. However, its function in bladder cancer (BLCA) remains largely unclear. In the present study, we found that low expression of METTL16 predicted poor survival in BLCA patients. METTL16 inhibited the proliferation and cisplatin-resistance function of bladder cancer cells in vitro and in vivo. In addition, METTL16 reduced the mRNA stability of prostate transmembrane protein androgen induced-1 (PMEPA1) via binding to its m6A site in the 3'-UTR, thereby inhibited the proliferation of bladder cancer cells and increased the sensitivity of cisplatin through PMEPA1-mediated autophagy pathway. Finally, we found that hypoxia-inducible factor 2α (HIF-2α) exerted its tumor-promoting effect by binding the METTL16 promoter region to repress its transcription. Taken together, High expression of METTL16 predicted better survival in BLCA. METTL16 significantly inhibited bladder cancer cell proliferation and sensitized bladder cancer cells to cisplatin via HIF-2α-METTL16-PMEPA1-autophagy axis in a m6A manner. These findings might provide fresh insights into BLCA therapy.

Neural Network With a Preference Sampling Paradigm for Imbalanced Data Classification.

Most data in real life are characterized by imbalance problems. One of the classic models for dealing with imbalanced data is neural networks. However, the data imbalance problem often causes the neural network to display negative class preference behavior. Using an undersampling strategy to reconstruct a balanced dataset is one of the methods to alleviate the data imbalance problem. However, most existing undersampling methods focus more on the data or aim to preserve the overall structural characteristics of the negative class through potential energy estimation, while the problems of gradient inundation and insufficient empirical representation of positive samples have not been well considered. Therefore, a new paradigm for solving the data imbalance problem is proposed. Specifically, to solve the problem of gradient inundation, an informative undersampling strategy is derived from the performance degradation and used to restore the ability of neural networks to work under imbalanced data. In addition, to alleviate the problem of insufficient empirical representation of positive samples, a boundary expansion strategy with linear interpolation and the prediction consistency constraint is considered. We tested the proposed paradigm on 34 imbalanced datasets with imbalance ratios ranging from 16.90 to 100.14. The test results show that our paradigm obtained the best area under the receiver operating characteristic curve (AUC) on 26 datasets.

A comprehensive pan-cancer analysis of CDH5 in immunological response.

Background: Cadherin 5 (CDH5) functions critically in maintaining cell adhesion and integrity of endothelial and vascular cells. The expression of CDH5 is abnormal in tumor cells, which may have great potential to serve as a new immune checkpoint. The current pan-cancer analysis was performed to better understand the role of CDH5 in tumor. Methods: The clinical significance and immunological function of CDH5 in pan-cancers were comprehensively analyzed based on the correlations between CDH5 and clinicopathologic features, prognosis values, tumor mutation burden (TMB), microsatellite instability (MSI), immune cells infiltration and immune response genes using 33 datasets from The Cancer Genome Atlas (TCGA). We further confirmed the expression of CDH5 in bladder cancer (BCa) tissues and cell lines. The CD8+ T cells were screened from peripheral blood of healthy controls and activated. BCa cell-CD8+ T cell co-culture assay and ELISA assay were carried out to verify the immunological function of CDH5. Results: The expression of CDH5 was down-regulated in 8 types of tumors including in BCa but up-regulated in 4 types of tumors. CDH5 was significantly correlated with tumor stage in 6 types of tumors. In addition, CDH5 was positively or negatively correlated with tumor prognosis. Furthermore, CDH5 was closely associated with TMB in 15 types of tumors and with MSI in 9 types of tumors. KEGG-GSEA and Hallmarks-GSEA analyses results indicated that CDH5 was positively related to immune response in most tumor types. In many tumors, CDH5 showed a positive correlation with immune cell infiltration. Enrichment analyses demonstrated that CDH5 was significantly related to the expression of many immunomodulators and chemokines. Further experiments showed that CDH5 was low-expressed in BCa tissues and cell lines in comparison to adjacent normal tissues and normal urothelial cell line, but it was positively associated with a better prognosis of BCa patients. The results of in vitro co-culture assay and ELISA assay demonstrated that CDH5 could promote the function of CD8+ T cells in TME of BCa. Conclusion: In summary, CDH5 was positively associated with a favorable prognosis and effective immune response in tumors, showing a great potential to serve as a novel tumor biomarker and immune checkpoint.

Robust Multi-View Clustering With Incomplete Information.

The success of existing multi-view clustering methods heavily relies on the assumption of view consistency and instance completeness, referred to as the complete information. However, these two assumptions would be inevitably violated in data collection and transmission, thus leading to the so-called Partially View-unaligned Problem (PVP) and Partially Sample-missing Problem (PSP). To overcome such incomplete information challenges, we propose a novel method, termed robuSt mUlti-view clusteRing with incomplEte information (SURE), which solves PVP and PSP under a unified framework. In brief, SURE is a novel contrastive learning paradigm which uses the available pairs as positives and randomly chooses some cross-view samples as negatives. To reduce the influence of the false negatives caused by random sampling, SURE is with a noise-robust contrastive loss that theoretically and empirically mitigates or even eliminates the influence of the false negatives. To the best of our knowledge, this could be the first successful attempt that simultaneously handles PVP and PSP using a unified solution. In addition, this could be one of the first studies on the noisy correspondence problem (i.e., the false negatives) which is a novel paradigm of noisy labels. Extensive experiments demonstrate the effectiveness and efficiency of SURE comparing with 10 state-of-the-art approaches on the multi-view clustering task.

Large-Scale Meta-Heuristic Feature Selection Based on BPSO Assisted Rough Hypercuboid Approach.

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO. To further alleviate the issue of high computational cost when processing large-scale datasets, parallelization approaches for calculating the hybrid feature evaluation criteria are presented by decomposing and recombining hypercuboid equivalence partition matrix via horizontal data partitioning. A distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm is thus developed and embedded in the Apache Spark cloud computing model. Extensive experimental results indicate that RH-BPSO is promising and can significantly outperform the other representative feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, experiments on distributed-memory multicore clusters show that DiRH-BPSO is significantly faster than its sequential counterpart and is perfectly capable of completing large-scale feature selection tasks that fail on a single node due to memory constraints. Parallel scalability and extensibility analysis also demonstrate that DiRH-BPSO could scale out and extend well with the growth of computational nodes and the volume of data.

Dual Contrastive Prediction for Incomplete Multi-View Representation Learning.

In this article, we propose a unified framework to solve the following two challenging problems in incomplete multi-view representation learning: i) how to learn a consistent representation unifying different views, and ii) how to recover the missing views. To address the challenges, we provide an information theoretical framework under which the consistency learning and data recovery are treated as a whole. With the theoretical framework, we propose a novel objective function which jointly solves the aforementioned two problems and achieves a provable sufficient and minimal representation. In detail, the consistency learning is performed by maximizing the mutual information of different views through contrastive learning, and the missing views are recovered by minimizing the conditional entropy through dual prediction. To the best of our knowledge, this is one of the first works to theoretically unify the cross-view consistency learning and data recovery for representation learning. Extensive experimental results show that the proposed method remarkably outperforms 20 competitive multi-view learning methods on six datasets in terms of clustering, classification, and human action recognition. The code could be accessed from https://pengxi.me.

Shunting at Arbitrary Feature Levels via Spatial Disentanglement: Toward Selective Image Translation.

The past few years have witnessed considerable efforts devoted to translating images from one domain to another, mainly aiming at editing global style. Here, we focus on a more general case, selective image translation (SLIT), under an unsupervised setting. SLIT essentially operates through a shunt mechanism that involves learning gates to manipulate only the contents of interest (CoIs), which can be either local or global, while leaving the irrelevant parts unchanged. Existing methods typically rely on a flawed implicit assumption that CoIs are separable at arbitrary levels, ignoring the entangled nature of DNN representations. This leads to unwanted changes and learning inefficiency. In this work, we revisit SLIT from an information-theoretical perspective and introduce a novel framework, which equips two opposite forces to disentangle the visual features. One force encourages independence between spatial locations on the features, while the other force unites multiple locations to form a "block" that jointly characterizes an instance or attribute that a single location may not independently characterize. Importantly, this disentanglement paradigm can be applied to visual features of any layer, enabling shunting at arbitrary feature levels, which is a significant advantage not explored in existing works. Our approach has undergone extensive evaluation and analysis, confirming its effectiveness in significantly outperforming the state-of-the-art baselines.

Identifying spatial agglomeration and driving forces of land use by green industries: a case study of Jiangsu Province, China.

In response to China's aims of becoming "carbon-neutral," the development of green industries such as renewable energy and recycling has flourished. Based on 2015 and 2019 data, this study uses spatial autocorrelation to analyze the evolution of land use by the green industries in Jiangsu Province. The Geodetector model was also applied to identify the driving factors underlying these spatial patterns. The spatial variability of green industrial land use in Jiangsu Province is significant, with the land-use area gradually decreasing from Southern to Northern Jiangsu. In terms of spatial-temporal changes, there is an increase in land use and a trend of expansion in the central and northern regions of Jiangsu. Land use by green industries in the province exhibits a more significant spatial clustering pattern but with a weakened clustering effect. The clustering types are mainly H-H and L-L, with the H-H type distributed mainly in the Su-Xi-Chang region and the L-L type distributed mainly in the Northern Jiangsu region. The levels of technology, economic development, industrialization, and industrial diversification are important individual driving factors, and the interaction among the different factors enhances their driving forces. This study suggests that spatial spillover effects should be focused to promote the coordinated development of regional energy-saving and environmental protection industries. At the same time, joint efforts should be made from the aspects of resources, government, economy, and related industries to promote the agglomeration of land for energy-saving and environmental protection industries.

Clinical significance, tumor immune landscape and immunotherapy responses of ADAR in pan-cancer and its association with proliferation and metastasis of bladder cancer.

BACKGROUND: ADAR is an enzyme involved in adenosine-inosine RNA editing. However, the role of ADAR in tumorigenesis, progression, and immunotherapy has not been fully elucidated. METHODS: The TCGA, GTEx and GEO databases were extensively utilized to explore the expression level of ADAR across cancers. Combined with the clinical information of patients, the risk profile of ADAR in various cancers was delineated. We identified pathways enriched in ADAR and their related genes and explored the association between ADAR expression and the cancer immune microenvironment score and response to immunotherapy. Finally, we specifically explored the potential value of ADAR in the treatment of the bladder cancer immune response and verified the critical role of ADAR in the development and progression of bladder cancer through experiments. RESULTS: ADAR is highly expressed in most cancers at both the RNA and protein level. ADAR is associated with the aggressiveness of some cancers, especially bladder cancer. In addition, ADAR is associated with immune-related genes, especially immune checkpoint genes, in the tumor immune microenvironment. Moreover, ADAR expression is positively correlated with tumor mutation burden and microsatellite instability in a variety of cancers, indicating that ADAR could be used as a biomarker of immunotherapy. Finally, we demonstrated that ADAR is a key pathogenic factor in bladder cancer. ADAR promoted proliferation and metastasis of bladder cancer cells. CONCLUSION: ADAR regulates the tumor immune microenvironment and can be used as a biomarker of the tumor immunotherapy response, providing a novel strategy for the treatment of tumors, especially bladder cancer.