eccDNA-pipe: an integrated pipeline for identification, analysis and visualization of extrachromosomal circular DNA from high-throughput sequencing data.

Extrachromosomal circular DNA (eccDNA) is currently attracting considerable attention from researchers due to its significant impact on tumor biogenesis. High-throughput sequencing (HTS) methods for eccDNA identification are continually evolving. However, an efficient pipeline for the integrative and comprehensive analysis of eccDNA obtained from HTS data is still lacking. Here, we introduce eccDNA-pipe, an accessible software package that offers a user-friendly pipeline for conducting eccDNA analysis starting from raw sequencing data. This dataset includes data from various sequencing techniques such as whole-genome sequencing (WGS), Circle-seq and Circulome-seq, obtained through short-read sequencing or long-read sequencing. eccDNA-pipe presents a comprehensive solution for both upstream and downstream analysis, encompassing quality control and eccDNA identification in upstream analysis and downstream tasks such as eccDNA length distribution analysis, differential analysis of genes enriched with eccDNA and visualization of eccDNA structures. Notably, eccDNA-pipe automatically generates high-quality publication-ready plots. In summary, eccDNA-pipe provides a comprehensive and user-friendly pipeline for customized analysis of eccDNA research.

Preparation and Performance of Ferric-Rich Bauxite-Tailing-Based Thermal Storage Ceramics.

In recent years, regenerative thermal oxidizer (RTO) has been widely used in the petroleum industry, chemical industry, etc. The massive storage required by solid waste has become a serious problem. Due to their chemical composition, bauxite tailings as raw materials for high-temperature thermal storage ceramics show enormous potential in the fields of research and application. In this study, we propose a method for preparing ferric-rich and high specific storage capacity by adding Fe2O3 powder to bauxite tailings. Based on a 7:3 mass ratio of bauxite tailings to lepidolite, Fe2O3 powder with different mass fractions (7 wt%, 15 wt%, 20 wt%, 30 wt%, and 40 wt%) was added to the ceramic material to improve the physical properties and thermal storage capacity of thermal storage ceramics. The results showed that ferric-rich thermal storage ceramics with optimal performance were obtained by holding them at a sintering temperature of 1000 °C for 2 h. When the Fe2O3 content was 15 wt%, the bulk density of the thermal storage ceramic reached 2.53 g/cm3, the compressive strength was 120.81 MPa, and the specific heat capacity was 1.06 J/(g·K). This study has practical guidance significance in the preparation of high thermal storage ceramics at low temperatures and low costs.

SPACEL: deep learning-based characterization of spatial transcriptome architectures.

Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning them to construct a three-dimensional (3D) stack of the tissue still remain a challenge. Here, we introduce spatial architecture characterization by deep learning (SPACEL) for ST data analysis. SPACEL comprises three modules: Spoint embeds a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot in a single ST slice; Splane employs a graph convolutional network approach and an adversarial learning algorithm to identify spatial domains that are transcriptomically and spatially coherent across multiple ST slices; and Scube automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a 3D architecture of the tissue. Comparisons against 19 state-of-the-art methods using both simulated and real ST datasets from various tissues and ST technologies demonstrate that SPACEL outperforms the others for cell type deconvolution, for spatial domain identification, and for 3D alignment, thus showcasing SPACEL as a valuable integrated toolkit for ST data processing and analysis.

Single-cell transcriptome profiling reveals immune and stromal cell heterogeneity in primary Sjögren's syndrome.

Primary Sjögren's syndrome (pSS) is a complex autoimmune disease characterized by lymphocytic infiltration and exocrine dysfunction, particularly affecting the salivary gland (SG). We employed single-cell RNA sequencing to investigate cellular heterogeneity in 11 patients with pSS and 5 non-SS controls. Notably, patients with pSS exhibited downregulated SOX9 in myoepithelial cells, potentially associated with impaired epithelial regeneration. An expanded ACKR1+ endothelial subpopulation in patients with pSS suggested a role in facilitating lymphocyte transendothelial migration. Our analysis of immune cells revealed expanded IGHD+ naive B cells in peripheral blood from patients with pSS. Pseudotime trajectory analysis outlined a bifurcated differentiation pathway for peripheral B cells, enriching three subtypes (VPREB3+ B, BANK1+ B, CD83+ B cells) within SGs in patients with pSS. Fibroblasts emerged as pivotal components in a stromal-immune interaction network, potentially driving extracellular matrix disruption, epithelial regeneration impairment, and inflammation. Our study illuminates immune and stromal cell heterogeneity in patients with pSS, offering insights into therapeutic strategies.

Three subtypes of postoperative ARDS that showing different outcomes and responses to mechanical ventilation and fluid management: A machine learning and latent profile analysis.

BACKGROUND: ARDS is a heterogeneous clinical syndrome, and operation and trauma are common indirect etiologies. The identification of postoperative ARDS subtypes may optimize individualized clinical management. OBJECTIVES: To identify the subtypes of postoperative ARDS and explore the impact of therapy on outcomes. METHODS: This retrospective study used data obtained from a database. Patients diagnosed with ARDS who underwent surgical procedures within 7 days were included in the study. Laboratory and clinical variables were used for latent profile analysis (LPA). XGBoost and multivariable logistic regression models were used to explore the association between therapy and outcomes. RESULTS: A total of 1065 patients were included. The LPA identified three subtypes of postoperative ARDS: Patients in profile 1 were mainly accepted neurosurgery, while those in profile 2 and 3 were treated with orthopedic and vascular or thoracic surgery, respectively. The XGBoost model effectively predicted mortality with an AUC of 0.935, which was higher than SOFA (0.622), APACHE 2 (0.629), SLIP (0.579), and SLIP-2 (0.550). CONCLUSIONS: This study identified three subtypes of postoperative ARDS with different clinical characteristics, mechanical support, and fluid resuscitation responses.

Development and Validation of a Prediction Model on Adult Emergency Department Patients for Early Identification of Fulminant Myocarditis.

OBJECTIVE: It is difficult to predict fulminant myocarditis at an early stage in the emergency department. The objective of this study was to construct and validate a simple prediction model for the early identification of fulminant myocarditis. METHODS: A total of 61 patients with fulminant myocarditis and 160 patients with acute myocarditis were enrolled in the training and internal validation cohorts. LASSO regression and multivariate logistic regression were selected to develop the prediction model. The selection of the model was based on overall performance and simplicity. A nomogram based on the optimal model was built, and its clinical usefulness was evaluated by decision curve analysis. The predictive model was further validated in an external validation group. RESULTS: The resulting prediction model was based on 4 factors: systolic blood pressure, troponin I, left ventricular ejection fraction, and ventricular wall motion abnormality. The Brier scores of the final model were 0.078 in the training data set and 0.061 in the internal testing data set, respectively. The C-indexes of the training data set and the testing data set were 0.952 and 0.968, respectively. Decision curve analysis showed that the nomogram model developed based on the 4 predictors above had a positive net benefit for predicting probability thresholds. In the external validation cohort, the model also showed good performance (Brier score=0.007, and C-index=0.989). CONCLUSION: We developed and validated an early prediction model consisting of 4 clinical factors (systolic blood pressure, troponin I, left ventricular ejection fraction, and ventricular wall motion abnormality) to identify potential fulminant myocarditis patients in the emergency department.

Single-cell landscape of primary central nervous system diffuse large B-cell lymphoma.

Understanding tumor heterogeneity and immune infiltrates within the tumor-immune microenvironment (TIME) is essential for the innovation of immunotherapies. Here, combining single-cell transcriptomics and chromatin accessibility sequencing, we profile the intratumor heterogeneity of malignant cells and immune properties of the TIME in primary central nervous system diffuse large B-cell lymphoma (PCNS DLBCL) patients. We demonstrate diverse malignant programs related to tumor-promoting pathways, cell cycle and B-cell immune response. By integrating data from independent systemic DLBCL and follicular lymphoma cohorts, we reveal a prosurvival program with aberrantly elevated RNA splicing activity that is uniquely associated with PCNS DLBCL. Moreover, a plasmablast-like program that recurs across PCNS/activated B-cell DLBCL predicts a worse prognosis. In addition, clonally expanded CD8 T cells in PCNS DLBCL undergo a transition from a pre-exhaustion-like state to exhaustion, and exhibit higher exhaustion signature scores than systemic DLBCL. Thus, our study sheds light on potential reasons for the poor prognosis of PCNS DLBCL patients, which will facilitate the development of targeted therapy.

The Photocatalytic Performance of P, Cl Doped Carboxylated Multiwalled Carbon Nanotube Modified Graphitic Carbon Nitride.

Graphitized carbonitride (g-C3N4) is widely used in CO2 reduction, hydrogen production, and degradation of toxic chemical dyes and antibiotics. It is a kind of photocatalytic material with excellent performance, and it has the advantages of being safe and nontoxic, having a suitable band gap (2.7 eV), and having a simple preparation and high stability, but because of its fast optical recombination speed and low visible light overutilization, the multifunctional application of g-C3N4 is seriously hindered. Compared with pure g-C3N4, MWCNTs/g-C3N4 have a red-shift in the visible range and a strong absorption in the visible region. Melamine and carboxylated multiwalled carbon nanotubes were used as raw materials to successfully prepare CMWCNT modified g-C3N4 doped with P, Cl by a high temperature calcination method. The effect of the addition amount of P, Cl on the photocatalytic performance of modified g-C3N4 was studied. The experimental results show that the multiwalled carbon nanotubes can accelerate the electron migration, and the doping of P, Cl elements can change the energy band structure of g-C3N4 and reduce the band gap. Through fluorescence analysis and photocurrent analysis, it is known that the incorporation of P, Cl reduces the recombination efficiency of photogenerated electron-hole pairs. In order to explore the application in the degradation of chemical dyes, the photocatalytic degradation efficiency of RhB under visible light was studied. The photocatalytic performance of the samples was evaluated by photodecomposition of aquatic hydrogen. The results showed that when the amount of ammonium dihydrogen phosphate was 10 wt %, the photocatalytic degradation efficiency was the highest, which was 21.13 times higher than that of g-C3N4.

Fine-grained Lesion Classification Framework for Early Auxiliary Diagnosis.

The deep neural networks are envisaged for the early disease diagnosis from medical images. However, in the early stage of the disease, the medical images of patients and healthy people have only subtle visual differences. Distinguishing the medical images for early diagnosis belongs to the Fine-Grained Visual Classification (FGVC) task. Many recent works are based on a standard FGVC learning paradigm: locate the discriminative regions first and then classify by fusing the information of these regions. However, it is still not enough for medical images. Because the shape and size of the lesions are variable, and the relationship between lesions and the background is complex. In order to solve these problems, we propose a fine-grained lesion classification framework for early auxiliary diagnosis. We first locate and extract multiple lesions with different sizes and shapes from the original image and then fuse the feature of lesion and background based on attention mechanism. As shown by experiment results in two real-world clinical data sets, our model can locate accurately and perform better.

Long intergenic non-protein coding RNA 00858 participates in the occurrence and development of esophageal squamous cell carcinoma through the activation of the FTO-m6A-MYC axis by recruiting ZNF184.

OBJECTIVES: We aimed at probing impact of LINC00858 on esophageal squamous cell carcinoma (ESCC) progression via ZNF184-FTO-m6A-MYC axis. METHODS: Expression of related genes (LINC00858, ZNF184, FTO, and MYC) was detected in ESCC tissues or cells and their relationships were assessed. After expression alterations in ESCC cells, cell proliferation, invasion, migration, and apoptosis were detected. Tumor formation in nude mice was conducted. RESULTS: LINC00858, ZNF184, FTO, and MYC were overexpressed in ESCC tissues and cells. LINC00858 enhanced ZNF184 expression to upregulate FTO, which augmented MYC expression. LINC00858 knockdown diminished ESCC cell proliferative, migratory, and invasive properties while elevating apoptosis, which was negated by FTO overexpression. FTO knockdown exerted similar functions of LINC00858 knockdown on ESCC cell movements, which was annulled by MYC upregulation. Silencing LINC00858 repressed tumor growth and related gene expression in nude mice. CONCLUSIONS: LINC00858 modulated MYC m6A modification via FTO by recruiting ZNF184, thus facilitating ESCC progression.

Single-cell profiling reveals Müller glia coordinate retinal intercellular communication during light/dark adaptation via thyroid hormone signaling.

Light adaptation enables the vertebrate visual system to operate over a wide range of ambient illumination. Regulation of phototransduction in photoreceptors is considered a major mechanism underlying light adaptation. However, various types of neurons and glial cells exist in the retina, and whether and how all retinal cells interact to adapt to light/dark conditions at the cellular and molecular levels requires systematic investigation. Therefore, we utilized single-cell RNA sequencing to dissect retinal cell-type-specific transcriptomes during light/dark adaptation in mice. The results demonstrated that, in addition to photoreceptors, other retinal cell types also showed dynamic molecular changes and specifically enriched signaling pathways under light/dark adaptation. Importantly, Müller glial cells (MGs) were identified as hub cells for intercellular interactions, displaying complex cell‒cell communication with other retinal cells. Furthermore, light increased the transcription of the deiodinase Dio2 in MGs, which converted thyroxine (T4) to active triiodothyronine (T3). Subsequently, light increased T3 levels and regulated mitochondrial respiration in retinal cells in response to light conditions. As cones specifically express the thyroid hormone receptor Thrb, they responded to the increase in T3 by adjusting light responsiveness. Loss of the expression of Dio2 specifically in MGs decreased the light responsive ability of cones. These results suggest that retinal cells display global transcriptional changes under light/dark adaptation and that MGs coordinate intercellular communication during light/dark adaptation via thyroid hormone signaling.

Eu3+ -activated red phosphor Ca3 YAl3 B4 O15 with low thermal quenching behaviour.

This paper reports a sequence of a Ca3 YAl3 B4 O15 :xEu3+ red phosphor prepared using a high-temperature solid-state reaction. At the excitation of 396 nm, the samples emitted intense red emission centred at ~623 nm, which could be attributed to the 5 D0 →7 F2 transition of the Eu3+ ion. The results showed that the optimum Eu3+ doping concentration of Ca3 YAl3 B4 O15 :Eu3+ phosphor was x = 80 mol%, and the concentration quenching mechanism of Ca3 YAl3 B4 O15 :Eu3+ red phosphor belonged to the exchange coupling between Eu3+ ions. The Commission Internationale de l'éclairage (CIE) coordinates and colour purity of Ca3 Y0.2 Al3 B4 O15 :0.8Eu3+ were calculated as (0.6375, 0.3476) and 95.5%, respectively. Moreover, the red emission of the obtained phosphor Ca3 YAl3 B4 O15 :0.8Eu3+ exhibited a low thermal quenching behaviour with an intensity retention rate of 92.85% at 150°C. The above results manifest that the Eu3+ -activated Ca3 YAl3 B4 O15 phosphor is predicted to be a promising red luminescent component for white light-emitting diodes.

Hub Genes and Long Noncoding RNAs That Regulates It Associated with the Prognosis of Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis.

Objective: Through bioinformatics analysis methods, the public databases GEO and TCGA were used to research mRNA and squamous cell carcinoma of the esophagus, construct a lncRNA-mRNA network, and screen hub genes and lncRNAs related to prognosis. Method: Download esophageal squamous cell carcinoma-related mRNA and lncRNA datasets GEO and TCGA public datasets, as well as clinical data, use bioinformatic tools to perform gene differential expression analysis on the datasets to obtain differentially expressing mRNA (DEmRNA) and lncRNA (DElncRNA), and plot volcano plots and cluster heatmaps. The differential intersection of differentially expressed DEmRNA and DElncRNA was extracted by Venn diagram and imported into CytoScape software, a regulatory network visualization software, to construct a lncRNA-mRNA network and use cytoHubba and MCODE plug-ins to screen hub genes and key lncRNAs. The DEmRNA in the network was imported into the Gene and Protein Interaction Retrieval Database (STRING), gene-encoded protein-protein interactions (PPI) network maps were created, and the genes in the PPI network maps were submitted to GO functional annotation and pathway enrichment analysis using Kyoto Encyclopedia of Gene Genomes (KEGG) (KEGG). The link between hub gene and prognosis was studied using the clinical data collected by TCGA. Result: Retrieve the datasets GSE23400 and GSE38129 from the GEO database and the esophageal squamous cell carcinoma-related mRNAs from TCGA databases and then obtain intersection. Differentially regulated genes revealed a correlation of 326 (up) with 191 (down) in terms of the differential intersection; for this study, we need to collect the GSE130078 dataset from GEO, as well as the lncRNAs from TCGA databases that are connected to esophageal squamous cell cancer. There were 184 differentially up- and downregulated genes in the differential intersection. A differential intersection network of the differential intersection lncRNA-mRNA network allowed us to identify the hub genes, including COL5A2 (COL3A1), COL1A1 (COL1A1), CTD-2171N6.1 (CTD-2171N6.1), and RP11-863P13.3 (RP11-863P13.3). The extracellular matrix, which is important in protein digestion and absorption, was shown to be the primary site of functional enrichment, as shown by GO/KEGG analysis. Squamous cell carcinoma of the mouth and throat is associated with a poor prognosis because of a change in the extracellular matrix structure caused by specific long noncoding RNA (lncRNA) regulatory upregulation. Conclusion: For the purpose of predicting the prognosis of cancer of the esophagus, researchers studied the esophageal squamous cell carcinoma-related hub genes and important noncoding RNAs (ncRNAs).

Energy transfer properties of a single-phase Na3Gd(PO4)2:Eu2+,Mn2+ phosphor with excellent thermal stability for w-LEDs.

A high temperature solid state method was used to prepare Na3Gd(PO4)2:Eu2+,Mn2+ phosphors with good thermal stability. The phosphor shows a broadband excitation region of 250-430 nm, which can be matched with the emissions of ultraviolet (UV)/near-ultraviolet (NUV) LED chips for white light emitting diodes (w-LEDs). The energy transfer efficiency is 74.46% from the sensitizer Eu2+ ions to the activator Mn2+ ions, which enhances the intensities of the Na3Gd(PO4)2-based phosphor. In addition, by increasing the Mn2+ doping level in the phosphor, the Na3Gd(PO4)2:Eu2+,Mn2+ phosphor first shows blue light, then turns to white light, and finally emits red light under 365 nm excitation. Besides, the temperature-dependent photoluminescence measurements indicate that the prepared phosphors exhibit good thermal stability. W-LEDs fabricated by combining a 365 nm chip with the Na3Gd(PO4)2:Eu2+,Mn2+ phosphor exhibit bright white light, which has a high color rendering index (CRI) = 91.5, and a relatively low correlated color temperature (CCT) = 5198 K. Moreover, the CIE point is calculated to be at (0.3337, 0.3465), which is located in the white light region. These results indicate that the as-prepared phosphors can be considered as potential candidates for UV/NUV light-excited w-LED applications.

Distribution and Drug Resistance of Pathogenic Bacteria and Prognosis in Patients with Septicemia Bloodstream Infection with Renal Insufficiency.

Objective: The aim of this study was to investigate the distribution and drug resistance of pathogenic bacteria and the prognosis of patients with sepsis bloodstream infection with renal insufficiency. Methods: One hundred and twelve patients with septicemic bloodstream infection with renal insufficiency and 112 patients with septic bloodstream infection without renal insufficiency were selected as study group and control group, respectively. We compare the distribution of pathogenic bacteria, analyze the drug resistance of major bacteria, and compare the efficacy, the incidence of septic shock, duration of mechanical ventilation, hospitalization time, and duration of antimicrobial drug administration between the two groups. Results: A total of 140 pathogenic strains were isolated from blood cultures in the study group, and 136 strains were isolated from blood cultures in the control group. The sepsis bloodstream infection was mainly caused by Gram-negative bacteria, accounting for 59.42% (164/276). Among the gram-negative bacteria, Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii had higher resistance rates to levofloxacin, ceftazidime, piperacillin sodium tazobactam, and amikacin. Among the gram-positive bacteria, Streptococcus pneumoniae, Enterococcus, and Staphylococcus aureus had high resistance rates to clindamycin, cefazolin, penicillin G, gentamicin, azithromycin, and levofloxacin. The rate of extended spectrum ß-lactamase (ESBLs)-producing enterobacteria and multi-drug resistant Pseudomonas aeruginosa (MDR-PA) infection was significantly higher in the study group than in the control group; there was no difference in multi-drug resistant Acinetobacter baumannii (MDR-AB), vancomycin-resistant enterococci (VRE), and methicillin-resistant Staphylococcus aureus (MRSA) between the two groups. The duration of hospitalization and the duration of antimicrobial drug administration were longer in the study group than in the control group. Conclusion: The pathogenic bacteria in patients with sepsis bloodstream infection with renal insufficiency are mainly Gram-negative bacteria, are more difficult to be cured, have a longer course of treatment, and need to use antibacterial drugs for a long time.

Tungsten-Based Nanocatalysts: Research Progress and Future Prospects.

The high price of noble metal resources limits its commercial application and stimulates the potential for developing new catalysts that can replace noble metal catalysts. Tungsten-based catalysts have become the most important substitutes for noble metal catalysts because of their rich resources, friendly environment, rich valence and better adsorption enthalpy. However, some challenges still hinder the development of tungsten-based catalysts, such as limited catalytic activity, instability, difficult recovery, and so on. At present, the focus of tungsten-based catalyst research is to develop a satisfactory material with high catalytic performance, excellent stability and green environmental protection, mainly including tungsten atomic catalysts, tungsten metal nanocatalysts, tungsten-based compound nanocatalysts, and so on. In this work, we first present the research status of these tungsten-based catalysts with different sizes, existing forms, and chemical compositions, and further provide a basis for future perspectives on tungsten-based catalysts.

(-)-Epicatechin Reduces Neuroinflammation, Protects Mitochondria Function, and Prevents Cognitive Impairment in Sepsis-Associated Encephalopathy.

Sepsis-associated encephalopathy is a common neurological complication of sepsis. Despite advances in pathological and diagnostic investigations, its treatment remains a major challenge. In sepsis-associated encephalopathy, neuroinflammatory overactivation and mitochondrial damage are thought to contribute to cognitive and behavioral impairments. In this study, we found that administration of (-)-Epicatechin, a dietary flavonoid of the flavan-3-ol subgroup, improves memory deficits and behavior performance by ameliorating neuroinflammation, regulating mitochondria function, enhancing synaptic plasticity, and reducing neuronal loss in a mouse model of lipopolysaccharide-induced sepsis. We further show that the AMPK signaling pathway might be among the mechanisms involved in the beneficial memory effects. Our data demonstrated the potential of (-)-Epicatechin as a new drug candidate for the treatment of sepsis-associated cognitive impairment by targeting AMPK.

A transcriptome atlas and interactive analysis platform for autoimmune disease.

With the rapid development of next-generation sequencing technology, many laboratories have produced a large amount of single-cell transcriptome data of blood and tissue samples from patients with autoimmune diseases, which enables in-depth studies of the relationship between gene transcription and autoimmune diseases. However, there is still a lack of a database that integrates the large amount of autoimmune disease transcriptome sequencing data and conducts effective analysis. In this study, we developed a user-friendly web database tool, Interactive Analysis and Atlas for Autoimmune disease (IAAA), which integrates bulk RNA-seq data of 929 samples of 10 autoimmune diseases and single-cell RNA-seq data of 783 203 cells in 96 samples of 6 autoimmune diseases. IAAA also provides customizable analysis modules, including gene expression, difference, correlation, similar gene detection and cell-cell interaction, and can display results in three formats (plot, table and pdf) through custom parameters. IAAA provides valuable data resources for researchers studying autoimmune diseases and helps users deeply explore the potential value of the current transcriptome data. IAAA is available. Database URL: http://galaxy.ustc.edu.cn/IAAA.

Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution.

Spatial transcriptomics approaches have substantially advanced our capacity to detect the spatial distribution of RNA transcripts in tissues, yet it remains challenging to characterize whole-transcriptome-level data for single cells in space. Addressing this need, researchers have developed integration methods to combine spatial transcriptomic data with single-cell RNA-seq data to predict the spatial distribution of undetected transcripts and/or perform cell type deconvolution of spots in histological sections. However, to date, no independent studies have comparatively analyzed these integration methods to benchmark their performance. Here we present benchmarking of 16 integration methods using 45 paired datasets (comprising both spatial transcriptomics and scRNA-seq data) and 32 simulated datasets. We found that Tangram, gimVI, and SpaGE outperformed other integration methods for predicting the spatial distribution of RNA transcripts, whereas Cell2location, SpatialDWLS, and RCTD are the top-performing methods for the cell type deconvolution of spots. We provide a benchmark pipeline to help researchers select optimal integration methods to process their datasets.

Downregulation of long noncoding RNA breast cancer anti-estrogen resistance 4 inhibits cell proliferation, invasion, and migration in esophageal squamous cell carcinoma by regulating the microRNA-181c-5p/LIM and SH3 protein 1 axis.

Recently, abnormal expression of long non-coding RNAs (lncRNAs) has been observed in esophageal squamous cell carcinoma (ESCC). In various human cancers, breast cancer anti­estrogen resistance 4 (BCAR4) was reported to be highly expressed, while the biological roles of BCAR4 in ESCC remain unclear. In ESCC cells and tissues, BCAR4 and microRNA -181c-5p (miR-181c-5p) expression, and phosphorylated signal transducer and activator of transcription (p-STAT3) and COX2 expression were evaluated by real-time reverse transcription PCR (qRT-PCR) and Western blot analysis. Cell function was evaluated by colony formation, CCK-8 assay, transwell and flow cytometer assays. Interactions between BCAR4 and miR-181c-5p, as well as miR-181c-5p and LIM and SH3 protein 1 (LASP1) were evaluated by RIP and luciferase reporter assay. ESCC cell malignancy with inhibition of BCAR4 was confirmed by a tumor xenograft model in vivo. In both ESCC tissues and cell lines, BCAR4 was upregulated. Downregulation of BCAR4 effectively induced cell apoptosis and inhibited invasion and migration in vitro, and reduced tumorigenesis in nude mice. BCAR4 was a sponge of miR-181c-5p to upregulate LASP1. Moreover, knockdown of BCAR4 and overexpression of miR-181c-5p inhibited the activation of the STAT3/COX2 signaling, which was reversed by overexpression of LASP1. In conclusion, BCAR4 promotes ESCC tumorigenesis by targeting the miR-181c-5p/LASP1 axis, which may act as a treatment and diagnosis biomarker for ESCC.