Chirality and Local Electron Density of States in a Two-Dimensional Molecular Crystal Self-Assembled by Prochiral Radical Molecules.

Creating and unveiling chiral systems is important to the development of new materials and devices. In this study, after depositing prochiral radical molecule 3-carbamoyl-2,2,5,5-tetramethyl pyrroline-1-oxyl (CTPO) on a Au(111) substrate, 2D molecular crystals with two chiralities of CTPO molecules have been discovered. A single CTPO molecule is an achiral molecule in three-dimensional space, and it can form chiral configurations after adsorbing on the substrate. The chiralities of 2D molecular crystals originate from the chiral properties of adsorbed CTPO molecules and their assembling. Molecules with different chiralities are connected with molecular recognition through hydrogen bond interactions. Through density functional theory simulations, the hydrogen bond networks and molecular structures of this system were explored. To gain a further understanding of this system, the electronic property of CTPO/Au(111) was studied with a local density of states (LDOS) characterization. A peculiar LDOS distribution related to the vibrational excitation of the molecules was mapped at the submolecular scale. These results are useful for understanding the nature of chirality formation, 2D molecular crystal construction, and radical molecule applications.

Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future.

Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.

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Understanding and alleviating informal caregiver burden through the development and validation of a caregiver strain index-based model in Taiwan.

BACKGROUND: Quantifying the informal caregiver burden is important for understanding the risk factors associated with caregiver overload and for evaluating the effectiveness of services provided in Long-term Care (LTC). OBJECTIVE: This study aimed to develop and validate a Caregiver Strain Index (CSI)-based score for quantifying the informal caregiver burden, while the original dataset did not fully cover evaluation items commonly included in international assessments. Subsequently, we utilized the CSI-based score to pinpoint key caregiver burden risk factors, examine the initial timing of LTC services adoption, and assess the impact of LTC services on reducing caregiver burden. METHODS: The study analyzed over 28,000 LTC cases in Southern Taiwan from August 2019 to December 2022. Through multiple regression analysis, we identified significant risk factors associated with caregiver burden and examined changes in this burden after utilizing various services. Survival analysis was employed to explore the relationship between adopting the first LTC services and varying levels of caregiver burden. RESULTS: We identified 126 significant risk factors for caregiver burden. The most critical factors included caregiving for other disabled family members or children under the age of three (ß = 0.74, p < 0.001), the employment status of the caregiver (ß = 0.30-0.53, p < 0.001), the frailty of the care recipient (ß = 0.28-0.31, p < 0.001), and the behavioral symptoms of dementia in care recipients (ß = 0.28-2.60, p < 0.05). Generally, caregivers facing higher burdens sought LTC services earlier, and providing home care services alleviated the caregiver's burden. CONCLUSION: This comprehensive study suggests policy refinements to recognize high-risk caregivers better early and provide timely support to improve the overall well-being of both informal caregivers and care recipients.

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alterations. Furthermore, CITRUS uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs) to learn the relationships between TFs and their target genes based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, transcriptomic, and epigenomic data from 17 cancer types profiled by The Cancer Genome Atlas. CITRUS predicts patient-specific TF activities and reveals transcriptional program variations between and within tumor types. We show that CITRUS yields biological insights into delineating TFs associated with somatic alterations in individual tumors. Thus, CITRUS is a promising tool for precision oncology.

An individualized Bayesian method for estimating genomic variants of hypertension.

BACKGROUND: Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual's genome that provide a strong explanation of the phenotype observed in this individual. RESULTS: We applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve. CONCLUSIONS: The results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.

Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution.

MOTIVATION: Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods. RESULTS: To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data. AVAILABILITY AND IMPLEMENTATION: EnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. The RNA microarray data from the TRAUMA study are available and can be accessed in GEO (GSE36809). The demographic and clinical phenotypes can be shared on reasonable request to the corresponding authors. The RNA-seq data from the EVAPR study cannot be shared publicly due to the privacy of individuals that participated in the clinical research in compliance with the IRB approval at the University of Pittsburgh. The RNA microarray data from the FHS study are available from dbGaP (phs000007.v32.p13). The RNA-seq data from ROS study is downloaded from AD Knowledge Portal. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Advanced LD pumped 3.3 J/1 Hz nanosecond Nd:glass preamplifier for SG-II upgrade laser facility.

We demonstrate a laser-diode-pumped multipass Nd:glass laser amplifier with a range of advanced characteristics. The amplifier exhibits high extraction efficiency, enables arbitrary shaping of spatial beam intensity, and effectively suppresses frequency modulation to amplitude modulation conversion. Our approach achieves excellent beam quality via thermal lensing and thermal depolarization compensation. When a 1.82 mJ/5 ns laser pulse was injected into the amplifier, the output energy reached up to 3.3 J with a repetition rate of 1 Hz at a central wavelength of 1053.3 nm. The near-field modulation of the amplified output beam was below 1.2, and the far-field focusing ability of the beam was 90% at 2.9 times the diffraction limit. This laser amplifier system holds potential for integration as a preamplifier within the SG-II upgrade high power laser facility.

An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors.

Cells within a tumor microenvironment (TME) dynamically communicate and influence each other's cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an individualized causal analysis framework for discovering tumor specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied individualized causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating individual ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.

Geometrical and magnetic properties of small titanium and chromium clusters on monolayer hexagonal boron nitride.

Magnetic clusters on an insulating substrate are potential candidates for spin-based quantum devices. Here we investigate the geometric, electronic, and magnetic structures of small Ti and Cr clusters, from dimers to pentamers, adsorbed on a single-layer hexagonal boron nitride (h-BN) sheet within the framework of density functional theory. The stable adsorption configurations of the Ti clusters and Cr clusters composed of the same number of atoms are found to be totally different from each other. The difference in their bonding mechanisms has been revealed by the density of states and the charge density difference of the corresponding adsorption systems. While chemical bonds are formed between the Ti atoms and the supporting sheet, the Cr clusters are found in the physisorption state on the substrate. In addition, it is shown that the h-BN sheet is energetically favorable for building three-dimensional Ti clusters. These findings support the use of h-BN as a suitable decoupling substrate for manipulation of quantum spin states in small transition metal (TM) clusters and fabrication of devices based on them.

Ionic Current Fluctuation and Orientation of Tetrahedral DNA Nanostructures in a Solid-State Nanopore.

Understanding the dynamic behavior of a nanostructure translocating through a nanopore is important for various applications. In this paper, the characteristics in ion current traces of tetrahedral DNA nanostructures (TDN) translocating through a solid-state nanopore are examined, by combined experimental and theoretical simulations. The results of finite element analysis reveal the correlation between orientation of TDN and the conductance blockade. The experimentally measured fluctuations in the conductance blockade, expressed as voltage-dependent histogram profiles, are consistent with the simulation, revealing the nature of a random distribution in orientation and weak influence of electrostatic and viscous torques. The step changes in orientation of a TDN during translocation are further explained by the collision with the nanopore, while the gradual changes in orientation illustrate the impact of a weak torque field in the nano-fluidic channel. The results demonstrate a general method and basic understanding in the dynamic behavior of nanostructures translocating through solid-state nanopores.

High-contrast OPCPA front end in high-power petawatt laser facility based on the ps-OPCPA seed system.

A high-energy, high-beam-quality, high-contrast picosecond optical parametric chirped-pulse amplification (ps-OPCPA) laser system was demonstrated. The pulse from a femtosecond oscillator was stretched to 4 ps, after which it was amplified from 140 pJ to 600 µJ by an 8 ps/6 mJ pump laser in two non-collinear OPCPA stages. The total gain was >106, and the root mean square of the energy stability of the laser system was 1.6% in 10 h. The contrasts of the solid and fiber mode-locked femtosecond oscillator-seeded ps-OPCPA systems were compared, and a signal-to-noise ratio of >1011 was achieved. Using this system, the contrast of the front end in high-power picosecond petawatt laser facility was improved by ∼40 dB to >1011, beyond ∼200 ps ahead of the main pulse with an output level of 60 mJ.

Dissecting expression profiles of gastric precancerous lesions and early gastric cancer to explore crucial molecules in intestinal-type gastric cancer tumorigenesis.

Intestinal-type gastric cancer (IGC) has a clear and multistep histological evolution. No studies have comprehensively explored gastric tumorigenesis from inflammation through low-grade intraepithelial neoplasia (LGIN) and high-grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC). We sought to investigate the characteristics participating in IGC tumorigenesis and identify related prognostic information within the process. RNA expression profiles of 94 gastroscopic biopsies from 47 patients, including gastric precancerous lesions (GPL: LGIN and HGIN), EGC, and paired controls, were detected by Agilent Microarray. During IGC tumorigenesis from LGIN through HGIN to EGC, the number of activity-changed tumor hallmarks increased. LGIN and HGIN had similar expression profiles when compared to EGC. We observed an increase in the stemness of gastric epithelial cells in LGIN, HGIN, and EGC, and we found 27 consistent genes that might contribute to dedifferentiation, including five driver genes. Remarkably, we perceived that the immune microenvironment was more active in EGC than in GPL, especially in the infiltration of lymphocytes and macrophages. We identified a five-gene signature from the gastric tumorigenesis process that could independently predict the overall survival and disease-free survival of GC patients (log-rank test: p < 0.0001), and the robustness was verified in an independent cohort (n > 300) and by comparing with two established prognostic signatures in GC. In conclusion, during IGC tumorigenesis, cancer-like changes occur in LGIN and accumulate in HGIN and EGC. The immune microenvironment is more active in EGC than in LGIN and HGIN. The identified signature from the tumorigenesis process has robust prognostic significance for GC patients. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

High-stability, high-energy Nd:glass rod regenerative amplifier with compensation for cavity misalignment.

We report on an Nd:glass large-mode rod regenerative amplifier with a pulse energy of 125 mJ at 1053 nm. The amplifier contains a linear-type resonator, which is designed in Stability Zone II with a misalignment sensitivity factor of 12.9 m. A method is proposed for analyzing the sensitivity of the mode displacement on the gain media to cavity misalignment, and the optimum solution to compensation for cavity misalignment is obtained and applied to the amplifier. The amplifier exhibits excellent energy stability with a fluctuation of 0.47% (RMS) within 7 h and high spatial beam quality with M2=1.17. The beam pointing stability in the horizontal and vertical axes within 7 h is 2.7 and 3.6 µrad (RMS), respectively.

Explicit representation of protein activity states significantly improves causal discovery of protein phosphorylation networks.

BACKGROUND: Protein phosphorylation networks play an important role in cell signaling. In these networks, phosphorylation of a protein kinase usually leads to its activation, which in turn will phosphorylate its downstream target proteins. A phosphorylation network is essentially a causal network, which can be learned by causal inference algorithms. Prior efforts have applied such algorithms to data measuring protein phosphorylation levels, assuming that the phosphorylation levels represent protein activity states. However, the phosphorylation status of a kinase does not always reflect its activity state, because interventions such as inhibitors or mutations can directly affect its activity state without changing its phosphorylation status. Thus, when cellular systems are subjected to extensive perturbations, the statistical relationships between phosphorylation states of proteins may be disrupted, making it difficult to reconstruct the true protein phosphorylation network. Here, we describe a novel framework to address this challenge. RESULTS: We have developed a causal discovery framework that explicitly represents the activity state of each protein kinase as an unmeasured variable and developed a novel algorithm called "InferA" to infer the protein activity states, which allows us to incorporate the protein phosphorylation level, pharmacological interventions and prior knowledge. We applied our framework to simulated datasets and to a real-world dataset. The simulation experiments demonstrated that explicit representation of activity states of protein kinases allows one to effectively represent the impact of interventions and thus enabled our framework to accurately recover the ground-truth causal network. Results from the real-world dataset showed that the explicit representation of protein activity states allowed an effective and data-driven integration of the prior knowledge by InferA, which further leads to the recovery of a phosphorylation network that is more consistent with experiment results. CONCLUSIONS: Explicit representation of the protein activity states by our novel framework significantly enhances causal discovery of protein phosphorylation networks.

Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference.

Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors.

Circulating miR-19a-3p and miR-483-5p as Novel Diagnostic Biomarkers for the Early Diagnosis of Gastric Cancer.

BACKGROUND MicroRNAs (miRNAs) are attracting substantial interest as promising noninvasive biomarkers for gastric cancer (GC). Our study aimed to identify circulating miRNAs that are potential noninvasive markers for precancerous lesions and early gastric cancers (EGCs). MATERIAL AND METHODS Plasma specimens were obtained from 58 gastritis subjects, 54 patients with precancerous lesions, and 38 EGC patients for study. RESULTS Significant differences in the plasma expression levels of miR-19a-3p, miR-22-3p, miR-146a-5p, and miR-483-5p (all P<0.05) were observed between EGC patients and gastritis subjects. Multivariable analysis showed that age (OR, 1.054; 95% CI, 1.006-1.104), miR-19a-3p expression (OR, 3.676; 95% CI, 1.914-7.061), and miR-483-5p expression (OR, 1.589; 95% CI, 1.242-2.033) were independently associated with EGCs and precancerous lesions. A combined diagnostic model incorporating these 3 variables for the prediction of EGCs and precancerous lesions was derived. The area under the receiver operating characteristic curve (AUC) of the model was 0.84; the sensitivity was 87.7% and the specificity was 62.8% at the cutoff value of -0.08. CONCLUSIONS Plasma miR-19a-3p and miR-483-5p are promising and powerful noninvasive markers for the early detection of GC. Patients are more willing to undergo noninvasive diagnostic procedures than gastroscopy for cancer screening, economizing limited medical resources.

High-stability, high-beam-quality, and pulse-width-tunable 1319 nm laser system for VISAR applications in high-power laser facilities.

A high-energy, high-beam-quality, and pulse-width-tunable Nd:YAG laser system, pumped by vertical cavity surface emitting laser arrays and laser diodes, is demonstrated and applied to a velocity interferometry system for any reflector (VISAR) application in a high power laser facility. A multistage multipass amplification structure is used to fully extract the amplifier energy and obtain a high-energy pulse. The temporal waveform is compensated to provide a square waveform, with a flatness less than 8% (peak-to-peak value). The peak power is greater than 100 kW with a frequency-doubling efficiency of 25% for a 50 ns pulse width. The laser operates as a single shot with 1-5 Hz repetition frequency and 0.7% rms energy stability.

Integrating data and knowledge to identify functional modules of genes: a multilayer approach.

BACKGROUND: Characterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics. This is made possible by the rising of high-throughput technology. Unfortunately, computational methods to identify functional modules were limited by the data quality issues of high-throughput techniques. This study aims to integrate knowledge extracted from literature to further improve the accuracy of functional module identification. RESULTS: Our new model and algorithm were applied to both yeast and human interactomes. Predicted functional modules have covered over 90% of the proteins in both organisms, while maintaining a comparable overall accuracy. We found that the combination of both mRNA expression information and biomedical knowledge greatly improved the performance of functional module identification, which is better than those only using protein interaction network weighted with transcriptomic data, literature knowledge, or simply unweighted protein interaction network. Our new algorithm also achieved better performance when comparing with some other well-known methods, especially in terms of the positive predictive value (PPV), which indicated the confidence of novel discovery. CONCLUSION: Higher PPV with the multiplex approach suggested that information from both sources has been effectively integrated to reduce false positive. With protein coverage higher than 90%, our algorithm is able to generate more novel biological hypothesis with higher confidence.

Structure based design of selective SHP2 inhibitors by De novo design, synthesis and biological evaluation.

SHP2 phosphatase, encoded by the PTPN11 gene, is a non-receptor PTP, which plays an important role in growth factor, cytokine, integrin, hormone signaling pathways, and regulates cellular responses, such as proliferation, differentiation, adhesion migration and apoptosis. Many studies have reported that upregulation of SHP2 expression is closely related to human cancer, such as breast cancer, liver cancer and gastric cancer. Hence, SHP2 has become a promising target for cancer immunotherapy. In this paper, we reported the identification of compound 1 as SHP2 inhibitor. Fragment-based ligand design, De novo design, ADMET and Molecular docking were performed to explore potential selective SHP2 allosteric inhibitors based on SHP836. The results of docking studies indicated that the selected compounds had higher selective SHP2 inhibition than existing inhibitors. Compound 1 was found to have a novel selectivity against SHP2 with an in vitro enzyme activity IC50 value of 9.97 µM. Fluorescence titration experiment confirmed that compound 1 directly bound to SHP2. Furthermore, the results of binding free energies demonstrated that electrostatic energy was the primary factor in elucidating the mechanism of SHP2 inhibition. Dynamic cross correlation studies also supported the results of docking and molecular dynamics simulation. This series of analyses provided important structural features for designing new selective SHP2 inhibitors as potential drugs and promising candidates for pre-clinical pharmacological investigations.

Analysis of Long Non-Coding RNA Expression Profile and Functional Study of LOC389332 in Early Gastric Cancer.

BACKGROUND Long non-coding RNAs (LncRNAs) could potentially function as diagnostic markers for gastric carcinoma. Nevertheless, the expression profile and biological feature of LncRNAs in early gastric cancer (EGC) remains to be explored. MATERIAL AND METHODS LncRNA expression microarray analysis was performed on 6 paired EGC tissues. One deregulated LncRNA, LOC389332, was validated using a quantitative reverse-transcription polymerase chain reaction (qRT-PCR) assay using independent tissue samples and cell lines. The Cell Counting Kit-8 (CCK-8) assay and wound healing assay were conducted to evaluate its influences on the proliferation and migration of gastric cancer cells. LncRNA expression microarray and gene ontology (GO) analysis were also performed on the LOC389332 knockdown cell line model to explore the molecular feature of LOC389332 in gastric carcinoma. RESULTS The LncRNA expression profiling showed that 72 LncRNAs were significantly differentially expressed in EGC tissues. The results in the validation phase revealed that LOC389332 was remarkably overexpressed in gastric carcinoma tissues, precancerous lesions, and gastric cancer cells. Functional study showed that knockdown of LOC389332 expression could inhibit cell proliferation and migration. LncRNA expression microarray on the LOC389332 knockdown cell line model revealed that 393 mRNAs were differentially expressed. The GO enrichment analysis indicated that the downregulated genes were mainly associated with cell membrane function, signal transmission process, and cell adhesion process. CONCLUSIONS The LncRNA expression profile between EGC and gastritis tissues was significantly different. LOC389332 was potential non-coding oncogenes in gastric cancer, and it may perform its function through altering cell membrane function, signal transmission, and cell adhesion.