Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning.

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation.

BACKGROUND: MicroRNA (miRNA) has been shown to play a key role in the occurrence and progression of diseases, making uncovering miRNA-disease associations vital for disease prevention and therapy. However, traditional laboratory methods for detecting these associations are slow, strenuous, expensive, and uncertain. Although numerous advanced algorithms have emerged, it is still a challenge to develop more effective methods to explore underlying miRNA-disease associations. RESULTS: In the study, we designed a novel approach on the basis of deep autoencoder and combined feature representation (DAE-CFR) to predict possible miRNA-disease associations. We began by creating integrated similarity matrices of miRNAs and diseases, performing a logistic function transformation, balancing positive and negative samples with k-means clustering, and constructing training samples. Then, deep autoencoder was used to extract low-dimensional feature from two kinds of feature representations for miRNAs and diseases, namely, original association information-based and similarity information-based. Next, we combined the resulting features for each miRNA-disease pair and used a logistic regression (LR) classifier to infer all unknown miRNA-disease interactions. Under five and tenfold cross-validation (CV) frameworks, DAE-CFR not only outperformed six popular algorithms and nine classifiers, but also demonstrated superior performance on an additional dataset. Furthermore, case studies on three diseases (myocardial infarction, hypertension and stroke) confirmed the validity of DAE-CFR in practice. CONCLUSIONS: DAE-CFR achieved outstanding performance in predicting miRNA-disease associations and can provide evidence to inform biological experiments and clinical therapy.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning.

BACKGROUND: Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping. METHODS: We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision. RESULTS: wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes. CONCLUSIONS: The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

Melatonin-mediated CcARP1 alters F-actin dynamics by phosphorylation of CcADF9 to balance root growth and salt tolerance in pigeon pea.

As a multifunctional hormone-like molecule, melatonin exhibits a pleiotropic role in plant salt stress tolerance. While actin cytoskeleton is essential to plant tolerance to salt stress, it is unclear if and how actin cytoskeleton participates in the melatonin-mediated alleviation of plant salt stress. Here, we report that melatonin alleviates salt stress damage in pigeon pea by activating a kinase-like protein, which interacts with an actin-depolymerizing factor. Cajanus cajan Actin-Depolymerizing Factor 9 (CcADF9) has the function of severing actin filaments and is highly expressed under salt stress. The CcADF9 overexpression lines (CcADF9-OE) showed a reduction of transgenic root length and an increased sensitivity to salt stress. By using CcADF9 as a bait to screen an Y2H library, we identified actin depolymerizing factor-related phosphokinase 1 (ARP1), a novel protein kinase that interacts with CcADF9. CcARP1, induced by melatonin, promotes salt resistance of pigeon pea through phosphorylating CcADF9, inhibiting its severing activity. The CcARP1 overexpression lines (CcARP1-OE) displayed an increased transgenic root length and resistance to salt stress, whereas CcARP1 RNA interference lines (CcARP1-RNAi) presented the opposite phenotype. Altogether, our findings reveal that melatonin-induced CcARP1 maintains F-actin dynamics balance by phosphorylating CcADF9, thereby promoting root growth and enhancing salt tolerance.

CcNFYB3-CcMATE35 and LncRNA CcLTCS-CcCS modules jointly regulate the efflux and synthesis of citrate to enhance aluminium tolerance in pigeon pea.

Aluminium (Al) toxicity decreases crop production in acid soils in general, but many crops have evolved complex mechanisms to resist it. However, our current understanding of how plants cope with Al stress and perform Al resistance is still at the initial stage. In this study, the citrate transporter CcMATE35 was identified to be involved in Al stress response. The release of citrate was increased substantially in CcMATE35 over-expression (OE) lines under Al stress, indicating enhanced Al resistance. It was demonstrated that transcription factor CcNFYB3 regulated the expression of CcMATE35, promoting the release of citrate from roots to increase Al resistance in pigeon pea. We also found that a Long noncoding RNA Targeting Citrate Synthase (CcLTCS) is involved in Al resistance in pigeon pea. Compared with controls, overexpression of CcLTCS elevated the expression level of the Citrate Synthase gene (CcCS), leading to increases in root citrate level and citrate release, which forms another module to regulate Al resistance in pigeon pea. Simultaneous overexpression of CcNFYB3 and CcLTCS further increased Al resistance. Taken together, these findings suggest that the two modules, CcNFYB3-CcMATE35 and CcLTCS-CcCS, jointly regulate the efflux and synthesis of citrate and may play an important role in enhancing the resistance of pigeon pea under Al stress.

SnRK1 kinase-mediated phosphorylation of transcription factor bZIP39 regulates sorbitol metabolism in apple.

Sorbitol is a major photosynthate produced in leaves and transported through the phloem of apple (Malus domestica) and other tree fruits in Rosaceae. Sorbitol stimulates its own metabolism, but the underlying molecular mechanism remains unknown. Here, we show that sucrose nonfermenting 1 (SNF1)-related protein kinase 1 (SnRK1) is involved in regulating the sorbitol-responsive expression of both SORBITOL DEHYDROGENASE 1 (SDH1) and ALDOSE-6-PHOSPHATE REDUCTASE (A6PR), encoding 2 key enzymes in sorbitol metabolism. SnRK1 expression is increased by feeding of exogenous sorbitol but decreased by sucrose. SnRK1 interacts with and phosphorylates the basic leucine zipper (bZIP) transcription factor bZIP39. bZIP39 binds to the promoters of both SDH1 and A6PR and activates their expression. Overexpression of SnRK1 in 'Royal Gala' apple increases its protein level and activity, upregulating transcript levels of both SDH1 and A6PR without altering the expression of bZIP39. Of all the sugars tested, sorbitol is the only 1 that stimulates SDH1 and A6PR expression, and this stimulation is blocked by RNA interference (RNAi)-induced repression of either SnRK1 or bZIP39. These findings reveal that sorbitol acts as a signal regulating its own metabolism via SnRK1-mediated phosphorylation of bZIP39, which integrates sorbitol signaling into the SnRK1-mediated sugar signaling network to modulate plant carbohydrate metabolism.

Phase correction strategy based on structured light fringe projection profilometry.

Fringe projection profilometry based on structured light has been widely used in 3-D vision due to its advantages of simple structure, good robustness, and high speed. The principle of this technique is to project multiple orders of stripes on the object, and the camera captures the deformed stripe map. Phase unwrapping and depth map calculation are important steps. Still, in actual situations, phase ambiguity is prone to occur at the edges of the object. In this paper, an adaptive phase segmentation and correction (APSC) method after phase unwrapping is proposed. In order to effectively distinguish the stable area and unstable area of the phase, a boundary identification method is proposed to obtain the structural mask of the phase. A phase compensation method is proposed to improve the phase accuracy. Finally, we obtain the 3-D reconstruction result based on the corrected phase. Specific experimental results verify the feasibility and effectiveness of this method.

Porous Ceramic Metal-Based Flow Battery Composite Membrane.

In metal-based flow battery, membranes significantly impact energy conversion efficiency and security. Unfortunately, damages to the membrane occur due to gradual accumulation of metal dendrites, causing short circuits and shortening cycle life. Herein, we developed a rigid hierarchical porous ceramic flow battery composite membrane with a sub-10-nm-thick polyelectrolyte coating to achieve high ion selectivity and conductivity, to restrain dendrite, and to realize long cycle life and high areal capacity. An aqueous zinc-iron flow battery prepared using this membrane achieved an outstanding energy efficiency of >80%, exhibiting excellent long-term stability (over 1000 h) and extremely high areal capacity (260 mAh cm-2). Low-field nuclear magnetic resonance (NMR) spectroscopy, small-angle X-ray scattering, in situ infrared spectroscopy, solid-state NMR analysis, and nano-computed tomography revealed that the rigid hierarchical pore structures and numerous hydrogen bonding networks in the membrane contributed to the stable operation and superior battery performance. This study contributes to the development of next-generation metal-based flow battery membranes for energy and power generation.

Integrated transcriptome and physiological analysis revealed core transcription factors that promote flavonoid biosynthesis in apricot in response to pathogenic fungal infection.

MAIN CONCLUSION: Integrated transcriptome and physiological analysis of apricot leaves after Fusarium solani treatment. In addition, we identified core transcription factors and flavonoid-related synthase genes which may function in apricot disease resistance. Apricot (Prunus armeniaca) is an important economic fruit species, whose yield and quality of fruit are limited owing to its susceptibility to diseases. However, the molecular mechanisms underlying the response of P. armeniaca to diseases is still unknown. In this study, we used physiology and transcriptome analysis to characterize responses of P. armeniaca subjected to Fusarium solani. The results showed increasing malondialdehyde (MDA) content, enhanced peroxidase (POD) and catalase (CAT) activity during F. solani infestation. A large number of differentially expressed genes (DEGs), which included 4281 upregulated DEGs and 3305 downregulated DEGs, were detected in P. armeniaca leaves exposed to F. solani infestation. Changes in expression of transcription factors (TFs), including bHLH, AP2/ERF, and WRKY indicated their role in triggering pathogen-responsive genes in P. armeniaca. During the P. armeniaca response to F. solani infestation, the content of total flavonoid was changed, and we identified enzyme genes associated with flavonoid biosynthesis. Ectopic overexpression of PabHLH15 and PabHLH102 in Nicotiana benthamiana conferred elevated resistance to Fspa_1. Moreover, PabHLH15 and PabHLH102 positively interact with the promoter of flavonoid biosynthesis-related genes. A regulatory network of TFs regulating enzyme genes related to flavonoid synthesis affecting apricot disease resistance was constructed. These results reveal the potential underlying mechanisms of the F. solani response of P. armeniaca, which would help improve the disease resistance of P. armeniaca and may cultivate high-quality disease-resistant varieties in the future.

Gene-based mediation analysis in epigenetic studies.

Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.

Identifying complex gene-gene interactions: a mixed kernel omnibus testing approach.

Genes do not function independently; rather, they interact with each other to fulfill their joint tasks. Identification of gene-gene interactions has been critically important in elucidating the molecular mechanisms responsible for the variation of a phenotype. Regression models are commonly used to model the interaction between two genes with a linear product term. The interaction effect of two genes can be linear or nonlinear, depending on the true nature of the data. When nonlinear interactions exist, the linear interaction model may not be able to detect such interactions; hence, it suffers from substantial power loss. While the true interaction mechanism (linear or nonlinear) is generally unknown in practice, it is critical to develop statistical methods that can be flexible to capture the underlying interaction mechanism without assuming a specific model assumption. In this study, we develop a mixed kernel function which combines both linear and Gaussian kernels with different weights to capture the linear or nonlinear interaction of two genes. Instead of optimizing the weight function, we propose a grid search strategy and use a Cauchy transformation of the P-values obtained under different weights to aggregate the P-values. We further extend the two-gene interaction model to a high-dimensional setup using a de-biased LASSO algorithm. Extensive simulation studies are conducted to verify the performance of the proposed method. Application to two case studies further demonstrates the utility of the model. Our method provides a flexible and computationally efficient tool for disentangling complex gene-gene interactions associated with complex traits.

Two-Dimensional MFI-Type Zeolite Flow Battery Membranes.

Vanadium flow battery (VFB) is one of the most reliable stationary electrochemical energy-storage technologies, and a membrane with high vanadium resistance and proton conductivity is essential for manufacturing high-performance VFBs. In this study, a two-dimensional (2D) MFI-type zeolite membrane was fabricated from zeolite nanosheet modules, which displayed excellent vanadium resistance (0.07 mmol L-1 h-1 ) and proton conductivity (0.16 S cm-1 ), yielding a coulombic efficiency of 93.9 %, a voltage efficiency of 87.6 %, and an energy efficiency of 82.3 % at 40 mA cm-2 . The self-discharge period of a VFB equipped with 2D MFI-type zeolite membrane increased up to 116.2 h, which was significantly longer than that of the commercial perfluorinated sulfonate membrane (45.9 h). Furthermore, the corresponding battery performance remained stable over 1000 cycles (>1500 h) at 80 mA cm-2 . These findings demonstrate that 2D MFI-type membranes are promising ion-conductive membranes applicable for stationary electrochemical energy-storage devices.

Crystallizing Self-Standing Covalent Organic Framework Membranes for Ultrafast Proton Transport in Flow Batteries.

Covalent organic frameworks (COFs) display great potential to be assembled into proton conductive membranes for their uniform and controllable pore structure, yet constructing self-standing COF membrane with high crystallinity to fully exploit their ordered crystalline channels for efficient ionic conduction remains a great challenge. Here, a macromolecular-mediated crystallization strategy is designed to manipulate the crystallization of self-standing COF membrane, where the -SO3 H groups in introduced sulfonated macromolecule chains function as the sites to interact with the precursors of COF and thus offer long-range ordered template for membrane crystallization. The optimized self-standing COF membrane composed of highly-ordered nanopores exhibits high proton conductivity (75 mS cm-1 at 100 % relative humidity and 20 °C) and excellent flow battery performance, outperforming Nafion 212 and reported membranes. Meanwhile, the long-term run of membrane is achieved with the help of the anchoring effect of flexible macromolecule chains. Our work provides inspiration to design self-standing COF membranes with ordered channels for permselective application.

The pigeon pea CcCIPK14-CcCBL1 pair positively modulates drought tolerance by enhancing flavonoid biosynthesis.

Calcineurin B-like (CBL)-interacting protein kinases (CIPKs) play a central role in Ca2+ signalling and promote drought tolerance in plants. The CIPK gene family in pigeon pea (Cajanus cajan L.), a major food crop affected by drought, has not previously been characterised. Here, we identified 28 CIPK genes in the pigeon pea genome. Five CcCIPK genes were strongly upregulated in roots upon drought treatment and were selected for further characterisation. Overexpression of CcCIPK13 and CcCIPK14 increased survival rates by two- to three-fold relative to controls after 14 days of drought. Furthermore, the three major flavonoids, genistin, genistein and apigenin, were significantly upregulated in the same transgenic plants. Using CcCIPK14 as bait, we performed a yeast two-hybrid screen and identified six interactors, including CcCBL1. CcCIPK14 exhibited autophosphorylation and phosphorylation of CcCBL1 in vitro. CcCBL1-overexpressed plants displayed higher survival rates upon drought stress as well as higher expression of flavonoid biosynthetic genes and flavonoid content. CcCIPK14-overexpressed plants in which CcCBL1 transcript levels were reduced by RNA interference had lower survival rates, which indicated CcCBL1 in the same pathway as CcCIPK14. Together, our results demonstrate a role for the CcCIPK14-CcCBL1 complex in drought stress tolerance through the regulation of flavonoid biosynthesis in pigeon pea.

Multilevel heterogeneous omics data integration with kernel fusion.

High-throughput omics data are generated almost with no limit nowadays. It becomes increasingly important to integrate different omics data types to disentangle the molecular machinery of complex diseases with the hope for better disease prevention and treatment. Since the relationship among different omics data features are typically unknown, a supervised learning model assuming a particular distribution with a specific structure will not serve the purpose to capture the underlying complex relationship between multiple features and a disease phenotype. In this work, we briefly reviewed methods for kernel fusion (KF) based on support vector machine and kernel partial least squares (KPLS) algorithms. We then proposed a fused KPLS (fKPLS) model for disease classification and prediction with multilevel omics data. The fused kernel can deal with effect heterogeneity in which different omic data types may have different effect contribution to the trait of interest, with the purpose to improve the prediction performance. We proposed to optimize the kernel parameters and kernel weights with the genetic algorithm (GA). The proposed GA-fKPLS model can substantially improve disease classification performance by integrating multiple omics data types, demonstrated via extensive simulations and real data analysis. With properly defined fitness functions during GA optimization, the proposed KF method can be extended to other kernel-based analyses such as in kernel association analysis with common or rare variants.

Hyperoside regulates its own biosynthesis via MYB30 in promoting reproductive development and seed set in okra.

Flavonoids are secondary metabolites that play important roles in fruit and vegetable development. Here, we examined the function of hyperoside, a unique flavonoid in okra (Abelmoschus esculentus), known to promote both flowering and seed set. We showed that the exogenous application of hyperoside significantly improved pollen germination rate and pollen tube growth by almost 50%, resulting in a 42.7% increase in the seed set rate. Of several genes induced by the hyperoside treatment, AeUF3GaT1, which encodes an enzyme that catalyzes the last step of hyperoside biosynthesis, was the most strongly induced. The transcription factor AeMYB30 enhanced AeUFG3aT1 transcription by directly binding to the AeUFG3aT1 promoter. We studied the effect of the hyperoside application on the expression of 10 representative genes at four stages of reproductive development, from pollination to seed maturity. We firstly developed an efficient transformation system that uses seeds as explants to study the roles of AeMYB30 and AeUFG3aT1. Overexpression of AeMYB30 or AeUF3GaT1 promoted seed development. Moreover, exogenous application of hyperoside partially restored the aberrant phenotype of AeUF3GaT1 RNA-interference plants. Thus, hyperoside promotes seed set in okra via a pathway involving AeUF3GaT and AeMYB30, and the exogenous application of this flavonoid is a simple method that can be used to improve seed quality and yield in okra.

Electrostatic-Induced Crystal-Rearrangement of Porous Organic Cage Membrane for CO2 Capture.

Porous Organic Cages (POCs) with tunable tailoring chemistry properties and polymer-like processing conditions are of great potential for molecular selective membranes, but it remains challenging in the assembly of high crystalline POCs with regular nanochannels for effective molecular sieving. Here we report an electrostatic-induced crystal-rearrangement strategy for the design of a POC membrane with heterostructure. Due to electrostatic attraction, ionic liquid molecules induced cage molecules to rearrange into a sub-10 nm uniform and defect-free crystal layer, which displayed competitive CO2 separation performance. The optimized hetero-structured membrane exhibited an attractive CO2 /N2 separation selectivity of over 130, which was superior to the state-of-the-art membranes, accompanied with excellent long-term and thermal shock stability. This strategy provides a new inspiration for the preparation of crystal-rearranged membranes with regular channels for gas molecule sieving.

High-Efficiency CO2 /N2 Separation Enabled by Rotation of Electrostatically Anchored Flexible Ligands in Metal-Organic Framework.

Metal organic frameworks (MOF) are of great potential for molecular separation, but the ligand rotation flexibility makes them remain challenging in the construction of fixed nanochannels for precise sieving. Here we report an electrostatic-anchoring strategy to fix the rotation of 2-methylimidazole (2-MIM) ligand in ZIF-8. Electrostatic inducer trifluoroacetate anchored at and blocked the six-membered windows of ZIF-8, and meanwhile induced the positive 2-MIM rotated from initial 49° to 68°, thus opening neighbored four-membered windows with a constant size of 3.4 Å. The obtained ZIF-8 significantly enhanced the CO2 /N2 adsorption selectivity from 14.02 to 332.86. Further membrane-based separation exhibited an outstanding CO2 /N2 selectivity of up to 137 with a desired permeability of 286 Barrer, which exceeded the 2019 upper bound. This strategy provides a new inspiration for fixing the ligand rotation in soft MOF for desired precise molecular sieving.

The effect of acetate Ringer's solution versus lactate Ringer's solution on acid base physiology in infants with biliary atresia.

BACKGROUND: The choice of the perioperative crystalloid is a key component of the fluid management and must take into account the liver function and the appearing metabolic disorders to avoid increase the liver extra metabolism. The aim of this study is to analyze the effect of acetate Ringer's solution or lactate Ringer's solution in biliary atresia patients. METHODS: We included 68 infant patients aged between 21 ~ 65 d, ASA physical status II or III, who underwent elective Kasai hepatoportoenterostomy, received either AR and LR for intravenous fluid resuscitation according to their group allocation. Lactate concentration, serum electrolytes and pH were noteded before skin incision (T1), end of surgery (T2) and postoperative 12 h. We also recorded the time of operation, stay of hospital, loss of blood and urinary, total volume of infusion of crystalloid. RESULTS: Lactate level was significantly higher in Group LR than in Group AR patients at T2 (0.76 ± 0.13 versus 0.57 ± 0.22, P = 0.03). Compared with T3, sodium and chlorine were significantly higher in two groups at T2 (145.2 ± 3.1 versus 143.4 ± 3.4 and 104.6 ± 3.7 versus 105.2 ± 2.1). No significant differences were noted in potassium, HCO3- and calcium. There was no statistically significant difference in pH. No glycopenia was recorded in two groups. No significant difference was noted in administration of vasoactive drug (0.7% versus 1%). CONCLUSIONS: Resuscitation with AR and LR was associated with similar clinical improvement in infants with biliary atresia. Use of AR reduced the level of lactate comparison with LR.

Comparison of the Effect of Laryngeal Mask Airway Versus Endotracheal Tube on Airway Management in Pediatric Patients with Tonsillar Hypertrophy.

PURPOSE: The laryngeal mask airway (LMA) has become an important choice in both routine and difficult airway management. We aimed to evaluate the safety and effectiveness of LMA use in pediatric patients with tonsillar hypertrophy. DESIGN: This study was a randomized controlled trial. METHODS: The study included 100 pediatric patients who had first or second degree tonsillar hypertrophy. Pediatric patients undergoing elective laparoscopic inguinal hernia repair were randomly divided into two groups (n = 50): LMA group and the endotracheal tube (ETT) group. The primary outcomes were ventilation leak volume, peak airway pressure, and partial pressure of end-tidal carbon dioxide (PETco2). Data for primary outcomes were collected before and 5-, 15-, and 25-minute after opening pneumoperitoneum, and on closing the pneumoperitoneum. Complications such as laryngospasm, bronchospasm, desaturation, severe coughing, blood on the device after removal, and sore throat were recorded. FINDINGS: A total of 100 pediatric patients were assessed and 97 eligible patients were randomly assigned to receive an LMA (n = 49) or an ETT (n = 48). There was no statistically significant difference in ventilation leak volume and PETco2 between the LMA and ETT groups (P > .05). Compared with T1-4, peak airway pressure was significantly lower in T0 (LMA group 12.6 ± 0.9, 95% confidence interval 12.2 to 13.0; ETT group 12.8 ± 1.2, 95% confidence interval 12.2 to 13.3; P < .05). The incidences of laryngospasm 11 (22.9%), desaturation 18 (37.5%), and severe coughing 13 (27%) were higher in the ETT group (11 [22.9%] vs 3 [6.1%], 18 [37.5%] vs 6 [12.2%], 13 [27%] vs 3 [6.1%]; P < .05). CONCLUSIONS: The application of LMA has a lower incidence of complications. LMA as an airway device is effective and perhaps superior in appropriate patients.