IDMIR: identification of dysregulated miRNAs associated with disease based on a miRNA-miRNA interaction network constructed through gene expression data.

Micro ribonucleic acids (miRNAs) play a pivotal role in governing the human transcriptome in various biological phenomena. Hence, the accumulation of miRNA expression dysregulation frequently assumes a noteworthy role in the initiation and progression of complex diseases. However, accurate identification of dysregulated miRNAs still faces challenges at the current stage. Several bioinformatics tools have recently emerged for forecasting the associations between miRNAs and diseases. Nonetheless, the existing reference tools mainly identify the miRNA-disease associations in a general state and fall short of pinpointing dysregulated miRNAs within a specific disease state. Additionally, no studies adequately consider miRNA-miRNA interactions (MMIs) when analyzing the miRNA-disease associations. Here, we introduced a systematic approach, called IDMIR, which enabled the identification of expression dysregulated miRNAs through an MMI network under the gene expression context, where the network's architecture was designed to implicitly connect miRNAs based on their shared biological functions within a particular disease context. The advantage of IDMIR is that it uses gene expression data for the identification of dysregulated miRNAs by analyzing variations in MMIs. We illustrated the excellent predictive power for dysregulated miRNAs of the IDMIR approach through data analysis on breast cancer and bladder urothelial cancer. IDMIR could surpass several existing miRNA-disease association prediction approaches through comparison. We believe the approach complements the deficiencies in predicting miRNA-disease association and may provide new insights and possibilities for diagnosing and treating diseases. The IDMIR approach is now available as a free R package on CRAN (https://CRAN.R-project.org/package=IDMIR).

CRdb: a comprehensive resource for deciphering chromatin regulators in human.

Chromatin regulators (CRs) regulate epigenetic patterns on a partial or global scale, playing a critical role in affecting multi-target gene expression. As chromatin immunoprecipitation sequencing (ChIP-seq) data associated with CRs are rapidly accumulating, a comprehensive resource of CRs needs to be built urgently for collecting, integrating, and processing these data, which can provide abundant annotated information on CR upstream and downstream regulatory analyses as well as CR-related analysis functions. This study established an integrative CR resource, named CRdb (http://cr.liclab.net/crdb/), with the aim of curating a large number of available resources for CRs and providing extensive annotations and analyses of CRs to help biological researchers clarify the regulation mechanism and function of CRs. The CRdb database comprised a total of 647 CRs and 2,591 ChIP-seq samples from more than 300 human tissues and cell types. These samples have been manually curated from NCBI GEO/SRA and ENCODE. Importantly, CRdb provided the abundant and detailed genetic annotations in CR-binding regions based on ChIP-seq. Furthermore, CRdb supported various functional annotations and upstream regulatory information on CRs. In particular, it embedded four types of CR regulatory analyses: CR gene set enrichment, CR-binding genomic region annotation, CR-TF co-occupancy analysis, and CR regulatory axis analysis. CRdb is a useful and powerful resource that can help in exploring the potential functions of CRs and their regulatory mechanism in diseases and biological processes.

SEdb 2.0: a comprehensive super-enhancer database of human and mouse.

Super-enhancers (SEs) are cell-specific DNA cis-regulatory elements that can supervise the transcriptional regulation processes of downstream genes. SEdb 2.0 (http://www.licpathway.net/sedb) aims to provide a comprehensive SE resource and annotate their potential roles in gene transcriptions. Compared with SEdb 1.0, we have made the following improvements: (i) Newly added the mouse SEs and expanded the scale of human SEs. SEdb 2.0 contained 1 167 518 SEs from 1739 human H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) samples and 550 226 SEs from 931 mouse H3K27ac ChIP-seq samples, which was five times that of SEdb 1.0. (ii) Newly added transcription factor binding sites (TFBSs) in SEs identified by TF motifs and TF ChIP-seq data. (iii) Added comprehensive (epi)genetic annotations of SEs, including chromatin accessibility regions, methylation sites, chromatin interaction regions and topologically associating domains (TADs). (iv) Newly embedded and updated search and analysis tools, including 'Search SE by TF-based', 'Differential-Overlapping-SE analysis' and 'SE-based TF-Gene analysis'. (v) Newly provided quality control (QC) metrics for ChIP-seq processing. In summary, SEdb 2.0 is a comprehensive update of SEdb 1.0, which curates more SEs and annotation information than SEdb 1.0. SEdb 2.0 provides a friendly platform for researchers to more comprehensively clarify the important role of SEs in the biological process.

TcoFBase: a comprehensive database for decoding the regulatory transcription co-factors in human and mouse.

Transcription co-factors (TcoFs) play crucial roles in gene expression regulation by communicating regulatory cues from enhancers to promoters. With the rapid accumulation of TcoF associated chromatin immunoprecipitation sequencing (ChIP-seq) data, the comprehensive collection and integrative analyses of these data are urgently required. Here, we developed the TcoFBase database (http://tcof.liclab.net/TcoFbase), which aimed to document a large number of available resources for mammalian TcoFs and provided annotations and enrichment analyses of TcoFs. TcoFBase curated 2322 TcoFs and 6759 TcoFs associated ChIP-seq data from over 500 tissues/cell types in human and mouse. Importantly, TcoFBase provided detailed and abundant (epi) genetic annotations of ChIP-seq based TcoF binding regions. Furthermore, TcoFBase supported regulatory annotation information and various functional annotations for TcoFs. Meanwhile, TcoFBase embedded five types of TcoF regulatory analyses for users, including TcoF gene set enrichment, TcoF binding genomic region annotation, TcoF regulatory network analysis, TcoF-TF co-occupancy analysis and TcoF regulatory axis analysis. TcoFBase was designed to be a useful resource that will help reveal the potential biological effects of TcoFs and elucidate TcoF-related regulatory mechanisms.

Kirsten rat sarcoma viral oncogene homolog G12C mutant advanced non-small-cell lung cancer treated with MEK1/2 inhibitor trametinib: a case report.

No targeted therapies are approved for non-small-cell lung cancer (NSCLC) with Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation to date. Trametinib, a selective allosteric inhibitor of the MEK1/2, demonstrated debatable clinical activity in KRAS-mutant NSCLC. In this case, we present a recurrent advanced NSCLC with KRAS G12C mutation successfully treated with single-agent trametinib therapy. An 87-year-old man who underwent radiotherapy for the right lung adenocarcinoma was admitted to clinical oncology center for recurrent lesions in bilateral lungs. He was unwilling to perform second-line chemotherapy, but underwent molecular profiling and revealed the KRAS G12C mutation. The single-agent target therapy of trametinib showed clinical benefit without obvious toxicity. Furthermore, this report reviewed the previous date of the preclinical and clinical and summarized that KRAS G12C mutation may be more sensitive to the inhibition of mitogen-activated protein kinase kinase. This case advocates for routine screening of KRAS point mutations in the utility of precision medicine and suggests that treatment with trametinib in advanced NSCLC cases with KRAS G12C mutation is well tolerated and effective, especially for those very elderly or unsuitable for more aggressive chemotherapy.

Genotoxicity detection of oil-containing drill cuttings by Comet assay based on a demersal marine fish Mugilogobius chulae.

An enormous amount of oil-containing drill cuttings have been produced by the marine oil and gas industry. The environmental impacts of discharged drilling waste have been extensively studied. However, there is still an urgent need to develop alternative methods to identify the genotoxicity of untreated and treated drill waste in a timely manner before it is discharged. In this study, we developed a relatively rapid, sensitive, and accurate genotoxicity-detection method using Comet assay and the marine benthic goby Mugilogobius chulae. This goby is sensitive to a standard toxicant mitomycin C (MMC). The optimal exposure period for genotoxicity detection using M. chulae was determined. Three genotoxic indices (tail length (TL), tail DNA content (TD), and tail moment (TM)) were used to assess the effectiveness of high-temperature treatment of oil-contaminated waste. Untreated oil-containing drill cuttings exhibited the highest genotoxicity to goby cells. Genotoxicity was dramatically reduced after thermal treatment of drill cuttings at 350 °C and 500 °C. TD and TM exhibited significant correlation with the concentration of total petroleum hydrocarbons (TPHs)/total polycyclic aromatic hydrocarbons (PAHs) according to Pearson and Mantel correlation analyses (P values were <0.05). Using redundancy analysis (RDA) and variation partition analysis (VPA), the genotoxic effects of the drill cuttings were ascribed to total alkanes and specific groups of PAHs. In conclusion, this newly established biological model has the potential to be widely used to detect the genetic damage of untreated or treated oil-containing drill cuttings discharged into the marine environment.

ARL4C stabilized by AKT/mTOR pathway promotes the invasion of PTEN-deficient primary human glioblastoma.

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) deficiency in primary human glioblastoma (GBM) is associated with increased invasiveness and poor prognosis with unknown mechanisms. Therefore, how loss of PTEN promotes GBM progression remains to be elucidated. Herein, we identified that ADP-ribosylation factor like-4C (ARL4C) was highly expressed in PTEN-deficient human GBM cells and tissues. Mechanistically, loss of PTEN stabilized ARL4C protein due to AKT/mTOR pathway-mediated inhibition of ARL4C ubiquitination. Functionally, ARL4C enhanced the progression of GBM cells in vitro and in vivo. Moreover, microarray profiling and GST pull-down assay identified that ARL4C accelerated tumor progression via RAC1-mediated filopodium formation. Importantly, targeting PTEN potently inhibited GBM tumor progression in vitro and in vivo, whereas overexpression of ARL4C reversed the tumor progression impaired by PTEN overexpression. Clinically, analyses with patients' specimens validated a negative correlation between PTEN and ARL4C expression. Elevated ARL4C expression but PTEN deficiency in tumor was associated with poorer disease-free survival and overall survival of GBM patients. Taken together, ARL4C is critical for PTEN-deficient GBM progression and acts as a novel prognostic biomarker and a potential therapeutic candidate. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Gain of function in the mouse model of a recurrent mutation p53N236S promotes the formation of double minute chromosomes and the oncogenic potential of p19ARF.

The mutation p53N236S (p53S) has been identified as one of the recurrent mutations in human cancers by TCGA database. Our in vitro data revealed the oncogenic gain of function of p53S. To understand the function of p53S in vivo, we generated the p53S knock-in mouse. The p53S/S mice manifested highly invasive lymphomas and metastatic sarcomas with dramatically increased double minute chromosomes. The survival curve, the incidence of tumors and the tumor spectrum of p53S/S mice is very similar to the p53R172H mouse model. The p53S/+ mice showed delayed onset of tumorigenesis and a high metastasis rate (40%) and low loss of heterozygosity rate (2/16). The activation of CDKN2A pathway in p53S/S MEF and tumors, and the accumulation of p19ARF protein in tumor tissues suggested p19ARF might contribute to the accumulation of mutant p53S protein in the tumor and promote tumorigenesis. The high expression of p19ARF correlated with mutant p53 accumulation and tumor progression, suggesting a dual role of p19ARF in tumor promotion or suppression that might depend on the p53 mutation status in tumor cells. The oncogenic gain of function of this recurrent mutation p53S prompts the reconsideration of p53 mutations function that occurs at a low frequency.

High-mobility group box 1 released by autophagic cancer-associated fibroblasts maintains the stemness of luminal breast cancer cells.

Cancer stem cells/cancer-initiating cells (CICs) and their microenvironmental niche play a vital role in malignant tumour recurrence and metastasis. Cancer-associated fibroblasts (CAFs) are major components of the niche of breast cancer-initiating cells (BCICs), and their interactions may profoundly affect breast cancer progression. Autophagy has been considered to be a critical process for CIC maintenance, but whether it is involved in the cross-talk between CAFs and CICs to affect tumourigenesis and pathological significance has not been determined. In this study, we found that the presence of CAFs containing high levels of microtubule-associated protein 1 light chain 3 (LC3II), a marker of autophagosomes, was associated with more aggressive luminal human breast cancer. CAFs in human luminal breast cancer tissues with high autophagy activity enriched BCICs with increased tumourigenicity. Mechanistically, autophagic CAFs released high-mobility group box 1 (HMGB1), which activated its receptor, Toll-like receptor (TLR) 4, expressed by luminal breast cancer cells, to enhance their stemness and tumourigenicity. Furthermore, immunohistochemistry of 180 luminal breast cancers revealed that high LC3II/TLR4 levels predicted an increased relapse rate and a poorer prognosis. Our findings demonstrate that autophagic CAFs play a critical role in promoting the progression of luminal breast cancer through an HMGB1-TLR4 axis, and that both autophagy in CAFs and TLR4 on breast cancer cells constitute potential therapeutic targets. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

PTP1B promotes aggressiveness of breast cancer cells by regulating PTEN but not EMT.

Metastasis is a complicated, multistep process and remains the major cause of cancer-related mortality. Exploring the molecular mechanisms underlying tumor metastasis is crucial for development of new strategies for cancer prevention and treatment. In this study, we found that protein tyrosine phosphatase 1B (PTP1B) promoted breast cancer metastasis by regulating phosphatase and tensin homolog (PTEN) but not epithelial-mesenchymal transition (EMT). By detecting PTP1B expression of the specimens from 128 breast cancer cases, we found that the level of PTP1B was higher in breast cancer tissues than the corresponding adjacent normal tissues. Notably, PTP1B was positively associated with lymph node metastasis (LNM) and estrogen receptor (ER) status. In vitro, disturbing PTP1B expression obviously attenuated cell migration and invasion. On the contrary, PTP1B overexpression significantly increased migration and invasion of breast cancer cells. Mechanistically, PTP1B knockdown upregulated PTEN, accompanied with an abatement of AKT phosphorylation and the expression of matrix metalloproteinase 2 (MMP2) and MMP7. Conversely, forced expression of PTP1B reduced PTEN and increased AKT phosphorylation as well as the expression of MMP2 and MMP7. Notably, neither EMT nor stemness of breast cancer cells was regulated by PTP1B. We also found that PTP1B acted as an independent prognostic factor and predicted poor prognosis in ER-positive breast cancer patients. Taken together, our findings provide advantageous evidence for the development of PTP1B as a potential therapeutic target for breast cancer, especially for ER-positive breast cancer patients.

DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19.

The Coronavirus Disease 2019 (COVID-19) pandemic is still wreaking havoc worldwide. Therefore, the urgent need for efficient treatments pushes researchers and clinicians into screening effective drugs. Drug repurposing may be a promising and time-saving strategy to identify potential drugs against this disease. Here, we developed a novel computational approach, named Drug Target Set Enrichment Analysis (DTSEA), to identify potent drugs against COVID-19. DTSEA first mapped the disease-related genes into a gene functional interaction network, and then it used a network propagation algorithm to rank all genes in the network by calculating the network proximity of genes to disease-related genes. Finally, an enrichment analysis was performed on drug target sets to prioritize disease-candidate drugs. It was shown that the top three drugs predicted by DTSEA, including Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways indicating the potential for use as promising COVID-19 inhibitors. In addition to these drugs, DTSEA also identified several drugs (such as Remdesivir and Olumiant), which have obtained emergency use authorization (EUA) for COVID-19. These results indicated that DTSEA could effectively identify the candidate drugs for COVID-19, which will help to accelerate the development of drugs for COVID-19. We then performed several validations to ensure the reliability and validity of DTSEA, including topological analysis, robustness analysis, and prediction consistency. Collectively, DTSEA successfully predicted candidate drugs against COVID-19 with high accuracy and reliability, thus making it a formidable tool to identify potential drugs for a specific disease and facilitate further investigation.

PathwayTMB: A pathway-based tumor mutational burden analysis method for predicting the clinical outcome of cancer immunotherapy.

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of the IPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB also exhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.

Cis-Cardio: A comprehensive analysis platform for cardiovascular-relavant cis-regulation in human and mouse.

Cis-regulatory elements are important molecular switches in controlling gene expression and are regarded as determinant hubs in the transcriptional regulatory network. Collection and processing of large-scale cis-regulatory data are urgent to decipher the potential mechanisms of cardiovascular diseases from a cis-regulatory element aspect. Here, we developed a novel web server, Cis-Cardio, which aims to document a large number of available cardiovascular-related cis-regulatory data and to provide analysis for unveiling the comprehensive mechanisms at a cis-regulation level. The current version of Cis-Cardio catalogs a total of 45,382,361 genomic regions from 1,013 human and mouse epigenetic datasets, including ATAC-seq, DNase-seq, Histone ChIP-seq, TF/TcoF ChIP-seq, RNA polymerase ChIP-seq, and Cohesin ChIP-seq. Importantly, Cis-Cardio provides six analysis tools, including region overlap analysis, element upstream/downstream analysis, transcription regulator enrichment analysis, variant interpretation, and protein-protein interaction-based co-regulatory analysis. Additionally, Cis-Cardio provides detailed and abundant (epi-) genetic annotations in cis-regulatory regions, such as super-enhancers, enhancers, transcription factor binding sites (TFBSs), methylation sites, common SNPs, risk SNPs, expression quantitative trait loci (eQTLs), motifs, DNase I hypersensitive sites (DHSs), and 3D chromatin interactions. In summary, Cis-Cardio is a valuable resource for elucidating and analyzing regulatory cues of cardiovascular-specific cis-regulatory elements. The platform is freely available at http://www.licpathway.net/Cis-Cardio/index.html.

Efficient Adsorption-Assisted Photocatalysis Degradation of Congo Red through Loading ZIF-8 on KI-Doped TiO2.

Zeolitic imidazolate framework-8 (ZIF-8) was evenly loaded on the surface of TiO2 doped with KI, using a solvent synthesis method, in order to produce a ZIF-8@TiO2 (KI) adsorption photocatalyst with good adsorption and photocatalytic properties. The samples were characterized by XRD, SEM, EDX, XPS, BET and UV-Vis. The photocatalytic efficiency of the material was obtained by photocatalytic tests. The results indicate that the doping with I inhibited the grain growth and reduced the crystallite size of TiO2, reduced the band gap width and improved the utilization rate for light. TiO2 (KI) was a single crystal of anatase titanium dioxide. The combination of ZIF-8 and TiO2 (KI) improved the specific surface area and increased the reaction site. The ZIF-8@TiO2 (KI) for Congo red was investigated to validate its photocatalytic performance. The optimal concentration of Congo red solution was 30 mg/L, and the amount of catalyst was proportional to the degradation efficiency. The degradation efficiency of ZIF-8@TiO2 (5%KI) was 76.42%, after being recycled four times.

DrugSim2DR: systematic prediction of drug functional similarities in the context of specific disease for drug repurposing.

BACKGROUND: Traditional approaches to drug development are costly and involve high risks. The drug repurposing approach can be a valuable alternative to traditional approaches and has therefore received considerable attention in recent years. FINDINGS: Herein, we develop a previously undescribed computational approach, called DrugSim2DR, which uses a network diffusion algorithm to identify candidate anticancer drugs based on a drug functional similarity network. The innovation of the approach lies in the drug-drug functional similarity network constructed in a manner that implicitly links drugs through their common biological functions in the context of a specific disease state, as the similarity relationships based on general states (e.g., network proximity or Jaccard index of drug targets) ignore disease-specific molecular characteristics. The drug functional similarity network may provide a reference for prediction of drug combinations. We describe and validate the DrugSim2DR approach through analysis of data on breast cancer and lung cancer. DrugSim2DR identified some US Food and Drug Administration-approved anticancer drugs, as well as some candidate drugs validated by previous studies in the literature. Moreover, DrugSim2DR showed excellent predictive performance, as evidenced by receiver operating characteristic analysis and multiapproach comparisons in various cancer datasets. CONCLUSIONS: DrugSim2DR could accurately assess drug-drug functional similarity within a specific disease context and may more effectively prioritize disease candidate drugs. To increase the usability of our approach, we have developed an R-based software package, DrugSim2DR, which is freely available on CRAN (https://CRAN.R-project.org/package=DrugSim2DR).

Characterization and Validation of ceRNA-Mediated Pathway-Pathway Crosstalk Networks Across Eight Major Cardiovascular Diseases.

Pathway analysis is considered as an important strategy to reveal the underlying mechanisms of diseases. Pathways that are involved in crosstalk can regulate each other and co-regulate downstream biological processes. Furthermore, some genes in the pathways can function with other genes via the relationship of the competing endogenous RNA (ceRNA) mechanism, which has also been demonstrated to play key roles in cellular biology. However, the comprehensive analysis of ceRNA-mediated pathway crosstalk is lacking. Here, we constructed the landscape of the ceRNA-mediated pathway-pathway crosstalk of eight major cardiovascular diseases (CVDs) based on sequencing data from ∼2,800 samples. Some common features shared by numerous CVDs were uncovered. A fraction of the pathway-pathway crosstalk was conserved in multiple CVDs and a core pathway-pathway crosstalk network was identified, suggesting the similarity of pathway-pathway crosstalk among CVDs. Experimental evidence also demonstrated that the pathway crosstalk was functioned in CVDs. We split all hub pathways of each pathway-pathway crosstalk network into three categories, namely, common hubs, differential hubs, and specific hubs, which could highlight the common or specific biological mechanisms. Importantly, after a comparison analysis of the hub pathways of networks, ∼480 hub pathway-induced common modules were identified to exert functions in CVDs broadly. Moreover, we performed a random walk algorithm on the hub pathway-induced sub-network and identified 23 potentially novel CVD-related pathways. In summary, our study revealed the potential molecular regulatory mechanisms of ceRNA crosstalk in pathway-pathway crosstalk levels and provided a novel routine to investigate the pathway-pathway crosstalk in cardiology. All CVD pathway-pathway crosstalks are provided in http://www.licpathway.net/cepathway/index.html.

Clinical and molecular microbiological characteristics of carbapenem-resistant Acinetobacter baumannii strains in an NICU.

BACKGROUND: Seventeen cases of Acinetobacter baumannii infection in a neonatal intensive care unit (NICU) were evaluated. The strains were characterized as resistant to carbapenems. The aim of the present study was therefore to investigate the clinical and molecular epidemiological characteristics of the 17 carbapenem-resistant A. baumannii strains. METHODS: Samples were isolated from blood or sputum from the patients in the NICU, cultured using conventional techniques and an automated system. Multiplex polymerase chain reaction (PCR) was used to detect blaOXA-51-like, blaOXA-23-like, OXA-24, OXA-58 and Ambler class B carbapenemases. The genotype of the strains was identified on pulsed-field gel electrophoresis (PFGE). RESULTS: BlaOXA-23 was detected in all of the isolates. PFGE genotype analysis suggested three clones among the 17 strains. Two clones were isolated from other wards of the hospital including the adult ICU and Department of Pulmonology. The other clone was proved to be the first appearance in the hospital as genotype analysis. CONCLUSION: BlaOXA-23 was the drug-resistant gene that made A. baumannii resistant to carbepenem. The source of blaOXA-23 in the 17 isolates was different.

Prognostic Role of Long Noncoding RNAs in Oral Squamous Cell Carcinoma: A Meta-Analysis.

Long noncoding RNAs (lncRNAs) have emerged as critical regulators of tumor progression, and lncRNA expression levels could serve as a potential molecular biomarker for the prognosis and diagnosis of some cancers. However, the prognostic value of lncRNAs in oral squamous cell carcinoma (OSCC) remains unclear. Thus, a meta-analysis was conducted to explore the potential prognostic value of lncRNAs in OSCC. We systematically searched PubMed, EBSCO, Web of Science, and Elsevier from 2005 to 2021 to identify all published studies that reported the association between lncRNAs and prognosis in OSCC. Then, we used meta-analytic methods to identify the actual effect size of lncRNAs on cancer prognosis. The hazard ratios (HRs) with 95% confidence intervals (95% CIs) were calculated to assess the strength of the association. The reliability of those results was then examined using measures of heterogeneity and testing for selective reporting biases. According to the inclusion and exclusion criteria, a total of 17 studies were eligible in our meta-analysis, involving 1384 Asian patients. The results identified a statistically significant association of high lncRNA expression with poor overall survival [adjusted pooled hazard ratio (AHR) = 1.52; 95% confidence interval (CI): [1.26-1.84], p ≤ 0.001]. The present meta-analysis demonstrated that lncRNA expression might be used as a predictive prognostic biomarker for Asian patients with OSCC.

Glutamate-activated chloride channels: Unique fipronil targets present in insects but not in mammals.

Selectivity to insects over mammals is one of the important characteristics for a chemical to become a useful insecticide. Fipronil was found to block cockroach GABA receptors more potently than rat GABA(A) receptors. Furthermore, glutamate-activated chloride channels (GluCls), which are present in cockroaches but not in mammals, were very sensitive to the blocking action of fipronil. The IC(50)s of fipronil block were 30 nM in cockroach GABA receptors and 1600 nM in rat GABA(A) receptors. Moreover, GluCls of cockroach neurons had low IC(50)s for fipronil. Two types of glutamate-induced chloride current were obswerved: desensitizing and non-desensitizing, with fipronil IC(50)s of 800 and 10 nM, respectively. We have developed methods to separately record these two types of GluCls. The non-desensitizing and desensitizing currents were selectively inhibited by trypsin and polyvinylpyrrolidone, respectively. In conclusion, in addition to GABA receptors, GluCls play a crucial role in selectivity of fipronil to insects over mammals. GluCls form the basis for development of selective and safe insecticides.