Perl Programming Research Articles

K-Means Clustering of the Iris dataset using the Perl language

Generally, the Perl programming language is not used in the domains of Machine Learning and Data Science. This study tries to find out whether Perl can be used for writing codes involving Machine Learning and Data Science. Here, it has been observed that Perl can be used to successfully implement the K-Means Clustering algorithm from scratch. Thus, Perl may be used for solving problems by applying Machine Learning and Data Science.

Pan-evolutionary and regulatory genome architecture delineated by an integrated macro- and microsynteny approach.

The forthcoming massive genome data generated by the Earth BioGenome Project will open up a new era of comparative genomics, for which genome synteny analysis provides an important framework. Profiling genome synteny represents an essential step in elucidating genome architecture, regulatory blocks/elements and their evolutionary history. Here we describe PanSyn, ( https://github.com/yhw320/PanSyn ), the most comprehensive and up-to-date genome synteny pipeline, providing step-by-step instructions and application examples to demonstrate its usage. PanSyn inherits both basic and advanced functions from existing popular tools, offering a user-friendly, highly customized approach for genome macrosynteny analysis and integrated pan-evolutionary and regulatory analysis of genome architecture, which are not yet available in public synteny software or tools. The advantages of PanSyn include: (i) advanced microsynteny analysis by functional profiling of microsynteny genes and associated regulatory elements; (ii) comprehensive macrosynteny analysis, including the inference of karyotype evolution from ancestors to extant species; and (iii) functional integration of microsynteny and macrosynteny for pan-evolutionary profiling of genome architecture and regulatory blocks, as well as integration with external functional genomics datasets from three- or four-dimensional genome and ENCODE projects. PanSyn requires basic knowledge of the Linux environment and Perl programming language and the ability to access a computer cluster, especially for large-scale genomic comparisons. Our protocol can be easily implemented by a competent graduate student or postdoc and takes several days to weeks to execute for dozens to hundreds of genomes. PanSyn provides yet the most comprehensive and powerful tool for integrated evolutionary and functional genomics.

The Expression and Prognostic Value of Co-stimulatory Molecules in Clear Cell Renal Cell Carcinoma (CcRcc).

Renal cell carcinoma (RCC) was one of the most common malignant cancers in the urinary system. Clear cell carcinoma (ccRCC) is the most common pathological type, accounting for approximately 80% of RCC. The lack of accurate and effective prognosis prediction methods has been a weak link in ccRCC treatment. Co-stimulatory molecules played the main role in increasing anti-tumor immune response, which determined the prognosis of patients. Therefore, the main objective of the present study was to explore the prognostic value of co-stimulatory molecules genes in ccRCC patients. The TCGA database was used to get gene expression and clinical characteristics of patients with ccRCC. A total of 60 co-stimulatory molecule genes were also obtained from TCGAccRCC, including 13 genes of the B7/ CD28 co-stimulatory molecules family and 47 genes of the TNF family. In the TCGA cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate a multigene signature. R and Perl programming languages were used for data processing and drawing. Real-time PCR was used to verify the expression of differentially expressed genes. The study's initial dataset included 539 ccRCC samples and 72 normal samples. The 13 samples have been eliminated. According to FDR<0.05, there were differences in the expression of 55 co-stimulatory molecule genes in ccRCC and normal tissues. LASSO Cox regression analysis results indicated that 13 risk genes were optimally used to construct a prognostic model of ccRCC. The patients were divided into a high-risk group and a low-risk group. Those in the high-risk group had significantly lower OS (Overall Survival rate) than patients in the low-risk group. Receiver operating characteristic (ROC) curve analysis confirmed the predictive value of the prognosis model of ccRCC (AUC>0.7). There are substantial differences in immune cell infiltration between high and low-risk groups. Functional analysis revealed that immune-related pathways were enriched, and immune status was different between the two risk groups. Real-time PCR results for genes were consistent with TCGA DEGs. By stratifying patients with all independent risk factors, the prognostic score model developed in this study may improve the accuracy of prognosis prediction for patients with ccRCC.

In Silico Typing and Identification Confirmation of Isolates.

Streptococcus suis is an important zoonotic pathogen causing severe infections in pigs and humans. Serotyping of S. suis strains is crucial for epidemiological surveillance, outbreak investigations, and understanding the pathogenesis of this bacterium. Here, we describe a step-by-step approach that enhances a previously developed pipeline by utilizing a computational script for efficient and accurate typing of S. suis strains. The pipeline is implemented in Perl programming language and leverages the Short Read Sequence Typing for Bacterial Pathogens (SRST2) tool. It integrates various bioinformatics techniques and utilizes multiple databases, including a serotype database, cpsH confirmation database, multi-locus sequence typing (MLST) database, recN species-specific gene database, and virulence gene database. These databases contain comprehensive information on S. suis serotypes, genetic markers, and virulence factors. The script can utilize paired-end or single-end fastq files as input and first confirms the species by sequence read data aligning to the recN gene, ensuring the accurate identification of S. suis strains. The pipeline next performs MLST typing and virulence factor identification using SRST2 while in a parallel processes it performs in silico serotyping of the strains. The pipeline offers a streamlined and semiautomated approach to serotyping S. suis strains, facilitating large-scale studies and reducing the manual effort required for data analysis.

Descriptive Statistics and Genome-Wide Copy Number Analysis of Milk Production Traits of Jiangsu Chinese Holstein Cows.

Milk production traits are the most important quantitative economic traits in dairy cow production; improving the yield and quality of milk is an important way to ensure the production efficiency of the dairy industry. This study carried out a series of in-depth statistical genetics studies and molecular analyses on the Chinese Holstein cows in the Jiangsu Province, such as descriptive statistics and copy number variation analysis. A genetic correlation, phenotypic correlation, and descriptive statistical analysis of five milk production traits (milk yield, milk fat percentage, milk fat yield, milk protein percentage, and milk protein yield) of the dairy cows were analyzed using the SPSS and DMU software. Through quality control, 4173 cows and their genomes were used for genomic study. Then, SNPs were detected using DNA chips, and a copy number variation (CNV) analysis was carried out to locate the quantitative trait loci (QTL) of the milk production traits by Perl program software Penn CNV and hidden Markov model (HMM). The phenotypic means of the milk yield, milk fat percentage, milk fat mass, milk protein percentage, and milk protein mass at the first trimester were lower than those at the other trimesters by 8.821%, 1.031%, 0.930%, 0.003%, and 0.826%, respectively. The five milk production traits showed a significant phenotypic positive correlation (p < 0.01) and a high genetic positive correlation among the three parities. Based on the GGPBovine 100 K SNP data, QTL-detecting research on the fist-parity milk performance of dairy cows was carried out via the CNV. We identified 1731 CNVs and 236 CNVRs in the 29 autosomes of 984 Holstein dairy cows, and 19 CNVRs were significantly associated with the milk production traits (p < 0.05). These CNVRs were analyzed via a bioinformatics analysis; a total of 13 gene ontology (GO) terms and 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched (p < 0.05), and these terms and pathways are mainly related to lipid metabolism, amino acid metabolism, and cellular catabolic processes. This study provided a theoretical basis for the molecular-marker-assisted selection of dairy cows by developing descriptive statistics on the milk production traits of dairy cows and by locating the QTL and functional genes that affect the milk production traits of first-born dairy cows. The results describe the basic status of the milk production traits of the Chinese Holstein cows in Jiangsu and locate the QTL and functional genes that affect the milk production traits of the first-born cows, providing a theoretical basis for the molecular-marker-assisted selection of dairy cows.

EasyCGTree: a pipeline for prokaryotic phylogenomic analysis based on core gene sets

BackgroundGenome-scale phylogenetic analysis based on core gene sets is routinely used in microbiological research. However, the techniques are still not approachable for individuals with little bioinformatics experience. Here, we present EasyCGTree, a user-friendly and cross-platform pipeline to reconstruct genome-scale maximum-likehood (ML) phylogenetic tree using supermatrix (SM) and supertree (ST) approaches.ResultsEasyCGTree was implemented in Perl programming languages and was built using a collection of published reputable programs. All the programs were precompiled as standalone executable files and contained in the EasyCGTree package. It can run after installing Perl language environment. Several profile hidden Markov models (HMMs) of core gene sets were prepared in advance to construct a profile HMM database (PHD) that was enclosed in the package and available for homolog searching. Customized gene sets can also be used to build profile HMM and added to the PHD via EasyCGTree. Taking 43 genomes of the genus Paracoccus as the testing data set, consensus (a variant of the typical SM), SM, and ST trees were inferred via EasyCGTree successfully, and the SM trees were compared with those inferred via the pipelines UBCG and bcgTree, using the metrics of cophenetic correlation coefficients (CCC) and Robinson–Foulds distance (topological distance). The results suggested that EasyCGTree can infer SM trees with nearly identical topology (distance < 0.1) and accuracy (CCC > 0.99) to those of trees inferred with the two pipelines.ConclusionsEasyCGTree is an all-in-one automatic pipeline from input data to phylogenomic tree with guaranteed accuracy, and is much easier to install and use than the reference pipelines. In addition, ST is implemented in EasyCGTree conveniently and can be used to explore prokaryotic evolutionary signals from a different perspective. The EasyCGTree version 4 is freely available for Linux and Windows users at Github (https://github.com/zdf1987/EasyCGTree4).

Uncovering the pathogenesis of obesity complicated with papillary thyroid carcinoma via bioinformatics and experimental validation.

This study aimed to investigate the common molecular mechanism between obesity and papillary thyroid cancer (PTC), the most common pathological type of thyroid cancer. In this study, we obtained gene expression datasets for obesity (GSE151839) and PTC (GSE33630) from the Gene Expression Omnibus (GEO). We used the Perl program and R software to identify differentially expressed genes (DEGs) and common genes, perform GO function and KEGG pathway enrichment analysis, construct a protein-protein interaction (PPI) network, identify hub genes, and perform transcription factors (TFs) analysis. After undergoing validation in external datasets and in vitro experiments, common targets for both diseases were ultimately identified. A total of 23 genes that were differentially expressed (DEGs) between obesity and papillary thyroid carcinoma (PTC) were identified in our study. Among these DEGs, 17 genes were up-regulated while 6 genes were down-regulated. Then the top ten key genes were identified from the PPI network using cytoHubba and MCODE plug-in. Further evidence from external datasets revealed that MMP9, MNDA, TNC, and CHIT1 were identified as hub genes for both diseases. The study utilized Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) to perform an enrichment analysis of TFs. This analysis identified ELF4 and STAT3 as common TFs for both diseases. Additionally, in vitro experiments were conducted to further analyze the clinical significance and biological functions of these TFs. The identification and investigation of hub genes and their corresponding TFs that regulate abnormalities in obesity and PTC can enhance our comprehension of the underlying connection between these two diseases, thus leading to the development of novel diagnostic approaches.

Rice WRKY13 TF protein binds to motifs in the promoter region to regulate downstream disease resistance-related genes.

In plants, pathogen resistance is brought about by the binding of certain transcription factor (TF) proteins to the cis-elements of certain target genes. These cis-elements are present upstream in the motif of the promoters of each gene. This ensures the binding of a specific TF to a specific promoter, therefore regulating the expression of that gene. Therefore, the study of each promoter sequence of all the rice genes would help identify the target genes of a specific TF. Rice 1kb upstream promoter sequences of 55,986 annotated genes were analyzed using the Perl program algorithm to detect WRKY13 binding motifs (bm). The resulting genes were grouped using Gene Ontology and gene set enrichment analysis. A gene with more than 4 TF bm in their promoter was selected. Ten genes reported to have a role in rice disease resistance were selected for further analysis. Cis-acting regulatory element analysis was carried out to find the cis-elements and confirm the presence of the corresponding motifs in the promoter sequences of these genes. The 3D structure of WRKY13 TF and the corresponding ten genes were built, and the interacting residues were determined. The binding capacity of WRKY13 to the promoter of these selected genes was analyzed using docking studies. WRKY13 was considered for docking analysis based on the prior reports of autoregulation. Molecular dynamic simulations provided more details regarding the interactions. Expression data revealed the expression of the genes that helped provide the mechanism of interaction. Further co-expression network helped to characterize the interaction of these selected disease resistance-related genes with the WRKY13 TF protein. This study suggests downstream target genes that are regulated by the WRKY13 TF. The molecular mechanism involving the gene network regulated by WRKY13 TF in disease resistance against rice fungal pathogens is explored.

Fast all versus all genotype comparison using DNA/RNA sequencing data: method and workflow

BackgroundMassively parallel sequencing includes many liquid handling steps which introduce the possibility of sample swaps, mixing, and duplication. The unique profile of inherited variants in human genomes allows for comparison of sample identity using sequence data. A comparison of all samples vs. each other (all vs. all) provides both identification of mismatched samples and the possibility of resolving swapped samples. However, all vs. all comparison complexity grows as the square of the number of samples, so efficiency becomes essential.ResultsWe have developed a tool for fast all vs. all genotype comparison using low level bitwise operations built into the Perl programming language. Importantly, we have also developed a complete workflow allowing users to start with either raw FASTQ sequence files, aligned BAM files, or genotype VCF files and automatically generate comparison metrics and summary plots. The tool is freely available at https://github.com/teerjk/TimeAttackGenComp/.ConclusionsA fast and easy to use method for genotype comparison as described here is an important tool to ensure high quality and robust results in sequencing studies.

The distribution characteristics of global blaOXA-carrying Klebsiella pneumoniae

ObjectiveTo analyze the distribution of blaOXA among global Klebsiella pneumoniae and the characteristics of blaOXA-carrying K. pneumoniae.Materials and MethodsThe genomes of global K. pneumoniae were downloaded from NCBI by Aspera software. After quality check, the distribution of blaOXA among the qualified genomes was investigated by annotation with the resistant determinant database. The phylogenetic tree was constructed for the blaOXA variants based on the single nucleotide polymorphism (SNP) to explore the evolutionary relationship between these variants. The MLST (multi-locus sequence type) website and blastn tools were utilized to determine the sequence types (STs) of these blaOXA-carrying strains. and sample resource, isolation country, date and host were extracted by perl program for analyzing the characteristics of these strains.ResultsA total of 12,356 K. pneumoniae genomes were downloaded and 11,429 ones were qualified. Among them, 4386 strains were found to carry 5610 blaOXA variants which belonged to 27 varieties of blaOXAs, blaOXA-1 (n = 2891, 51.5%) and blaOXA-9 (n = 969, 17.3%) were the most prevalent blaOXA variants, followed by blaOXA-48 (n = 800, 14.3%) and blaOXA-232 (n = 480, 8.6%). The phylogenetic tree displayed 8 clades, three of them were composed of carbapenem-hydrolyzing oxacillinase (CHO). Totally, 300 distinct STs were identified among 4386 strains with ST11 (n = 477, 10.9%) being the most predominant one followed by ST258 (n = 410, 9.4%). Homo sapiens (2696/4386, 61.5%) was the main host for blaOXA-carrying K. pneumoniae isolates. The blaOXA-9-carrying K. pneumoniae strains were mostly found in the United States and blaOXA-48-carrying K. pneumoniae strains were mainly distributed in Europe and Asia.ConclusionAmong the global K. pneumoniae, numerous blaOXA variants were identified with blaOXA-1, blaOXA-9, blaOXA-48 and blaOXA-232 being the most prevalent ones, indicating that blaOXA rapidly evolved under the selective pressure of antimicrobial agents. ST11 and ST258 were the main clones for blaOXA-carrying K. pneumoniae.

Hybrid Metabolic Activity-Related Prognostic Model and Its Effect on Tumor in Renal Cell Carcinoma.

Tumor cells with a hybrid metabolic state, in which glycolysis and oxidative phosphorylation (OXPHOS) can be used, usually have a strong ability to adapt to different stress environments due to their metabolic plasticity. However, few studies on tumor cells with this phenotype have been conducted in the field of renal cell carcinoma (RCC). The metabolic pathway (glycolysis, OXPHOS) related gene sets were obtained from the Molecular Signatures Database (V7.5.1). The gene expression matrix, clinical information, and mutation data were obtained by Perl programming language (5.32.0) mining, the Cancer Genome Atlas and International Cancer Genome Consortium database. Gene Set Enrichment Analysis (GSEA) software (4.0.3) was utilised to analyse glycolysis-related gene sets. Analysis of survival, immune infiltration, mutation, etc. was performed using the R programming language (4.1.0). Eight genes that are highly associated with glycolysis and OXHPOS were used to construct the cox proportional hazards model, and risk scores were calculated based on this to predict the prognosis of clear cell RCC patients and to classify patients into risk groups. Gene Ontology, the Kyoto Encyclopaedia of Genes and Genomes, and GSEA were analysed according to the differential genes to investigate the signal pathways related to the hybrid metabolic state. Immunoinfiltration analysis revealed that CD8+T cells, M2 macrophages, etc., had significant differences in infiltration. In addition, the analysis of mutation data showed significant differences in the number of mutations of PBRM1, SETD2, and BAP1 between groups. Cell experiments demonstrated that the DLD gene expression was abnormally high in various tumor cells and is associated with the strong migration ability of RCC. We successfully constructed a risk score system based on glycolysis and OXPHOS-related genes to predict the prognosis of RCC patients. Bioinformatics analysis and cell experiments also revealed the effect of the hybrid metabolic activity on the migration ability and immune activity of RCC and the possible therapeutic targets for patients.

AutomAted RepeaT Identifier (AARTI): A tool to identify common, polymorphic, and unique microsatellites

Here we are presenting an automated computational pipeline used to mine 5976 mitochondrial genomes to identify common, polymorphic, and unique microsatellites also known as simple sequence repeats (SSRs). Microsatellites are repetitive motifs of 1–6 bases in a DNA sequence. Due to their abundance and highly polymorphic nature, microsatellites have become one of the widely used molecular/genetic markers valuable for many studies including gene tagging, genetic diversity, and species identification. Several computational tools dedicated to mine and categorize microsatellites in nucleotide sequences were developed; however, there is no tool which can identify unique, common and polymorphic microsatellites between each pair of nucleotide sequences. To explore such microsatellites, we have developed a fully automated computational pipeline named AutomAted RepeaT Identifier (AARTI). The AARTI is the only tool till date, which identifies common, polymorphic, and unique microsatellites between each pair of nucleotide sequences. The computational pipeline was constructed using the PERL programming language and the web server for the pipeline was developed with the help of PHP, HTML, CSS, and JavaScript. It was successfully tested to reproduce the results of previous study on 7 mitochondrial genomes of genus Orthotrichum. Moreover, the pipeline was also applied on 5846 (Metazoa) and 130 (Viridiplantae) mitochondrial genomes. The AARTI is freely available at https://lms.snu.edu.in/aarti/ and will certainly accelerate the studies of length variation in microsatellites between species. Additionally, it will be useful in comparative genomic studies, for the computational characterization of microsatellites, and has the potential to be a routine genome analysis pipeline for mitochondrial genomes.

Thrombospondin-2 acts as a bridge between tumor extracellular matrix and immune infiltration in pancreatic and stomach adenocarcinomas: an integrative pan-cancer analysis

BackgroundThrombospondin-2 (THBS2) is a versatile glycoprotein that regulates numerous biological functions, including the apoptosis-proliferation balance in endothelial cells, and it has been linked to tumor angiogenesis. However, the exact role of THBS2 in human cancer remains unknown. This study aimed to determine THBS2 expression in a pan-cancer analysis and its association with pan-cancer prognosis and to further identify its possible roles in tumor immunity and the extracellular matrix (ECM).MethodsData on THBS2 expression in cancers and normal tissues were downloaded from the Genotype-Tissue Expression portal and UCSC Xena visual exploration tool and analyzed using the ONCOMINE database, Perl programming language, and Gene Expression Profiling and Interactive Analyses vision 2 webserver. In addition, survival prognosis was analyzed using the survival, survminer, limma, and forestplot packages in R v. 4.0.3.Immune and matrix components were also analyzed using R v. 4.0.3. Most importantly, we partially validated the role and mechanism of THBS2 in pancreatic and gastric cancers in vitro using PANC1 and BGC-823 cell lines.ResultsTHBS2 was significantly overexpressed in 17 of the 33 investigated cancers and linked to a poor prognosis in pan-cancer survival analysis. High THBS2 expression was an independent unfavorable prognostic factor in kidney renal papillary cell, mesothelioma, and stomach and pancreatic adenocarcinomas. Immune infiltration and THBS2 expression were also related. THBS2 expression has been linked to immune and stromal scores and immune checkpoint markers in various cancers. The protein–protein interaction network revealed that THBS2 is associated with multiple ECM and immune proteins. THBS2 knockdown decreased the expression of CD47 and matrix metallopeptidase 2 (MMP-2) as well as the proliferation, migration, and invasion of PANC1 and BGC-823 cells in vitro.ConclusionsOur findings suggested that THBS2 might promote cancer progression by remodeling the tumor microenvironment, affecting CD47-mediated signaling pathways, activating the pro-tumor functions of a disintegrin and metalloproteinase with thrombospondin motifs, and enhancing MMP-2 expression. Furthermore, it functions as a bridge between the ECM and immune infiltration in cancer and serves as a potential prognostic biomarker for several cancers, especially pancreatic and gastric adenocarcinomas.

Web interface - an example of the universal platform of engineering calculations in the educational process

The current trend of technology development in the educational process is based on Internet communications, online calculations and mobile microprocessor gadgets.&#x0D; The article shows an example of developing a universal and reliable server platform for interactive online applications that can be used in the learning process. A web service implemented in the PERL programming language using secure CGI client-server technology is presented.&#x0D; The implementation of the technology is considered in detail on the example of a typical problem of the course "Technical Mechanics of Liquids and Gases" to determine the critical depth of flow in the open channel.&#x0D; The analysis of possibilities and - most importantly - limitations of various modern trends in the implementation of computer algebra systems in comparison with the development of an interactive web form of online calculations, which is developed specifically for each educational task.&#x0D; An experimental project of educational and methodical material with web forms of online calculation of individual tasks is a variant of modern universal online technology. This example combines a wide range of microprocessor gadgets and operating systems without restrictions.&#x0D; The calculations are presented in tabular format of the output of the entire volume of iterative calculations and graphical visualization of the results. Conclusions of calculations in a graphical format with scaling avoids conventionality or formal schematics in the process of analysis and conclusions about the results of the calculation.&#x0D; At each stage of using the web form, the size, size and format of data entry are controlled. The main purpose of the implementation of an interactive web form with the conclusion of scaled visualization of the results of calculations - to bring conditional learning examples to real practical calculations.

BreakAlign: a Perl program to align chimaeric (split) genomic NGS reads and allow visual confirmation of novel retroviral integrations

BackgroundRetroviruses replicate by integrating a DNA copy into a host chromosome. Detecting novel retroviral integrations (ones not in the reference genome sequence of the host) from genomic NGS data is bioinformatically challenging and frequently produces many false positives. One common method of confirmation is visual inspection of an alignment of the chimaeric (split) reads that span a putative novel retroviral integration site. We perceived the need for a program that would facilitate this by producing a multiple alignment containing both the viral and host regions that flank an integration.ResultsBreakAlign is a Perl program that uses blastn to produce such a multiple alignment. In addition to the NGS dataset and a reference viral sequence, the program requires either (a) the ~ 500nt host genome sequence that spans the putative integration or (b) coordinates of this putative integration in an installed copy of the reference human genome (multiple integrations can be processed automatically). BreakAlign is freely available from https://github.com/marchiem/breakalign and is accompanied by example files allowing a test run.ConclusionBreakAlign will confirm and facilitate characterisation of both (a) germline integrations of endogenous retroviruses and (b) somatic integrations of exogenous retroviruses such as HIV and HTLV. Although developed for use with genomic short-read NGS (second generation) data and retroviruses, it should also be useful for long-read (third generation) data and any mobile element with at least one conserved flanking region.

Mechanism of Huashi Xingyu Qingre recipe in treating oral lichen planus based on network pharmacology and clinical trial verification.

To identify the main active components and targets of Huashi Xingyu Qingre recipe (, HXQR) and to investigate its mechanism in the treatment of oral lichen planus (OLP). The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was searched to identify the active ingredients and corresponding targets of HXQR. Disease genes were obtained from the GeneCards database, and a "drug-disease regulatory network" was constructed using Cytoscape software and PERL programming language. The STRING database was used to build a protein-protein interaction network. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms were analyzed using R software with a Bioconducter plugin. Finally, the results and the efficacy of HXQR in treating OLP were validated in a clinical trial that included enzyme-linked immunosorbent assay (ELISA) testing and observations of the post-treatment changes in clinical symptoms. HXQR contained 167 active components and 261 targets, with 391 disease targets. The intersection of these two categories in a Venn diagram revealed 57 drug-disease common targets. A compound-target network was constructed and revealed that the six key pharmaceutical ingredients of HXQR were quercetin, luteolin, wogonin, kaempferol, beta-carotene, and baicalein. The protein-protein interaction network mainly involved core proteins such as ALB, interleukin-6, and AKT1. Drug-disease common targets were enriched in 1628 GO terms and 117 KEGG terms, mainly involving inflammatory responses, viral infections, and tumor-related pathways. ELISA testing indicated that HXQR inhibited the tumor necrosis factor (TNF) signaling pathway by reducing the expression of interleukin-6, matrix metalloproteinase-9, and intercellular adhesion molecule-1. The clinical symptoms of the patients with OLP were significantly improved after 8 weeks of treatment with HXQR. HXQR treats OLP by regulating the TNF signaling pathway, resulting in a marked treatment effect with few adverse effects.

Identifying the Hub Genes and Immune Cell Infiltration in Synovial Tissue between Osteoarthritic and Rheumatoid Arthritic Patients by Bioinformatic Approach.

Osteoarthritis (OA) and rheumatoid arthritis (RA) are two common diseases that result in limb disability and a decrease in quality of life. The major symptoms of OA and RA are pain, swelling, stiffness, and malformation of joints, and each disease also has unique characteristics. To compare the pathological mechanisms of OA and RA via weighted correlation network analysis (WGCNA) and immune infiltration analysis and find potential diagnostic and pharmaceutical targets for the treatment of OA and RA. The gene expression profiles of ten OA and ten RA synovial tissue samples were downloaded from the Gene Expression Omnibus (GEO) database (GSE55235). After obtaining differentially expressed genes (DEGs) via GEO2R, WGCNA was conducted using an R package, and modules and genes that were highly correlated with OA and RA were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network analyses were also conducted. Hub genes were identified using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Immune infiltration analysis was conducted using the Perl program and CIBERSORT software. Two hundred ninety-nine DEGs, 24 modules, 16 GO enrichment terms, 6 KEGG pathway enrichment terms, 10 hub genes (CXCL9, CXCL10, CXCR4, CD27, CD69, CD3D, IL7R, STAT1, RGS1, and ISG20), and 8 kinds of different infiltrating immune cells (plasma cells, CD8 T cells, activated memory CD4 T cells, T helper follicular cells, M1 macrophages, Tregs, resting mast cells, and neutrophils) were found to be involved in the different pathological mechanisms of OA and RA. Inflammation-associated genes were the top differentially expressed hub genes between OA and RA, and their expression was downregulated in OA. Genes associated with lipid metabolism may have upregulated expression in OA. In addition, immune cells that participate in the adaptive immune response play an important role in RA. OA mainly involves immune cells that are associated with the innate immune response.

Prediction of the mechanism of miRNAs in laryngeal squamous cell carcinoma based on the miRNA-mRNA regulatory network.

In this study, a bioinformatics analysis is conducted to screen differentially expressed miRNAs and mRNAs in laryngeal squamous cell carcinoma (LSCC). Based on this information, we explored the possible roles of miRNAs in the pathogenesis of LSCC. The RNA-Seq data from 79 laryngeal cancer samples in the Gene Expression Omnibus (GEO) database were sorted. Differentially expressed miRNAs and mRNAs in LSCC are screened using the PERL programming language, and it was analysed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The miRNA-mRNA regulatory network of LSCC is constructed using Cytoscape software. Then, quantitative real-time PCR (QRT- PCR), Cell Counting Kit-8 (CCK8) and flow cytometry analysis we are used to further validate key miRNAs. We identified 99 differentially expressed miRNAs and 2,758 differentially expressed mRNAs in LSCC tissues from the GEO database. Four more important miRNAs displaying a high degree of connectivity are selected, these results suggest that they play an important role in the pathogenesis of LSCC. As shown in the present study, we identified specific miRNA-mRNA networks associated with the occurrence and development of LSCC through bioinformatics analysis. We found a miRNA molecule closely related to LSCC based on miRNA-mRNA network: miR-140-3p was down-regulated in LSCC. In addition, the potential antitumor effect of miR-140-3p in LSCC was verified in the experiment, and it was proved that overexpression of miR-140-3p could inhibit the proliferation of LSCC cells and promote cell apoptosis, suggesting that miR-140-3p may be a potential tumor marker in LSCC.

Upregulation of Stress-Induced Protein Kinase CK1 Delta is associated with a Poor Prognosis for patients with Hepatocellular Carcinoma.

Objective: This study was designed to analyze the expression of CSNK1D in hepatocellular carcinoma (HCC) and investigate the relationship between the expression of CSNK1D and the prognosis of HCC patients. Methods: The CSNK1D and alpha-fetoprotein (AFP) expression levels in patients with HCC and their corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) and sorted with a Perl program. CSNK1D and AFP expression differences in liver tissue and liver cancer were compared and analyzed, based on the online database human cancer metastasis database, the relationships between the expression levels of CSNK1D and AFP and the proliferation and metastases of HCC were explored. The immunohistochemical data obtained from the Human Protein Atlas Database further verified the differences in the expression levels of CSNK1D and AFP in liver tissues and liver cancer tissues. Through Kaplan-Meier survival analysis, the effects of CSNK1D and AFP expression levels on the prognosis of patients with HCC were investigated, and the influences of and patients' gender, age and grades of cancer cells, tumor size, the status of lymph node metastasis, distant metastasis, and tumor stage on the expression of CSNK1D were analyzed with R language. The influence of differential expressions of CSNK1D on survival time was compared and the prognostic factors influencing the survival of HCC patients were statistically explored by univariate analysis and multivariate analysis. The potential influencing mechanism of CSNK1D on the prognosis of HCC patients was explored by Gene Set Enrichment Analysis (GSEA) enrichment. Results: The expression level of CSNK1D and AFP in cancer foci was significantly higher than that in normal tissues, However, in the same patient, the expression levels of AFP in paracarcinoma tissues and cancer tissues showed no significant difference. The expression level of CSNK1D in HCC with distant metastases was higher than that in those without metastasis, but the expression level of AFP in metastatic HCC was lower than that in those HCC without metastases. In immunohistochemical tests, CSNK1D was moderately positive in normal liver tissues, slightly positive in normal bile duct tissues, and highly positive in HCC. AFP was slightly positive in normal liver tissues and negative in HCC, but it was not detected in normal intrahepatic bile duct tissue. Survival analysis results suggested that the higher expression level of CSNK1D corresponded to the shorter survival period, whereas the expression level of AFP showed no significant influence on survival time. The expression level of CSNK1D was not correlated with gender, age, the status of lymph node metastasis status, or distant metastasis of patients. The main factors influencing the expression level of CSNK1D included tumor size, cancer cell grade, and tumor stage. The expression levels of CSNK1D in T2 and T3 were higher than that in T1. The expression levels of CSNK1D in G3 and G4 were higher than that in G1. The expression levels of CSNK1D in Stage II and Stage III were higher than that in Stage I. Univariate analysis suggested that tumor size, cell grade, distant metastasis, clinical stage, and CSNK1D expression level were the prognostic factors influencing the survival of patients. Multivariate analysis suggested that CSNK1D expression level was an independent factor influencing the prognosis of HCC patients. GSEA enrichment analysis indicated that CSNK1D mainly affected the prognosis of HCC patients through cell cycle, WNT signaling pathway, amino acid degradation metabolism, and other pathways. Conclusion: CSNK1D is an independent influencing factor for the prognosis of HCC patients and has the potential to be developed as a potential therapeutic target for HCC, and better than AFP in predicting the prognosis of HCC.

Genome-wide analyses of tandem repeats and transposable elements in patchouli.

Patchouli, Pogostemon cablin (Blanco) Benth., is a traditional Chinese medicinal plant from the order Lamiales. It is considered a valuable herb due to its essential oil content and range of therapeutic effects. This study aimed to explore the evolutionary history of repetitive sequences in the patchouli genome by analyzing tandem repeats and transposable elements (TEs). We first retrieved genomic data for patchouli and four other Lamiales species from the GenBank database. Next, the content of tandem repeats with different period sizes was identified. Long terminal repeats (LTRs) were then identified with LTR_STRUC. Finally, the evolutionary landscape of TEs was explored using an in-house PERL program. The analysis of repetitive sequences revealed that tandem repeats constitute a higher proportion of the patchouli genome compared to the four other species. Analyses of TE families showed that most of the repetitive sequences in the patchouli genome are TEs, and that recently inserted TEs make up a comparatively larger proportion than older ones. Our analyses of LTR retrotransposons in their host genome indicated the existence of ancient LTR retrotransposon expansion, and the escape of these elements from natural selection revealed their ages. Our identification and analyses of repetitive sequences should provide new insights for further investigation of patchouli evolution.