Investigations into the therapeutic potential of Astragalus Mongholicus (AM, huáng qí) and Largehead Atractylodes (LA, bái zhú) reveal significant efficacy in mitigating the onset and progression of knee osteoarthritis (KOA), albeit with an elusive mechanistic understanding. This study delineates the primary bioactive constituents and their molecular targets within the AM-LA synergy by harnessing the comprehensive Traditional Chinese Medicine (TCM) network databases, including TCMSP, TCMID, and ETCM. Furthermore, an analysis of 3 gene expression datasets, sourced from the gene expression omnibus database, facilitated the identification of differential genes associated with KOA. Integrating these findings with data from 5 predominant databases yielded a refined list of KOA-associated targets, which were subsequently aligned with the gene signatures corresponding to AM and LA treatment. Through this alignment, specific molecular targets pertinent to the AM-LA therapeutic axis were elucidated. The construction of a protein-protein interaction network, leveraging the shared genetic markers between KOA pathology and AM-LA intervention, enabled the identification of pivotal molecular targets via the topological analysis facilitated by CytoNCA plugins. Subsequent GO and KEGG enrichment analyses fostered the development of a holistic herbal-ingredient-target network and a core target-signal pathway network. Molecular docking techniques were employed to validate the interaction between 5 central molecular targets and their corresponding active compounds within the AM-LA complex. Our findings suggest that the AM-LA combination modulates key biological processes, including cellular activity, reactive oxygen species modification, metabolic regulation, and the activation of systemic immunity. By either augmenting or attenuating crucial signaling pathways, such as MAPK, calcium, and PI3K/AKT pathways, the AM-LA dyad orchestrates a comprehensive regulatory effect on immune-inflammatory responses, cellular proliferation, differentiation, apoptosis, and antioxidant defenses, offering a novel therapeutic avenue for KOA management. This study, underpinned by gene expression omnibus gene chip analyses and network pharmacology, advances our understanding of the molecular underpinnings governing the inhibitory effects of AM and LA on KOA progression, laying the groundwork for future explorations into the active components and mechanistic pathways of TCM in KOA treatment.

OBJECTIVE: Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and HCC.
METHODS: Based on multiple GEO datasets containing cirrhosis and HCC samples, we used lasso regression, random forest (RF)-recursive feature elimination (RFE) and receiver operator characteristic analysis to screen for characteristic genes. Subsequently, we integrated these genes into a multivariable logistic regression model and validated the linear prediction scores in both training and validation cohorts. The ssGSEA algorithm was used to estimate the fraction of infiltrating immune cells in the samples. Finally, molecular typing for patients with cirrhosis was performed using the CCP algorithm.
RESULTS: The study identified 137 differentially expressed genes (DEGs) and selected five significant genes (CXCL14, CAP2, FCN2, CCBE1 and UBE2C) to construct a diagnostic model. In both the training and validation cohorts, the model exhibited an area under the curve (AUC) greater than 0.9 and a kappa value of approximately 0.9. Additionally, the calibration curve demonstrated excellent concordance between observed and predicted incidence rates. Comparatively, HCC displayed overall downregulation of infiltrating immune cells compared to cirrhosis. Notably, CCBE1 showed strong correlations with the tumour immune microenvironment as well as genes associated with cell death and cellular ageing processes. Furthermore, cirrhosis subtypes with high linear predictive scores were enriched in multiple cancer-related pathways.
CONCLUSIONS: In conclusion, we successfully identified diagnostic markers distinguishing between cirrhosis and hepatocellular carcinoma and developed a novel diagnostic model for discriminating the two conditions. CCBE1 might exert a pivotal role in regulating the tumour microenvironment, cell death and senescence.

OBJECTIVE: To evaluate the clinical efficacy and safety of Shenzhu Guanxin recipe granules (, SGR) in treating patients with intermediate coronary lesions (ICL), and to investigate the potential mechanism though a transcriptome sequencing approach.
METHODS: ICL patients with Qi deficiency and phlegm stasis were adopted and randomly assigned to a case group or a control by random number generator in a 1:1 randomization ratio to evaluate the clinical efficacy.
RESULTS: There was no significant difference between the two groups in coronary computed tomography angiography related indexes in the two groups before and after intervention. Through the gene chip expression analysis, it is finally concluded that there are 355 differential mRNAs (190 up-regulated genes and 165 down regulated genes) when compared the SGR group and placebo group. Through protein-protein interaction network analysis of differentially expressed genes, 10 hub genes were finally obtained: CACNA2D2, CACNA2D3, DNAJC6, FGF12, SGSM2, CACNA1G, LRP6, KIF25, OXTR, UPB1.
CONCLUSIONS: SGR combined with Western Medicine can be safely used to treat ICL patients with Qi deficiency and phlegm stasis. The possible mechanism of action and relevant gene loci and pathway were proposed.

Gene chip is a high-throughput technique for detecting specific DNA sequences by DNA or DNA-RNA complementary hybridization, among which SNP genotyping chips have been widely employed in the animal genetics and breeding, and have made great achievements in cattle (Bos taurus), pigs (Sus scrofa), sheep (Caprinae), chickens (Gallus gallus) and other livestock. However, genomic selection applied in production merely uses genomic information and cannot fully explain the molecular mechanism of complex traits genetics, which limits the accuracy of genomic selection. With the continuous progresses in epigenetic research, the development of commercial methylation chips and the application of the epigenome-wide association study (EWAS), DNA methylation has been extensively used to draw the causal connections between genetics and phenotypes. In the future, it is hopefully expected to develop methylation chips customized for livestock and poultry and explore methylation sites significantly related to economic traits of livestock and poultry through EWAS thereby extending the understanding of causal variation of complex traits. Combining methylation chips and SNP chips, we can capture the epigenomic and genomic information of livestock and poultry, interpret genetic variation more precisely, improve the accuracy of genome selection, and promote the fine evolution of molecular genetic breeding of livestock and poultry. In this review, we summarize the application of SNP chips and depict the prospects of the application of methylation chips in livestock and poultry. This review will provide valuable insights for further application of gene chips in farm animal breeding.
基因芯片是一种通过DNA双链或DNA-RNA互补杂交检测特定DNA序列的高通量技术，其中SNP基因分型芯片已经广泛用于畜禽的遗传育种工作，在牛(Bos taurus)、猪(Sus scrofa)、羊(Caprinae)、鸡(Gallus gallus)等畜禽中取得了重大成就。但是在实际生产中使用的基因组选择仅利用了基因组信息，无法完全解释复杂性状的分子遗传基础，限制了基因组选择的准确性。随着表观遗传学研究的不断深入、商用甲基化芯片的推出、表观基因组关联分析(epigenome-wide association study，EWAS)的提出，DNA甲基化已被广泛用于解释遗传与表型的因果关系。未来，有望开发专门针对畜禽的甲基化芯片，通过EWAS探索与畜禽经济性状显著相关的甲基化位点，深化对复杂性状因果变异的理解。结合甲基化芯片与SNP芯片捕获畜禽表观基因组和基因组信息，更准确地解读遗传变异，提高基因组选择的准确性，推动畜禽分子遗传育种工作的精细化发展。本文综述了SNP芯片在畜禽上的应用，并对甲基化芯片在畜禽上的应用进行了展望，以期为基因芯片在动物育种中的进一步应用提供借鉴和参考。.

OBJECTIVE: Abnormal immune system activation and inflammation are crucial in causing Parkinson\'s disease. However, we still don\'t fully understand how certain immune-related genes contribute to the disease\'s development and progression. This study aims to screen key immune-related gene in Parkinson\'s disease based on weighted gene co-expression network analysis (WGCNA) and machine learning.
METHODS: This study downloaded the gene chip data from the Gene Expression Omnibus (GEO) database, and used WGCNA to screen out important gene modules related to Parkinson\'s disease. Genes from important modules were exported and a Venn diagram of important Parkinson\'s disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson\'s disease. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the the functions of immune-related genes and signaling pathways involved. Immune cell infiltration analysis was performed using the CIBERSORT package of R language. Using bioinformatics method and 3 machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM)], the immune-related genes of Parkinson\'s disease were further screened. A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes (hub) gene. The downstream proteins of the Parkinson\'s disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn.
RESULTS: A total of 218 immune genes related to Parkinson\'s disease were identified, including 45 upregulated genes and 50 downregulated genes. Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways. The results of immune infiltration analysis showed that the infiltration percentages of CD4+ T cells, NK cells, CD8+ T cells, and B cells were higher in the samples of Parkinson\'s disease patients, while resting NK cells and resting CD4+ T cells were significantly infiltrated in the samples of Parkinson\'s disease patients. ANK1 was screened out as the hub gene. The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction, iron homeostasis regulation, and immune system activation.
CONCLUSIONS: This study identifies the Parkinson\'s disease immune-related key gene ANK1 via WGCNA and machine learning methods, suggesting its potential as a candidate therapeutic target for Parkinson\'s disease.
目的: 在帕金森病的发病过程中，免疫系统的异常激活和炎症反应起着重要作用。然而，目前对于免疫相关关键基因在帕金森病发生和发展中的具体作用和作用机制的了解仍然有限。本研究旨在通过加权基因共表达网络分析(weighted gene co-expression network analysis，WGCNA)和机器学习筛选帕金森病免疫相关关键基因。方法: 从基因表达综合(Gene Expression Omnibus，GEO)数据库下载基因芯片数据，采用WGCNA筛选出与帕金森病相关的重要基因模块；将重要模块中的基因导出，绘制帕金森病重要相关基因与免疫相关基因的韦恩图，从而筛选出帕金森病免疫相关基因。采用基因本体(gene ontology，GO)分析和京都基因和基因组百科全书(Kyoto Encyclopedia of Genes and Genomes，KEGG)深入分析免疫相关基因的功能及参与的信号通路。通过R语言的CIBERSORT包进行免疫细胞浸润分析。采用生物信息学方法和3种机器学习方法[最小绝对收缩和选择算子(least absolute shrinkage and selection operator，LASSO)回归、随机森林(random forest，RF)和支持向量机(support vector machine，SVM)]对筛选出的帕金森病免疫相关基因进行进一步筛选研究，绘制4种方法筛选的差异表达基因的韦恩图，筛选交集基因即中心节点(hub node，hub)基因。通过STRING数据库搜索帕金森病hub基因的下游蛋白质，绘制蛋白质互作网络图。结果: 筛选出帕金森病重要模块基因中与免疫相关的基因218个，其中45个为上调基因，50个为下调基因。富集分析结果显示218个基因主要在免疫系统对外来物反应和病毒感染通路富集。免疫浸润分析结果表明，CD4+ T细胞、NK细胞、CD8+ T细胞、B细胞在帕金森病患者样本中的浸润百分率较高，静息NK细胞、静息CD4+ T细胞在帕金森病患者样本中显著浸润。4种方法筛选出的hub基因为ANK1基因。交集基因蛋白质互作网络分析结果显示，ANK1基因翻译表达的11个蛋白质主要参与信号转导、铁稳态调节及免疫系统激活等功能。结论: 通过WGCNA和机器学习方法，筛选出帕金森病免疫相关关键基因ANK1，该基因可能成为帕金森病诊断和治疗的候选靶点。.

OBJECTIVE: This study aimed to explore potential hub genes and pathways of plaque vulnerability and to investigate possible therapeutic targets for acute coronary syndrome (ACS).
RESULTS: Four microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene coexpression networks (WGCNA) and immune cell infiltration analysis (IIA) were used to identify the genes for plaque vulnerability. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology, Gene Ontology annotation and protein-protein interaction (PPI) network analyses were performed to explore the hub genes. Random forest and artificial neural networks were constructed for validation. Furthermore, the CMap and Herb databases were employed to explore possible therapeutic targets. A total of 168 DEGs with an adjusted P < 0.05 and approximately 1974 IIA genes were identified in GSE62646. Three modules were detected and associated with CAD-Class, including 891 genes that can be found in GSE90074. After removing duplicates, 114 hub genes were used for functional analysis. GO functions identified 157 items, and 6 pathways were enriched for the KEGG pathway at adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). Random forest and artificial neural network models were built based on the GSE48060 and GSE34822 datasets, respectively, to validate the previous hub genes. Five genes (GZMA, GZMB, KLRB1, KLRD1 and TRPM6) were selected, and only two of them (GZMA and GZMB) were screened as therapeutic targets in the CMap and Herb databases.
CONCLUSIONS: We performed a comprehensive analysis and validated GZMA and GZMB as a target for plaque vulnerability, which provides a therapeutic strategy for the prevention of ACS. However, whether it can be used as a predictor in blood samples requires further experimental verification.

BACKGROUND: China has thousands years of goat breeding and abundant goat genetic resources. Additionally, the Hainan black goat is one of the high-quality local goat breeds in China. In order to conserve the germplasm resources of the Hainan black goat, facilitate its genetic improvement and further protect the genetic diversity of goats, it is urgent to develop a single nucleotide polymorphism (SNP) chip for Hainan black goat.
RESULTS: In this study, we aimed to design a 10K liquid chip for Hainan black goat based on genotyping by pinpoint sequencing of liquid captured targets (cGPS). A total of 45,588 candidate SNP sites were obtained, 10,677 of which representative SNP sites were selected to design probes, which finally covered 9,993 intervals and formed a 10K cGPS liquid chip for Hainan black goat. To verify the 10K cGPS liquid chip, some southern Chinese goat breeds and a sheep breed with similar phenotype to the Hainan black goat were selected. A total of 104 samples were used to verify the clustering ability of the 10K cGPS liquid chip for Hainan black goat. The results showed that the detection rate of sites was 97.34% -99.93%. 84.5% of SNP sites were polymorphic. The heterozygosity rate was 3.08%-36.80%. The depth of more than 99.4% sites was above 10X. The repetition rate was 99.66%-99.82%. The average consistency between cGPS liquid chip results and resequencing results was 85.58%. In addition, the phylogenetic tree clustering analysis verified that the SNP sites on the chip had better clustering ability.
CONCLUSIONS: These results indicate that we have successfully realized the development and verification of the 10K cGPS liquid chip for Hainan black goat, which provides a useful tool for the genome analysis of Hainan black goat. Moreover, the 10K cGPS liquid chip is conducive to the research and protection of Hainan black goat germplasm resources and lays a solid foundation for its subsequent breeding work.

The purpose of this study was to screen differentially expressed genes in PCOS using gene chip data and investigate the biological functions of these DEGs in PCOS. Additionally, the study aimed to analyze the potential clinical significance of these genes using clinical data. In this study, we first screened the DEGs related to PCOS by using the gene chip data (GSE5090) from GEO database. Target gene prediction software was used to predict the target genes for these DEGs, and their functional enrichment was analysed. Subsequently, the STRING online tool and Cytoscape software were utilized to identify key genes by constructing protein-protein interaction networks (PPI). In the analysis of the GSE5090 dataset, seventeen differentially expressed genes (DEGs) were identified. Functional enrichment analysis revealed that these DEGs are predominantly associated with biological functions related to polycystic ovary syndrome (PCOS). Moreover, the tissue-specific expression analysis highlighted immune system markers, with a notable difference observed in 18 of these markers, accounting for 20.5% of the total. By constructing PPI networks and key gene regulatory networks, a total of three genes (RPL13, LEP, and ANXA1) were identified as key genes. In addition, the column-line graphical model performed well in predicting the risk of PCOS. Using ROC curves, the model proved to be effective in diagnosis. This study represents the first application of a bioinformatics approach to identify and confirm high expression levels of RPL13, LEP, and ANXA1 in patients with Polycystic Ovary Syndrome (PCOS). These key genes-RPL13, LEP, and ANXA1-may present viable targets for therapeutic interventions in PCOS, underscoring their potential clinical importance.

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

OBJECTIVE: Research indicates that long noncoding RNAs (lncRNAs) contribute significantly to fibrotic diseases. Although lncRNAs may play a role in hypertrophic scars after burns, its mechanisms remain poorly understood.
METHODS: Using chip technology, we compared the lncRNA expression profiles of burn patients and healthy controls (HCs). Microarray results were examined by quantitative reverse-transcription polymerase chain reaction (RT-PCR) to verify their reliability. The biological functions of differentially expressed mRNAs and the relationships between genes and signaling pathways were investigated by Gene Ontology (GO) and pathway analyses, respectively.
RESULTS: In contrast with HCs, it was found that 2738 lncRNAs (1628 upregulated) and 2166 mRNAs (1395 upregulated) were differentially expressed in hypertrophic scars after burn. Results from RT-PCR were consistent with those from microarray. GO and pathway analyses revealed that the differentially expressed mRNAs are mainly associated with processes related to cytokine secretion in the immune system, notch signaling, and MAPK signaling.
CONCLUSIONS: The lncRNA expression profiles of hypertrophic scars after burn changed significantly compared with HCs. It was believed that the transcripts could be used as potential targets for inhibiting abnormal scar formation in burn patients.