To explore the differentially expressed genes (DEGs) related to biosynthesis of active ingredients in wolfberry fruits of different varieties of Lycium barbarum L. and reveal the molecular mechanism of the differences of active ingredients, we utilized Illumina NovaSeq 6000 high-throughput sequencing technology to conduct transcriptome sequencing on the fruits of 'Ningqi No.1' and 'Ningqi No.7' during the green fruit stage, color turning stage and maturity stage. Subsequently, we compared the profiles of related gene expression in the fruits of the two varieties at different development stages. The results showed that a total of 811 818 178 clean reads were obtained, resulting in 121.76 Gb of valid data. There were 2 827, 2 552 and 2 311 DEGs obtained during the green fruit stage, color turning stage and maturity stage of 'Ningqi No. 1' and 'Ningqi No. 7', respectively, among which 2 153, 2 050 and 1 825 genes were annotated in six databases, including gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) and clusters of orthologous groups of proteins (KOG). In GO database, 1 307, 865 and 624 DEGs of green fruit stage, color turning stage and maturity stage were found to be enriched in biological processes, cell components and molecular functions, respectively. In the KEGG database, the DEGs at three developmental stages were mainly concentrated in metabolic pathways, biosynthesis of secondary metabolites and plant-pathogen interaction. In KOG database, 1 775, 1 751 and 1 541 DEGs were annotated at three developmental stages, respectively. Searching the annotated genes against the PubMed database revealed 18, 26 and 24 DEGs related to the synthesis of active ingredients were mined at the green fruit stage, color turning stage and maturity stage, respectively. These genes are involved in carotenoid, flavonoid, terpenoid, alkaloid, vitamin metabolic pathways, etc. Seven DEGs were verified by RT-qPCR, which showed consistent results with transcriptome sequencing. This study provides preliminary evidences for the differences in the content of active ingredients in different Lycium barbarum L. varieties from the transcriptional level. These evidences may facilitate further exploring the key genes for active ingredients biosynthesis in Lycium barbarum L. and analyzing their expression regulation mechanism.
Flavonoids/metabolism* ; Fruit/genetics* ; Gene Expression Profiling/methods* ; Gene Expression Regulation, Plant ; Lycium/metabolism* ; Metabolic Networks and Pathways ; Transcriptome

Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes, and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichment analysis, protein-protein interaction (PPI) network, and survival analysis based on the Gene Expression Omnibus (GEO) database.Methods By screening with highly expressed genes, we mainly analyzed proteins MUC13 and EPCAM with transmembrane domain and antigen epitope from TMHMM and IEDB websites. Significant genes and pathways associated with the pathogenesis of esophageal cancer were identified using enrichment analysis, PPI network, and survival analysis. Several software and platforms including Prism 8, R language, Cytoscape, DAVID, STRING, and GEPIA platform were used in the search and/or figure creation.Results Genes MUC13 and EPCAM were over-expressed with several antigen epitopes in esophageal squamous cell carcinoma (ESCC) tissue. Enrichment analysis revealed that the process of keratinization was focused and a series of genes were related with the development of esophageal cancer. Four genes including ALDH3A1, C2, SLC6A1,and ZBTB7C were screened with significant P value of survival curve.Conclusions Genes MUC13 and EPCAM may be promising antigen targets or biomarkers for esophageal cancer. Keratinization may greatly impact the pathogenesis of esophageal cancer. Genes ALDH3A1, C2, SLC6A1,and ZBTB7C may play important roles in the development of esophageal cancer.
Humans ; Esophageal Neoplasms/metabolism* ; Esophageal Squamous Cell Carcinoma/metabolism* ; Epithelial Cell Adhesion Molecule/metabolism* ; Gene Expression Profiling/methods* ; Gene Regulatory Networks ; Gene Expression ; Gene Expression Regulation, Neoplastic ; Intracellular Signaling Peptides and Proteins

Objective: Using bioinformatics methods to analyze the core pathogenic genes and related pathways in elderly osteoporosis. Methods: From November 2020 and August 2021, eight elderly osteoporosis patients who received treatment and five healthy participants who underwent physical examinations in Beijing Jishuitan Hospital were selected as subjects. The expression level of RNA in the peripheral blood of eight elderly osteoporosis patients and five healthy participants was collected for high-throughput transcriptome sequencing and analysis. The gene ontology (GO) analysis Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed for the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was constructed using the STRING website and Cytoscape software, and the most significant modules and hub genes were screened out. Results: Among the eight elderly osteoporosis patients, there were seven females and one male, with an average age of 72.4 years (SD=4.2). Among the five healthy participants, there were four females and one male, with an average age of 68.2 years (SD=5.7). A total of 1 635 DEGs (847 up-regulated and 788 down-regulated) were identified. GO analysis revealed that the molecular functions of DEGs were mainly enriched in structural constituents of the ribosome, protein dimerization activity, and cellular components were mainly enriched in the nucleosome, DNA packaging complex, cytosolic part, protein-DNA complex and the cytosolic ribosome. KEGG pathway analysis showed that DEGs were mainly enriched in systemic lupus erythematosus and ribosome. Gene UBA52, UBB, RPS27A, RPS15, RPS12, RPL13A, RPL23A, RPL10A, RPS25 and RPS6 were selected and seven of them could encode ribosome proteins. Conclusion: The pathogenesis of elderly osteoporosis may be associated with ribosome-related genes and pathways.
Female ; Humans ; Male ; Aged ; Gene Expression Profiling/methods* ; Transcriptome ; Protein Interaction Maps/genetics* ; Computational Biology/methods* ; Osteoporosis/genetics*

Carcinoma of unknown primary (CUP) is a kind of metastatic tumor whose primary origin cannot be identified after adequate examination and evaluation. The main treatment modality of CUP is empiric chemotherapy, and the median overall survival time is less than 1 year. Compared with immunohistochemistry, novel method based on gene expression profiling have improved the sensitivity and specificity of CUP detection, but its guiding value for treatment is still controversial. The approval of immune checkpoint inhibitors and pan-cancer antitumor agents has improved the prognosis of patients with CUP, and targeted therapy and immunotherapy based on specific molecular characteristics are the main directions of future research. Given the high heterogeneity and unique clinicopathological characteristics of CUP, "basket trial" is more suitable for clinical trial design in CUP.
Humans ; Neoplasms, Unknown Primary/genetics* ; Carcinoma/drug therapy* ; Gene Expression Profiling/methods* ; Microarray Analysis ; Prognosis

Gelsemium elegans is a traditional Chinese herb of medicinal importance, with indole terpene alkaloids as its main active components. To study the expression of the most suitable housekeeping reference genes in G. elegans, the root bark, stem segments, leaves and inflorescences of four different parts of G. elegans were used as materials in this study. The expression stability of 10 candidate housekeeping reference genes (18S, GAPDH, Actin, TUA, TUB, SAND, EF-1α, UBC, UBQ, and cdc25) was assessed through real-time fluorescence quantitative PCR, GeNorm, NormFinder, BestKeeper, ΔCT, and RefFinder. The results showed that EF-1α was stably expressed in all four parts of G. elegans and was the most suitable housekeeping gene. Based on the coexpression pattern of genome, full-length transcriptome and metabolome, the key candidate targets of 18 related genes (AS, AnPRT, PRAI, IGPS, TSA, TSB, TDC, GES, G8H, 8-HGO, IS, 7-DLS, 7-DLGT, 7-DLH, LAMT, SLS, STR, and SGD) involved in the Gelsemium alkaloid biosynthesis were obtained. The expression of 18 related enzyme genes were analyzed by qRT-PCR using the housekeeping gene EF-1α as a reference. The results showed that these genes' expression and gelsenicine content trends were correlated and were likely to be involved in the biosynthesis of the Gelsemium alkaloid, gelsenicine.
Genes, Essential ; Gelsemium/genetics* ; Peptide Elongation Factor 1/genetics* ; Transcriptome ; Gene Expression Profiling/methods* ; Alkaloids ; Real-Time Polymerase Chain Reaction/methods* ; Reference Standards

This study aims to identify the novel biomarkers of cold-dampness syndrome(RA-Cold) of rheumatoid arthritis(RA) by gene set enrichment analysis(GSEA), weighted gene correlation network analysis(WGCNA), and clinical validation. Firstly, transcriptome sequencing was carried out for the whole blood samples from RA-Cold patients, RA patients with other traditional Chinese medicine(TCM) syndromes, and healthy volunteers. The differentially expressed gene(DEG) sets of RA-Cold were screened by comparison with the RA patients with other TCM syndromes and healthy volunteers. Then, GSEA and WGCNA were carried out to screen the key DEGs as candidate biomarkers for RA-Cold. Experimentally, the expression levels of the candidate biomarkers were determined by RT-qPCR for an independent clinical cohort(not less than 10 cases/group), and the clinical efficacy of the candidates was assessed using the receiver operating characteristic(ROC) curve. The results showed that 3 601 DEGs associated with RA-Cold were obtained, including 106 up-regulated genes and 3 495 down-regulated genes. The DEGs of RA-Cold were mainly enriched in the pathways associated with inflammation-immunity regulation, hormone regulation, substance and energy metabolism, cell function regulation, and synovial pannus formation. GSEA and WGCNA showed that recombinant proteasome 26S subunit, ATPase 2(PSMC2), which ranked in the top 50% in terms of coefficient of variation, representativeness of pathway, and biological modules, was a candidate biomarker of RA-Cold. Furthermore, the validation results based on the clinical independent sample set showed that the F1 value, specificity, accuracy, and precision of PSMC2 for RA-Cold were 70.3%, 61.9%, 64.5%, and 81.3%, respectively, and the area under the curve(AUC) value was 0.96. In summary, this study employed the "GSEA-WGCNA-validation" integrated strategy to identify novel biomarkers of RA-Cold, which helped to improve the TCM clinical diagnosis and treatment of core syndromes in RA and provided an experimental basis for TCM syndrome differentiation.
Humans ; Arthritis, Rheumatoid/drug therapy* ; Biomarkers/metabolism* ; Medicine, Chinese Traditional ; Gene Expression Profiling/methods* ; Computational Biology ; Gene Regulatory Networks ; ATPases Associated with Diverse Cellular Activities/therapeutic use* ; Proteasome Endopeptidase Complex/therapeutic use*

Objective To identify immune-related dysregulation mechanisms and potential diagnostic predictive biomarkers in osteoporosis. Methods Gene expression data for both osteoporosis and control populations were retrieved from the GSE35958 and GSE56815 datasets. Immune-related differentially expressed genes (DEGs) were obtained by screening DEGs and were compared with the immunology database and analysis portal (ImmPort) database. Enrichment analysis of these immune-related DEGs was conducted using the Clusterprofiler software package. A protein-protein interaction network was built with the STRING database, which is a search tool for finding interacting genes/proteins, and the top 10 genes with the highest network connectivity were identified as candidate genes. Subsequently, the diagnostic predictive effect of candidate genes was evaluated using receiver operating characteristic (ROC) curves, logistic regression, and column plots. Finally, PCR and Western blot analysis were applied to detect the differential expression of these genes in bone marrow tissue of patients with osteoporosis. Results A total of 138 immune-related DEGs were obtained through intersection analysis. The results of the enrichment analysis indicated that these genes were involved in biological functions such as immune inflammation and signaling pathways including T cell receptors, mitogen activated protein kinase (MAPK), rat sarcoma virus oncogene homologs (Ras), osteoclast differentiation, and B cell receptors. In addition, among the candidate genes, upregulated vascular endothelial growth factor A (VEGFA) and epidermal growth factor receptor (EGFR) and downregulated AKT1, SRC, and JUN in osteoporosis showed the highest connectivity. Among them, VEGFA, EGFR, JUN, and AKT1 demonstrated the best diagnostic predictive value. Conclusion The screening of immune-related DEGs will enhance the understanding of osteoporosis and facilitate the development of immunotherapy targets.
Humans ; Vascular Endothelial Growth Factor A/genetics* ; Biomarkers ; Osteoporosis/genetics* ; Computational Biology/methods* ; ErbB Receptors/genetics* ; Gene Expression Profiling/methods*

Paeonia veitchii and P. lactiflora are both original plants of the famous Chinese medicinal drug Paeoniae Radix Rubra in the Chinese Pharmacopoeia. They have important medicinal value and great potential in the flower market. The selection of stable and reliable reference genes is a necessary prerequisite for molecular research on P. veitchii. In this study, two reference genes, Actin and GAPDH, were selected as candidate genes from the transcriptome data of P. veitchii. The expression levels of the two candidate genes in different tissues(phloem, xylem, stem, leaf, petiole, and ovary) and different growth stages(bud stage, flowering stage, and dormant stage) of P. veitchii were detected using real-time fluorescence quantitative technology(qRT-PCR). Then, the stability of the expression of the two reference genes was comprehensively analyzed using geNorm, NormFinder, BestKeeper, ΔCT, and RefFinder. The results showed that the expression patterns of Actin and GAPDH were stable in different tissues and growth stages of P. veitchii. Furthermore, the expression levels of eight genes(Pv-TPS01, Pv-TPS02, Pv-CYP01, Pv-CYP02, Pv-CYP03, Pv-BAHD01, Pv-UGT01, and Pv-UGT02) in different tissues were further detected based on the transcriptome data of P. veitchii. The results showed that when Actin and GAPDH were used as reference genes, the expression trends of the eight genes in different tissues of P. veitchii were consistent, validating the reliability of Actin and GAPDH as reference genes for P. veitchii. In conclusion, this study finds that Actin and GAPDH can be used as reference genes for studying gene expression levels in different tissues and growth stages of P. veitchii.
Real-Time Polymerase Chain Reaction/methods* ; Paeonia/genetics* ; Actins/genetics* ; Reproducibility of Results ; Transcriptome ; Glyceraldehyde-3-Phosphate Dehydrogenases/genetics* ; Reference Standards ; Gene Expression Profiling/methods*

OBJECTIVES: To explore the mRNA differential expressions and the sequential change pattern in acute myocardial infarction (AMI) mice. METHODS: The AMI mice relevant dataset GSE4648 was downloaded from Gene Expression Omnibus (GEO). In the dataset, 6 left ventricular myocardial tissue samples were selected at 0.25, 1, 4, 12, 24 and 48 h after operation in AMI group and sham control group, and 6 left ventricular myocardial tissue samples were selected in blank control group, a total of 78 samples were analyzed. Differentially expressed genes (DEGs) were analyzed by R/Bioconductor package limma, functional pathway enrichment analysis was performed by clusterProfiler, protein-protein interaction (PPI) network was constructed by STRING database and Cytoscape software, the key genes were identified by Degree topological algorithm, cluster sequential changes on DEGs were analyzed by Mfuzz. RESULTS: A total of 1 320 DEGs were associated with the development of AMI. Functional enrichment results included cellular catabolic process, regulation of inflammatory response, development of muscle system and vasculature system, cell adhesion and signaling pathways mainly enriched in mitogen-activated protein kinase (MAPK) signaling pathway. The key genes of AMI included MYL7, TSC22D2, HSPA1A, BTG2, NR4A1, RYR2 were up-regulated or down-regulated at 0.25-48 h after the occurrence of AMI. CONCLUSIONS The functional signaling pathway of DEGs and the sequential expression of key genes in AMI may provide a reference for the forensic identification of AMI.
Animals ; Computational Biology/methods* ; Gene Expression Profiling/methods* ; Mice ; Mitogen-Activated Protein Kinases/metabolism* ; Myocardial Infarction/metabolism* ; RNA, Messenger ; Ryanodine Receptor Calcium Release Channel/metabolism* ; Transcriptome

Objective: Bioinformatics methods were used to mine differentially expressed genes (DEGs) and enriched signal pathways induced by chronic pesticide exposure, and explore its potential pathogenic mechanisms and key genes. Methods: In July 2021, high-throughput gene expression profile data related to pesticide toxicity was downloaded from Gene Expression Omnibus (GEO) database to obtain DEGs. The samples were from American male farm workers who had been exposed to pesticides for a long time and other industry workers. The functional enrichment analysis of GO, KEGG and Geme Set Enrichment Analysis (GSEA) were performed by R package clusterProfiler. STRING, Cytoscape and other tools were applied to construct and visualize the protein interaction network. With the help of MCODE and Cytohubba plugins, gene function modules were obtained, and hub gene was screened out. Results: 189 DEGs were screened from GSE30335 dataset, including 101 up-regulated genes and 88 down-regulated genes. The results of GO, KEGG and GSEA were mainly enriched in biological functions such as regulation of neuron projection development, regulation of locomotion, ribosomal protein synthesis, and pathways related to complex nervous system diseases such as Parkinson's disease. And the comprehensive screening showed that KLF1 was the hub gene of pesticide exposure, with a fold change of 0.456 (t=-3.82, P=0.021) . Conclusion: Long term exposure to pesticides results in the differential expression of multiple genes in the exposed population, which may be involved in the pathological changes of nervous system by down regulating KLF1 and related biological pathways.
Computational Biology/methods* ; Gene Expression Profiling/methods* ; Gene Regulatory Networks ; Genetic Association Studies ; Humans ; Male ; Pesticides/toxicity*