Gene Ontology Database Research Articles

Metabolomic and Transcriptomic Analysis of Unique Floral Coloration in Osmanthus fragrans Cultivars

The floral color phenotypes of Osmanthus fragrans cultivars range from light yellow to orange yellow, with ‘Yanzhi Hong’ being the only reported cultivar with a red color. However, the underlying reason for this unique floral coloration remains unclear. The study conducted targeted metabolomics and transcriptomics analyses on the petals of ‘Yanzhi Hong’ at both initial and peak flowering stages. Candidate gene expression was validated, and expression levels of the petals of three cultivars were compared using RT-qPCR. The results revealed the presence of 27 components in the petals of ‘Yanzhi Hong’, including 5 carotenoids, 8 xanthophylls, and 14 xanthophyll esters. Notably, lycopene was detected in abundance for the first time in O. fragrans cultivars. Carotenes accounted for 78.82 ± 3.17% and 91.19 ± 1.69% of the total carotenoid content in petals during the initial and peak flowering stages, respectively, with all carotene contents increasing during the peak flowering period. β-carotene, lycopene, and γ-carotene were identified as the top three carotene components in petals during both initial and full flowering stages. The unique blush red color of ‘Yanzhi Hong’ petals could be attributed to the low content of α-carotene and the rich accumulation of lycopene. Furthermore, a total of 1550 differentially expressed genes (DEGs) were identified in petals at the peak flowering stage relative to the initial flowering stage, with 1003 genes being downregulated and 547 genes being upregulated during the full flowering stage. There are 926 differentially expressed genes (DEGs) annotated in the Gene Ontology (GO) database. Among these DEGs, those that were downregulated and upregulated during the peak flowering period showed significant enrichment in carbohydrate metabolism and oxidation–reduction processes, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified 14 structural genes associated with phenylpropanoid biosynthesis and 7 structural genes linked to carotenoid biosynthesis. Expression levels of candidate genes involved in carotenoid biosynthesis were examined in the petals of three cultivars (‘Yanzhi Hong’, ‘Liuye Jingui’, and ‘Gecheng Dangui’) at both the initial and peak flowering stages. The results indicated that the decreased expression of LYG009054 (LYCE) and LYG018651 (LYCB) in ‘Yanzhi Hong’ resulted in higher lycopene accumulation and lower α-carotene content in the petals. This study offers valuable insights into the mechanisms underlying the unique flower color phenotype of O. fragrans, proving a basis for further research on carotenoid metabolism pathways and the breeding of new cultivars with a variety of flower colors in O. fragrans.

Experimental confirmation and bioinformatics reveal biomarkers of immune system infiltration and hypertrophy ligamentum flavum.

Hypertrophy ligamentum flavum is a prevalent chronic spinal condition that affects middle-aged and older adults. However, the molecular pathways behind this disease are not well comprehended. The objective of this work is to implement bioinformatics techniques in order to identify crucial biological markers and immune infiltration that are linked to hypertrophy ligamentum flavum. Further, the study aims to experimentally confirm the molecular mechanisms that underlie the hypertrophy ligamentum flavum. The corresponding gene expression profiles (GSE113212) were selected from a comprehensive gene expression database. The gene dataset for hypertrophy ligamentum flavum was acquired from GeneCards. A network of interactions between proteins was created, and an analysis of functional enrichment was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. An study of hub genes was performed to evaluate the infiltration of immune cells in patient samples compared to tissues from the control group. Finally, samples of the ligamentum flavum were taken with the purpose of validating the expression of important genes in a clinical setting. Overall, 27 hub genes that were differently expressed were found through molecular biology. The hub genes were found to be enriched in immune response, chemokine-mediated signaling pathways, inflammation, ossification, and fibrosis processes, as demonstrated by GO and KEGG studies. The main signaling pathways involved include the TNF signaling pathway, cytokine-cytokine receptor interaction, and TGF-β signaling pathway. An examination of immunocell infiltration showed notable disparities in B cells (naïve and memory) and activated T cells (CD4 memory) between patients with hypertrophic ligamentum flavum and the control group of healthy individuals. The in vitro validation revealed markedly elevated levels of ossification and fibrosis-related components in the hypertrophy ligamentum flavum group, as compared to the normal group. The TGF-β signaling pathway, TNF signaling pathway, and related hub genes play crucial roles in the progression of ligamentum flavum hypertrophic. Our study may guide future research on fibrosis of the ligamentum flavum.

The expression and clinical significance of CFAP65 in colon cancer

BackgroundCFAP65 (cilia and flagella associated protein 65) is a fundamental protein in the development and formation of ciliated flagella, but few studies have focused on its role in cancer. This study aimed to investigate the prognostic significance of CFAP65 in colon cancer.MethodsThe functionally enriched genes related to CFAP65 were analyzed through the Gene Ontology (GO) database. Subsequently, CFAP65 expression levels in colon cancer were evaluated by reverse transcription and quantitative polymerase chain reaction (RT-qPCR) and immunoblotting in 20 pairs of frozen samples, including tumors and their matched paratumor tissue. Furthermore, protein expression of CFAP65 in 189 colon cancer patients were assessed via immunohistochemical staining. The correlations between CFAP65 expression and clinical features as well as long-term survival were statistically analyzed.ResultsCFAP65-related genes are significantly enriched on cellular processes of cell motility, ion channels, and GTPase-associated signaling. The expression of CFAP65 was significantly higher in colon cancer tissue compared to paratumor tissue. The proportion of high expression and low expression of CFAP65 in the clinical samples of colon cancer were 61.9% and 38.1%, respectively, and its expression level was not associated with the clinical parameters including gender, age, tumor location, histological differentiation, tumor stage, vascular invasion and mismatch repair deficiency. The five-year disease-free survival rate of the patients with CFAP65 low expression tumors was significantly lower than that those with high expression tumors (56.9% vs. 72.6%, P = 0.03), but the overall survival rate has no significant difference (69% vs. 78.6%, P = 0.171). The cox hazard regression analysis model showed that CFAP65 expression, tumor stage and tumor location were independent prognostic factors.ConclusionsIn conclusion, we demonstrate CFAP65 is a potential predictive marker for tumor progression in colon cancer.

Resource: A curated database of brain-related functional gene sets (Brain.GMT)

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain “ignorome”. To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types (“Brain.GMT”) that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, “brain ignorome” genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation.•We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types (“Brain.GMT”).•Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.

GOAT: efficient and robust identification of gene set enrichment

Gene set enrichment analysis is foundational to the interpretation of high throughput biology. Identifying enriched Gene Ontology (GO) terms or disease-associated gene sets within a list of gene effect sizes that represent experimental outcomes is an everyday task in life science that crucially depends on robust and sensitive statistical tools. We here present GOAT, a parameter-free algorithm for gene set enrichment analysis of preranked gene lists. The algorithm can precompute null distributions from standardized gene scores, enabling enrichment testing of the GO database in one second. Validations using synthetic data show that estimated gene set p-values are well calibrated under the null hypothesis and invariant to gene list length and gene set size. Application to various real-world proteomics and gene expression studies demonstrates that GOAT identifies more significant GO terms as compared to current methods. GOAT is freely available as an R package and user-friendly online tool for gene set enrichment analyses that includes interactive data visualizations: https://ftwkoopmans.github.io/goat.

Exploring the Potential Lipid-Lowering and Weight-Reducing Mechanisms of FH06 Fermented Beverages Based on Non-Targeted Metabolomics and Network Pharmacology

Investigating the intricate pathways through which FH06 fermentation broth exerts lipid-lowering and weight-loss effects is pivotal for advancing our comprehension of metabolic regulation and therapeutic interventions. Ultrahigh-performance liquid chromatography quadrupole electrostatic field orbit trap mass spectrometry (UHPLC-QE-MS) detection and the ChEMBL database were used to determine the effective compounds in the FH06 fermentation broth and predict their targets. The TTD database and DisGeNET database were used to query obesity-related targets. The STRING database was used to construct protein interaction information. The Gene Ontology (GO) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used to perform biological function annotation (GO) and KEGG pathway enrichment analyses of the targets. Results: A total of 85 effective compounds were screened from the fermentation broth of FH06; these compounds may act on TP53, PPARG, TNF, and other targets through 10 signaling pathways, such as the chemical carcinogenesis-receptor activation and lipid and atherosclerosis pathways, and exert pharmacological effects, such as hypoglycemic effects and weight loss. They also have anti-inflammatory, antioxidant, antitumor, and immunoregulatory effects. These findings reveal the active ingredients of FH06 fermentation broth and its multi-target and multi-channel characteristics in lipid lowering and weight loss. This study has positive implications for the clinical treatment of obesity using FH06, providing a theoretical and scientific basis for further developing of FH06-assisted lipid-lowering products.

Identification of hub genes and pathways in hepatitis B virus-associated hepatocellular carcinoma: A comprehensive in silico study.

The hepatitis B virus (HBV) is one of the most common causes of liver cancer in the world. This study aims to provide a better understanding of the mechanisms involved in the development and progression of HBV-associated hepatocellular carcinoma (HCC) by identifying hub genes and the pathways related to their functions. GSE83148 and GSE94660 were selected from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) with an adjusted p-value < 0.05 and a |logFC| ≥1 were identified. Common DEGs of two data sets were identified using the GEO2R tool. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) databases were used to identify pathways. Protein-protein interactions (PPIs) analysis was performed by using the Cytoscap and Gephi. A Gene Expression Profiling Interactive Analysis(GEPIA) analysis was carried out to confirm the target genes. One hundred and ninety-eight common DEGs and 49 hub genes have been identified through the use of GEO and PPI, respectively. The GO and KEGG pathways analysis showed DEGs were enriched in the G1/S transition of cell cycle mitotic, cell cycle, spindle, and extracellular matrix structural constituent. The expression of fourgenes (TOP2A, CDK1, CCNA2, and CCNB2) with high scores in module 1 were more in tumor samples and have been identified by GEPIA analysis. In this study, the hub genes and their related pathways involved in the development of HBV-associated HCC were identified. These genes, as potential diagnostic biomarkers, may provide a potent opportunity to detect HBV-associated HCC at the earliest stages, resulting in a more effective treatment.

The genomic database of fruits: A comprehensive fruit information database for comparative and functional genomic studies

Fruit has an important role in human nutrition and health; therefore, the systematic study of fruit genomic data is essential. The Genomic Database of Fruits (TGDF, http://tgdf.bio2db.com/), established through whole-genome analyses of 44 fruit species, is a comprehensive, user-friendly fruit database. TGDF contains a wealth of functional genes, including 11,350 flowering genes, 3161 auxin signaling genes, 2164 anthocyanin synthesis genes, 1464 abscisic acid (ABA) synthesis genes, 10,931 ​cell division and expansion genes, 1786 starch synthesis genes, 294 fruit size genes, and 6311 sugar transporter genes. Additionally, TGDF contains 1,433,368 CRISPR guide sequences from various fruit genomes, along with information on homologous genes and duplication types for the 44 fruit species. TGDF contains 6,417,060 gene annotations sourced from TrEMBL, SwissProt, Nr, and Gene Ontology databases, along with tools such as Sequence Fetch, BLAST, Synteny, and JBrowse for bioinformatics analyses. Transcriptomic data were also collected and collated from fruits, including details on instruments, tissues, or growth stages. This comprehensive, user-friendly resource is the first collection of fruit genomic data. Users can easily download genomic sequences, gene annotations, and bioinformatics analysis results from TGDF, which will be updated continually. We anticipate that TGDF will become a primary resource for fruit comparative and functional genomic studies.

The causal effects of intelligence and fluid intelligence on Parkinson’s disease: a Mendelian randomization study

BackgroundParkinson’s disease (PD) is a chronic neurodegenerative disease that affects the central nervous system, primarily the motor nervous system, and occurs most often in older adults. A large number of studies have shown that high intelligence leads to an increased risk of PD. However, whether there is a causal relationship between intelligence on PD has not yet been reported.MethodsIn this study, Mendelian randomization (MR) analysis was performed with intelligence (ebi-a-GCST006250) and fluid intelligence score (ukb-b-5238) as exposure factors and PD (ieu-b-7) as an outcome, which the datasets were mined from the IEU OpenGWAS database. MR analysis was performed through 3 methods [MR Egger, weighted median, inverse variance weighted (IVW)], of which IVW was the primary method. In addition, the reliability of the results of the MR analysis was assessed via the heterogeneity test, the horizontal polytropy test, and Leave-One-Out (LOO). Finally, based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, the genes corresponding to intelligence and fluid intelligence score related to SNPs were enriched for functional features and pathways.ResultsThe results of MR analysis suggested that elevated intelligence indicators can increase the risk of PD [p = 0.015, Odd Ratio (OR) = 1.316]. Meanwhile, fluid intelligence score was causally associated with the PD (p = 0.035), which was a risk factor (OR = 1.142). The reliability of the results of MR analysis was demonstrated by sensitivity analysis. Finally, the results of GO enrichment analysis for 87 genes corresponding to intelligence related SNPs mainly included regulation of synapse organization, developmental cell growth, etc. These genes were enriched in the synaptic vessel cycle, polycomb expressive complex in KEGG. Similarly, 44 genes corresponding to SNPs associated with fluid intelligence score were used for enrichment analysis. Based on the GO database, these genes were mainly enriched in regulation of developmental growth, negative regulation of neuron projection development, etc. In KEGG, 44 genes corresponding to SNPs associated with fluid intelligence score were enriched in signaling pathways including Alzheimer’s disease, the cellular senescence, etc.ConclusionThe causal relationships between intelligence and fluid intelligence scores, and PD were demonstrated through MR analysis, providing an important reference and evidence for the study of PD.

Gossypium hirsutum calmodulin-like protein (CML 11) interaction with geminivirus encoded protein using bioinformatics and molecular techniques

Plant viruses are the most abundant destructive agents that exist in every ecosystem, causing severe diseases in multiple crops worldwide. Currently, a major gap is present in computational biology determining plant viruses interaction with its host. We lay out a strategy to extract virus-host protein interactions using various protein binding and interface methods for Geminiviridae, a second largest virus family. Using this approach, transcriptional activator protein (TrAP/C2) encoded by Cotton leaf curl Kokhran virus (CLCuKoV) and Cotton leaf curl Multan virus (CLCuMV) showed strong binding affinity with calmodulin-like (CML) protein of Gossypium hirsutum (Gh-CML11). Higher negative value for the change in Gibbs free energy between TrAP and Gh-CML11 indicated strong binding affinity. Consensus from gene ontology database and in-silico nuclear localization signal (NLS) tools identified subcellular localization of TrAP in the nucleus associated with Gh-CML11 for virus infection. Data based on interaction prediction and docking methods present evidences that full length and truncated C2 strongly binds with Gh-CML11. This computational data was further validated with molecular results collected from yeast two-hybrid, bimolecular fluorescence complementation system and pull down assay. In this work, we also show the outcomes of full length and truncated TrAP on plant machinery. This is a first extensive report to delineate a role of CML protein from cotton with begomoviruses encoded transcription activator protein.

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.

Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics. The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed. A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM. The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.

A novel microRNA miR-4433a-3p as a potential diagnostic biomarker for lung adenocarcinoma

BackgroundLung adenocarcinoma is one of the leading causes of cancer-related deaths because of the lack of early specific clinical indicators. MicroRNAs (miRNAs) have become the focus in lung cancer diagnosis. Further studies are required to explore miRNA expression in the serum of lung adenocarcinoma patients and their correlation with therapy and analyse specific messenger RNA targets to improve the specificity and sensitivity of early diagnosis. MethodsThe Toray 3D-Gene miRNA array was used to compare the expression levels of various miRNAs in the sera of patients with lung adenocarcinoma and healthy volunteers. Highly expressed miRNAs were selected for further analysis. To verify the screening results, serum and pleural fluid samples were analysed using qRT-PCR. Serum levels of the miRNAs and their correlation with the clinical information of patients with lung adenocarcinoma were analysed. The functions of miRNAs were further analysed using the Kyoto Encyclopedia of Gene and Genomes and Gene Ontology databases. ResultsMicroarray analysis identified 60 and 50 miRNAs with upregulated and downregulated expressions, respectively, in the serum of patients with lung adenocarcinoma compared to those in healthy individuals. Using qRT-qPCR to detection of miRNAs expression in the serum or pleural effusion of patients with early and advanced lung adenocarcinoma, we found that miR-4433a-3p could be used as a diagnostic marker and therapeutic evaluation indicator for lung adenocarcinoma. Serum of miR-4433a-3p levels significantly correlated with the clinical stage. miR-4433a-3p may be more suitable than other tumour markers for the early diagnosis and evaluation of therapeutic effects in lung adenocarcinoma. miR-4433a-3p may affect tumour growth and metastasis by acting on target genes (PIK3CD, UBE2J2, ICMT, PRDM16 and others) and regulating tumour-related signalling pathways (MAPK signal pathway, Ras signalling pathway and others). ConclusionmiR-4433a-3p may serve as a biomarker for the early diagnosis of lung adenocarcinoma and monitoring of therapeutic effects.

Research hotspots and evolving trends of barrier dysfunction in acute lung injury and acute respiratory distress syndrome

Endothelial and epithelial barrier dysfunction due to increased permeability and heightened inflammatory reactions influences the emergence of acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). Nevertheless, bibliometric research comparing endothelial and epithelial barriers is limited. Therefore, this bibliometric study analyzed the Web of Science Core Collection (WoSCC) of the Science Citation Index Expanded literature to explore present research priorities and development tendencies within this field. We conducted a comprehensive search (October 18, 2023) on WoSCC from January 1, 2010, to October 18, 2023, focusing on articles related to endothelial and epithelial barriers in ALI and ARDS. Retrieved data were visualized and analyzed using R-bibliometrix, VOS viewer 1.6.19, and CiteSpace 6.2. R4. Functional enrichment analysis of gene targets identified in the keyword list using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology databases, and based on the STRING database to construct a PPI network to predict core genes. A total of 941 original articles and reviews were identified. The United States had the highest number of publications and citations and the highest H-index and G-index. According to the Collaboration Network Analysis graph, the United States and China had the strongest collaboration. Birukova AA had the most publications and citations among all authors, while eight of the top ten institutions with mediator centrality were located in the United States. The American Journal of Physiology-Lung Cellular and Molecular Physiology was the leading journal and had the most well-established publication on endothelial and epithelial barriers in ALI and ARDS. Bibliometric analysis revealed that the most frequently used keywords were acute lung injury, ARDS, activation, expression, and inflammation. RHOA appeared most frequently among gene-related keywords, and the PI3K-AKT signaling pathway had the highest count in KEGG pathway enrichment. Research on endothelial versus epithelial barriers in ALI and ARDS remains preliminary. This bibliometric study examined cooperative network connections among countries, authors, journals, and network associations in the cited references. Investigation of the functions of the endothelial and epithelial barriers in ALI/ARDS associated with COVID-19 has recently gained significant attention.

Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation Analysis Reveal Insights into the Molecular Mechanism of Cordia myxa in the Treatment of Liver Cancer.

Traditional treatments of cancer have faced various challenges, including toxicity, medication resistance, and financial burdens. On the other hand, bioactive phytochemicals employed in complementary alternative medicine have recently gained interest due to their ability to control a wide range of molecular pathways while being less harmful. As a result, we used a network pharmacology approach to study the possible regulatory mechanisms of active constituents of Cordia myxa for the treatment of liver cancer (LC). Active constituents were retrieved from the IMPPAT database and the literature review, and their targets were retrieved from the STITCH and Swiss Target Prediction databases. LC-related targets were retrieved from expression datasets (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790) through gene expression omnibus (GEO). The DAVID Gene Ontology (GO) database was used to annotate target proteins, while the Kyoto Encyclopedia and Genome Database (KEGG) was used to analyze signaling pathway enrichment. STRING and Cytoscape were used to create protein-protein interaction networks (PPI), while the degree scoring algorithm of CytoHubba was used to identify hub genes. The GEPIA2 server was used for survival analysis, and PyRx was used for molecular docking analysis. Survival and network analysis revealed that five genes named heat shot protein 90 AA1 (HSP90AA1), estrogen receptor 1 (ESR1), cytochrome P450 3A4 (CYP3A4), cyclin-dependent kinase 1 (CDK1), and matrix metalloproteinase-9 (MMP9) are linked with the survival of LC patients. Finally, we conclude that four extremely active ingredients, namely cosmosiin, rosmarinic acid, quercetin, and rubinin influence the expression of HSP90AA1, which may serve as a potential therapeutic target for LC. These results were further validated by molecular dynamics simulation analysis, which predicted the complexes with highly stable dynamics. The residues of the targeted protein showed a highly stable nature except for the N-terminal domain without affecting the drug binding. An integrated network pharmacology and docking study demonstrated that C. myxa had a promising preventative effect on LC by working on cancer-related signaling pathways.

Identification of genes related to ribosomal proteins in colorectal cancer: exploring their potential as biomarkers, prognostic indicators, and therapeutic targets.

Colorectal cancer (CRC) ranks as the third most commonly diagnosed cancer in both females and males, underscoring the need for the identification of effective biomarkers. We assessed the expression levels of ribosomal proteins (RPs) at both mRNA and protein levels. Subsequently, leveraging the STRING database, we constructed a protein-protein interaction network and identified hub genes. The co-expression network of differentially expressed genes associated with CRC and their target hub RPs was constructed using the weighted gene co-expression network analysis algorithm. Gene ontology and molecular signatures database were conducted to gain insights into the biological roles of genes associated with the identified module. To confirm the results, the expression level of the candidate genes in the CRC samples compared to the adjacent healthy was evaluated by the RT-qPCR method. Our findings indicated that the genes related to RPs were predominantly enriched in biological processes associated with Myc Targets, Oxidative Phosphorylation, and cell proliferation. Also, results demonstrated that elevated levels of GRWD1, MCM5, IMP4, and RABEPK that related to RPs were associated with poor prognostic outcomes for CRC patients. Notably, IMP4 and RABEPK exhibited higher diagnostic value. Moreover, the expression of IMP4 and RABEPK showed a significant association with drug resistance using cancer cell line encyclopedia and genomics of drug sensitivity in cancer databases. Also, the results showed that the expression level of IMP4 and RABEPK in cancerous samples was significantly higher compared to the adjacent healthy ones. The general results of this study have shown that many genes related to RPs are increased in cancer and could be associated with the death rate of patients. We also highlighted the therapeutic and prognostic potentials of RPs genes in CRC.

Abstract 7494: Pan-cancer analysis of circulating tumor organoid drug sensitivity revealed differential distribution of viability dependence among cancer hallmark modules

Abstract Background: Patient-derived organoids represent an opportunity into deciphering human cancer biology at the personal-level resolution. Phenotyping living cell properties, such as drug sensitivity, complements deep sequencing to improve the prediction of cancer treatment response and holds promises for functional precision medicine. Modern cancer therapeutics target genetic alterations in modular networks, also known as cancer hallmarks, which drive biological essentialities for cancer progression. However, it remains elusive the relative robustness of each module among the naturally occurring solid tumors. Methods: In order to explore this issue, we analyzed a drug sensitivity database of a collection of circulating tumor cells-derived organoids (CDO) from 532 patients with advanced/metastatic solid tumors to 15 chemicals. In this collection, each CDO was derived from 20 ml peripheral blood on a binary colloid crystal cell culture system over 4 weeks. CDO were tested for survival fraction at the Cmax of the designated chemicals. Cell viability was measured with ATP abundance normalized to vehicle controls. For each chemical tested, the probability density function (PDF) of drug sensitivity was fitted to log normal distribution and goodness of fit was analyzed with Kolmogorov Smirnov test with the significance cut-off set to 0.05. The targeted biological pathway of each chemical was mapped to canonical human pathways in the Gene Ontology and KEGG databases. Strength of the specific evolution conserved module (ECM) was evaluated with the published analysis using clustering by inferred models of evolution (CLIME) algorithm. Results: There were 25 types of tumors in the database. The top 3 tumor types were soft-tissue sarcoma, breast cancers, central nervous system tumor. The average number in each tumor type was 21. The PDF of commonly known chemotherapeutic agents, such as gemcitabine, doxorubicin, docetaxel followed log normal distribution. In contrast, the PDF of commonly known biological targeted agents such as everolimus, cabozantinib or enzalutamide do not. For chemicals whose PDF followed a log normal distribution, the biological pathways that were affected tend to have a higher strength of ECM value. Conclusions: Our early analysis revealed the feasibility to quantitatively estimate the robustness of biological pathways in the cancer hallmark modules with a CDO bank. The biological implication of deviation from classical log normal distribution for biologically targeted agents warrants further investigation. Citation Format: Yihsuan Chen, Shih-Pei Wu, Yin-Ju Chen, Long-Sheng Lu. Pan-cancer analysis of circulating tumor organoid drug sensitivity revealed differential distribution of viability dependence among cancer hallmark modules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7494.

Analysis of global gene expression using RNA-sequencing reveals novel mechanism of Yanghe Pingchuan decoction in the treatment of asthma

BackgroundYanghe Pingchuan decoction (YPD) has been used for asthma treatment for many years in China. We sought to understand the mechanism of YPD, and find more potential targets for YPD-based treatment of asthma.MethodsAn ovalbumin-induced asthma model in rats was created. Staining (hematoxylin and eosin, Masson) was used to evaluate the treatment effect of YPD. RNA-sequencing was carried out to analyze global gene expression, and differentially expressed genes (DEGs) were identified. Analysis of the functional enrichment of genes was done using the Gene Ontology database (GO). Analysis of signaling-pathway enrichment of genes was done using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Real-time reverse transcription-quantitative polymerase chain reaction was undertaken to measure expression of DEGs.ResultsPathology showed that YPD had an improvement effect on rats with asthma. RNA-sequencing showed that YPD led to upregulated and downregulated expression of many genes. The YPD-based control of asthma pathogenesis may be related to calcium ion (Ca2+) binding, inorganic cation transmembrane transporter activity, microtubule motor activity, and control of canonical signaling (e.g., peroxisome proliferator-activated receptor, calcium, cyclic adenosine monophosphate). Enrichment analyses suggested that asthma pathogenesis may be related to Ca2 + binding and contraction of vascular smooth muscle. A validation experiment showed that YPD could reduce the Ca2 + concentration by inhibiting the Angiopoietin-II (Ang-II)/Phospholipase (PLA)/calmodulin (CaM0 signaling axis.ConclusionControl of asthma pathogenesis by YPD may be related to inhibition of the Ang-II/PLA/CaM signaling axis, reduction of the Ca2+ concentration, and relaxation of airway smooth muscle (ASM).

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer.

Treatment options for patients with gastric cancer (GC) continue to improve, but the overall prognosis is poor. The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present, and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups. To determined the effects of differentially expressed immune-related genes (DEIRGs) on the development, prognosis, tumor microenvironment (TME), and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations. Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were searched from ImmPort. DEIRGs were extracted from the intersection of the differentially-expressed genes (DEGs) and IRGs lists. The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGGs) and Gene Ontology (GO) databases. To identify genes associated with prognosis, a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression. The tumor risk score was divided into high- and low-risk groups. The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model. The infiltration of immune cells was obtained using 'CIBERSORT,' and the infiltration of immune subgroups in high- and low-risk groups was analyzed. The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed. We collected 412 GC and 36 adjacent tissue samples, and identified 3627 DEGs and 1311 IRGs. A total of 482 DEIRGs were obtained. GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes, receptor ligand activity, and signaling receptor activators. KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors, neuroactive ligand receptor interactions, and viral protein interactions. We ultimately obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, and risk curves confirmed that the risk model had good predictive ability. Multivariate COX analysis showed that age, stage, and risk score were independent prognostic factors for patients with GC. Meanwhile, patients in the low-risk group had higher tumor mutation burden and immunophenotype, which can be used to predict the immune checkpoint inhibitor response. Both cytotoxic T lymphocyte antigen4+ and programmed death 1+ patients with lower risk scores were more sensitive to immunotherapy. In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

Immune Gene Networks from Lung Cancer Patients Treated with Immune Checkpoint Inhibitors.

The association between immune checkpoint inhibitors (ICIs) and immune gene networks in squamous lung cancer (LUSC) and lung adenocarcinoma (LUAD) was studied. Immune gene networks were constructed using RNA-seq data from the gene expression omnibus (GEO) database. Datasets with more than 10 samples of normal control and tumor tissues were selected; of these, GSE87340, GSE120622, and GSE111907 were suitable for analysis. Gene set enrichment for pathway analysis was performed. For immune gene network construction, 998 unique immune genes were selected from 21 pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene function annotation was performed based on the KEGG, Gene Ontology, and Reactome databases. Tumor tissues showed decreased coagulation, hematopoiesis, and innate immune pathways, whereas complement- and coagulation-related genes were prominent in the tumor immune gene network. The average numbers of neighbors, clustering coefficients, network diameters, path lengths, densities, and heterogeneities were highest for normal tissue, followed by LUAD and LUSC. Decreased coagulation genes, which were prominent in tumor immune networks, imply functional attenuation. LUAD was deviated from normal tissue, based on network parameters. Tumor tissues showed decreased immune function, and the deviation of LUSC from normal tissue might explain LUSC's better therapeutic response to ICI treatment.

Anoikis-related lncRNA signature predicts prognosis and is associated with immune infiltration in hepatocellular carcinoma.

Anoikis is a programmed cell death process triggered when cells are dislodged from the extracellular matrix. Numerous long noncoding RNAs (lncRNAs) have been identified as significant factors associated with anoikis resistance in various tumor types, including glioma, breast cancer, and bladder cancer. However, the relationship between lncRNAs and the prognosis of hepatocellular carcinoma (HCC) has received limited research attention. Further research is needed to investigate this potential link and understand the role of lncRNAs in the progression of HCC. We developed a prognostic signature based on the differential expression of lncRNAs implicated in anoikis in HCC. A co-expression network of anoikis-related mRNAs and lncRNAs was established using data obtained from The Cancer Genome Atlas (TCGA) for HCC. Cox regression analyses were conducted to formulate an anoikis-related lncRNA signature (ARlncSig) in a training cohort, which was subsequently validated in both a testing cohort and a combined dataset comprising the two cohorts. Receiver operating characteristic curves, nomograms, and decision curve analyses based on the ARlncSig score and clinical characteristics demonstrated robust predictive ability. Moreover, gene set enrichment analysis revealed significant enrichment of several immune processes in the high-risk group compared to the low-risk group. Furthermore, significant differences were observed in immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy between the high- and low-risk groups. Lastly, we validated the expression levels of the five lncRNAs included in the signature using quantitative real-time PCR. In conclusion, our ARlncSig model holds substantial predictive value regarding the prognosis of HCC patients and has the potential to provide clinical guidance for individualized immunotherapy. In this study, we obtained 36 genes associated with anoikis from the Gene Ontology and Gene Set Enrichment Analysis databases. We also identified 22 differentially expressed lncRNAs that were correlated with these genes using data from TCGA. Using Cox regression analyses, we developed an ARlncSig in a training cohort, which was then validated in both a testing cohort and a combined cohort comprising data from both cohorts. Additionally, we collected eight pairs of liver cancer tissues and adjacent tissues from the Affiliated Tumor Hospital of Nantong University for further analysis. The aim of this study was to investigate the potential of ARlncSig as a biomarker for liver cancer prognosis. The study developed a risk stratification system called ARlncSig, which uses five lncRNAs to categorize liver cancer patients into low- and high-risk groups. Patients in the high-risk group exhibited significantly lower overall survival rates compared to those in the low-risk group. The model's predictive performance was supported by various analyses including the receiver operating characteristic curve, nomogram calibration, clinical correlation analysis, and clinical decision curve. Additionally, differential analysis of immune function, immune checkpoint, response to chemotherapy, and immune cell subpopulations revealed significant differences between the high- and low-risk groups. Finally, quantitative real-time PCR validated the expression levels of the five lncRNAs. In conclusion, the ARlncSig model demonstrates critical predictive value in the prognosis of HCC patients and may provide clinical guidance for personalized immunotherapy.