bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

CXCL10 and its receptor in patients with chronic hepatitis B and their ability to predict HBeAg seroconversion during antiviral treatment with TDF

The serum chemokine C-X-C motif ligand-10 (CXCL10) and its unique receptor (CXCR3) may predict the prognosis of patients with chronic hepatitis B (CHB) treated with tenofovir disoproxil fumarate (TDF). Nevertheless, there are few reports on the profile of CXCL10 and CXCR3 and their clinical application in HBeAg (+) CHB patients during TDF antiviral therapy. CXCL10 and CXCR3 were determined in 118 CHB patients naively treated with TDF for at least 96 weeks at baseline and at treatment weeks 12 and 24. In addition, gene set enrichment analysis was used to examine the associated dataset from Gene Expression Omnibus and explore the gene sets associated with HBeAg seroconversion (SC). The change of CXCL10 (ΔCXCL10, baseline to 48-week TDF treatment) and CXCR3 (ΔCXCR3) is closely related to the possibility of HBeAg SC of CHB patients under TDF treatment. Immunohistochemical analysis of CXCL10/CXCR3 protein in liver tissue shows that there is a significant difference between paired liver biopsy samples taken before and after 96 weeks of successful TDF treatment of CHB patients (11 pairs) but no significance for unsuccessful TDF treatment (14 pairs). Multivariate Cox analysis suggests that the ΔCXCL10 is an independent predictive indicator of HBeAg SC, and the area under the receiver operating characteristic curve of the ΔCXCL10 in CHB patients is 0.8867 (p < 0.0001). Our results suggest that a lower descending CXCL10 level is associated with an increased probability of HBeAg SC of CHB patients during TDF therapy. Moreover, liver tissue CXCL10 might be involved in the immunological process of HBeAg SC.

Platelet RNA sequencing reveals profile of caffeic acid affecting hemostasis in mice

Background

Caffeic acid (CA) is a naturally occurring phenolic compound with diverse pharmacologic properties. CA plays a crucial role in hemostasis by increasing platelet count. However, the mechanism by which CA regulates platelets to promote hemostasis remains unclear.

Objectives

We aim to identify the potential target pathways and genes by which CA regulates platelets to promote hemostasis.

Methods

We performed RNA sequencing (RNA-seq) analysis of mouse platelet pools in both the CA-gavaged group and phosphate-buffered saline-gavaged group.

Results

The 12,934 expressed transcripts had been annotated after platelet RNA-seq. Compared with the phosphate-buffered saline group, 987 differentially expressed genes (DEGs) were identified, of which 466 were downregulated and 521 were upregulated in CA group. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome gene set enrichment analysis demonstrated that upregulated DEGs were enriched in the pathways of hemostasis, platelet activation, signaling, aggregation, and degranulation. Moreover, Kyoto Encyclopedia of Genes and Genomes and Reactome gene set enrichment analysis revealed that 5 of the 25 cosignificantly upregulated DEGs were essential in CA-mediated platelet regulation to promote hemostasis.

Conclusion

Our findings of platelet RNA-seq analysis demonstrate that CA regulates the gene expression of hemostasis and platelet activation-related pathways to increase platelet count and promote hemostasis. It will also provide reference molecular resources for future research on the function and mechanism by which CA regulates platelets to promote hemostasis.

Benchmarking enrichment analysis methods with the disease pathway network

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.

Deciphering Immune-Related Gene Signatures in Diabetic Retinopathy: Insights from In Silico Analysis and In vitro Experiment

BACKGROUND

Diabetes retinopathy (DR) is one of the most common microvascular consequences of diabetes, and the economic burden is increasing. Our aim is to decipher the relevant mechanisms of immune-related gene features in DR and explore biomarkers targeting DR. Provide a basis for the treatment and prevention of DR.

METHOD

The immune infiltration enrichment score of DR patients was evaluated from the single- cell RNA sequencing dataset, and the samples were divided into low immune subgroups and high immune subgroups based on this result. Through weighted gene correlation network analysis, differentially expressed genes (DEGs) between two subgroups were identified and crossed with genes with the strongest immune association, resulting in significant key genes. Then divide the DR individuals into two immune related differentially expressed gene (IDEG) clusters, A and B. Submit cross DEGs between two clusters through Gene Set Enrichment Analysis (GSEA) to further explore their functions. A protein-protein interaction (PPI) network of IDEG was established to further identify central genes associated with DR. Use the discovered central genes to predict the regulatory network involved in the pathogenesis of DR. Then, the role of the identified hub gene in the pathogenesis of DR was further studied through in vitro experiments.

RESULT

We found that the immune scores of DR and control groups were different, and 27 IDEGs were found in the DR subgroup. Compared with cluster A, the proportion of cytotoxic lymphocytes, B lineage, monocyte lineage, and fibroblasts in DR patients in cluster B is significantly enriched. GSEA indicates that these genes are associated with T cell activation, regulation of immune response processes, lymphocyte-mediated immunity, TNF signaling pathway, and other signaling pathways. The PPI network subsequently identified 10 hub genes in DR, including SIGLEC10, RGS10, PENK, FGD2, LILRA6, CIITA, EGR2, SIGLEC7, LILRB1, and CD300LB. The upstream regulatory network and lncRNA miRNA mRNA ceRNA network of these hub genes were ultimately constructed. The discovery and identification of these genes will provide biomarkers for targeted prediction and treatment of DR.

CONCLUSION

By integrating bioinformatics analysis and in vitro experiments, we have identified a set of central genes, indicating that these genes can serve as potential biomarkers for DR, which may be promising targets for future DR immunotherapy interventions.

AMIGO2 is a pivotal therapeutic target related to M2 polarization of macrophages in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a common kind of lethal cancer, with low early diagnostic rate and poor prognosis. In this study, we identified and verified the AMIGO2 with significant diagnostic and prognostic value in PDAC through LASSO regression combined with multiple machines learning methods, including RVM-RFE and Random Forest in TCGA and GEO datasets. The relevance between the expression of AMIGO2 and M2 polarization of macrophages was identified through pancancer, normal tissue, and cell lines data in TCGA, GTEx and CCLE datasets. The relevance between AMIGO2 and M2 polarization was then further identified in our local PDAC cohort. Finally, the role of AMIGO2 as cancer promoter and pivotal factor enrolled in M2 polarization was verified through siRNA transfection and M2 macrophages induction. These findings could facilitate diagnosis and treatment of PDAC. In addition, further research was deemed necessary on the deep mechanism between AMIGO2 and M2 polarization of macrophages in PDAC.

Identification of reversible and druggable pathways to improve beta-cell function and survival in Type 2 diabetes

Targeting β-cell failure could prevent, delay or even partially reverse Type 2 diabetes. However, development of such drugs is limited as the molecular pathogenesis is complex and incompletely understood. Further, while β-cell failure can be modeled experimentally, only some of the molecular changes will be pathogenic. Therefore, we used a novel approach to identify molecular pathways that are not only changed in a diabetes-like state but also are reversible and can be targeted by drugs. INS1E cells were cultured in high glucose (HG, 20 mM) for 72 h or HG for an initial 24 h followed by drug addition (exendin-4, metformin and sodium salicylate) for the remaining 48 h. RNAseq (Illumina TruSeq), gene set enrichment analysis (GSEA) and pathway analysis (using Broad Institute, Reactome, KEGG and Biocarta platforms) were used to identify changes in molecular pathways. HG decreased function and increased apoptosis in INS1E cells with drugs partially reversing these effects. HG resulted in upregulation of 109 pathways while drug treatment downregulated 44 pathways with 21 pathways in common. Interestingly, while hyperglycemia extensively upregulated metabolic pathways, they were not altered with drug treatment, rather pathways involved in the cell cycle featured more heavily. GSEA for hyperglycemia identified many known pathways validating the applicability of our cell model to human disease. However, only a fraction of these pathways were downregulated with drug treatment, highlighting the importance of considering druggable pathways. Overall, this provides a powerful approach and resource for identifying appropriate targets for the development of β-cell drugs.

SPP2 plays a role in the tumorigenesis of hepatocellular carcinoma: A bioinformatic based analysis

OBJECTIVES

Hepatocellular carcinoma (HCC) patients at the same stage exhibit different prognosis, and the underlying molecular mechanism remains unclear. This study aims to identify the key genes impacting the prognosis of HCC patients.

METHODS

Differentially expressed gene analyses were performed between HCC samples and normal ones, and between patients with long overall survival (OS) and those with short OS, in TCGA-LIHC and GSE14520 datasets. The Kaplan-Meier method with log-rank test was used to evaluate the role of secreted phosphoprotein 2 (SPP2) in the prognosis of HCC patients. Gene set enrichment analysis (GSEA) was used to understand the difference of enriched signaling pathways between SPP2-stratified HCC subgroups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to predict the potential functional pathways in which SPP2 might participate.

RESULTS

SPP2 was significantly down-regulated in tumors when compared with normal tissues, or in tumor samples with short OS when compared with those with long OS [fold change (FC)>2 and false discovery rate (FDR)<0.05]. Low expression of SPP2 was associated with worse clinicopathological features like vascular invasion (P=1.6e-05), poor cancer status (with tumor, P=0.021), advanced T stage (T3 or T4, P=4.5e-04), advanced TNM stage (stage III or IV, P=3.1e-04), and with unfavorable prognosis (shorter OS, P=0.002). Gene enrichment analyses revealed that SPP2 might involve in the metabolic homeostasis of HCC and in the development of liver fibrosis and cirrhosis.

CONCLUSIONS

SPP2 might inhibit the development of liver fibrosis and cirrhosis and the tumorigenesis of HCC, and analogs of SPP2 might be potential drugs in the prevention of these diseases.

Longitudinal Transcription Profiling of Bladder Cancers Dictate the Response to BCG Treatment and Disease Progression

Although the intravesical instillation of Bacillus Calmette-Guerin (BCG) is widely used as adjuvant treatment for nonmuscle-invasive bladder cancers, the clinical benefit is variable across patients, and the molecular mechanisms underlying the sensitivity to BCG administration and disease progression are poorly understood. To establish the molecular signatures that predict the responsiveness and disease progression of bladder cancers treated with BCG, we performed transcriptome sequencing (RNA-seq) for 13 treatment-naïve and 22 post-treatment specimens obtained from 14 bladder cancer patients. To overcome disease heterogeneity, we used non-negative matrix factorization to identify the latent molecular features associated with drug responsiveness and disease progression. At least 12 molecular features were present, among which the immune-related feature was associated with drug responsiveness, indicating that pre-treatment anti-cancer immunity might dictate BCG responsiveness. We also identified disease progression-associated molecular features indicative of elevated cellular proliferation in post-treatment specimens. The progression-associated molecular features were validated in an extended cohort of BCG-treated bladder cancers. Our study advances understanding of the molecular mechanisms of BCG activity in bladder cancers and provides clinically relevant gene markers for evaluating and monitoring patients.

[Identification and validation of novel biomarkers for cold-dampness syndrome of rheumatoid arthritis based on integration of multiple bioinformatics methods]

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.

Identification of key genes and functional enrichment analysis of liver fibrosis in nonalcoholic fatty liver disease through weighted gene co-expression network analysis

Nonalcoholic fatty liver disease (NAFLD) is a common type of chronic liver disease, with severity levels ranging from nonalcoholic fatty liver to nonalcoholic steatohepatitis (NASH). The extent of liver fibrosis indicates the severity of NASH and the risk of liver cancer. However, the mechanism underlying NASH development, which is important for early screening and intervention, remains unclear. Weighted gene co-expression network analysis (WGCNA) is a useful method for identifying hub genes and screening specific targets for diseases. In this study, we utilized an mRNA dataset of the liver tissues of patients with NASH and conducted WGCNA for various stages of liver fibrosis. Subsequently, we employed two additional mRNA datasets for validation purposes. Gene set enrichment analysis (GSEA) was conducted to analyze gene function enrichment. Through WGCNA and subsequent analyses, complemented by validation using two additional datasets, we identified five genes (BICC1, C7, EFEMP1, LUM, and STMN2) as hub genes. GSEA analysis indicated that gene sets associated with liver metabolism and cholesterol homeostasis were uniformly downregulated. BICC1, C7, EFEMP1, LUM, and STMN2 were identified as hub genes of NASH, and were all related to liver metabolism, NAFLD, NASH, and related diseases. These hub genes might serve as potential targets for the early screening and treatment of NASH.

Identification and verification of pivotal genes promoting the progression of atherosclerosis based on WGCNA

As the main pathological basis for the development of cardiovascular and cerebrovascular diseases, atherosclerosis (AS) seriously affects human health. The key targets of biological information analysis of AS can help exploit therapeutic targets. The expression data of early and progressive atherosclerotic tissues were downloaded from the Gene Expression Omnibus (GEO) database. Based on GSE28829 and GSE120521, 74 key genes were obtained through differential expression analysis and weighted correlation network analysis (WGCNA) analysis, which were mainly enriched in the regulating of inflammatory response, chemokine signalling pathway, apoptosis, lipid and AS, Toll-like receptor signalling pathway and so on according to the results of the enrichment analysis. Cytoscape software was applied to screen four pivotal genes (TYROBP, ITGB2, ITGAM and TLR2) based on PPI. The results of the correlation analysis showed that the expression level of pivotal genes was positively related to macrophages M0, and was negatively related to T cells follicular helper. In addition, the expression of ITGB2 was positively related to Tregs. In this study, bioinformatics was applied to screen pivotal genes affecting the progress of AS, which were significantly related to immune-related biological functions and signal pathways of atherosclerotic tissues and the infiltration level of immune cells. Therefore, pivotal genes were expected to become therapeutic targets for AS.

Analysis of small EV proteomes reveals unique functional protein networks regulated by VAP-A

Extracellular vesicles (EVs) influence cell phenotypes and functions via protein, nucleic acid, and lipid cargoes. EVs are heterogeneous, due to diverse biogenesis mechanisms that remain poorly understood. Our previous study revealed that the endoplasmic reticulum (ER) membrane contact site (MCS) linker protein vesicle associated protein associated protein A (VAP-A) drives biogenesis of a subset of RNA-enriched EVs. Here, we examine the protein content of VAP-A-regulated EVs. Using label-free proteomics, we identified down- and upregulated proteins in small EVs (SEVs) purified from VAP-A knockdown (KD) colon cancer cells. Gene set enrichment analysis (GSEA) of the data revealed protein classes that are differentially sorted to SEVs dependent on VAP-A. Search Tool for the Retrieval of Reciprocity Genes (STRING) protein-protein interaction network analysis of the RNA-binding protein (RBP) gene set identified several RNA functional machineries that are downregulated in VAP-A KD SEVs, including ribosome, spliceosome, mRNA surveillance, and RNA transport proteins. We also observed downregulation of other functionally interacting protein networks, including cadherin-binding, unfolded protein binding, and ATP-dependent proteins.

Screening of Genes Co-Associated with Sudden Infant Death Syndrome and Infectious Sudden Death in Infancy and Bioinformatics Analysis of Their Regulatory Networks

OBJECTIVES

The common differentially expressed mRNAs in brain, heart and liver tissues of deceased sudden infant death syndrome (SIDS) and infectious sudden death in infancy (ISDI) confirmed by autopsy was screened by bioinformatics to explore the common molecular markers and pathogenesis of SIDS and ISDI.

METHODS

The datasets of GSE70422 and GSE136992 were downloaded, the limma of R software was used to screen differentially expressed mRNA in different tissue samples of SIDS and ISDI decedents for overlapping analysis. The clusterProfiler of R software was used to conduct gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The protein-protein interaction (PPI) network was constructed by STRING database, while the hub gene was screened by cytoHubba plug-in.

RESULTS

Compared with the control group, there were 19 significant differentially expressed genes in the tissue samples of SIDS and ISDI decedents, among which 16 in the heart tissue and 3 in the liver tissue, and the astrotactin 1 (ASTN1) gene expression difference in the heart tissue was most significant. The PPI network identified Ras homolog family member A (RHOA), integrin subunit alpha 1 (ITGA1), and H2B clustered histone 5 (H2BC5) were hub genes. The analysis of GO and KEGG showed that differentially expressed genes were enriched in the molecular pathways of actin cytoskeleton regulation, focal adhesion and response to mycophenolic acid.

CONCLUSIONS

ASTN1, RHOA and ITGA1 may participate in the development of SIDS and ISDI. The enrichment of differentially expressed genes in immune and inflammatory pathways suggests a common molecular regulatory mechanism between SIDS and ISDI. These findings are expected to provide new biomarkers for molecular anatomy and forensic identification of SIDS and ISDI.

Importance of metabolic and immune profile as a prognostic indicator in patients with diabetic clear cell renal cell carcinoma

Background

ccRCC, also known as clear cell renal cell carcinoma, is a cancer that is highly metabolically active and has a strong connection with the immune system. The objective of this research was to investigate the correlation between pathways associated with metabolism, diabetes, immune infiltration, and their impact on the prognosis of ccRCC.

Method

We conducted an extensive examination utilizing ssGSEA, ESTIMATE algorithm, WGCNA, and GSVA for gene set enrichment analysis, gene co-expression network analysis, and gene set variation analysis. An established prognostic model, utilizing immune-related WGCNA findings, was evaluated for its association with clinical characteristics and the tumor microenvironment (TME).

Result

The ssGSEA effectively categorized ccRCC into groups based on low and high metabolism. Strong associations were observed between scores related to metabolism and immune scores, ESTIMATE scores, stromal scores, and gene expression related to HLA. The analysis conducted by WGCNA revealed a module called the 'yellow module' that exhibited a significant correlation with the infiltration of immune cells and the survival rates of patients. A risk model was developed, demonstrating reliable predictive performance for patient survival outcomes. The risk model also correlated significantly with immune scores and HLA-related gene expressions, suggesting potential immune evasion mechanisms. The analysis of mutations in TCGA data revealed the mutational patterns of ccRCC, and there was a significant correlation between the risk score and clinical characteristics. The GSVA analysis revealed a notable enrichment of pathways associated with cancer in patients at high risk. Finally, in order to evaluate the role of CX3CL1 in renal cell carcinoma cells, we then performed the cell proliferation assays. The results demonstrated that the over expression of CXCL1 could promote the cell proliferation ability in renal cell carcinoma cells.

Conclusion

Our findings provide a novel perspective on the interactions between diabetes, metabolic pathways, and the immune landscape in ccRCC. The predictive value of the prognostic model established in this research has the potential to guide the development of new therapeutic and prognostic approaches for patients with ccRCC.

A Systematic Review of Gene Expression Studies in Critically Ill Patients with Sepsis and Community-Acquired Pneumonia

(1) Background: Sepsis is present in nearly 90% of critically ill patients with community-acquired pneumonia (CAP). This systematic review updates the information on studies that have assessed gene expression profiles in critically ill septic patients with CAP. (2) Methods: We searched for studies that satisfied the following criteria: (a) expression profile in critically ill patients with sepsis due to CAP, (b) presence of a control group, and (c) adult patients. Over-representation analysis was performed with clusterProfiler using the Hallmark and Reactome collections. (3) Results: A total of 4312 differentially expressed genes (DEGs) and sRNAs were included in the enrichment analysis. In the Hallmark collection, genes regulated by nuclear factor kappa B in response to tumor necrosis factor, genes upregulated by signal transducer and activator of transcription 5 in response to interleukin 2 stimulation, genes upregulated in response to interferon-gamma, genes defining the inflammatory response, a subgroup of genes regulated by MYC-version 1 (v1), and genes upregulated during transplant rejection were significantly enriched in critically ill septic patients with CAP. Moreover, 88 pathways were identified in the Reactome database. (4) Conclusions: This study summarizes the reported DEGs in critically ill septic patients with CAP and investigates their functional implications. The results highlight the complexity of immune responses during CAP.

Identification of two immune subtypes and four hub immune-related genes in ovarian cancer through multiple analysis

Immune classification of ovarian cancer (OV) becomes more and more influential for its immunotherapy. However, current studies had few immune subtypes of OV. It is urgent to explore the immune subtypes and deeper hub immune-related genes (IRGs) of OV for follow-up treatment. A total number of 379 OV samples were obtained from UCSC online website. Single sample gene set enrichment analysis of 29 immune gene sets was used for identifying immune subtypes of OV and gene set variation analysis were used for exploring the hallmarks and Kyoto Encyclopedia of Genes and Genomes pathways of immune types. Two immunity subtypes (Immunity_H and Immunity_L) were identified by single sample gene set enrichment analysis. The OV patients in Immunity_H group had longer overall survival compared with those in Immunity_L group. The Immunity_H had higher stromal score, immune score and estimate score and the tumor purity had the adverse tendency. Besides, the gene set variation analysis enrichment results showed positive relationship between improved immunoreaction and pathways correlated to classical signaling pathway (PI3K/AKT/MTOR, P53, TNFA/NFkB signaling pathways) and immune responses (T/B cell receptor signaling pathways and primary immunodeficiency). Furthermore, 4 hub IRGs (CCR5, IL10RA, ITGAL and PTPRC) were jointly dug by weighted gene co-expression network construction and Cytoscape. Our team also explored the mutations of 4 hub IRGs and PTPRC showed nearly 7% amplification. Besides, 8 immune-checkpoint genes had higher expression in Immuity_H group compared with Immuity_L group, except CD276. The correlation between PD-1/PD-L1 and 4 hub IRGs were explored and gene set enrichment analysis were conducted to explore the underlying mechanisms of PTPRC in OV. Finally, western-blotting showed PTPRC could regulate immune checkpoint PD-L1 expression via JAK-STAT signaling pathway. In a word, 2 immune subtypes and 4 hub IRGs of OV were identified by multiple analysis.

Nutrient Deficiencies Impact on the Cellular and Metabolic Responses of Saxitoxin Producing Alexandrium minutum: A Transcriptomic Perspective

Dinoflagellate Alexandrium minutum Halim is commonly associated with harmful algal blooms (HABs) in tropical marine waters due to its saxitoxin production. However, limited information is available regarding the cellular and metabolic changes of A. minutum in nutrient-deficient environments. To fill this gap, our study aimed to investigate the transcriptomic responses of A. minutum under nitrogen and phosphorus deficiency. The induction of nitrogen and phosphorus deficiency resulted in the identification of 1049 and 763 differently expressed genes (DEGs), respectively. Further analysis using gene set enrichment analysis (GSEA) revealed 702 and 1251 enriched gene ontology (GO) terms associated with nitrogen and phosphorus deficiency, respectively. Our results indicate that in laboratory cultures, nitrogen deficiency primarily affects meiosis, carbohydrate catabolism, ammonium assimilation, ion homeostasis, and protein kinase activity. On the other hand, phosphorus deficiency primarily affects the carbon metabolic response, cellular ion transfer, actin-dependent cell movement, signalling pathways, and protein recycling. Our study provides valuable insights into biological processes and genes regulating A. minutum's response to nutrient deficiencies, furthering our understanding of the ecophysiological response of HABs to environmental change.

Mesenchymal Stromal Cell-Derived Small Extracellular Vesicles Modulate Apoptosis, TNF Alpha and Interferon Gamma Response Gene mRNA Expression in T Lymphocytes

Recent studies have highlighted the therapeutic potential of small extracellular bodies derived from mesenchymal stem cells (MSC-sEVs) for various diseases, notably through their ability to alter T-cell differentiation and function. The current study aimed to explore immunomodulatory pathway alterations within T cells through mRNA sequencing of activated T cells cocultured with bone marrow-derived MSC-sEVs. mRNA profiling of activated human T cells cocultured with MSC-sEVs or vehicle control was performed using the QIAGEN Illumina sequencing platform. Pathway networks and biological functions of the differentially expressed genes were analyzed using Ingenuity pathway analysis (IPA)® software, KEGG pathway, GSEA and STRING database. A total of 364 differentially expressed genes were identified in sEV-treated T cells. Canonical pathway analysis highlighted the RhoA signaling pathway. Cellular development, movement, growth and proliferation, cell-to-cell interaction and inflammatory response-related gene expression were altered. KEGG enrichment pathway analysis underscored the apoptosis pathway. GSEA identified enrichment in downregulated genes associated with TNF alpha and interferon gamma response, and upregulated genes related to apoptosis and migration of lymphocytes and T-cell differentiation gene sets. Our findings provide valuable insights into the mechanisms by which MSC-sEVs implement immunomodulatory effects on activated T cells. These findings may contribute to the development of MSC-sEV-based therapies.

Comprehensive analysis of ferroptosis-related genes in immune infiltration and prognosis in multiple myeloma

Background: One particular type of cellular death that is known as ferroptosis is caused by the excessive lipid peroxidation. It is a regulated form of cell death that can affect the response of the tumor cells. Currently, it is not known if the presence of this condition can affect the prognosis of patients with multiple myeloma (MM). Methods: In this study, we studied the expression differences and prognostic value of ferroptosis-related genes (FRGs) in MM, and established a ferroptosis risk scoring model. In order to improve the prediction accuracy and clinical applicability, a nomogram was also established. Through gene enrichment analysis, pathways closely related to high-risk groups were identified. We then explored the differences in risk stratification in drug sensitivity and immune patterns, and evaluated their value in prognostic prediction and treatment response. Lastly, we gathered MM cell lines and samples from patients to confirm the expression of marker FRGs using quantitative real-time PCR (qRT-PCR). Results: The ability to predict the survival of MM patients is a challenging issue. Through the use of a risk model derived from ferroptosis, we were able to develop a more accurate prediction of the disease's prognosis. They were then validated by a statistical analysis, which showed that the model is an independent factor in the prognosis of MM. Patients of high ferroptosis risk scores had a much worse chance of survival than those in the low-risk groups. The calibration and power of the nomogram were also strong. We noted that the link between the ferroptosis risk score and the clinical treatment was suggested by the FRG's significant correlation with the immune checkpoint genes and the medication sensitivity. We validated the predictive model using qRT-PCR. Conclusion: We demonstrated the association between FRGs and MM, and developed a new risk model for prognosis in MM patients. Our study sheds light on the potential clinical relevance of ferroptosis in MM and highlights its potential as a therapeutic target for patients with this disease.

Diagnostic yield and clinical impact of chromosomal microarray analysis in autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is characterized by high heritability estimates and recurrence rates; its genetic underpinnings are very heterogeneous and include variable combinations of common and rare variants. Array-comparative genomic hybridization (aCGH) offers significant sensitivity for the identification of copy number variants (CNVs), which can act as susceptibility or causal factors for ASD.

METHODS

The aim of this study was to evaluate both diagnostic yield and clinical impact of aCGH in 329 ASD patients of Italian descent.

RESULTS

Pathogenic/likely pathogenic CNVs were identified in 50/329 (15.2%) patients, whereas 89/329 (27.1%) carry variants of uncertain significance. The 10 most enriched gene sets identified by Gene Ontology Enrichment Analysis are primarily involved in neuronal function and synaptic connectivity. In 13/50 (26.0%) patients with pathogenic/likely pathogenic CNVs, the outcome of array-CGH led to the request of 25 additional medical exams which would not have otherwise been prescribed, mainly including brain MRI, EEG, EKG, and/or cardiac ultrasound. A positive outcome was obtained in 12/25 (48.0%) of these additional tests.

CONCLUSIONS

This study confirms the satisfactory diagnostic yield of aCGH, underscoring its potential for better, more in-depth care of children with autism when genetic results are analyzed also with a focus on patient management.

Investigating possible dilated cardiomyopathy targets via bioinformatic analysis

Dilated cardiomyopathy (DCM) is the most common cardiomyopathy associated with heart failure; however, the underlying mechanism remains unclear. Initially, gene expression data of patients with DCM from the GSE4172 and GSE21610 datasets were obtained from the Gene Expression Omnibus website. Differentially expressed genes (DEGs) were analyzed with a false discovery rate < 0.05 and log2 fold change > 1.2. Furthermore, both the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA) were used to investigate the functional annotations. STRING and Cytoscape tools were used to form the protein-protein interaction (PPI) network and authenticate hub genes. Thereafter, the signature of immune-related genes (IRGs) was selected from the DEGs and data via the IMMPORT website. Hub genes were selected from the differentially expressed IRGs that formed the PPI network. Finally, the receiver-operating characteristic curves of the key genes were measured as biomarkers of DCM. A total of 173 independent DEGs (103 upregulated and 70 downregulated genes) were found in the microarray datasets GSE4172 and GSE21610. KEGG analysis and GSEA indicated that the BMP signaling pathway and apoptosis-related signals have a key effect on DCM development. The 10 hub genes also indicated the key effect of the BMP signaling pathway on DCM. A total of 224 differentially expressed IRGs and 20 featured IRGs were identified. Finally, BMP6, CD69, RUNX2, and SPP1 were identified as possible targets for DCM. Our data suggest a possible molecular regulatory mechanism for DCM therapy. Moreover, BMP6, CD69, RUNX2, and SPP1 may have key effects on the development of DCM.

Ichthyosis linked to sphingosine 1-phosphate lyase insufficiency is due to aberrant sphingolipid and calcium regulation

Sphingosine 1-phosphate lyase (SGPL1) insufficiency (SPLIS) is a syndrome which presents with adrenal insufficiency, steroid-resistant nephrotic syndrome, hypothyroidism, neurological disease, and ichthyosis. Where a skin phenotype is reported, 94% had abnormalities such as ichthyosis, acanthosis, and hyperpigmentation. To elucidate the disease mechanism and the role SGPL1 plays in the skin barrier we established clustered regularly interspaced short palindromic repeats-Cas9 SGPL1 KO and a lentiviral-induced SGPL1 overexpression (OE) in telomerase reverse-transcriptase immortalised human keratinocytes (N/TERT-1) and thereafter organotypic skin equivalents. Loss of SGPL1 caused an accumulation of S1P, sphingosine, and ceramides, while its overexpression caused a reduction of these species. RNAseq analysis showed perturbations in sphingolipid pathway genes, particularly in SGPL1_KO, and our gene set enrichment analysis revealed polar opposite differential gene expression between SGPL1_KO and _OE in keratinocyte differentiation and Ca2+ signaling genesets. SGPL1_KO upregulated differentiation markers, while SGPL1_OE upregulated basal and proliferative markers. The advanced differentiation of SGPL1_KO was confirmed by 3D organotypic models that also presented with a thickened and retained stratum corneum and a breakdown of E-cadherin junctions. We conclude that SPLIS associated ichthyosis is a multifaceted disease caused possibly by sphingolipid imbalance and excessive S1P signaling, leading to increased differentiation and an imbalance of the lipid lamellae throughout the epidermis.

Effect of feeding regimen on circadian activity rhythms of food anticipatory by ghrelin hormone in a pig model

Increasingly diverse meal patterns affect the internal body clock. Ghrelin secretion is closely associated with the anticipation of a regularly scheduled mealtime, leading ghrelin to be a putative candidate for food-related entraining signals that drive activity rhythms. Here, growing pigs with different meal frequencies were used to construct an irregular eating pattern model. We found that irregular eating patterns changed central ghrelin levels of pigs, affected the circadian entrainment and circadian rhythm pathways in hypothalamus tissue, and altered the daily behavior and food anticipatory activity (FAA). To determine whether ghrelin exerts an effect, growing pigs were intravenously injected with ghrelin antagonist [D-Lys3]-GHRP-6 for 7 days. We showed here that [D-Lys3]-GHRP-6 administration decreased locomotor activity of growing pigs in the 4-h window preceding onset of food availability. In addition, we also confirmed that the direct role of ghrelin in molecular mechanism of regulating clock genes expression via calcium mobilization through intracellular PKC/PLC and AC/PKA pathways in vitro. Collectively, irregular eating patterns affect the central circadian system by ghrelin, supporting ghrelin as a temporal messenger of food-entrainment in hypothalamic circadian functions.

Differences in Whole-Blood Transcriptional Profiles in Inflammatory Bowel Disease Patients Responding to Vedolizumab Compared with Non-Responders

Vedolizumab is efficacious in the treatment of Crohn's disease (CD) and ulcerative colitis (UC). However, a significant proportion of patients present with a non-response. To investigate whether differences in the clinical response to vedolizumab is reflected in changes in gene expression levels in whole blood, samples were collected at baseline before treatment, and at follow-up after 10-12 weeks. Whole genome transcriptional profiles were established by RNA sequencing. Before treatment, no differentially expressed genes were noted between responders (n = 9, UC 4, CD 5) and non-responders (n = 11, UC 3, CD 8). At follow-up, compared with baseline, responders displayed 201 differentially expressed genes, and 51 upregulated (e.g., translation initiation, mitochondrial translation, and peroxisomal membrane protein import) and 221 downregulated (e.g., Toll-like receptor activating cascades, and phagocytosis related) pathways. Twenty-two of the upregulated pathways in responders were instead downregulated in non-responders. The results correspond with a dampening of inflammatory activity in responders. Although considered a gut-specific drug, our study shows a considerable gene regulation in the blood of patients responding to vedolizumab. It also suggests that whole blood is not optimal for identifying predictive pre-treatment biomarkers based on individual genes. However, treatment outcomes may depend on several interacting genes, and our results indicate a possible potential of pathway analysis in predicting response to treatment, which merits further investigation.

EVI2B Is a Prognostic Biomarker and Is Correlated with Monocyte and Macrophage Infiltration in Osteosarcoma Based on an Integrative Analysis

Osteosarcoma (OS) is the most common malignant bone tumor. However, treatment strategies have not changed over the past 30 years. The relationship between OS and the immune microenvironment may provide a basis for the establishment of novel therapeutic targets. In this study, a large-scale gene expression dataset (GSE42352) was used to identify key genes in OS. A Target-OS dataset from the Cancer Genome Atlas was used as a validation set. Ecotropic viral integration site 2B (EVI2B) was significantly upregulated in OS tumor samples. Differentially expressed genes (DEGs) were identified between samples with high and low EVI2B expression in both the test and validation cohorts. The top three functions of DEGs determined by a gene set enrichment analysis (GSEA) were chemokine signaling, cytokine-cytokine receptor interaction, and Human T-cell leukemia virus 1 infection. A prognostic prediction model including EVI2B, DOCK2, and CD33 was constructed by a Cox regression analysis. This model indicated that EVI2B is an independent protective prognostic marker in OS. An analysis of immune infiltration further showed that high EVI2B expression levels were correlated with high levels of macrophage infiltration. Protein expression data derived from the Human Protein Atlas suggested EVI2B to be highly expressed in monocytes. Finally, we validated the elevated expression of EVI2B in OS cell lines and OS tissue samples; these results were consistent with those of the analyses of the GSE42352 and Target-OS datasets. Our integrative bioinformatics analysis and experimental results provide clear evidence for the prognostic value of EVI2B in OS and its close relationship with monocyte and macrophage infiltration.

Comprehensive analysis reveals signal and molecular mechanism of mitochondrial energy metabolism pathway in pancreatic cancer

Pancreatic cancer (PAAD) is one of the most malignant tumors with the worst prognosis. The abnormalities in the mitochondrial energy metabolism pathway are intimately correlated with the occurrence and progression of cancer. For the diagnosis and treatment of pancreatic cancer, abnormal genes in the mitochondrial energy metabolism system may offer new targets and biomarkers. In this study, we compared the dysregulated mitochondrial energy metabolism-associated pathways in PAAD based on pancreatic cancer samples in the Cancer Genome Atlas (TCGA) database and normal pancreas samples from the Genotype Tissue Expression project (GTEx) database. Then identified 32 core genes of mitochondrial energy metabolism pathway-related genes (MMRG) were based on the gene set enrichment analysis (GSEA). We found most of these genes were altered among different clinical characteristic groups, and showed significant prognostic value and association with immune infiltration, suggesting critical roles of MMRG involve tumor genesis of PAAD. Therefore, we constructed a four-gene (LDHA, ALDH3B1, ALDH3A1, and ADH6) prognostic biomarker after eliminating redundant factors, and confirming its efficiency and independence. Further analysis indicated the potential therapeutic compounds based on the mitochondrial energy metabolism-associated prognostic biomarker. All of the above analyses dissected the critical role of mitochondrial energy metabolism signaling in pancreatic cancer and gave a better understanding of the clinical intervention of PAAD.

Whole-Blood Gene Expression Profiles Associated with Mortality in Community-Acquired Pneumonia

(1) Background: Information regarding gene expression profiles and the prognosis of community-acquired pneumonia (CAP) is scarce. We aimed to examine the differences in the gene expression profiles in peripheral blood at hospital admission between patients with CAP who died during hospitalization and those who survived. (2) Methods: This is a multicenter study of nonimmunosuppressed adult patients who required hospitalization for CAP. Whole blood samples were obtained within 24 h of admission for genome-expression-profile analysis. Gene expression profiling identified both differentially expressed genes and enriched gene sets. (3) Results: A total of 198 samples from adult patients who required hospitalization for CAP were processed, of which 13 were from patients who died. Comparison of gene expression between patients who died and those who survived yielded 49 differentially expressed genes, 36 of which were upregulated and 13 downregulated. Gene set enrichment analysis (GSEA) identified four positively enriched gene sets in survivors, mainly associated with the interferon-alpha response, apoptosis, and sex hormone pathways. Similarly, GSEA identified seven positively enriched gene sets, associated with the oxidative stress, endoplasmic reticulum stress, oxidative phosphorylation, and angiogenesis pathways, in the patients who died. Protein-protein-interaction-network analysis identified FOS, CDC42, SLC26A10, EIF4G2, CCND3, ASXL1, UBE2S, and AURKA as the main gene hubs. (4) Conclusions: We found differences in gene expression profiles at hospital admission between CAP patients who died and those who survived. Our findings may help to identify novel candidate pathways and targets for potential intervention and biomarkers for risk stratification.

Ultra-Rare Variants Identify Biological Pathways and Candidate Genes in the Pathobiology of Non-Syndromic Cleft Palate Only

In recent decades, many efforts have been made to elucidate the genetic causes of non-syndromic cleft palate (nsCPO), a complex congenital disease caused by the interaction of several genetic and environmental factors. Since genome-wide association studies have evidenced a minor contribution of common polymorphisms in nsCPO inheritance, we used whole exome sequencing data to explore the role of ultra-rare variants in this study. In a cohort of 35 nsCPO cases and 38 controls, we performed a gene set enrichment analysis (GSEA) and a hypergeometric test for assessing significant overlap between genes implicated in nsCPO pathobiology and genes enriched in ultra-rare variants in our cohort. GSEA highlighted an enrichment of ultra-rare variants in genes principally belonging to cytoskeletal protein binding pathway (Probability Density Function corrected p-value = 1.57 × 10^-4); protein-containing complex binding pathway (p-value = 1.06 × 10^-2); cell adhesion molecule binding pathway (p-value = 1.24 × 10^-2); ECM-receptor interaction pathway (p-value = 1.69 × 10^-2); and in the Integrin signaling pathway (p-value = 1.28 × 10^-2). Two genes implicated in nsCPO pathobiology, namely COL2A1 and GLI3, ranked among the genes (n = 34) with nominal enrichment in the ultra-rare variant collapsing analysis (Fisher's exact test p-value < 0.05). These genes were also part of an independent list of genes highly relevant to nsCPO biology (n = 25). Significant overlap between the two sets of genes (hypergeometric test p-value = 5.86 × 10^-3) indicated that enriched genes are likely to be implicated in physiological palate development and/or the pathological processes of oral clefting. In conclusion, ultra-rare variants collectively impinge on biological pathways crucial to nsCPO pathobiology and point to candidate genes that may contribute to the individual risk of disease. Sequencing can be an effective approach to identify candidate genes and pathways for nsCPO.

Novel diagnostic biomarkers related to immune infiltration in Parkinson's disease by bioinformatics analysis

Background

Parkinson's disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers.

Materials and methods

Three substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the "limma" package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein-protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers.

Results

A total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package "UpSet." We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905).

Conclusion

By affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.

Gene Set Enrichment Analysis and Genetic Experiment Reveal Changes in Cell Signaling Pathways Induced by α-Synuclein Overexpression

Abnormal accumulation of alpha synuclein (α-Syn) in sporadic and familial Parkinson's disease (PD) may be a key step in its pathogenesis. In this study, the expression matrix of the GSE95427 dataset after α-Syn overexpression in human glioma cell line H4 was obtained from the GEO database. We used the Gene Set Enrichment Analysis (GSEA) method to reanalyze this dataset to evaluate the possible functions of α-Syn. The results showed that the tumor necrosis factor alpha (TNF-α) signal was significantly activated in α-Syn-overexpressing cells, and oxidative phosphorylation signal, extracellular matrix signal, cell cycle related signal and fatty acid metabolism signal were significantly inhibited. Moreover, we employed the α-Syn-expressing transgenic Drosophila model of Parkinson's disease and knocked-down eiger, a TNF superfamily ligand homologue, indicating that the TNF-α pathway plays a role in the common pathogenesis of synucleinopathies. Our analysis based on GSEA data provides more clues for a better understanding of α-Syn function.

Gene set enrichment analysis and ingenuity pathway analysis to verify the impact of Wnt signaling in psoriasis treated with Taodan granules

BACKGROUND

Taodan granules (TDGs), traditional Chinese herbals, have effectiveness in relieving skin erythema, scales, and other symptoms of psoriasis. Yet mechanisms of TDGs remain indistinct.

OBJECTIVE

To indicate the molecular mechanisms of TDGs in treating psoriasis.

MATERIALS AND METHODS

Primarily, transcriptional profiling was applied to identify differentially expressed genes (DEGs), proceeding with Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) analysis were used for functional enrichment analysis. Subsequently, levels of selected genes were validated by RT-PCR and western blotting.

RESULTS

The GSEA results revealed TDGs could down-regulate the Wnt signaling pathway to ameliorate skin lesions of imiquimod (IMQ)-induced psoriatic models mice. IPA core network associated with Wnt signaling pathways in TDGs for psoriasis was established. Thereinto zeste homolog 2 (EZH2), CTNNB1, tumor protein p63 (TP63), and WD repeat domain 5 (WDR5) were considered as upstream genes in the Wnt signaling pathway. Experimental verification indicated TDGs could down-regulate EZH2, CTNNB1, and WDR5 at the mRNA and protein levels, along with up-regulate TP63 levels. Moreover, TDGs were confirmed to reduce RAC2 and WNT5A at mRNA and protein levels of the Wnt signaling pathway.

CONCLUSIONS

TDGs may improve psoriasis through the regulation for upstream genes (down-regulating levels of EZH2, CTNNB1, and WDR5; up-regulating TP63 levels) of Wnt signaling pathway, thus reducing levels of RAC2 and WNT5A in the Wnt signaling pathway.

Candidate Marker Genes for Diagnosis of Osteoarthritis and Prediction of Their Regulatory Mechanisms

We have screened candidate marker genes for the diagnosis of osteoarthritis and predicted their regulatory mechanisms. Six expression chips of tissue samples and one expression chip of peripheral blood mononuclear cell (PMBC) samples were obtained from the GEO database. Differential analysis, GSEA, and WGCNA were performed on the integra-ted tissue sample data with batch correction. Can-didate genes were obtained from the intersection of the genes significantly related to osteoarthritis in the WGCNA and the differentially expressed genes. ROC analysis was performed on the candidate genes in the tissue and PMBC samples. Genes with AUC values greater than 0.6 were retained as final candidates, and their upstream regulatory miRNAs were predicted. A total of 106 genes with differential expression were found in osteoarthritis tissue samples, which were mainly enriched in cell cycle and p53 signalling pathways. WGCNA selected a gene module significantly correlated with the occurrence of osteoarthritis. Fourteen candidate genes were obtained from the intersection of the genes in the module and the differentially expressed genes. ROC analysis showed that among these 14 candidate genes, only ADM, CX3CR1 and GADD45A had AUC values greater than 0.6 in both tissue and PMBC samples. The AUC values of the gene set of these three genes were greater than 0.7. Multiple miRNAs were predicted to be regulators of these three genes. ADM, CX3CR1 and GADD45A have potential as diagnostic marker genes for osteoarthritis and may be regulated by multiple miRNAs.

Screening of Key Part in IFN Pathway for Herpes Zoster: Evidence from Bioinformatics Analysis

BACKGROUND

Herpes zoster is one of the most common diseases in middle and old ages, and the incidence rate is constantly increasing. Long-term, severe neuropathological pain continues to afflict the patients, causing trouble and even the inability to live a normal life. Since the occurrence and development of herpes zoster are related to many mechanisms, there is no uniform conclusion and specific treatment method, and only a limited number of people are currently vaccinated against HZ.

OBJECTIVE

This study aimed at exploring the potential mechanism or biomarkers for Herpes zoster.

METHODS

In this study, a data set GSE165112 containing 12 samples was downloaded, out of which, 6 samples were treated with interferon, and 6 samples were not treated. Differentially expressed genes (DEG) analysis, KEGG, GO enrichment analysis, and GSEA were carried out.

RESULTS

A total of 264 DEGs were identified, including 32 uP-regulated DEGs and 232 downregulated DEGs. DEGs are mainly enriched in immune response, inflammatory response, chemotaxis, etc. Four key pathways were found to be related to HZ, including IL2-STAT5 signaling, inflammatory response, TNF-a signaling via NF-κB, and IFN-α. Moreover, ten hub genes were also identified.

CONCLUSION

This study shows that exploring DEGs and pathways through bioinformatics analysis is of great significance for understanding the molecular mechanism of HZ, especially the defect of the IFN pathway. It may be helpful in improving the treatment for HZ.

Antagonistic Functions of Androgen Receptor and NF-κB in Prostate Cancer-Experimental and Computational Analyses

Prostate cancer is very frequent and is, in many countries, the third-leading cause of cancer related death in men. While early diagnosis and treatment by surgical removal is often curative, metastasizing prostate cancer has a very bad prognosis. Based on the androgen-dependence of prostate epithelial cells, the standard treatment is blockade of the androgen receptor (AR). However, nearly all patients suffer from a tumor relapse as the metastasizing cells become AR-independent. In our study we show a counter-regulatory link between AR and NF-κB both in human cells and in mouse models of prostate cancer, implying that inhibition of AR signaling results in induction of NF-κB-dependent inflammatory pathways, which may even foster the survival of metastasizing cells. This could be shown by reporter gene assays, DNA-binding measurements, and immune-fluorescence microscopy, and furthermore by a whole set of computational methods using a variety of datasets. Interestingly, loss of PTEN, a frequent genetic alteration in prostate cancer, also causes an upregulation of NF-κB and inflammatory activity. Finally, we present a mathematical model of a dynamic network between AR, NF-κB/IκB, PI3K/PTEN, and the oncogene c-Myc, which indicates that AR blockade may upregulate c-Myc together with NF-κB, and that combined anti-AR/anti-NF-κB and anti-PI3K treatment might be beneficial.

Phosphoglycerate mutase 1 that is essential for glycolysis may act as a novel metabolic target for predicating poor prognosis for patients with gastric cancer

BACKGROUND

To identify a novel marker for gastric cancer, we examined the usefulness of phosphoglycerate mutase 1 (PGAM1) as a potential diagnostic marker using isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomics and evaluated its clinical significance.

METHODS

Proteins from a discovery group of four paired gastric cancer tissues and adjacent gastric tissues were labeled with iTRAQ reagents and then identified and quantified using LC-MS/MS. The expression of PGAM1 was further validated in 139 gastric cancer patients using immunohistochemistry. Furthermore, the correlation of PGAM1 expression with clinical parameters was analyzed. Gene set enrichment analysis (GSEA) was performed to identify gene sets that were activated in PGAM1-overexpressing patients with gastric cancer.

RESULTS

PGAM1 was significantly overexpressed in most cancers but particularly so in gastric cancer, with a sensitivity of 82.01% (95% confidence interval [CI]: 75.5%-88.5%) and specificity of 79.13% (95% CI: 72.3%-86%). Its expression was significantly associated with histological grade II and III tumors (p = 0.033), lymph node metastasis (p = 0.031), and TNM III-IV staging (p = 0.025). The area under the receiver operating characteristic (ROC) curve for the detection of PGAM1 overexpression in gastric cancer was 0.718 (p < 0.01). Furthermore, GSEA revealed that several important pathways such as glycolysis pathway and immune pathways were significantly enriched in patients with gastric cancer with PGAM1 overexpression.

CONCLUSIONS

This study provided a sensitive method for detecting PGAM1, which may serve as a novel indicator for poor prognosis of gastric cancer, as well as a potent drug target for gastric cancer.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

Meta-Analysis of Transcriptomes in Insects Showing Density-Dependent Polyphenism

With increasing public data, a statistical analysis approach called meta-analysis, which combines transcriptome results obtained from multiple studies, has succeeded in providing novel insights into targeted biological processes. Locusts and aphids are representative of insect groups that exhibit density-dependent plasticity. Although the physiological mechanisms underlying density-dependent polyphenism have been identified in aphids and locusts, the underlying molecular mechanisms remain largely unknown. In this study, we performed a meta-analysis of public transcriptomes to gain additional insights into the molecular underpinning of density-dependent plasticity. We collected RNA sequencing data of aphids and locusts from public databases and detected differentially expressed genes (DEGs) between crowded and isolated conditions. Gene set enrichment analysis was performed to reveal the characteristics of the DEGs. DNA replication (GO:0006260), DNA metabolic processes (GO:0006259), and mitotic cell cycle (GO:0000278) were enriched in response to crowded conditions. To date, these processes have scarcely been the focus of research. The importance of the oxidative stress response and neurological system modifications under isolated conditions has been highlighted. These biological processes, clarified by meta-analysis, are thought to play key roles in the regulation of density-dependent plasticity.

DYSF promotes monocyte activation in atherosclerotic cardiovascular disease as a DNA methylation-driven gene

Dysferlin (DYSF) has drawn much attention due to its involvement in dysferlinopathy and was reported to affect monocyte functions in recent studies. However, the role of DYSF in the pathogenesis of atherosclerotic cardiovascular diseases (ASCVD) and the regulation mechanism of DYSF expression have not been fully studied. In this study, Gene Expression Omnibus (GEO) database and epigenome-wide association study (EWAS) literatures were searched to find the DNA methylation-driven genes (including DYSF) of ASCVD. The hub genes related to DYSF were also identified through weighted correlation network analysis (WGCNA). Regulation of DYSF expression through its promoter methylation status was verified using peripheral blood leucocytes (PBLs) from ASCVD patients and normal controls, and experiments on THP1 cells and Apoe^-/- mice. Similarly, the expressions of DYSF related hub genes, mainly contained SELL, STAT3 and TMX1, were also validated. DYSF functions were then evaluated by phagocytosis, transwell and adhesion assays in DYSF knock-down and overexpressed THP1 cells. The results showed that DYSF promoter hypermethylation up-regulated its expression in clinical samples, THP1 cells and Apoe^-/- mice, confirming DYSF as a DNA methylation-driven gene. The combination of DYSF expression and methylation status in PBLs had a considerable prediction value for ASCVD. Besides, DYSF could enhance the phagocytosis, migration and adhesion ability of THP1 cells. Among DYSF related hub genes, SELL was proven to be the downstream target of DYSF by wet experiments. In conclusion, DYSF promoter hypermethylation upregulated its expression and promoted monocytes activation, which further participated in the pathogenesis of ASCVD.

The glioblastoma multiforme tumor site promotes the commitment of tumor-infiltrating lymphocytes to the TH17 lineage in humans

Glioblastoma multiforme (GBM) has been impervious to immune interventional therapies. Here, we analyzed the transcriptome of highly pure CD4⁺ and CD8⁺ T cells from the tumor bed, normal-appearing brain tissue, and peripheral blood of treatment-naive GBM patients. While the transcriptome of tumor-infiltrating CD8⁺ T cells was consistent with a potentially robust antitumor response, tumor-infiltrating CD4⁺ T cells showed a strong commitment to the T_H17 lineage. Since intratumoral T_H17 cells might exert a dominant-negative function as to a productive antitumor response, our data suggest that a site-directed anti-T_H17 intervention may be a prerequisite for efficient antitumor immunity in GBM.

Although glioblastoma multiforme (GBM) is not an invariably cold tumor, checkpoint inhibition has largely failed in GBM. In order to investigate T cell–intrinsic properties that contribute to the resistance of GBM to endogenous or therapeutically enhanced adaptive immune responses, we sorted CD4⁺ and CD8⁺ T cells from the peripheral blood, normal-appearing brain tissue, and tumor bed of nine treatment-naive patients with GBM. Bulk RNA sequencing of highly pure T cell populations from these different compartments was used to obtain deep transcriptomes of tumor-infiltrating T cells (TILs). While the transcriptome of CD8⁺ TILs suggested that they were partly locked in a dysfunctional state, CD4⁺ TILs showed a robust commitment to the type 17 T helper cell (T_H17) lineage, which was corroborated by flow cytometry in four additional GBM cases. Therefore, our study illustrates that the brain tumor environment in GBM might instruct T_H17 commitment of infiltrating T helper cells. Whether these properties of CD4⁺ TILs facilitate a tumor-promoting milieu and thus could be a target for adjuvant anti-T_H17 cell interventions needs to be further investigated.

Identification of MEDAG and SERPINE1 Related to Hypoxia in Abdominal Aortic Aneurysm Based on Weighted Gene Coexpression Network Analysis

Purpose: Abdominal aortic aneurysm (AAA) is a severe cardiovascular disease that often results in high mortality due to sudden rupture. This paper aims to explore potential molecular mechanisms and effective targeted therapies to prevent and delay AAA rupture. Methods: We downloaded two microarray datasets (GSE98278 and GSE17901) from the Gene Expression Omnibus (GEO) database. Differential analysis and single-sample gene set enrichment analysis (ssGSEA) of hypoxia scores were performed on 48 AAA patients in GSE98278. We identified hypoxia- and ruptured AAA-related gene modules using weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the R package clusterProfiler. For candidate genes, validation was conducted on the mouse dataset GSE17901. Finally, we predicted drug candidates associated with the hub genes using the HERB Chinese medicine database. Results: Eighty-two differentially expressed genes were screened in the ruptured and stable groups; 103 differentially expressed genes were identified between the high- and low-hypoxia groups; and WGCNA identified 58 differentially expressed genes. Finally, nine candidate genes were screened, including two hub genes (MEDAG and SERPINE1). We identified pathways such as cytokine-cytokine receptor interaction and T-helper 1-type immune response involved in AAA hypoxia and rupture. We predicted 93 traditional Chinese medicines (TCMs) associated with MEDAG and SERPINE1. Conclusion: We identified the hypoxic molecules MEDAG and SERPINE1 associated with AAA rupture. Our study provides an additional direction for the association between hypoxia and AAA rupture.

Transcriptional progressive patterns from mild to severe renal ischemia/reperfusion-induced kidney injury in mice

The renal ischemia/reperfusion (I/R)-induced acute kidney injury incidence after nephron-sparing surgery for localized renal tumors is 20%, but the biological determinant process of postoperative acute kidney injury remains unclear. Using Gene Expression Omnibus database (GSE192883) and several bioinformatics analyses (discrete time points analysis, gene set enrichment analysis, dynamic network biomarker analysis, etc), combined with the establishment of the I/R model for verification, we identified three progressive patterns involving five core pathways confirmed using gene set enrichment analysis and six key genes (S100a10, Pcna, Abat, Kmo, Acadm, and Adhfe1) verified using quantitative polymerase chain reaction The dynamic network biomarker (DNB) subnetwork composite index value is the highest in the 22-min ischemia group, suggesting the transcriptome expression level fluctuated sharply in this group, which means 22-min ischemia is an critical warning point. This study illustrates the core molecular progressive patterns from mild to severe I/R kidney injury, laying the foundation for precautionary biomarkers and molecular intervention targets for exploration. In addition, the safe renal artery blocking time of nephron-sparing surgery that we currently accept may not be safe anymore.

High Expression of PRNP Predicts Poor Prognosis in Korean Patients with Gastric Cancer

Gastric cancer (GC) has the highest occurrence and fourth-highest mortality rate of all cancers in Korea. Although survival rates are improving with the development of diagnosis and treatment methods, the five-year survival rate for stage 4 GC in Korea remains <10%. Therefore, it is important to identify candidate prognostic factors for predicting poor prognosis. PRNP is a gene encoding the prion protein PrP, which has been noted for its role in the nervous system and is known to be upregulated in various cancers and associated with both cell proliferation and metastasis. However, the value of PRNP as a prognostic factor for Korean GC patients remains unclear. Here, we analyzed the relationship between PRNP expression and survival in three independent datasets for Korean patients with GC as well as the TCGA-STAD dataset. Survival analysis indicates that high levels of PRNP expression are associated with poor overall survival of patients with GC. Gene set enrichment analysis showed that PRNP is associated with epithelial mesenchymal transition and Hedgehog signaling. In addition, proliferation of GC cell lines was inhibited after siRNA-mediated knockdown of PRNP. In conclusion, our study suggests a potential role for PRNP as a candidate prognostic factor for patients with GC.

Liver transcriptome analysis reveals biological pathways and transcription factors in response to high ammonia exposure

Aim: Ammonia is a toxic gas that not only causes environmental pollution, but also is harmful to human health after inhalation. Liver is an important detoxification organ that can convert external or metabolized toxic substances into nontoxic substances. However, the toxic effects of ammonia exposure on livers have not been well studied.Method: In this study, pigs were used as an animal model and were exposed to 80 ppm ammonia (8 h during 12 days), and then, RNA-seq were conducted to explore the key genes in response to high ammonia exposure in livers.Result: Gene set enrichment analysis (GSEA) showed that the genes associated with hypoxia, inflammatory response, and apoptosis were up-regulated in the ammonia group, but the genes associated with DNA replication, linoleic acid metabolism, and glycolysis were down-regulated. Totally, 556 differentially expressed genes (DEGs) including 54 genes that encode the transcription factors (TFs) were identified between the exposure and control groups. GO and KEGG pathway analysis suggested that these DEGs were involved in inflammatory response, oxidative stress, apoptosis, immune, and cell cycle. Furthermore, the TF-target interaction analysis showed that FOS, HIF-1α, JUNB, ATF3, REL, and KLF4 were important TFs in regulating the hepatic gene expression in response to high ammonia exposure.Conclusion: Altogether, our findings not only presented a comprehensive mRNA transcriptome profile of liver after high ammonia exposure, but also found some key genes and TFs that could be used to investigate the toxicity mechanism of high ammonia on livers.

F-box protein 17 promotes glioma progression by regulating glycolysis pathway

F-box protein 17 (FBXO17) is associated with high-grade glioma and acted as a promotor of glioma development. This study investigated the effect and underlying pathway of FBXO17 on glioma. The Cancer Genome Atlas database was applied to analyze FBXO17 expression information in glioma. First, high FBXO17 expressions are associated with glioma and poor prognosis. Then, FBXO17 was upregulated in glioma cells. Meanwhile, knock-down of FBXO17 inhibited cell proliferation, migration, and invasion, but increased the cell apoptosis. Besides, knock-down of FBXO17 inhibited mitochondrial membrane potential and increased reactive oxygen species. Furthermore, knock-down of FBXO17 decreased level of adenosine triphosphate, glucose, lactate, GLUT1, HK2, PFKP, PKM2, and LDHA. In conclusion, FBXO17 was high expression in glioma, and FBXO17 regulates glioma by regulating glycolysis pathway, providing novel theoretical for the treatment of glioma.

Thyroidal Transcriptomic Profiles of Pathoadaptive Responses to Congenital Hypothyroidism in XB130 Knockout Mice

Congenital hypothyroidism is a genetic condition in which the thyroid gland fails to produce sufficient thyroid hormone (TH), resulting in metabolic dysfunction and growth retardation. Xb130-/- mice exhibit perturbations of thyrocyte cytoskeleton and polarity, and develop postnatal transient growth retardation due to congenital hypothyroidism, leading ultimately to multinodular goiter. To determine the underlying mechanisms, we performed transcriptomic analyses on thyroid glands of mice at three age points: week 2 (W2, before visible growth retardation), W4 (at the nadir of growth); and W12 (immediately before full growth recovery). Using gene set enrichment analysis, we compared a defined set of thyroidal genes between Xb130+/+ and Xb130-/- mice to identify differentially enriched gene clusters. At the earliest postnatal stage (W2), the thyroid glands of Xb130-/- mice exhibited significantly downregulated gene clusters related to cellular metabolism, which continued to W4. Additionally, mutant thyroids at W4 and W12 showed upregulated gene clusters related to extracellular matrix, angiogenesis, and cell proliferation. At W12, despite nearly normal levels of serum TH and TSH and body size, a significantly large number of gene clusters related to inflammatory response were upregulated. Early postnatal TH deficiency may suppress cellular metabolism within the thyroid gland itself. Upregulation of genes related to extracellular matrix and angiogenesis may promote subsequent thyroid growth. Chronic inflammatory responses may contribute to the pathogenesis of multinodular goiter in later life. Some of the pathoadaptive responses of Xb130-/- mice may overlap with those from other mutations causing congenital hypothyroidism.

The Rationale for the Dual-Targeting Therapy for RSK2 and AKT in Multiple Myeloma

Multiple myeloma (MM) is characterized by remarkable cytogenetic/molecular heterogeneity among patients and intraclonal diversity even in a single patient. We previously demonstrated that PDPK1, the master kinase of series of AGC kinases, is universally active in MM, and plays pivotal roles in cell proliferation and cell survival of myeloma cells regardless of the profiles of cytogenetic and genetic abnormalities. This study investigated the therapeutic efficacy and mechanism of action of dual blockade of two major PDPK1 substrates, RSK2 and AKT, in MM. The combinatory treatment of BI-D1870, an inhibitor for N-terminal kinase domain (NTKD) of RSK2, and ipatasertib, an inhibitor for AKT, showed the additive to synergistic anti-tumor effect on human MM-derived cell lines (HMCLs) with active RSK2-NTKD and AKT, by enhancing apoptotic induction with BIM and BID activation. Moreover, the dual blockade of RSK2 and AKT exerted robust molecular effects on critical gene sets associated with myeloma pathophysiologies, such as those with MYC, mTOR, STK33, ribosomal biogenesis, or cell-extrinsic stimuli of soluble factors, in HMCLs. These results provide the biological and molecular rationales for the dual-targeting strategy for RSK2 and AKT, which may overcome the therapeutic difficulty due to cytogenetic/molecular heterogeneity in MM.

Comprehensive bioinformatics analysis reveals the hub genes and pathways associated with multiple myeloma

PURPOSE

While the prognosis of multiple myeloma (MM) has significantly improved over the last decade because of new treatment options, it remains incurable. Aetiological explanations and biological targets based on genomics may provide additional help for rational disease intervention.

MATERIALS AND METHODS

Three microarray datasets associated with MM were downloaded from the Gene Expression Omnibus (GEO) database. GSE125364 and GSE39754 were used as the training set, and GSE13591 was used as the verification set. The differentially expressed genes (DEGs) were obtained from the training set, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate their functions. The hub genes were derived from the combined results of a protein-protein interaction (PPI) network and weighted gene coexpression network analysis (WGCNA). The receiver operating characteristic (ROC) curves of hub genes were plotted to evaluate their clinical diagnostic value. Biological processes and signaling pathways associated with hub genes were explained by gene set enrichment analysis (GSEA).

RESULTS

A total of 1759 DEGs were identified. GO and KEGG pathway analyses suggested that the DEGs were related to the process of protein metabolism. RPN1, SEC61A1, SPCS1, SRPR, SRPRB, SSR1 and TRAM1 were proven to have clinical diagnostic value for MM. The GSEA results suggested that the hub genes were widely involved in the N-glycan biosynthesis pathway.

CONCLUSION

The hub genes identified in this study can partially explain the potential molecular mechanisms of MM and serve as candidate biomarkers for disease diagnosis.

Developing an immune signature for triple-negative breast cancer to predict prognosis and immune checkpoint inhibitor response

Aim: We aimed to develop a new signature based on immune-related genes to predict prognosis and response to immune checkpoint inhibitors in patients with triple-negative breast cancer (TNBC). Materials & methods: Single-sample gene set enrichment was used to develop an immune-based prognostic signature (IPRS) for TNBC patients. We conducted multivariate Cox analysis to evaluate the prognosis value of the IPRS. Result: An IPRS based on 66 prognostic genes was developed. Multivariate Cox analysis indicated that the IPRS was an independent factor for prognosis. PD-1, PD-L1, PD-L2 and CTLA4 gene expression was higher in the low-risk group, suggesting IPRS could predict the response to immune checkpoint inhibitors. Conclusion: The IPRS might be a reliable signature to predict TNBC patients' prognosis and response to immune checkpoint inhibitors, but needs prospective validation.

Clinical Significance and Potential Role of LSM4 Overexpression in Hepatocellular Carcinoma: An Integrated Analysis Based on Multiple Databases

Background: Hepatocellular carcinoma (HCC) is a solid tumor with high recurrence rate and high mortality. It is crucial to discover available biomarkers to achieve early diagnosis and improve the prognosis. The effect of LSM4 in HCC still remains unrevealed. Our study is dedicated to exploring the expression of LSM4 in HCC, demonstrating its clinical significance and potential molecular mechanisms.

Methods: Clinical information and LSM4 expression values of HCC were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Survival analysis and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prognostic and diagnostic significance of LSM4. Calculating pooled standardized mean difference (SMD) and performing summary receiver operating characteristic (sROC) curve analysis to further determine its expression status and diagnostic significance. LSM4-related co-expressed genes (CEGs) were obtained and explored their clinical significance in HCC. LSM4-associated pathways were identified through Gene set enrichment analysis (GSEA).

Results: Up-regulated LSM4 was detected in HCC tissues (SMD = 1.56, 95% CI: 1.29–1.84) and overexpressed LSM4 had excellent distinguishing ability (AUC = 0.91, 95% CI: 0.88–0.93). LSM4 was associated with clinical stage, tumor grade, and lymph node metastasis status (p < 0.05). Survival analysis showed that high LSM4 expression was related to poor overall survival (OS) of HCC patients. Cox regression analysis suggested that high LSM4 expression may be an independent risk factor for HCC. We obtained nine up-regulated CEGs of LSM4 in HCC tissues, and six CEGs had good prognostic and diagnostic significance. GSEA analysis showed that up-regulated LSM4 was closely related to the cell cycle, cell replication, focal adhesion, and several metabolism-associated pathways, including fatty acid metabolism.

Conclusion: Overexpressed LSM4 may serve as a promising diagnostic and prognostic biomarker of HCC. Besides, LSM4 may play a synergistic effect with CEGs in promoting the growth and metastasis of HCC cells via regulating crucial pathways such as cell cycle, focal adhesion, and metabolism-associated pathways.