Target antigens are crucial for developing chimeric antigen receptor (CAR)-T cells, but their application to ovarian cancers is limited. This study aimed to identify potential genes as CAR-T-cell antigen candidates for ovarian cancers. A differential gene expression analysis was performed on ovarian cancer samples from four datasets obtained from the GEO datasets. Functional annotation, pathway analysis, protein localization, and gene expression analysis were conducted using various datasets and tools. An oncogenicity analysis and network analysis were also performed. In total, 153 differentially expressed genes were identified in ovarian cancer samples, with 60 differentially expressed genes expressing plasma membrane proteins suitable for CAR-T-cell antigens. Among them, 21 plasma membrane proteins were predicted to be oncogenes in ovarian cancers, with nine proteins playing crucial roles in the network. Key genes identified in the oncogenic pathways of ovarian cancers included MUC1, CXCR4, EPCAM, RACGAP1, UBE2C, PRAME, SORT1, JUP, and CLDN3, suggesting them as recommended antigens for CAR-T-cell therapy for ovarian cancers. This study sheds light on potential targets for immunotherapy in ovarian cancers.

Chinese hamster ovary (CHO) cells represent the most preferential host cell system for therapeutic monoclonal antibody (mAb) production. Enhancing mAb production in CHO cells can be achieved by adding chemical compounds that regulate the cell cycle and cell survival pathways. This study investigated the impact of ectoine supplementation on mAb production in CHO cells. The results showed that adding ectoine at a concentration of 100 mM on the 3rd day of cultivation improved mAb production by improving cell viability and extending the culture duration. RNA sequencing analysis revealed differentially expressed genes associated with cell cycle regulation, cell proliferation, and cellular homeostasis, in particular promotion of cell cycle arrest, which was then confirmed by flow cytometry analysis. Ectoine-treated CHO cells exhibited an increase in the number of cells in the G0/G1 phase. In addition, the cell diameter was also increased. These findings support the hypothesis that ectoine enhances mAb production in CHO cells through mechanisms involving cell cycle arrest and cellular homeostasis. Overall, this study highlights the potential of ectoine as a promising supplementation strategy to enhance mAb production not only in CHO cells but also in other cell lines.

BACKGROUND: Exposure to particulate matter (PM) and house dust mite (HDM) can change the expression patterns of inflammation-, oxidative stress-, and cell death-related genes. We investigated the changes in gene expression patterns owing to PM exposure.
OBJECTIVE: This study examined the changes in gene expression patterns following PM exposure.
METHODS: We searched for differentially expressed genes (DEGs) following PM exposure using five cell line-based RNA-seq or microarray datasets and six human-derived datasets. The enrichment terms of the DEGs were assessed.
RESULTS: DEG analysis yielded two gene sets. Thus, enrichment analysis was performed for each gene set, and the enrichment terms related to respiratory diseases were presented. The intersection of six human-derived datasets and two gene sets was obtained, and the expression patterns following PM exposure were observed.
CONCLUSIONS: Two gene sets were obtained for cells treated with PM and their expression patterns were presented following verification in human-derived cells. Our findings suggest that exposure to PM2.5 and HDM may reveal changes in genes that are associated with diseases, such as allergies, highlighting the importance of mitigating PM2.5 and HDM exposure for disease prevention.

The molecular mechanisms underlying window of implantation (WOI) displacement in patients with recurrent implantation failure (RIF) remain unclear. This study aims to explore the transcriptomic signatures of endometrium with normal and displaced WOIs and to identify the causes of endometrial receptivity (ER) abnormalities and WOI displacement in RIF patients.
In this study, 40 RIF patients were recruited and underwent personalized embryo transfer (pET) guided by the predicted results of endometrial receptivity diagnosis (ERD) model. Transcriptome analysis of endometrium from patients with clinical pregnancies after pET was performed to identify differentially expressed genes (DEGs) associated with WOI displacement. Gene expression data from HRT and natural cycle endometrium were compared to identify specific gene expression patterns of ER-related genes during WOI.
The ERD results indicated that 67.5% of RIF patients (27/40) were non-receptive in the conventional WOI (P+5) of the HRT cycle. The clinical pregnancy rate in RIF patients improved to 65% (26/40) after ERD-guided pET, indicating the effectiveness of transcriptome-based WOI prediction. Among the 26 patients with clinical pregnancy, the gene expression profiles of P+5 endometrium from advanced (n=6), normal (n=10) and delayed (n=10) WOI groups were significantly different from each other. Furthermore, 10 DEGs identified among P+5 endometrium of 3 groups were involved in immunomodulation, transmembrane transport and tissue regeneration, which could accurately classify the endometrium with different WOIs. Additionally, a large number of ER-related genes showed significant correlation and similar gene expression patterns in P+3, P+5, and P+7 endometrium from HRT cycles and LH+5, LH+7, and LH+9 endometrium from natural cycles.
Our study shows that ER-related genes share similar gene expression patterns during WOI in both natural and HRT cycles, and their aberrant expression is associated with WOI displacements. The improvement of pregnancy outcomes in RIF patients by adjusting ET timing according to ERD results demonstrates the importance of transcriptome-based endometrial receptivity assessment and the clinical efficiency of ERD model.

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering has been widely used to classify DEGs with similar expression patterns, but rarely used to identify DEGs themselves. We recently reported that the clustering-based method (called MBCdeg1 and 2) for identifying DEGs has great potential. However, these methods left room for improvement. This study reports on the improvement (named MBCdeg3). We compared a total of six competing methods: three conventional R packages (edgeR, DESeq2, and TCC) and three versions of MBCdeg (i.e., MBCdeg1, 2, and 3) corresponding to three different normalization algorithms. As MBCdeg3 performs well in many simulation scenarios of RNA-seq count data, MBCdeg3 replaces MBCdeg1 and 2 in our previous report. •MBCdeg3 is a method for both identification and classification of DEGs from RNA-seq count data.•MBCdeg3 is available as a function of R, which is common in the field of expression analysis.•MBCdeg3 performs well in a variety of simulation scenarios for RNA-seq count data.

Long non-coding RNAs (lncRNAs) has been proven by many to play a crucial part in the process of sepsis. To obtain a better understanding of sepsis, the molecular biomarkers associated with it, and its possible pathogenesis, we obtained data from RNA-sequencing analysis using serum from three sepsis patients and three healthy controls (HCs). Using edgeR (one of the Bioconductor software package), we identified 1118 differentially expressed mRNAs (DEmRNAs) and 1394 differentially expressed long noncoding RNAs (DElncRNAs) between sepsis patients and HCs. We identified the biological functions of these disordered genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analyses. The GO analysis showed that the homophilic cell adhesion via plasma membrane adhesion molecules was the most significantly enriched category. The KEGG signaling pathway analysis indicated that the differentially expressed genes (DEGs) were most significantly enriched in retrograde endocannabinoid signaling. Using STRING, a protein-protein interaction network was also created, and Cytohubba was used to determine the top 10 hub genes. To examine the relationship between the hub genes and sepsis, we examined three datasets relevant to sepsis that were found in the gene expression omnibus (GEO) database. PTEN and HIST2H2BE were recognized as hub gene in both GSE4607, GSE26378, and GSE9692 datasets. The receiver operating characteristic (ROC) curves indicate that PTEN and HIST2H2BE have good diagnostic value for sepsis. In conclusion, this two hub genes may be biomarkers for the early diagnosis of sepsis, our findings should deepen our understanding of the pathogenesis of sepsis.

BACKGROUND: Liver ischemia reperfusion injury (LIRI) is not only a common injury during liver transplantation and major hepatic surgery, but also one of the primary factors that affect the outcome of postoperative diseases. However, there are still no reliable ways to tackle the problem. Our study aimed to find some characteristic genes associated with immune infiltration that affect LIRI, which can provide some insights for future research in the future. Therefore, it is essential for the treatment of LIRI, the elucidation of the mechanisms of LIRI, and exploring the potential biomarkers. Efficient microarray and bioinformatics analyses can promote the understanding of the molecular mechanisms of disease occurrence and development.
METHODS: Data from GSE151648 were downloaded from GEO data sets, and we performed a comprehensive analysis of the differential expression, biological functions and interactions of LIRI-associated genes. Then we performed Gene ontology (GO) analysis and Kyotoencydlopedia of genes and genomes (KEGG) enrichment analysis of DEGs. At last, we performed a protein-protein interaction network to screen out hub genes.
RESULTS: A total of 161 differentially expressed genes (DEGs) were identified. GO analysis results revealed that the changes in the modules were mostly enriched in the neutrophil degranulation, neutrophil activation involved in immune response, and neutrophil mediated immunity. KEGG enrichment analysis of DEGs demonstrated that LIRI mainly involved the cytokine-cytokine receptor interaction. Our data indicated that macrophages and neutrophils are closely related to LIRI. 9 hub genes were screened out in the protein-protein interaction network.
CONCLUSIONS: In summary, our data indicated that neutrophil degranulation, neutrophil activation involved in immune response, neutrophil mediated immunity and cytokine-cytokine receptor interaction may play a key role in LIRI, HRH1, LRP2, P2RY6, PKD1L1, SLC8A3 and TNFRSF8, which were identified as potential biomarkers in the occurrence and development of LIRI. However, further studies are needed to validate these findings and explore the molecular mechanism of these biomarkers in LIRI.

BACKGROUND: Women are more likely than men to develop the chronic, progressive autoimmune disease known as rheumatoid arthritis (RA). Although there may be a complex interplay between sex-based differences and autoimmune dysfunction. Their function in RA is largely unknown, though. The purpose of this study was to pinpoint the crucial genes and metabolic pathways that control biological variations in RA between men and women.
METHODS: First, the Gene Expression Omnibus database\'s gene expression information for GSE39340 and GSE55457 was downloaded (GEO). R software was used to find each of the individually identified differentially expressed genes (DEGs) between the sexes. DEGs that overlapped were found. The interactions between the overlapping DEGs were then further examined using a protein-protein interaction (PPI) network. The Kyoto Encyclopedia of Genes and Genomes and Gene Ontology tools, respectively, were used to perform enrichment analyses.
RESULTS: According to our findings, there were 1169 DEGs that overlapped between RA males and females, comprising 845 up-regulated genes and 324 down-regulated genes. Ten hub genes, including PIK3R1, RAC1, HRAS, PTPN11, UQCRB, NDUFV1, EGF, UBA1, UBE2G1, and UBE2E1, were discovered in the PPI network. According to a functional enrichment analysis, these genes were primarily enriched in neurodegenerative illnesses, including various disease pathways, MAPK signaling, insulin signaling, and autophagy.
CONCLUSIONS: The current data point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. Key Points • Our research point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. • PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. • These findings may aid in the development of novel diagnostic and treatment techniques for RA in men and women.

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

UNASSIGNED: Alzheimer\'s disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized.
UNASSIGNED: Differentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls.
UNASSIGNED: DEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder.
UNASSIGNED: AD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.