Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Molecular biomarkers identification and applications in CHO bioprocessing

Biomarkers are valuable tools in clinical research where they allow to predict susceptibility to diseases, or response to specific treatments. Likewise, biomarkers can be extremely useful in the biomanufacturing of therapeutic proteins. Indeed, constraints such as short timelines and the need to find hyper-productive cells could benefit from a data-driven approach during cell line and process development. Many companies still rely on large screening capacities to develop productive cell lines, but as they reach a limit of production, there is a need to go from empirical to rationale procedures. Similarly, during bioprocessing runs, substrate consumption and metabolism wastes are commonly monitored. None of them possess the ability to predict the culture behavior in the bioreactor. Big data driven approaches are being adapted to the study of industrial mammalian cell lines, enabled by the publication of Chinese hamster and CHO genome assemblies which allowed the use of next-generation sequencing with these cells, as well as continuous proteome and metabolome annotation. However, if these different -omics technologies contributed to the characterization of CHO cells, there is a significant effort remaining to apply this knowledge to biomanufacturing methods. The correlation of a complex phenotype such as high productivity or rapid growth to the presence or expression level of a specific biomarker could save time and effort in the screening of manufacturing cell lines or culture conditions. In this review we will first discuss the different biological molecules that can be identified and quantified in cells, their detection techniques, and associated challenges. We will then review how these markers are used during the different steps of cell line and bioprocess development, and the inherent limitations of this strategy.

Cadmium exacerbates liver injury by remodeling ceramide metabolism: Multiomics and laboratory evidence

Cadmium (Cd) is a toxic heavy metal that primarily targets the liver. Cd exposure disrupts specific lipid metabolic pathways; however, the underlying mechanisms remain unclear. This study aimed to investigate the lipidomic characteristics of rat livers after Cd exposure as well as the potential mechanisms of Cd-induced liver injury. Our analysis of established Cd-exposed rat and cell models showed that Cd exposure resulted in liver lipid deposition and hepatocyte damage. Lipidomic detection, transcriptome sequencing, and experimental analyses revealed that Cd mainly affects the sphingolipid metabolic pathway and that the changes in ceramide metabolism are the most significant. In vitro experiments revealed that the inhibition of ceramide synthetase activity or activation of ceramide decomposing enzymes ameliorated the proapoptotic and pro-oxidative stress effects of Cd, thereby alleviating liver injury. In contrast, the exogenous addition of ceramide aggravated liver injury. In summary, Cd increased ceramide levels by remodeling ceramide synthesis and catabolism, thereby promoting hepatocyte apoptosis and oxidative stress and ultimately aggravating liver injury. Reducing ceramide levels can serve as a potential protective strategy to mitigate the liver toxicity of Cd. This study provides new evidence for understanding Cd-induced liver injury at the lipidomic level and insights into the health risks and toxicological mechanisms associated with Cd.

Male Breast Cancer: Current Scenario and Future Perspectives

Male breast cancer (MBC), one of the rare types of cancer among men where the global incidence rate is 1.8% of all breast cancers cases with a yearly increase in a pace of 1.1%. Since the last 10 years, the incidence has been increased from 7.2% to 10.3% and the mortality rate was decreased from 11% to 3.8%. Nevertheless, the rate of diagnoses has been expected to be around 2.6% in the near future, still there is a great lack in studies to characterize the MBC including the developed countries. Based on our search, it is evidenced from the literature that the number of risk factors for the cause of MBC are significant, which includes the increase in age, family genetic history, mutations in specific genes due to various environmental impacts, hormonal imbalance and unregulated expression receptors for specific hormones of high levels of estrogen or androgen receptors compared to females. MBCs are broadly classified into ductal and lobular carcinomas with further sub-types, with some of the symptoms including a lump or swelling in the breast, redness of flaky skin in the breast, irritation and nipple discharge that is similar to the female breast cancer (FBC). The most common diagnostic tools currently in use are the ultrasound guided sonography, mammography, and biopsies. Treatment modalities for MBC include surgery, radiotherapy, chemotherapy, hormonal therapy, and targeted therapies. However, the guidelines followed for the diagnosis and treatment modalities of MBC are mostly based on FBC that is due to the lack of prospective studies related to MBC. However, there are distinct clinical and molecular features of MBC, it is a need to develop different clinical methods with more multinational approaches to help oncologist to improve care for MBC patients.

Genome-wide expression profiling reveals novel biomarkers in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the most aggressive and frequent malignancy detected among women worldwide. The pathophysiology of OC should, therefore be better understood to identify diagnostic, prognostic, and predictive novel biomarkers necessary for early detection, management, and prognostication. In this study, we aimed to investigate transcriptomic landscape and biomarker through RNA-seq data analysis. Further analysis by Protein Protein network identified top 10 Differentially Expressed Genes (DEGs). KEGG pathway enrichment analysis revealed the significant enrichment of DEGs in basal cell carcinoma, cell cycle and FoxO signalling pathway. The RNA-seq results of 10 DEGs were validated by QRT-PCR and TCGA database. Correlation studies were also performed between gene expression and clinical characteristics followed by survival analysis. Finally, 8 DEGs (CDKN1A, BCL6, CDC45, WNT2, TLR5, AQP5) including two novel DEGs (CSN1S1 and NKILA) were identified showing significant correlations with EOC characteristics. These may serve as interesting biomarkers and novel treatment targets and warrant further investigation into the functional outcome of EOC.

Nitrogen removal performance of aerobic denitrifying bacteria enhanced by an iron-anode pulsed electric field

Pulsed electric field (PEF) technology has attracted considerable attention because it can efficiently treat pollutants that are difficult to degrade. In this study, a PEF system using iron as the electrode was constructed to investigate the effect of PEF-Fe on the growth and metabolism of aerobic denitrifying bacteria and the effectiveness of wastewater nitrogen removal. The chemical oxygen demand, NO3--N and nitrate removal rates were 98.93%, 97.60% and 24.40 mg·L-1·h-1, respectively, under optimal conditions. As confirmed in this study, PEF-Fe could improve the key enzyme activities of W207-14. Scanning electron microscopy revealed that the surface of PEF-Fe-treated W207-14 was intact and smooth without any irreversible deformation. Flow cytometry combined with fluorescence staining analysis also confirmed reversible electroporation on the cell membrane surface of PEF-Fe-treated W207-14. Differentially expressed gene enrichment analysis showed that PEF-Fe activated the transmembrane transport function of ATP-binding cassette transporte (ABC) transport proteins and enhanced the cell membrane permeability of aerobic denitrifying bacteria. The significant differential expression of iron-sulphur cluster proteins facilitated the regulation of electron transport and maintenance of the dynamic balance of iron ions within the PEF-Fe system.

Transcriptomic analysis of esophageal cancer reveals hub genes and networks involved in cancer progression

Esophageal carcinoma (ESCA) has a 5-year survival rate of fewer than 20%. The study aimed to identify new predictive biomarkers for ESCA through transcriptomics meta-analysis to address the problems of ineffective cancer therapy, lack of efficient diagnostic tools, and costly screening and contribute to developing more efficient cancer screening and treatments by identifying new marker genes. Nine GEO datasets of three kinds of esophageal carcinoma were analyzed, and 20 differentially expressed genes were detected in carcinogenic pathways. Network analysis revealed four hub genes, namely RAR Related Orphan Receptor A (RORA), lysine acetyltransferase 2B (KAT2B), Cell Division Cycle 25B (CDC25B), and Epithelial Cell Transforming 2 (ECT2). Overexpression of RORA, KAT2B, and ECT2 was identified with a bad prognosis. These hub genes modulate immune cell infiltration. These hub genes modulate immune cell infiltration. Although this research needs lab confirmation, we found interesting biomarkers in ESCA that may aid in diagnosis and treatment.

Interpreting omics data with pathway enrichment analysis

Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.

Metformin attenuates multiple myeloma cell proliferation and encourages apoptosis by suppressing METTL3-mediated m6A methylation of THRAP3, RBM25, and USP4

Based on the results of epidemiological and preclinical studies, metformin can improve the prognosis of patients with malignant tumors. Studies have confirmed that metformin inhibits multiple myeloma (MM) cell proliferation and promotes apoptosis. Nevertheless, the specific mechanism remains to be elucidated. MM cells were intervened with different doses of metformin to detect cell proliferation and apoptosis. Western blotting and RT-qPCR were employed to assess the expression of METTL3, METTL14, WTAP, FTO, and ALKBH5 after metformin intervention. The microarray dataset GSE29023 was retrieved from the Gene Expression Omnibus (GEO) database and calculated using the R language (limma package) to authenticate differentially expressed genes (DEGs). The database for annotation, visualization, and integrated discovery (David) was applied for GO annotation analysis of DEGs. Subsequently, the string database and Cytoscape software were applied to construct protein-protein interaction (PPI) and DEM hub gene networks. Bioinformatics analysis and MeRIP were applied to predict and test METTL3-mediated m6A levels on mRNA of THRAP3, RBM25, and USP4 in METTL3 knocked-down cells. Then rescue experiments were performed to explore effects of METTL3 and THRAP3, RBM25, or USP4 on cell proliferation and apoptosis. The effect on MM cell xenograft tumor growth was observed by injection of metformin or/and overexpression of METTL3 in in vivo experiments. Metformin decreased cell proliferation and encouraged cell apoptosis in a dose-dependent manner. Global m6A modification was elevated in MM cells compared to normal cells, which was counteracted by metformin treatment. Furthermore, THRAP3, RBM25, and USP4 were identified as possible candidate genes for metformin treatment by GSE29023 data mining. METTL3 interference impaired m6A modification on mRNA of THRAP3, RBM25, and USP4 as well as expression levels. The mRNA stability and expression of THRAP3, RBM25, and USP4 was decreased after metformin treatment, which was reversed by METTL3 overexpression. THRAP3, RBM25 or USP4 knockdown reversed the assistance of METTL3 overexpression on the malignant behavior of MM cells. Finally, upregulation of METTL3 was shown to exert facilitative effects on xenograft tumor growth by blocking metformin injection. The present study demonstrates that metformin can repress the expression of THRAP3, RBM25, and USP4 by inhibiting METTL3-mediated m6A modification, which in turn hamper cell proliferation and promotes cell apoptosis.Abbreviations: multiple myeloma (MM), Gene Expression Omnibus (GEO), differentially expressed genes (DEGs), database for annotation, visualization and integrated discovery (David), protein-protein interaction (PPI), epithelial‑mesenchymal transition (EMT), methyltransferase like 3 (METTL3), methyltransferase like 14 (METTL14), wilms tumor 1-associated protein (WTAP), methyltransferase like 16 (METTL16), acute myeloid leukemia (AML), non-small lung cancer (NSCLC), glioma stem cells (GSCs), normal bone marrow-derived plasma cells (nPCs), false discovery rate (FDR), biological process (BP), optical density (OD), horseradish peroxidase (HRP), M6A RNA immunoprecipitation assay (MeRIP).

Classical epithelial-mesenchymal transition (EMT) and alternative cell death process-driven blebbishield metastatic-witch (BMW) pathways to cancer metastasis

Metastasis is a pivotal event that accelerates the prognosis of cancer patients towards mortality. Therapies that aim to induce cell death in metastatic cells require a more detailed understanding of the metastasis for better mitigation. Towards this goal, we discuss the details of two distinct but overlapping pathways of metastasis: a classical reversible epithelial-to-mesenchymal transition (hybrid-EMT)-driven transport pathway and an alternative cell death process-driven blebbishield metastatic-witch (BMW) transport pathway involving reversible cell death process. The knowledge about the EMT and BMW pathways is important for the therapy of metastatic cancers as these pathways confer drug resistance coupled to immune evasion/suppression. We initially discuss the EMT pathway and compare it with the BMW pathway in the contexts of coordinated oncogenic, metabolic, immunologic, and cell biological events that drive metastasis. In particular, we discuss how the cell death environment involving apoptosis, ferroptosis, necroptosis, and NETosis in BMW or EMT pathways recruits immune cells, fuses with it, migrates, permeabilizes vasculature, and settles at distant sites to establish metastasis. Finally, we discuss the therapeutic targets that are common to both EMT and BMW pathways.

Reveal the Mechanisms of Yi-Fei-Jian-Pi-Tang on Covid-19 through Network Pharmacology Approach

Objectives

The Traditional Chinese Medicine (TCM) formula Yi-Fei-Jian-Pi-Tang (YFJPT) has been demonstrated effective against Corona Virus Disease 2019 (Covid-19). The aim of this article is to make a thorough inquiry about its active constituent as well as mechanisms against Covid-19 via TCM network pharmacology.

Methods

All the ingredients of YFJPT are obtained from the pharmacology database of the TCM system. The genes which are associated with the targets are obtained by utilizing UniProt. The herb-target network is built up by utilizing Cytoscape. The target protein-protein interaction network is built by utilizing the STRING database and Cytoscape. The critical targets of YFJPT are explored by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG).

Results

The outcomes show that YFJPT might has 33 therapeutic targets on Covid-19, namely, interleukin 2 (IL2), heme oxygenase 1 (HMOX1), interleukin 4 (IL4), interferon gamma (FNG), α nuclear factor of kappa light polypeptide gene enhancer in Bcells inhibitor, alpha (NFKBIA), nuclear factor-k-gene binding (NFKB), nitric oxide synthase 3 (NOS3), intercellular adhesion molecule 1 (ICAM1), hypoxia inducible factor 1 subunit alpha (HIF1A), mitogen-activated protein kinase 3 (MAPK3), epidermal growth factor receptor (EGFR), interleukin 10 (IL10), jun proto-oncogene (JUN), C-C motif chemokine ligand 2 (CCL2), C-X-C motif chemokine ligand 8 (CXCL8), tumor protein p53 (TP53), interleukin 1 beta (IL1B), AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor (TNF), interleukin 6 (IL6), erb-b2 receptor tyrosine kinase 2 (ERBB2), RELA proto-oncogene (RELA), NF-κB subunit, caspase 8 (CASP8), peroxisome proliferator activated receptor alpha (PPARA), TIMP metallopeptidase inhibitor 1 (TIMP1), transforming growth factor beta 1 (TGFB1), interleukin 1 alpha (IL1A), signal transducer and activator of transcription 1 (STAT1), mitogen-activated protein kinase 8 (MAPK8), myeloperoxidase (MPO), matrix metallopeptidase 3 (MMP3), matrix metallopeptidase 1 (MMP1), and NFE2 like bZIP transcription factor 2 (NFE2L2). The gene enrichment analysis prompts that YFJPT most likely contributes to patients related to Covid-19 by regulating the pathways of cancers.

Conclusions

That will lay a foundation for the clinical rational application and further experimental research of YFJPT.

Ontologies and Knowledge Graphs in Oncology Research

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.