Translational Bioinformatics Applied to the Study of Complex Diseases

Analysis of total RNA as a potential biomarker of Parkinson's disease in silico

Knowledge about total RNA molecules in Parkinson's disease is limited. This gene expression profiling study was conducted with a preclinical experimental design using a mouse model to examine the molecular-biological characteristics and the pathological implication of total RNA gene interaction in Parkinson's disease in silico. In silico analysis of total RNA molecules, the Gene Expression Omnibus database, published results, and preliminary findings of available patient samples apply. The potential signaling network and the effect of the interaction of molecules with total RNA was predicted and confirmed. The research consists of four parts. At first, we analyzed the control and MPTP groups. In the second part, we analyzed FVB-N control and MPTP. In the third part, we analyzed controls. In the fourth part, we analyzed MTPT separately. The constructed network contains total RNA, where the Kyoto Encyclopedia of Genes and Genomes database analysis showed that genes from the signaling pathway are involved in the development and complications of Parkinson's disease in male and female rats. Identified total RNA and genes are involved in altered signaling. There is direct interconnection and interdependence of interactions in the signaling network. Results identified the significant total-RNA molecules of the signaling pathway that connect other molecules. In silico analysis shows upregulated and downregulated genes in Parkinson's disease rats. Preliminary data shows that total RNA molecules interact with other genes, and they are applicable in Parkinson's disease course monitoring, shedding light on how factors impact the expression of genes and offering strategies for management.

Bioinformatics analysis of ferroptosis in frozen shoulder

OBJECTIVES

Frozen shoulder is a common shoulder disease that significantly affects the patient's life and work. Ferroptosis is a new type of programmed cell death, which is involved in many diseases. However, there have been no studies reporting the relationship between frozen shoulders and ferroptosis. This study identified potential molecular markers of ferroptosis in frozen shoulders to provide more effective strategies for the treatment of frozen shoulders.

METHODS

GSE238053 was downloaded from the Gene Expression Omnibus (GEO) dataset and intersected with ferroptosis genes to obtain differentially expressed genes (DEGs). The signaling pathways and biological functions of DEGs were performed by WebGestalt and Metascape. The interactions related to these DEGs and the key genes between frozen shoulders and ferroptosis was performed by STRING and Cytoscape. A frozen shoulders rat model was used to validate our predicted genes, Western Blot and qRT-PCR was used to assess the expression levels of our genes of interest.

RESULTS

A total of 34 DEGs between GSE238053 and Ferroptosis Database were obtained, most of which were involved in the HIF-1 signaling pathway and inflammatory response. A protein-protein interaction network was obtained by Cytoscape and the key genes (IL-6, HMOX1 and TLR4) were screened by MCODE. Our results of Western Blot showed that the protein expression level of TLR4 and HMOX1 were elevated, and the protein level of IL-6 decreased in frozen shoulders rat model. The mRNA level after frozen shoulders showed that IL-6 was upregulated, whereas TLR4 and HMOX1were downregulated.

CONCLUSIONS

The results demonstrated that ferroptosis may affect the pathological process of frozen shoulders through these signaling pathways and genes. The identification of IL-6, HMOX1 and TLR4 genes can provide new therapeutic targets for frozen shoulders.

Integrating frontiers: a holistic, quantum and evolutionary approach to conquering cancer through systems biology and multidisciplinary synergy

Cancer therapy is facing increasingly significant challenges, marked by a wide range of techniques and research efforts centered around somatic mutations, precision oncology, and the vast amount of big data. Despite this abundance of information, the quest to cure cancer often seems more elusive, with the "war on cancer" yet to deliver a definitive victory. A particularly pressing issue is the development of tumor treatment resistance, highlighting the urgent need for innovative approaches. Evolutionary, Quantum Biology and System Biology offer a promising framework for advancing experimental cancer research. By integrating theoretical studies, translational methods, and flexible multidisciplinary clinical research, there's potential to enhance current treatment strategies and improve outcomes for cancer patients. Establishing stronger links between evolutionary, quantum, entropy and chaos principles and oncology could lead to more effective treatments that leverage an understanding of the tumor's evolutionary dynamics, paving the way for novel methods to control and mitigate cancer. Achieving these objectives necessitates a commitment to multidisciplinary and interprofessional collaboration at the heart of both research and clinical endeavors in oncology. This entails dismantling silos between disciplines, encouraging open communication and data sharing, and integrating diverse viewpoints and expertise from the outset of research projects. Being receptive to new scientific discoveries and responsive to how patients react to treatments is also crucial. Such strategies are key to keeping the field of oncology at the forefront of effective cancer management, ensuring patients receive the most personalized and effective care. Ultimately, this approach aims to push the boundaries of cancer understanding, treating it as a manageable chronic condition, aiming to extend life expectancy and enhance patient quality of life.

Bioinformatics and molecular biology tools for diagnosis, prevention, treatment and prognosis of COVID-19

Since December 2019, a new form of Severe Acute Respiratory Syndrome (SARS) has emerged worldwide, caused by SARS coronavirus 2 (SARS-CoV-2). This disease was called COVID-19 and was declared a pandemic by the World Health Organization in March 2020. Symptoms can vary from a common cold to severe pneumonia, hypoxemia, respiratory distress, and death. During this period of world stress, the medical and scientific community were able to acquire information and generate scientific data at unprecedented speed, to better understand the disease and facilitate vaccines and therapeutics development. Notably, bioinformatics tools were instrumental in decoding the viral genome and identifying critical targets for COVID-19 diagnosis and therapeutics. Through the integration of omics data, bioinformatics has also improved our understanding of disease pathogenesis and virus-host interactions, facilitating the development of targeted treatments and vaccines. Furthermore, molecular biology techniques have accelerated the design of sensitive diagnostic tests and the characterization of immune responses, paving the way for precision medicine approaches in treating COVID-19. Our analysis highlights the indispensable contributions of bioinformatics and molecular biology to the global effort against COVID-19. In this review, we aim to revise the COVID-19 features, diagnostic, prevention, treatment options, and how molecular biology, modern bioinformatic tools, and collaborations have helped combat this pandemic. An integrative literature review was performed, searching articles on several sites, including PUBMED and Google Scholar indexed in referenced databases, prioritizing articles from the last 3 years. The lessons learned from this COVID-19 pandemic will place the world in a much better position to respond to future pandemics.

Exploring the current landscape of single-cell RNA sequencing applications in gastric cancer research

Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.

From genome to clinic: The power of translational bioinformatics in improving human health

Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.

BNIPL is a promising biomarker of laryngeal cancer: novel insights from bioinformatics analysis and experimental validation

BACKGROUND

Laryngeal cancer (LC) is a malignant tumor with high incidence and mortality. We aim to explore key genes as novel biomarkers to find potential target of LC in clinic diagnosis and treatment.

METHODS

We retrieved GSE143224 and GSE84957 datasets from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). Hub genes were identified from protein-protein interaction networks and further determined using receiver operating characteristic curves and principal component analysis. The expression of hub gene was verified by quantitative real time polymerase chain reaction. The transfection efficiency of BCL2 interacting protein like (BNIPL) was measured by western blot. Proliferation, migration, and invasion abilities were detected by Cell Counting Kit-8, wound-healing, and transwell assays, respectively.

RESULTS

Total 96 overlapping DEGs were screened out from GSE143224 and GSE84957 datasets. Six hub genes (BNIPL, KRT4, IGFBP3, MMP10, MMP3, and TGFBI) were identified from PPI network. BNIPL was selected as the target gene. The receiver operating characteristic curves of BNIPL suggested that the false positive rate was 18.5% and the true positive rate was 81.5%, showing high predictive values for LC. The expression level of BNIPL was downregulated in TU212 and TU686 cells. Additionally, overexpression of BNIPL suppressed the proliferation, migration, and invasion of TU212 and TU686 cells.

CONCLUSION

BNIPL is a novel gene signature involved in LC progression, which exerts an inhibitory effect on LC development. These findings provide a novel insight into the pathogenesis of LC.