Big data-driven precision medicine: Starting the custom-made era of iatrology

Visualization analysis of research hotspots and trends on gastrointestinal tumor organoids

BACKGROUND

Gastrointestinal tumor organoids serve as an effective model for simulating cancer in vitro and have been applied in basic biology and preclinical research. Despite over a decade of development and increasing research achievements in this field, a systematic and comprehensive analysis of the research hotspots and future trends is lacking.

AIM

To address this problem by employing bibliometric tools to explore the publication years, countries/regions, institutions, journals, authors, keywords, and references in this field.

METHODS

The literature was collected from Web of Science databases. CiteSpace-6.2R4, a widely used bibliometric analysis software package, was used for institutional analysis and reference burst analysis. VOSviewer 1.6.19 was used for journal co-citation analysis, author co-authorship and co-citation analysis. The 'online platform for bibliometric analysis (https://bibliometric.com/app)' was used to assess the total number of publications and the cooperation relationships between countries. Finally, we employed the bibliometric R software package (version R.4.3.1) in R-studio, for a comprehensive scientific analysis of the literature.

RESULTS

Our analysis included a total of 1466 publications, revealing a significant yearly increase in articles on the study of gastrointestinal tumor organoids. The United States (n = 393) and Helmholtz Association (n = 93) have emerged as the leading countries and institutions, respectively, in this field, with Hans Clevers and Toshiro Sato being the most contributing authors. The most influential journal in this field is Gastroenterology. The most impactful reference is "Long term expansion of epithelial organs from human colon, adenoma, adenocarcinoma, and Barrett's epithelium". Keywords analysis and citation burst analysis indicate that precision medicine, disease modeling, drug development and screening, and regenerative medicine are the most cutting-edge directions. These focal points were further detailed based on the literature.

CONCLUSION

This bibliometric study offers an objective and quantitative analysis of the research in this field, which can be considered as an important guide for next scientific research.

Serum Proteomic Signatures of Common Health Outcomes among Older Adults

INTRODUCTION

In aging populations, the coexistence of multiple health comorbidities represents a significant challenge for clinicians and researchers. Leveraging advances in omics techniques to characterize these health conditions may provide insight into disease pathogenesis as well as reveal biomarkers for monitoring, prognostication, and diagnosis. Researchers have previously established the utility of big data approaches with respect to comprehensive health outcome measurements in younger populations, identifying protein markers that may provide significant health information with a single blood sample.

METHODS

Here, we employed a similar approach in two cohorts of older adults, the Baltimore Longitudinal Study of Aging (mean age = 76.12 years) and InCHIANTI Study (mean age = 66.05 years), examining the relationship between levels of serum proteins and 5 key health outcomes: kidney function, fasting glucose, physical activity, lean body mass, and percent body fat.

RESULTS

Correlations between proteins and health outcomes were primarily shared across both older adult cohorts. We further identified that most proteins associated with health outcomes in the older adult cohorts were not associated with the same outcomes in a prior study of a younger population. A subset of proteins, adiponectin, MIC-1, and NCAM-120, were associated with at least three health outcomes in both older adult cohorts but not in the previously published younger cohort, suggesting that they may represent plausible markers of general health in older adult populations.

CONCLUSION

Taken together, these findings suggest that comprehensive protein health markers have utility in aging populations and are distinct from those identified in younger adults, indicating unique mechanisms of disease with aging.

High-accuracy gastric cancer cell viability evaluation based on multi-impedance spectrum characteristics

The increasing attention to precision medicine is widely paid to greatly rise the cure rate of cancer. Improving the stability and accuracy of cancer cell viability evaluation is one of the keys for precision medicine, as excess dosage of anti-cancer drugs not only kills the cancer cells, but also does harm to normal cells. Electrochemical impedance sensing (EIS) method is well known as a label-free, non-invasive approach for real-time, online monitoring of cell viability. However, the existing EIS methods using single-frequency impedances cannot reflect the comprehensive information of cellular impedance spectroscopy (CIS), ultimately leading to a poor stability and low accuracy of cancer cell viability evaluation. In this paper, we proposed a multi-frequency approach for improving the stability and accuracy of cancer cell viability evaluation based on multi-physical properties of CIS, including cell adhesion state and cell membrane capacitance. The results show that the mean relative error of multi-frequency method is reduced by 50% compared with single-frequency method, while the maximum relative error of the former is 7∼fold smaller than that of the latter. The accuracy of cancer cell viability evaluation is up to 99.6%.

Genomic instability drives tumorigenesis and metastasis and its implications for cancer therapy

Genetic instability can be caused by external factors and may also be associated with intracellular damage. At the same time, there is a large body of research investigating the mechanisms by which genetic instability occurs and demonstrating the relationship between genomic stability and tumors. Nowadays, tumorigenesis development is one of the hottest research areas. It is a vital factor affecting tumor treatment. Mechanisms of genomic stability and tumorigenesis development are relatively complex. Researchers have been working on these aspects of research. To explore the research progress of genomic stability and tumorigenesis, development, and treatment, the authors searched PubMed with the keywords "genome instability" "chromosome instability" "DNA damage" "tumor spread" and "cancer treatment". This extracts the information relevant to this study. Results: This review introduces genomic stability, drivers of tumor development, tumor cell characteristics, tumor metastasis, and tumor treatment. Among them, immunotherapy is more important in tumor treatment, which can effectively inhibit tumor metastasis and kill tumor cells. Breakthroughs in tumorigenesis development studies and discoveries in tumor metastasis will provide new therapeutic techniques. New tumor treatment methods can effectively prevent tumor metastasis and improve the cure rate of tumors.

Molecular effects of site-specific phosphate-methylated primer on the structure and motions of Taq DNA polymerase

Polymerase chain reaction (PCR) is a powerful molecular biology assay for gene detection and quantification. Conventional DNA primers for PCR often suffer from poor sensitivity in specific gene detection. Recently, oligonucleotides containing methyl phosphotriester (MPTE-DNA) have been developed with enhanced DNA hybridization and improved gene detection sensitivity. Yet, site-specific MPTE-modifications on DNA primers have been reported to affect PCR amplification efficiencies while the detailed mechanism remains elusive. Here, we utilized molecular dynamics (MD) simulation to examine the effects of site-specific MPTE-modified primers on the structure and motions of DNA/Taq polymerase complexes. All tested MPTE-DNA/Taq complexes exhibited RMSD values below 2 Å, indicating insignificant effects of all methylation sites on the complex stability. The energetic and hydrogen-bonding analyses suggest minor effects of methylation at t-3, t-4, t-6, and t-7 positions on the DNA-Taq interaction. Principal component analyses further reveal that only t-3, and t-7 methylations preserve the motions of the Taq thumb domain. The site-specific methylation affects the interaction between DNA and the surrounding protein residues, resulting in allosteric-like effects on the DNA/Taq complex. The MD data complement the best experimentally observed PCR efficacies for the t-3 and t-7 positions among all tested MPTE-primers. The unveiled molecular insights can benefit the design of novel PCR primers for improving genetic testing platforms.

Exploring the Role of Obesity in Dilated Cardiomyopathy Based on Bio-informatics Analysis

(1) Background: Obesity is a major risk factor for cardiovascular disease (CVD), contributing to increasing global disease burdens. Apart from heart failure, coronary artery disease, and arrhythmia, recent research has found that obesity also elevates the risk of dilated cardiomyopathy (DCM). The main purpose of this study was to investigate the underlying biological role of obesity in increasing the risk of DCM. (2) Methods: The datasets GSE120895, GSE19303, and GSE2508 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using GSE120895 for DCM and GSE2508 for obesity, and the findings were compiled to discover the common genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted for the common genes in RStudio. In addition, CIBERSORT was used to obtain the immune cellular composition from DEGs. The key genes were identified in the set of common genes by the least absolute shrinkage and selection operator (LASSO) algorithm, the prognostic risk models of which were verified by receiver operator characteristic (ROC) curves in GSE19303. Finally, Spearman's correlation was used to explore the connections between key genes and immune cells. (3) Results: GO and KEGG pathway enrichment analyses showed that the main enriched terms of the common genes were transforming growth factor-beta (TGF-β), fibrillar collagen, NADPH oxidase activity, and multiple hormone-related signaling pathways. Both obesity and DCM had a disordered immune environment, especially obesity. The key genes NOX4, CCDC80, COL1A2, HTRA1, and KLHL29 may be primarily responsible for the changes. Spearman's correlation analysis performed for key genes and immune cells indicated that KLHL29 closely correlated to T cells and M2 macrophages, and HTRA1 very tightly correlated to plasma cells. (4) Conclusions: Bio-informatics analyses performed for DCM and obesity in our study suggested that obesity disturbed the immune micro-environment, promoted oxidative stress, and increased myocardial fibrosis, resulting in ventricular remodeling and an increased risk of DCM. The key genes KLHL29 and HTRA1 may play critical roles in obesity-related DCM.

The Roles of Optogenetics and Technology in Neurobiology: A Review

Optogenetic is a technique that combines optics and genetics to control specific neurons. This technique usually uses adenoviruses that encode photosensitive protein. The adenovirus may concentrate in a specific neural region. By shining light on the target nerve region, the photosensitive protein encoded by the adenovirus is controlled. Photosensitive proteins controlled by light can selectively allow ions inside and outside the cell membrane to pass through, resulting in inhibition or activation effects. Due to the high precision and minimally invasive, optogenetics has achieved good results in many fields, especially in the field of neuron functions and neural circuits. Significant advances have also been made in the study of many clinical diseases. This review focuses on the research of optogenetics in the field of neurobiology. These include how to use optogenetics to control nerve cells, study neural circuits, and treat diseases by changing the state of neurons. We hoped that this review will give a comprehensive understanding of the progress of optogenetics in the field of neurobiology.

Precision Medicine in Lung Cancer Theranostics: Paving the Way from Traditional Technology to Advance Era

Precision medicine for lung cancer theranostics is an advanced model combining prevention, diagnosis, and treatment for individual or specific population diseases to match individual patient differences. It involves collection and integration of genome, transcriptome, proteome, and metabolome features of lung cancer patients, combined with clinical characteristics. Subsequently, large data and artificial intelligence (AI) analysis have emerged to identify the most suitable therapeutic targets and personal treatment strategies for treatment of patients with lung cancer. We review the development and challenges associated with diagnosis and therapy of lung cancer from traditional technology, including immunotherapy prediction markers, liquid biopsy, surgery, and tumor immune microenvironment and patient-derived xenograft models, to AI in the era of precision medicine. AI has improved precision medicine and the predictive ability and accuracy of patient outcomes. Finally, we discuss some opportunities and challenges for lung cancer theranostics. Precision medicine in lung cancer can help us find the optimum treatment dose and time for a specific patient, which can advance the development of lung cancer therapeutics.

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

The Mechanism of Stimulating and Mobilizing the Immune System Enhancing the Anti-Tumor Immunity

Cancer immunotherapy is a kind of therapy that can control and eliminate tumors by restarting and maintaining the tumor-immune cycle and restoring the body's normal anti-tumor immune response. Although immunotherapy has great potential, it is currently only applicable to patients with certain types of tumors, such as melanoma, lung cancer, and cancer with high mutation load and microsatellite instability, and even in these types of tumors, immunotherapy is not effective for all patients. In order to enhance the effectiveness of tumor immunotherapy, this article reviews the research progress of tumor microenvironment immunotherapy, and studies the mechanism of stimulating and mobilizing immune system to enhance anti-tumor immunity. In this review, we focused on immunotherapy against tumor microenvironment (TME) and discussed the important research progress. TME is the environment for the survival and development of tumor cells, which is composed of cell components and non-cell components; immunotherapy for TME by stimulating or mobilizing the immune system of the body, enhancing the anti-tumor immunity. The checkpoint inhibitors can effectively block the inhibitory immunoregulation, indirectly strengthen the anti-tumor immune response and improve the effect of immunotherapy. We also found the checkpoint inhibitors have brought great changes to the treatment model of advanced tumors, but the clinical treatment results show great individual differences. Based on the close attention to the future development trend of immunotherapy, this study summarized the latest progress of immunotherapy and pointed out a new direction. To study the mechanism of stimulating and mobilizing the immune system to enhance anti-tumor immunity can provide new opportunities for cancer treatment, expand the clinical application scope and effective population of cancer immunotherapy, and improve the survival rate of cancer patients.

Clinical and Prognostic Pan-Cancer Analysis of N6-Methyladenosine Regulators in Two Types of Hematological Malignancies: A Retrospective Study Based on TCGA and GTEx Databases

N6-methyladenosine (m6A) is one of the most active modification factors of mRNA, which is closely related to cell proliferation, differentiation, and tumor development. Here, we explored the relationship between the pathogenesis of hematological malignancies and the clinicopathologic parameters. The datasets of hematological malignancies and controls were obtained from the TCGA [AML (n = 200), DLBCL (n = 48)] and GTEx [whole blood (n = 337), blood vascular artery (n = 606)]. We analyzed the m6A factor expression differences in normal tissue and tumor tissue and their correlations, clustered the express obvious clinical tumor subtypes, determined the tumor risk score, established Cox regression model, performed univariate and multivariate analysis on all datasets. We found that the AML patients with high expression of IGF2BP3, ALKBH5, and IGF2BP2 had poor survival, while the DLBCL patients with high expression of METTL14 had poor survival. In addition, "Total" datasets analysis revealed that IGF2BP1, ALKBH5, IGF2BP2, RBM15, METTL3, and ZNF217 were potential oncogenes for hematologic system tumors. Collectively, the expressions of some m6A regulators are closely related to the occurrence and development of hematologic system tumors, and the intervention of specific regulatory factors may lead to a breakthrough in the treatment in the future.

Imaging genomics for accurate diagnosis and treatment of tumors: A cutting edge overview

Imaging genomics refers to the establishment of the connection between invasive gene expression features and non-invasive imaging features. Tumor imaging genomics can not only understand the macroscopic phenotype of tumor, but also can deeply analyze the cellular and molecular characteristics of tumor tissue. In recent years, tumor imaging genomics has been a key in the field of medicine. The incidence of cancer in China has increased significantly, which is the main reason of disease death of urban residents. With the rapid development of imaging medicine, depending on imaging genomics, many experts have made remarkable achievements in tumor screening and diagnosis, prognosis evaluation, new treatment targets and understanding of tumor biological mechanism. This review analyzes the relationship between tumor radiology and gene expression, which provides a favorable direction for clinical staging, prognosis evaluation and accurate treatment of tumors.

How wide is the application of genetic big data in biomedicine

In the era of big data, massive genetic data, as a new industry, has quickly swept almost all industries, especially the pharmaceutical industry. As countries around the world start to build their own gene banks, scientists study the data to explore the origins and migration of humans. Moreover, big data encourage the development of cancer therapy and bring good news to cancer patients. Big datum has been involved in the study of many diseases, and it has been found that analyzing diseases at the gene level can lead to more beneficial treatment options than ordinary treatments. This review will introduce the development of extensive data in medical research from the perspective of big data and tumor, neurological and psychiatric diseases, cardiovascular diseases, other applications and the development direction of big data in medicine.

Breakthrough in targeted therapy for non-small cell lung cancer

Non-small cell lung cancer (NSCLC) remains by far the single most common malignancy of lung cancer which causes more and more mortality in recent years. NSCLC accounts for more than 80 % of lung cancers, and the vast majority of patients were found to be in advanced inoperable stages. Chemotherapy used to be the main treatment for NSCLC, but due to its obvious side effects. Chemotherapy gradually withdrew from the stage of history. In recent years, cellular and molecular biotechnology has developed rapidly, and researchers have begun to target key genes and regulatory molecules for treatment. Targeted drugs have also emerged. The purpose of this review is to introduce important research achievements in recent years and the treatment progress of new drugs.