Genomics and Transcriptomics: The Powerful Technologies in Precision Medicine

Multi-omics joint analysis reveals the mechanism underlying Chinese herbal Yougui Pill in the treatment of knee osteoarthritis

ETHNOPHARMACOLOGICAL RELEVANCE

Yougui Pill (YGP), originating from Jingyue Quanshu, comprises 10 traditional Chinese medicines (TCMs). This classic prescription is renowned for its ability to tonify the kidney, warm the kidney, promote yang, warm the interior, and dispel cold. YGP has proven effective in treating degenerative knee arthritis, osteoporosis, delayed fracture healing, and other orthopedic conditions, making it a widely used clinical prescription for knee osteoarthritis (KOA).

AIM OF THE STUDY

Although YGP is commonly used in clinical practice, its pharmacodynamic material basis and anti-arthritis mechanisms remain unclear. This study aims to comprehensively analyze the chemical constituents of YGP and elucidate its anti-arthritis mechanisms.

MATERIALS AND METHODS

Ultra-high performance liquid chromatography coupled with electrospray ionization-triple quadrupole-linear ion trap mass spectrometry(ESI-Q TRAP-MS/MS) was used to identify the chemical constituents of YGP. The Hulth method was utilized to establish KOA rat model, and pathological examinations were performed to assess the anti-arthritis effects of YGP. Integrated metabolomics and transcriptomics analyses were conducted to explore the anti-arthritis mechanisms of YGP. Key targets were confirmed via immunohistochemistry.

RESULTS

A total of 1981 chemical components were identified in YGP, predominantly phenolic acids and flavonoids. Compared with the model group, 422 differentially expressed metabolites and 214 differentially expressed genes were identified, primarily involving the MAPK signaling pathway, FoxO signaling pathway, and PI3K-Akt pathway. YGP exerted an anti-osteoarthritis effect by inhibiting the excessive activation of the EGFR/ERT/FOS signaling pathway.

CONCLUSIONS

TCM offers significant advantages in the treatment of KOA, addressing the shortcomings of current clinical medications. YGP displayed exceptional pharmacodynamic effects. This study elucidated its pharmacodynamic material basis and anti-osteoarthritis mechanisms, providing substantial support for its clinical application and the development of related drugs.

Novel clinical trial designs emerging from the molecular reclassification of cancer

Next-generation sequencing has revealed the disruptive reality that advanced/metastatic cancers have complex and individually distinct genomic landscapes, necessitating a rethinking of treatment strategies and clinical trial designs. Indeed, the molecular reclassification of cancer suggests that it is the molecular underpinnings of the disease, rather than the tissue of origin, that mostly drives outcomes. Consequently, oncology clinical trials have evolved from standard phase 1, 2, and 3 tissue-specific studies; to tissue-specific, biomarker-driven trials; to tissue-agnostic trials untethered from histology (all drug-centered designs); and, ultimately, to patient-centered, N-of-1 precision medicine studies in which each patient receives a personalized, biomarker-matched therapy/combination of drugs. Innovative technologies beyond genomics, including those that address transcriptomics, immunomics, proteomics, functional impact, epigenetic changes, and metabolomics, are enabling further refinement and customization of therapy. Decentralized studies have the potential to improve access to trials and precision medicine approaches for underserved minorities. Evaluation of real-world data, assessment of patient-reported outcomes, use of registry protocols, interrogation of exceptional responders, and exploitation of synthetic arms have all contributed to personalized therapeutic approaches. With greater than 1 × 1012 potential patterns of genomic alterations and greater than 4.5 million possible three-drug combinations, the deployment of artificial intelligence/machine learning may be necessary for the optimization of individual therapy and, in the near future, also may permit the discovery of new treatments in real time.

Neuroinflammation and nociception in intervertebral disc degeneration: a review of precision medicine perspective

Intervertebral disc (IVD) degeneration is a major cause of low back pain (LBP), which results in disability worldwide. However, the pathogenesis of IVD degeneration mediating LBP remains unclear. Current conservative treatments and surgical interventions are both to relieve the symptoms and minimise pain; nevertheless, they are unable to reverse the degeneration. Previous studies have shown that inflammation and nociception markers are important indicators of pain mechanisms in IVD degeneration underlying LBP. As such, multiomics profiling allows the discovery of these target markers to understand the key pathological mechanisms mediating IVD degeneration underpinnings of LBP. This article provides insights into a precision medicine approach for identifying and understanding the pathophysiology of IVD degeneration associated with LPB based on the severity of the disease from early and mild to severe degenerative stages. Molecular profiling of key markers in degenerative IVDs based on patient stratification at early, mild, and severe stages will contribute to the identification of target markers associated with signalling pathways in mediating neuroinflammation, innervation, and nociception underlying painful IVD degeneration. This approach will offer an understanding of establishing personalised clinical strategies tailored to the severity of IVD degeneration for the treatment of LBP.

Isolating high-quality RNA for RNA-Seq from 10-year-old blood samples

There is much interest in analysing RNA, particularly with RNA Sequencing, across both research and diagnostic domains. However, its inherent instability renders it susceptible to degradation. Given the imperative for RNA integrity in such applications, proper storage and biobanking of blood samples and successful subsequent RNA isolation is essential to guarantee optimal integrity for downstream analyses. Especially for larger collections, it would be particularly beneficial if these methods would additionally offer affordability, minimal blood volume requirements and also long-term storage. In this study, RNA of high quality, suitable for transcriptomics, has been successfully isolated from 400 µL of EDTA and citrated whole blood samples in Boom's lysis buffer stored at -85 °C for 10 years. Isolation was carried out using a modified Zymo Research Quick-RNA kit protocol. This isolation method showed significant improvement in RNA integrity when compared to RNA extracted using the original Boom method. RNA Sequencing provided high-quality data comparable to that of other studies using recently frozen blood in RNA stabilisation tubes. Additionally, sequencing data from blood collected in citrate and EDTA anticoagulants also showed excellent correlation.

Application of multi-omics techniques to androgenetic alopecia: Current status and perspectives

The rapid advancement of sequencing technologies has enabled the generation of vast datasets, allowing for the in-depth analysis of sequencing data. This analysis has facilitated the validation of novel pathogenesis hypotheses for understanding and treating diseases through ex vivo and in vivo experiments. Androgenetic alopecia (AGA), a common hair loss disorder, has been a key focus of investigators attempting to uncover its underlying mechanisms. Abnormal changes in mRNA, proteins, and metabolites have been identified in individuals with AGA, and future developments in sequencing technologies may reveal new biomarkers for AGA. By integrating multiple omics analysis datasets such as genomics, transcriptomics, proteomics, and metabolomics-along with clinical phenotype data-we can achieve a comprehensive understanding of the molecular underpinnings of AGA. This review summarizes the data-mining studies conducted on various omics analysis datasets as related to AGA that have been adopted to interpret the biological data obtained from different omics layers. We herein discuss the challenges of integrative omics analyses, and suggest that collaborative multi-omics studies can enhance the understanding of the complete pathomechanism(s) of AGA by focusing on the interaction networks comprising DNA, RNA, proteins, and metabolites.

Flavonoids attenuate inflammation of HGF and HBMSC while modulating the osteogenic differentiation based on microfluidic chip

BACKGROUND

When inflammation occurs in periodontal tissues, a dynamic cellular crosstalk interacts between gingival fibroblasts and bone marrow mesenchymal stem cells (BMSCs), which plays a crucial role in the biological behaviour and differentiation of the cells. Recently, flavonoids are increasingly recognized for their therapeutic potential in modulating inflammation and osteogenic differentiation. Owing to their varied molecular structures and mechanisms, there are more needs that flavonoid compounds should be identified by extensive screening. However, current drug research mostly relies on static, single-type cell cultures. In this study, an innovative bionic microfluidic chip system tailored for both soft and hard tissues was developed to screen for flavonoids suitable for treating periodontitis.

METHODS

This study developed a microfluidic system that bionically simulates the soft and hard structures of periodontal tissues. Live/dead staining, reactive oxygen species (ROS) staining, and RT-qPCR analysis were employed. These techniques evaluated the effects of flavonoid compounds on the levels of inflammatory factors and ROS contents in HGF and HBMSC under LPS stimulation. Additionally, the impact of these compounds on osteogenic induction in HBMSC and the exploration of the underlying mechanisms were assessed.

RESULTS

The microfluidic chip used in this study features dual chambers separated by a porous membrane, allowing cellular signal communication via bioactive factors secreted by cells in both layers under perfusion. The inflammatory response within the chip under LPS stimulation was lower compared to individual static cultures of HGF and HBMSC. The selected flavonoids-myricetin, catechin, and quercetin-significantly reduced cellular inflammation, decreased ROS levels, and enhanced osteogenic differentiation of BMSCs. Additionally, fisetin, silybin, and icariside II also demonstrated favorable outcomes in reducing inflammation, lowering ROS levels, and promoting osteogenic differentiation through the Wnt/β-catenin pathway.

CONCLUSIONS

The bionic microfluidic chip system provides enhanced capabilities for drug screening and evaluation, delivering a more precise assessment of drug efficacy and safety compared to traditional in vitro methods. This study demonstrates the efficacy of flavonoids in influencing osteogenic processes in BMSCs primarily through the Wnt/β-catenin pathway. These results uncover the potential of flavonoids as therapeutic medicine for treating periodontitis, meriting further research and development.

Exploring a new signature for lung adenocarcinoma: analyzing cuproptosis-related genes through Integrated single-cell and bulk RNA sequencing

OBJECTIVES

Lung adenocarcinoma (LUAD) continues to pose a significant global health challenge. This research investigates cuproptosis and its association with LUAD progression. Employing various bioinformatics techniques, the study explores the heterogeneity of LUAD cells, identifies prognostic cuproptosis-related genes (CRGs), examines cell-to-cell communication networks, and assesses their functional roles.

METHODS

We downloaded single-cell RNA sequencing data from TISCH2 and bulk RNA sequencing data from TCGA for exploring LUAD cell heterogeneity. Subsequently, "CellChat" package was employed for intercellular communication network analysis, while weighted correlation network analysis was applied for identification of hub CRGs. Further, A cuproptosis related prognostic signature was constructed via LASSO regression, validated through survival analysis, nomogram development, and ROC curves. We assessed immune infiltration, gene mutations, and GSEA of prognostic CRGs. Finally, in vitro experiments were applied to validate CDC25C's role in LUAD.

RESULTS

We identified 15 clusters and nine cell type in LUAD. Malignant cells showed active communication and pathway enrichment in "oxidative phosphorylation" and "glycolysis". Meanwhile, prognostic hub CRGs including PFKP, CDC25C, F12, SIGLEC6, and NLRP7 were identified, with a robust prognostic signature. Immune infiltration, gene mutations, and functional enrichment correlated with prognostic CRGs. In vitro cell experiments have shown that CDC25C-deficient LUAD cell lines exhibited reduced activity.

CONCLUSION

This research reveals the heterogeneity of LUAD cells, identifies key prognostic CRGs, and maps intercellular communication networks, providing insights into LUAD pathogenesis. These findings pave the way for developing targeted therapies and precision medicine approaches.

Machine learning and related approaches in transcriptomics

Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.

Transcriptomic Characterization of Male Formosan Pangolin (Manis pentadactyla pentadactyla) Reproductive Tract and Evaluation of Domestic Cat (Felis catus) as a Potential Model Species

The Formosan pangolin (Manis pentadactyla pentadactyla) is an endemic animal of Taiwan. Due to their reduced population and behavior, very little is known about this enigmatic species. To unravel male pangolin reproduction, in the present study, we built a complete genomic database of the male Formosan pangolin reproductive tract and revealed highly expressing genes as well as critical signaling pathways and their associated biological processes in both the testis and the epididymis. Moreover, we evaluated the domestic cat (Felis catus) as a potential model species for male pangolin reproduction by comparing their testicular transcriptomes. We demonstrated a clear tissue-specific gene expression supporting the unique biological signature of each reproductive tissue and identified critical genes of the different reproductive organs. Pathway enrichment analysis revealed unique pathways in the testis as well as a clear epididymal transition. Furthermore, domestic cats, despite being the closest domestic species to pangolin, demonstrated their unfitness as a male reproduction model species as clear differences in spermatid differentiation and metabolism were observed. These results enable a better understanding of male pangolin reproduction characteristics and may inspire improvements in in Formosan pangolin conservation strategies.

Lung Cancer Subtyping: A Short Review

As of 2022, lung cancer is the most commonly diagnosed cancer worldwide, with the highest mortality rate. There are three main histological types of lung cancer, and it is more important than ever to accurately identify the subtypes since the development of personalized, type-specific targeted therapies that have improved mortality rates. Traditionally, the gold standard for the confirmation of histological subtyping is tissue biopsy and histopathology. This, however, comes with its own challenges, which call for newer sampling techniques and adjunctive tools to assist in and improve upon the existing diagnostic workflow. This review aims to list and describe studies from the last decade (n = 47) that investigate three such potential omics techniques-namely (1) transcriptomics, (2) proteomics, and (3) metabolomics, as well as immunohistochemistry, a tool that has already been adopted as a diagnostic adjunct. The novelty of this review compared to similar comprehensive studies lies with its detailed description of each adjunctive technique exclusively in the context of lung cancer subtyping. Similarities between studies evaluating individual techniques and markers are drawn, and any discrepancies are addressed. The findings of this study indicate that there is promising evidence that supports the successful use of omics methods as adjuncts to the subtyping of lung cancer, thereby directing clinician practice in an economical and less invasive manner.

Exploring the apoptotic effects of sericin on HCT116 cells through comprehensive nanostring transcriptomics and proteomics analysis

Sericin, a silk protein from Bombyx mori (silkworms), has many applications, including cosmetics, anti-inflammation, and anti-cancer. Sericin complexes with nanoparticles have shown promise for breast cancer cell lines. Apoptosis, a programmed cell death mechanism, stops cancer cell growth. This study found that Sericin urea extract significantly affected HCT116 cell viability (IC50 = 42.00 ± 0.002 µg/mL) and caused apoptosis in over 80% of treated cells. S-FTIR analysis showed significant changes in Sericin-treated cells' macromolecule composition, particularly in the lipid and nucleic acid areas, indicating major cellular modifications. A transcriptomics study found upregulation of the apoptotic signaling genes FASLG, TNFSF10, CASP3, CASP7, CASP8, and CASP10. Early apoptotic proteins also showed that BAD, AKT, CASP9, p53, and CASP8 were significantly upregulated. A proteomics study illuminated Sericin-treated cells' altered protein patterns. Our results show that Sericin activated the extrinsic apoptosis pathway via the caspase cascade (CASP8/10 and CASP3/7) and the death receptor pathway, involving TNFSF10 or FASLG, in HCT116 cells. Upregulation of p53 increases CASP8, which activates CASP3 and causes HCT116 cell death. This multi-omics study illuminates the molecular mechanisms of Sericin-induced apoptosis, sheds light on its potential cancer treatment applications, and helps us understand the complex relationship between silk-derived proteins and cellular processes.

Integration and implementation of precision medicine in the multifaceted inflammatory bowel disease

Inflammatory bowel disease (IBD) is a complex disease with variability in genetic, environmental, and lifestyle factors affecting disease presentation and course. Precision medicine has the potential to play a crucial role in managing IBD by tailoring treatment plans based on the heterogeneity of clinical and temporal variability of patients. Precision medicine is a population-based approach to managing IBD by integrating environmental, genomic, epigenomic, transcriptomic, proteomic, and metabolomic factors. It is a recent and rapidly developing medicine. The widespread adoption of precision medicine worldwide has the potential to result in the early detection of diseases, optimal utilization of healthcare resources, enhanced patient outcomes, and, ultimately, improved quality of life for individuals with IBD. Though precision medicine is promising in terms of better quality of patient care, inadequacies exist in the ongoing research. There is discordance in study conduct, and data collection, utilization, interpretation, and analysis. This review aims to describe the current literature on precision medicine, its multiomics approach, and future directions for its application in IBD.

The immune checkpoint VISTA is associated with prognosis in patients with malignant uveal melanoma

Introduction

Uveal melanoma (UM) is a rare yet deadly tumor. It is known for its high metastatic potential, which makes it one of the most aggressive and lethal cancers. Recently, immune checkpoints such as Programmed cell Death protein-1 (PD1) and Cytotoxic T-Lymphocyte-Associated significantly increasing patient survival in multiple human cancers, especially cutaneous melanoma. However, patients with UMs were excluded from these studies because of their molecular characteristics, which tend to be widely different from those of cutaneous melanoma. This study aimed to analyze the expression of V domain Ig Suppressor T-cell Activation (VISTA), a novel immune checkpoint, to evaluate its prognosis significance and its correlation with PD1 and CTLA-4.

Methods

Evaluation of VISTA, CTLA-4, and PD1 expression was performed through TCGA database analysis and immunohistochemistry using two independent cohorts with primary malignant UM.

Results and discussion

Our results showed that VISTA expression was associated with tumor aggressiveness, T cell exhaustion, and the shortest median overall survival among patients. Surprisingly, PD1 protein expression was negative in all patients, whereas CTLA-4 expression was high in patients with advanced stages. Our findings suggest that VISTA may be a prognostic marker and an attractive treatment strategy for immunotherapy in patients with UM. Exploring its expression profile may predict response to immunotherapy and may lead to the improvement of precision therapy in malignant uveal melanoma patients.

Whole-Genome Transcriptomics of PBMC to Identify Biomarkers of Early Metabolic Risk in Apparently Healthy People with Overweight-Obesity and in Normal-Weight Subjects

SCOPE

Peripheral blood mononuclear cells (PBMC) provide a useful and minimally invasive source of biomarkers. Here to identify PBMC transcriptomic biomarkers predictive of metabolic impairment related to increased adiposity is aimed.

METHODS AND RESULTS

The study analyzed the global PBMC transcriptome in metabolically healthy (normoglycemic) volunteers with overweight-obesity (OW-OB, n = 12), and in subjects with metabolically obese normal-weight (MONW, n = 5) phenotype, in comparison to normal-weight (NW, n = 12) controls. The study identifies 1072 differentially expressed genes (DEGs) in OW-OB versus NW and 992 in MONW versus NW. Hierarchical clustering of the top 100 DEGs clearly distinguishes OW-OB and MONW from NW. Remarkably, the OW-OB and MONW phenotypes share 257 DEGs regulated in the same direction. The top up-regulated gene CXCL8, coding for interleukin 8, with a role in obesity-related pathologies, is of special interest as a potential marker for predicting increased metabolic risk. CXCL8 expression is increased mainly in the MONW group and correlated directly with C-reactive protein levels.

CONCLUSIONS

PBMC gene expression analysis of CXCL8 or a pool of DEGs may be used to identify early metabolic risk in an apparently healthy population regardless of their BMI, i.e., subjects with OW-OB or MONW phenotype and to apply adequate and personalized nutritional preventive strategies.

Genome-wide identification and analysis of bZIP gene family reveal their roles during development and drought stress in Wheel Wingnut (Cyclocarya paliurus)

Background

The bZIP gene family has important roles in various biological processes, including development and stress responses. However, little information about this gene family is available for Wheel Wingnut (Cyclocarya paliurus). 

Results

In this study, we identified 58 bZIP genes in the C. paliurus genome and analyzed phylogenetic relationships, chromosomal locations, gene structure, collinearity, and gene expression profiles. The 58 bZIP genes could be divided into 11 groups and were unevenly distributed among 16 C. paliurus chromosomes. An analysis of cis-regulatory elements indicated that bZIP promoters were associated with phytohormones and stress responses. The expression patterns of bZIP genes in leaves differed among developmental stages. In addition, several bZIP members were differentially expressed under drought stress. These expression patterns were verified by RT-qPCR.

Conclusions

Our results provide insights into the evolutionary history of the bZIP gene family in C. paliurus and the function of these genes during leaf development and in the response to drought stress. In addition to basic genomic information, our results provide a theoretical basis for further studies aimed at improving growth and stress resistance in C. paliurus, an important medicinal plant.

Intervertebral Disc Degeneration: Biomaterials and Tissue Engineering Strategies toward Precision Medicine

Intervertebral disc degeneration is a common cause of discogenic low back pain resulting in significant disability. Current conservative or surgical intervention treatments do not reverse the underlying disc degeneration or regenerate the disc. Biomaterial-based tissue engineering strategies exhibit the potential to regenerate the disc due to their capacity to modulate local tissue responses, maintain the disc phenotype, attain biochemical homeostasis, promote anatomical tissue repair, and provide functional mechanical support. Despite preliminary positive results in preclinical models, these approaches have limited success in clinical trials as they fail to address discogenic pain. This review gives insights into the understanding of intervertebral disc pathology, the emerging concept of precision medicine, and the rationale of personalized biomaterial-based tissue engineering tailored to the severity of the disease targeting early, mild, or severe degeneration, thereby enhancing the efficacy of the treatment for disc regeneration and ultimately to alleviate discogenic pain. Further research is required to assess the relationship between disc degeneration and lower back pain for developing future clinically relevant therapeutic interventions targeted towards the subgroup of degenerative disc disease patients.

Omics technologies in poultry health and productivity - part 1: current use in poultry research

In biology, molecular terms with the suffix "-omics" refer to disciplines aiming at the collective characterization of pools of molecules derived from different layers (DNA, RNA, proteins, metabolites) of living organisms using high-throughput technologies. Such omics analyses have been widely implemented in poultry research in recent years. This first part of a bipartite review on omics technologies in poultry health and productivity examines the use of multiple omics and multi-omics techniques in poultry research. More specific present and future applications of omics technologies, not only for the identification of specific diagnostic biomarkers, but also for potential future integration in the daily monitoring of poultry production, are discussed in part 2. Approaches based on omics technologies are particularly used in poultry research in the hunt for genetic markers of economically important phenotypical traits in the host, and in the identification of key bacterial species or functions in the intestinal microbiome. Integrative multi-omics analyses, however, are still scarce. Host physiology is investigated via genomics together with transcriptomics, proteomics and metabolomics techniques, to understand more accurately complex production traits such as disease resistance and fertility. The gut microbiota, as a key player in chicken productivity and health, is also a main subject of such studies, investigating the association between its composition (16S rRNA gene sequencing) or function (metagenomics, metatranscriptomics, metaproteomics, metabolomics) and host phenotypes. Applications of these technologies in the study of other host-associated microbiota and other host characteristics are still in their infancy.

Precision medicine journey through omics approach

It has been well established that understanding the underlying heterogeneity of numerous complex disease process needs new strategies that present in precision medicine for prediction, prevention and personalized treatment strategies. This approach must be tailored for each individual's unique omics that lead to personalized management of disease. The correlation between different omics data should be considered in precision medicine approach. The interaction provides a hypothesis which is called domino effect in the present minireview. Here we review the various potentials of omics data including genomics, transcriptomics, proteomics, metabolomics, pharmacogenomics. We comprehensively summarize the impact of omics data and its major role in precision medicine and provide a description about the domino effect on the pathophysiology of diseases. Each constituent of the omics data typically provides different information in associated with disease. Current research, although inadequate, clearly indicate that the information of omics data can be applicable in the concept of precision medicine. Integration of different omics data type in domino effect hypothesis can explain the causative changes of disease as it is discussed in the system biology too. While most existing studies investigate the omics data separately, data integration is needed on the horizon of precision medicine by using machine learning.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Precision Medicine and Precision Nursing: The Era of Biomarkers and Precision Health

Precision health, by means of the support of precision medicine and precision nursing, is able to support clinical decision making in order to tailor optimal health-care decisions, around the individual characteristics of patients. The operational arm of precision health is represented by the use of biomarkers that can give useful information about disease susceptibility, exposure, evolution and response to treatment. Omics, imaging and clinical biomarkers are actually studied for their ability to positively impact health-care management. In this article, we try to address the role of biomarkers in the context of modern medicine and nursing with the view of improving patients care.

Cancer Omics in Africa: Present and Prospects

During the last century, cancer biology has been arguably one of the most investigated research fields. To gain deeper insight into cancer mechanisms, scientists have been attempting to integrate multi omics data in cancer research. Cancer genomics, transcriptomics, metabolomics, proteomics, and metagenomics are the main multi omics strategies used currently in the diagnosis, prognosis, treatment, and biomarker discovery in cancer. In this review, we describe the use of different multi omics strategies in cancer research in the African continent and discuss the main challenges facing the implementation of these approaches in African countries such as the lack of training programs in bioinformatics in general and omics strategies in particular and suggest paths to address deficiencies. As a way forward, we advocate for the establishment of an "African Cancer Genomics Consortium" to promote intracontinental collaborative projects and enhance engagement in research activities that address indigenous aspects for cancer precision medicine.