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Journal of Drug Delivery and Therapeutics

Open Access to Pharmaceutical and Medical Research

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Open Access Full Text Article   Review Article

Multi-Omics Integration in Personalized Medicine: Advancing Laboratory Diagnostics and Precision Therapeutics in the Era of Individualized Healthcare

Tochukwu Anthony Ikwelle ¹, Augustine Chinedu Ihim ¹*, Donatus F.N. Ozuruoke ⁵, Patrick Chinedu Obi ³, Collins Uchechukwu Obi ¹, Ifeoma Joy Onuora ², Awalu Joseph Chimezie ⁶, Ogai Ogalagu Romanus ⁴

¹ Clinical Chemistry Department, Faculty of Medical Laboratory Science Nnamdi Azikiwe University, Nigeria

² Department of Medical Laboratory Science, Faculty of health sciences and technology, Chukwuemeka Odumegwu Ojukwu University Igbariam, Anambra State Nigeria

³ Department of Internal Medicine, Federal University Teaching Hospital Owerri, Imo State, Nigeria

⁴ Department of Biochemistry, Tansian University, Umunya, Anambra State, Nigeria

⁵ Department of Medical Laboratory Science,Faculty of Medical and health sciences, New Gate University, Minna

⁶ Chemical Pathology Unit,Department of Medical Laboratory Science, Ebonyi State University Abakaliki

 

 

Introduction

Personalized medicine is a healthcare approach that tailors disease prevention, diagnosis, and treatment to an individual’s unique genetic, environmental, and lifestyle factors. By customizing medical interventions, it seeks to maximize treatment effectiveness while minimizing side effects. This approach leverages advanced diagnostic and therapeutic tools, including genomics, proteomics, and pharmacogenomics, to analyze a person’s genetic makeup, molecular profile, and clinical data, enabling targeted and precise medical decisions. Precision medicine is a specialized subset of personalized medicine that emphasizes tailoring treatments to an individual’s unique molecular characteristics¹. While personalized medicine encompasses a broader approach, considering various factors that tailor healthcare to an individual’s needs and circumstances, precision medicine specifically focuses on molecular-level interventions. The advancement of targeted therapies and companion diagnostics, tools that identify patients most likely to benefit from specific treatments, has been a key driver in the growing adoption of precision medicine². The first successful application of precision medicine was the use of imatinib to treat chronic myelogenous leukemia (CML) in 2001, which demonstrated the power of targeting specific genetic mutations to treat cancer³. Precision medicine represents a paradigm shift in the approach to healthcare⁴. While the completion of the Human Genome Project in 2003 significantly accelerated progress in this field, earlier medical breakthroughs had already demonstrated the potential of tailoring treatments based on individual patient characteristics. 

The Human Genome Project, which aimed to map the entire human genome, played a crucial role in identifying genetic variations linked to various diseases, thereby laying the groundwork for the development of personalized medical interventions⁵. The revolutionary breakthroughs made by these scientists allowed researchers, for the first time in history, to connect diseases and human health to a wide range of individual genetic and environmental factors. The scientific discoveries helped researchers to gain a better understanding of what specific molecules within the human body determine a patient’s individuality and, more importantly, their susceptibility to certain diseases. As a systematic survey recently concluded, whether called precision medicine or personalized medicine, the phrase has come to refer to the way personal data and biomarkers, particularly genetic biomarkers, might be used to tailor treatments for individual patients⁶. Precision medicine is not a new concept but has its roots in the early days of medicine, where physicians practiced personalized medicine by observing and treating individual patients based on their unique symptoms and characteristics⁷. The advent of next-generation sequencing and other high-throughput technologies has accelerated the pace of precision medicine research, enabling rapid identification of disease-causing mutations and personalized treatment options.

Precision medicine has many potential applications in healthcare, such as cancer treatment, where specific genetic mutations or biomarkers in cancer cells can be identified and targeted with drugs for more effective and personalized treatments⁸. In pharmacogenomics, precision medicine can identify genetic variations that affect an individual's response to medications, enabling doctors to prescribe the most effective treatment with minimal side effects. It can also identify the genetic causes of rare diseases and genetic disorders, allowing for earlier diagnosis and more targeted treatments. For infectious diseases, precision medicine can help identify genetic variations that make individuals more susceptible to certain diseases, leading to more targeted prevention and treatment¹⁰. Similarly, precision medicine can help identify the genetic variations that cause neurological disorders, such as Alzheimer's and Parkinson's diseases, enabling earlier diagnosis and more targeted treatments. 

Personalized medicine is being revolutionized by the integration of multi-omics data, primarily genomics, transcriptomics, proteomics, and metabolomics¹¹. This integrated approach provides a holistic view of individual health by capturing genetic, molecular, and biochemical information. The ability to generate and synthesize multi-omics data enables the development of highly specific and personalized therapeutic strategies, enhancing treatment efficacy and minimizing adverse reactions. Despite its promise, the full implementation of personalized medicine faces substantial barriers¹¹. Chief among these is the complexity of integrating diverse omics datasets, the requirement for advanced bioinformatics and computational infrastructure, and the high costs associated with large-scale data generation. The challenges related to data privacy, lack of standardization across platforms, and limited validation in diverse populations continue to impede widespread clinical application.

History and Development

The evolution of personalized medicine has been driven by key breakthroughs in genetics, technology, and clinical practice, marking a clear departure from the traditional one-size-fits-all model toward a more precise, individualized approach that targets the molecular mechanisms underlying health, disease, and therapeutic response¹². The origins of personalized medicine can be traced back to Hippocrates, who recognized that individual variability among patients could influence how they respond to treatment, an idea that laid the foundation for today’s precision healthcare approaches. However, the scientific foundation of personalized medicine was solidified in the 20th century through the discovery of DNA and the elucidation of the genetic code¹³, which provided the molecular basis for understanding individual variability in health and disease. The use of the term "personalized medicine" has gained momentum in recent years, driven by advances in diagnostic technologies and bioinformatics, particularly comparative genomics, which have deepened our understanding of the molecular basis of disease and enabled more precise patient stratification and targeted interventions.

Over the past six decades, extensive research has demonstrated that a substantial proportion of variability in drug response is influenced by genetic factors, alongside diet, health status, environmental exposures, and combination therapies¹⁴. Progress in pharmacogenomics and pharmacogenetics has been instrumental in uncovering the molecular mechanisms through which an individual’s genetic makeup affects disease susceptibility and therapeutic efficacy, thereby advancing the principles of personalized medicine¹⁵. Consequently, drug therapies must be tailored to individual patient profiles to achieve more predictable and effective outcomes across geographically and ethnically diverse populations, accounting for genetic variability and other individual-specific factors that influence treatment response. Pharmacogenomics emerged from advances in molecular medicine aimed at understanding the molecular mechanisms that drive individual drug responses. This application of scientific research is what defines personalized medicine. While the concept remains largely unfamiliar to the average patient, it holds the potential to transform the entire healthcare system by enabling more precise, effective, and patient-centered care¹⁶. Today, personalized medicine has significantly transformed cancer care. Advances in genomic sequencing and molecular diagnostics have enabled oncologists to classify cancers with greater precision, leading to more accurate diagnoses, improved prognostic assessments, and better prediction of treatment responses¹⁷. Targeted therapies such as HER2 inhibitors for breast cancer and EGFR inhibitors for lung cancer are developed based on specific genetic mutations identified within individual tumors¹⁸, marking a shift toward more effective, individualized oncology treatment strategies.

Genomics and personalized medicine

Genomics has played a pivotal role in advancing personalized medicine, allowing for targeted therapies and precision medicine approaches to improve patient outcomes¹⁹. Genomics has been instrumental in advancing personalized medicine, and the field has rapidly evolved from sequencing a few genes to analyzing entire genomes, enabling tailored treatments based on an individual’s genetic profile. Multi-omics integration techniques, particularly genomic data acquired from new sequencing technologies, have made a significant contribution to expanding and deepening understanding of the molecular mechanisms of diseases²⁰. By analyzing an individual's genetic information, healthcare providers can gain insights into their unique risk factors for disease, drug response, and potential side effects. Genomics testing involves analyzing an individual's DNA to identify variations or mutations in specific genes or regions of the genome, and its purpose can vary depending on the type of test being performed²¹. The testing process involves collecting a sample of DNA, usually through a blood or saliva sample, and analyzing it in a laboratory setting. Different types of genetic tests can be performed, including sequencing, which determines the order of the nucleotides (A, C, G, T) that make up an individual's DNA, and genotyping, which identifies specific variations or mutations in the DNA sequence. Genomic testing is revolutionizing the field of personalized medicine, allowing for more precise diagnoses and targeted treatments. Genomics testing encompasses various types of testing that serve distinct purposes. 

Cancer treatment, especially, benefits immensely from genomic analysis and precision medicine. Genomic analysis identifies specific genetic mutations that drive cancer progression²². By focusing on these mutations unique to each patient's cancer, targeted therapies can be developed to more effectively eradicate cancer cells while sparing healthy tissue. For instance, mutations in the BRCA1 and BRCA2 genes are crucial biomarkers for breast and ovarian cancers^23,24, enabling more tailored and effective treatment strategies. Gene therapy and gene-editing technologies like CRISPR allow for precise alterations to DNA²⁵. These techniques involve introducing, removing, or modifying genetic material within a person's cells to treat or prevent diseases. They have the potential to correct genetic defects at their source, potentially offering cures for genetic disorders that were once deemed untreatable²⁶. Overall, the convergence of genomics, biotechnology, and data analytics is propelling personalized medicine forward, offering transformative potential for healthcare systems. By enabling more precise, predictive, and preventive approaches, personalized medicine is set to improve patient outcomes and elevate public health on a broader scale.

Advantages and Limitations of Genetic Testing

Health information is increasingly recognized as a pivotal element in determining diagnosis and treatment within a patient's healthcare²⁷, with genetic data being a crucial component of this information. Genetic data can be obtained through various types of genetic testing, including molecular genetic tests that examine single genes or DNA strands, chromosomal tests that analyze long DNA strands and entire chromosomes, and biochemical tests that study protein behavior. Genetic testing plays an important role in healthcare as it offers valuable insights into an individual’s genetic makeup and its potential impact on their health²⁸. Critical to precision medicine, genetic testing informs various aspects of patient care, including risk assessment, prevention, diagnosis, treatment, and ongoing monitoring of genetic disorders like cancer. It provides patients and their families with important health risk information and tailored management plans. 

The accuracy and reliability of genetic testing depend on factors such as the type of test, the quality of the sample, and the expertise of the laboratory performing the test. When conducted and interpreted by qualified professionals, genetic testing can be highly accurate and beneficial in guiding treatment decisions and identifying potential risks for family members. It is an invaluable tool for diagnosing and managing genetic disorders and conditions. However, genetic testing is not without limitations. Genetic testing has limitations and cannot provide definitive answers about a person's health status or future risks²⁹. Even with its availability, genetic testing may not always be the most helpful option, as other medical tests may offer more direct benefits. For instance, in the case of hereditary hemochromatosis, an autosomal recessive disease common among Caucasians, genetic testing can identify carriers; however, current screening guidelines recommend measuring serum iron levels, total iron-binding capacity, and serum ferritin levels. These tests, which are less expensive than DNA-based testing, provide more relevant information about a patient’s iron storage status. While genetic testing for hereditary hemochromatosis can reveal whether someone is a carrier or homozygous for the gene, it does not provide insight into the patient’s current iron load³⁰

Genetic testing cannot predict future health outcomes with absolute certainty³¹. It can also reveal unexpected or incidental findings, which may cause unnecessary anxiety and lead to unwarranted medical interventions. However, counseling services are now available to help individuals understand these risks and benefits. It is crucial for individuals to carefully consider the potential risks before undergoing genetic testing, as it has limitations related to accuracy, predictive power, and clinical validity. Direct-to-consumer genetic testing allows individuals to access their genetic risk information without consulting healthcare providers, but it raises concerns over the accuracy and clinical validity of some tests. The absence of expert guidance and genetic counseling in these cases increases the risk of misinterpreting results. As a result, there is a call for greater regulation and oversight of direct-to-consumer genetic testing to ensure its responsible use and minimize potential harm to consumers³².

Pharmacogenomics and personalized medicine

Pharmacogenomics investigates how an individual’s genetic makeup affects their response to medications, combining principles from pharmacology, the study of drug action, with genomics, which focuses on genes and their functions. By aligning drug therapies with a patient’s genetic profile, pharmacogenomics supports the development of personalized treatment plans that enhance efficacy and reduce adverse effects. As research advances and technological accessibility improves, the integration of pharmacogenomics into routine clinical practice is expected to expand, driving the shift toward more precise, individualized healthcare strategies^33,34. Understanding how genetic variations affect individual responses to medications is essential, given the diversity in genetic profiles, lifestyles, and environmental exposures among patients. Differences in DNA sequences can alter drug metabolism, leading to significant variability in drug efficacy and the risk of adverse effects. This insight enables clinicians to select medications and dosages that are optimized for each individual’s genetic makeup, resulting in more effective treatment outcomes and minimized side effects key principles at the core of personalized medicine³⁵.

Genome-wide association study approaches are now being successfully applied to pharmacogenomics research³⁶. This is a powerful tool that has already produced some novel insights: HLAB5701 as a predisposing factor for flucloxacillin induced liver injury³⁷; IL28B genotypes as predictors of hepatitis C treatment outcomes³⁸; the ABO blood group genes as predictors of the antihypertensive efficacy of angiotensin-converting-enzyme inhibitors³⁹; and several genotypes as tentative biomarkers for antipsychotic induced Parkinsonism⁴⁰. Hopefully, many other genome-wide response studies will soon follow.

Advantages and Limitations of Pharmacogenomics

Pharmacogenomics plays a critical role in advancing precision medicine and its allied disciplines by enabling enhancements at every stage of the drug lifecycle from discovery to delivery. In drug research and development, pharmacogenomic data informs how candidate therapies are identified and refined for specific genetic profiles, improving the alignment between disease biology and therapeutic intervention⁴¹. Genome-wide association studies (GWAS) have been pivotal in identifying population subgroups that respond differently to specific medications, allowing for more stratified and effective therapeutic strategies⁴². In later stages of drug development, pharmacogenomic insights are increasingly embedded in the regulatory and manufacturing process. Drug labeling now frequently includes pharmacogenomic (PGx) data, guiding clinicians toward more precise prescribing based on genetic markers, with many labels containing clinically actionable information⁴¹. In clinical practice, PGx data also supports tailored prescribing decisions, enabling adjustments in drug type and dosage based on individual variations such as single nucleotide polymorphisms⁴³.

The application of pharmacogenomics in clinical trials is equally significant. Selecting genetically appropriate participants enables better assessment of pharmacokinetics and pharmacodynamics across genetically diverse populations, thereby supporting more population-specific dose optimization and enhancing trial efficiency⁴⁴. This precision reduces the likelihood of adverse drug reactions while improving treatment efficacy⁴⁵. Integrating pharmacogenomics into the drug development pipeline holds the potential to reduce R&D costs, shorten timelines, and improve healthcare outcomes by delivering more targeted therapies⁴⁶. 

Nevertheless, the field faces substantial implementation challenges. These include the complexity of gene-drug and gene-environment interactions, limited availability of diverse genomic data, and high costs associated with genetic testing and data integration. Additionally, ethical, regulatory, and infrastructural barriers, along with the limited number of drugs with well-defined genetic targets, continue to restrict their clinical uptake⁴⁷. However, as PGx testing becomes more widespread and cost-effective, it is expected to yield measurable improvements in clinical outcomes, mitigate adverse drug reactions, and contribute to more sustainable healthcare systems.

Proteomics and personalized medicine

Proteomics studies the complete set of proteins present in a biological sample, including their structures, functions, and interactions⁴⁸. It is a large-scale approach to understanding the dynamic and complex nature of protein expression and its roles in biological processes. Proteomics techniques identify, quantify, and characterize proteins, their modifications, and interactions. The aim is to gain a comprehensive understanding of protein expression, regulation, and function, which can lead to the discovery of new biomarkers, drug targets, and therapeutic interventions⁴⁹. Human phenotypes play a key role in biomedical research and clinical practice towards better diagnosis, patient stratification, and the selection of effective treatment strategies. Computational approaches developed for the integration of multiple omics data types allow for a more holistic understanding of molecular mechanisms in health and disease-related processes⁵⁰. Such combined approaches can lead to the discovery of biomarkers that enable personalized medicine approaches by representing personalized prognosis and treatment efficacy. The acquisition of data derived from genomic, transcriptomic, and proteomic personal phenotypes (among other types of omics techniques) holds implications for both personalized medicine and data privacy-related issues, considering technical, ethical, and legal aspects⁵¹. Through rapid proteomic screening of easily accessible tissue samples, early determination of the presence and/or severity of disease allows for quick response from medical personnel and improved patient outcome. The power of proteomics is in discovering the meaningful unknown in a bouillabaisse of unrelated molecules with high mass accuracy and sensitivity. For this reason, clinical proteomics will be most helpful for the diagnosis of diseases with rare or unknown etiology, monitoring the therapeutic effect and activity of drug regimens, improving treatment options for individual patients, and, as always, discovering a brave new world of medical possibilities. 

Precision medicine aims to match patients with the best course of therapy to ensure optimal outcomes, and genetic profiling of HLA loci has reduced transplant rejection rates by matching donors and recipients. Despite this level of scrutiny, as many as 10% of transplants result in rejection or graft versus host disease (GVHD)^52,53. Differential alteration of the self and transplant peptidome after infection has been implicated as a potential cause for these complications⁵⁴. Personalized proteomic analysis of the HLA loci, minor antigen loci, and peptide repertoire of potential donor/recipient pairs could reduce this rate even further⁵⁴.

Molecular diagnosis is moving beyond genomics to proteomics⁵⁵. To understand proteomics, understanding what the proteome is very necessary. According to the American Medical Association (AMA) and the Office of Cancer Clinical Proteomics Research at the National Cancer Institute, the term proteome was taken from two words: protein and genome, so “prote- “was taken from protein and “-ome” from genome. Therefore, proteomes are proteins that are expressed by different genomes and other cells⁵⁶. Proteomics, the study of the proteome, is important because proteins represent the actual functional molecules in the cell. Importantly, proteomics provides important insights into our understanding of cell signaling; specifically, the quantitative identification of protein cargo and biological insights of extracellular vesicles⁵⁷. 

Proteomics typically gives us a better understanding of an organism than genomics.  The level of complexity resulting from co- and posttranslational modification events can only be dissected and understood through qualitative and quantitative studies at the level of the functional proteins themselves⁵⁵. Proteins, not genes, are responsible for the phenotype of cells. Finally, protein degradation rate plays an important role in protein content⁵⁸. Particularly, when interpretation of protein expression considers the dynamics of this expression in specific biological contexts. Chung et al. developed a method for analyzing serum potential biomarkers in patients with bronchial asthma⁵⁹. Unlike other diseases (e.g., cancer), the goal of proteomics in patients with bronchial asthma is not to develop a new method for asthma diagnosis through analysis of body fluids; instead, it aims to determine the causal relationship between changes of proteins in body fluids and clinical phenotypes, to identify target molecules that play key roles in the pathogenesis of asthma, eventually leading to the development of new treatment protocols⁵⁵. Proteomics can aid in the diagnosis and treatment of various medical conditions such as bronchial asthma, oncology, cancer, heart diseases, and neurodegenerative diseases such as Parkinson's and Alzheimer's. It also has applications in Met proteomics. These benefits make proteomics a valuable tool in medical laboratory diagnosis. 

Advantages and Limitations of Proteomics

Proteomics has the potential to revolutionize lab management by providing more efficient and comprehensive analysis of biological samples⁶⁰. It is a valuable tool for lab management, as it can improve the efficiency and reproducibility of experiments, facilitate quality control, and enable identification of novel biomarkers⁶¹. Proteomics has been noted to have significant benefits in lab management. Gromski and colleagues ⁶² suggest that the detection of protein markers specific to a disease through proteomic analysis may enhance diagnostic precision and expediency, thereby facilitating the development of customized treatment strategies.

Proteomics, despite its vast potential, continues to face significant technical and biological challenges. These include difficulties in the accurate identification and quantification of proteins, restricted dynamic range, and incomplete proteome coverage, primarily due to the inherent complexity of biological samples and current technological limitations⁶³. Data analysis in proteomics remains particularly problematic, with persistent issues such as the inability to reliably detect low-abundance proteins and distinguish between true and false identifications, even with notable advancements in analytical methods⁶⁴. Although technological progress has improved certain aspects, major hurdles persist related to sample heterogeneity, limited sensitivity, low throughput, and the dynamic nature of the proteome⁶⁵. Additional complications stem from challenges in detecting specific proteins and post-translational modifications, ensuring reproducibility, and managing the high cost and complexity associated with proteomic data analysis.

Metabolomics and Personalized Medicine

Metabolomics emerged in the late 1990s as a progression from proteomics⁶⁶. It focuses on the detection, identification, and quantification of low molecular weight compounds within a biological system. The metabolome encompasses all small molecules typically less than 1000 to 1500 Daltons found in a biological sample, excluding macromolecules such as proteins and nucleic acids⁶⁷. These metabolites include essential components of cellular energy metabolism, such as amino acids, sugars, nucleotides, and fatty acids. Additionally, some metabolites are species-specific and play specialized biological roles, for example, glucocorticoids and neurotransmitters in mammals or alkaloids in plants. Xenobiotics, which are chemicals present in living organisms but not naturally produced by them, such as drugs and environmental pollutants, are also considered part of the metabolome⁶⁸. This inclusion is based on their presence in biological systems and their detectability using the analytical chemistry techniques employed in metabolomics.

Metabolomics presents a level of complexity that far exceeds the simplified metabolic pathways typically illustrated in biochemistry textbooks. Unlike theoretical proteomes, which can be relatively well predicted from genomic data, the total number of metabolites in living organisms remains undefined. This is due in part to the vast chemical diversity among metabolites, which range from polar, hydrophilic molecules like sugar derivatives to apolar, hydrophobic compounds such as lipids. As a result, there is no single analytical method capable of capturing the entire metabolome. Comprehensive metabolite detection, therefore, depends on the use of complementary analytical techniques in parallel⁶⁹. The two primary technologies driving metabolomics data acquisition are nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). MS can be used directly or in conjunction with separation techniques such as gas chromatography (GC-MS), liquid chromatography (LC-MS), or capillary electrophoresis (CE-MS). Since the early 2000s, metabolomics workflows encompassing sample preparation, analytical measurements, data pre-processing, statistical analysis, and visualization have evolved significantly and are now well-established. Today, metabolomics is recognized as a standalone analytical platform and is routinely applied in systems biology across diverse fields, including environmental science⁷⁰, plant biology^71,72, nutrition, and human and animal health⁷³.

In biomedical research, metabolomics plays a pivotal role within the broader framework of systems medicine, a holistic, data-driven approach to health and disease that underpins personalized medicine. Biomarker discovery has become a cornerstone of modern healthcare, essential for advancing precision diagnostics, prognosis, and therapeutic monitoring. Omics-based strategies, including metabolomics, are particularly valuable for identifying novel biomarkers or panels of biomarkers that reflect disease states or treatment responses. By enabling the comprehensive profiling of low molecular weight compounds in patient samples, metabolomics offers a promising avenue for uncovering biologically and clinically relevant molecular signatures. However, despite extensive research efforts over the past decade, the number of omics-derived biomarkers that have received regulatory approval and been integrated into clinical practice remains disappointingly low⁷⁴.

Advantages and Limitations of Metabolomics

The metabolome is highly responsive to both genetic and environmental influences⁷⁵. As such, conducting a metabolomic study demands careful consideration of multiple variables to minimize confounding factors and maximize data integrity. Robust experimental design is critical for reducing interindividual variability, and selecting the most appropriate analytical platform, based on the specific objectives and capabilities of the laboratory, is a key decision that significantly impacts both the quality of results and the extent of data recovery. A persistent challenge, particularly in LC-MS-based metabolomics, lies in the accurate identification of biomarkers, which continues to be a major limitation in the field⁷⁶. The metabolome is influenced by genetic, environmental, and gut microbiota pressures, thus, subtle variations between individuals can result in large perturbations to metabolite concentrations and flux.

Environmental factors, such as diet, stress, lifestyle, disease, and exposure to xenobiotics, along with genetic variations including gender differences, gene polymorphisms, and epigenetic modifications, significantly influence the metabolome⁷⁷.  Additionally, the gut microbiota plays a critical role in co-metabolizing dietary compounds, such as phenols and phenylalanine, as well as xenobiotics like prontosil, fostamatinib, and acetaminophen. Disruptions in the gut microbial environment have also been linked to conditions like obesity⁷⁸. Collectively, these genetic, environmental, and microbiome-related factors contribute to substantial interindividual variability in metabolomic profiles. The complex interactions among them further complicate data interpretation, posing a challenge to achieving consistent and reliable results in metabolomic studies. To overcome the confounding effects of variation, the concept of a metabolic fingerprint or the metabotype was conceived to encompass and identify all the possible variations in an individual⁷⁹. The most challenging part of a metabolomic study is confirmation of biomarker identity⁸⁰. This is an essential step toward understanding the biological changes occurring within the system and remains a major bottleneck in metabolomics investigations. 

Transcriptomics and Personalized Medicine

The transcriptome encompasses the complete set of RNA transcripts present within a cell or tissue⁸¹. This includes various alternative splice variants. A primary reason for studying the transcriptome is that the majority of human genes undergo alternative splicing. In eukaryotic genes, exons and introns are common structural components; however, only exons are ultimately translated into proteins. Introns are removed during RNA processing. The mechanism responsible for excising introns and joining exons to form a mature mRNA is known as splicing⁸². Transcriptomics is the study of the transcriptome and is involved in the function of cells, tissues, or organisms, across a wide range of biological conditions⁸³. The relationship between the transcriptome and the genome suggests that the information of an organism is stored in the DNA (the genome) and expressed by RNA (the transcriptome). The main focus of transcriptomics is to discover how transcripts of a cell, tissue, or living organism are influenced by disease or environmental factors (such as drugs, hormones, etc.)⁸⁴. Non-coding RNA is another very important aspect of transcriptomics. These functional elements play an important role in the occurrence of a variety of diseases and their response to treatment. Transcriptomics encompasses the post-transcriptional phase of gene expression, enabling the detection of molecular alterations that may not be evident at the genomic level⁸⁵. These transcript-level changes, if associated with pathogenic mechanisms, are critical to identify for the development of more effective and targeted therapeutic strategies. In essence, transcriptomics serves as a bridge between genomics and proteomics, complementing both and playing a pivotal role in advancing precision medicine⁸⁶. Various techniques are employed in transcriptome analysis, depending on the scope of investigation, whether focused on a single gene or a limited set of genes, or aiming to profile the entire transcriptome of a cell, tissue, or organism.

Advantages and Limitations of Transcriptomics

One of the primary challenges in RNA-seq experiments is the extraction of sufficient quantities of high-quality RNA from the target tissue. RNA is highly unstable and degrades rapidly, making timely and precise tissue dissection essential, especially when working with small or delicate regions like specific brain areas. This is particularly demanding in small organisms such as zebrafish, where dissections must be completed within 2–3 minutes to preserve the integrity of the sample without compromising anatomical accuracy. Furthermore, obtaining adequate RNA yields is difficult due to the low abundance of messenger RNA (mRNA), which constitutes only 1–2% of total cellular RNA, the portion relevant for sequencing. In contrast, ribosomal RNA, which makes up approximately 90% of total RNA, must be selectively removed before analysis, adding another layer of complexity to the preparation process⁸⁷. 

Pooling samples involves combining multiple individual biological samples into a single composite sample before transcriptomic analysis. This pooled sample is then processed as one library and represents a single biological replicate. In contrast, analyzing each sample individually involves constructing a separate library for each biological specimen, preserving the integrity of biological replicates. Pooling is often employed to reduce sequencing costs, address limitations in tissue availability, or compensate for low RNA yield. It may also be used to mitigate batch effects in sequencing runs⁸⁸. Despite its practical benefits, pooling introduces significant limitations. Since each pool is treated as a single data point, it obscures individual biological variation and reduces the statistical power of downstream analyses. Therefore, results derived from pooled transcriptomic data must be interpreted with caution, and researchers must not mistake pooled samples for independent biological replicates.

In transcriptomic studies, researchers face a critical trade-off between sequencing depth and the number of biological replicates, both of which significantly influence the cost, statistical power, and reliability of the experiment⁸⁹. This decision is particularly important in the context of ongoing concerns about reproducibility, especially in fields like psychology and neuroscience, where underpowered studies have led to questionable findings. In RNA-seq experiments, a common practice is to include a minimum of three biological replicates per condition. However, this threshold is largely arbitrary and may be insufficient. The appropriate number of replicates should be determined based on the specific research objective, the inherent biological variability of the system under investigation, and the level of statistical power required to detect meaningful biological differences. Balancing these factors is essential for producing robust, reproducible results that support genuine scientific discovery.

Future Interconnections Between Multi-Omics Data and Personalized Medicine

Modern advancements in personalized medicine are increasingly dependent on technologies that analyze a patient’s fundamental biology, such as DNA, RNA, and protein profiling, which are crucial for accurate disease diagnosis⁹⁰. As technological progress continues, the integration of DNA analysis with precision medicine is anticipated to broaden, offering new possibilities for disease prevention, treatment, and management. For example, personalized approaches like genome sequencing can identify DNA mutations that influence a wide range of diseases, from cystic fibrosis to cancer⁹¹. Omics sciences focus on the comprehensive characterization of biological molecules, offering valuable insights into an organism’s structure, function, and dynamics. In precision medicine, multi-omics combines data from genomics, transcriptomics, proteomics, metabolomics, and other fields to understand how molecular variations impact disease development, progression, and treatment responses. This integrated approach provides a deeper understanding of complex biological processes and disease mechanisms, ultimately advancing personalized healthcare.

Several international networks and projects are currently dedicated to applying multi-omics approaches in precision medicine, including The Cancer Genome Atlas (TCGA), The Precision Medicine Initiative (PMI) Cohort Program, Pan-Cancer Analysis of Whole Genomes (PCAWG), and the Human Proteome Project (HPP)⁹². A notable example is the European Joint Programme on Rare Diseases (EJP RD), a Europe-wide initiative focused on accelerating the diagnosis and treatment of rare diseases by integrating multi-omics data to gain deeper insights into disease mechanisms and improve therapeutic precision⁹². In a 2018 study on pancreatic ductal adenocarcinoma, researchers integrated genomics, transcriptomics, and proteomics data to identify distinct molecular subtypes with varying responses to treatment, thereby improving patient classification for personalized therapy⁹³. Similarly, a 2020 study utilized a multi-omics approach to analyze cerebrospinal fluid samples from Alzheimer's patients, combining proteomics and metabolomics data to identify new biomarkers linked to the disease, furthering the understanding of its molecular mechanisms⁹⁴. These examples highlight how multi-omics approaches have revolutionized the understanding of complex biological systems and diseases, enhancing diagnostic and therapeutic strategies across multiple fields.

Conclusion

In conclusion, personalized medicine marks a transformative advancement in healthcare, shifting from a generalized treatment paradigm to a patient-centric approach grounded in molecular and genomic insights. The integration of genomics, transcriptomics, proteomics, metabolomics, and pharmacogenomics has enabled clinicians and researchers to unravel complex disease mechanisms, stratify patients more accurately, and tailor therapies to individual profiles. This approach holds particular promise in oncology, pharmacotherapy, and rare disease management, where molecular characterization directly informs therapeutic decisions. However, the widespread clinical implementation of personalized medicine remains constrained by significant challenges, including data integration complexities, high costs, limited genomic diversity in research cohorts, and infrastructure demands. Transcriptomics, by revealing post-transcriptional changes invisible at the genomic level, serves as a vital link between genotypic data and proteomic outcomes, enhancing our ability to detect disease signatures and therapeutic targets. The reproducibility of transcriptomic data, however, depends heavily on adequate biological replication and methodological rigor, emphasizing the need for well-powered experimental designs. Despite existing limitations, personalized medicine is poised to redefine modern clinical practice, offering the potential for earlier diagnoses, improved treatment efficacy, reduced adverse reactions, and more efficient healthcare delivery. Continued investment in multi-omics research, ethical data governance, and equitable access will be pivotal in translating its full potential into routine patient care.

Conflicts of Interest: The authors declare no potential conflict of interest concerning the contents, authorship, and/or publication of this article.

Authors Contributions: All authors have equal contributions in the preparation of the manuscript and compilation.

Source of Support: Nil 

Funding: The authors declared that this study has received no financial support. 

Informed Consent Statement: Not applicable. 

Data Availability Statement: The data supporting this paper are available in the cited references. 

Ethical approvals: Not applicable.

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