Development of Locus-Directed Editing of the Epigenome from Basic Mechanistic Engineering to First Clinical Applications.

The introduction of CRISPR/Cas systems has resulted in a strong impulse for the field of gene-targeted epigenome/epigenetic reprogramming (EpiEditing), where EpiEditors consisting of a DNA binding part for targeting and an enzymatic part for rewriting of chromatin modifications are applied in cells to alter chromatin modifications at targeted genome loci in a directed manner. Pioneering studies preceding this era indicated causal relationships of chromatin marks instructing gene expression. The accumulating evidence of chromatin reprogramming of a given genomic locus resulting in gene expression changes opened the field for mainstream applications of this technology in basic and clinical research. The growing knowledge on chromatin biology and application of EpiEditing tools, however, also revealed a lack of predictability of the efficiency of EpiEditing in some cases. In this perspective, the dependence of critical parameters such as specificity, effectivity, and sustainability of EpiEditing on experimental settings and conditions including the expression levels and expression times of the EpiEditors, their chromatin binding affinity and specificity, and the crosstalk between EpiEditors and cellular epigenome modifiers are discussed. These considerations highlight the intimate connection between the outcome of epigenome reprogramming and the details of the technical approaches toward EpiEditing, which are the main topic of this volume of Methods in Molecular Biology. Once established in a fully functional "plug-and-play" mode, EpiEditing will allow to better understand gene expression control and to translate such knowledge into therapeutic tools. These expectations are beginning to be met as shown by various in vivo EpiEditing applications published in recent years, several companies aiming to exploit the therapeutic power of EpiEditing and the first clinical trial initiated.

Reader-Effectors as Actuators of Epigenome Editing.

Epigenome editing applications are gaining broader use for targeted transcriptional control as more enzymes with diverse chromatin-modifying functions are being incorporated into fusion proteins. Development of these fusion proteins, called epigenome editors, has outpaced the study of proteins that interact with edited chromatin. One type of protein that acts downstream of chromatin editing is the reader-effector, which bridges epigenetic marks with biological effects like gene regulation. As the name suggests, a reader-effector protein is generally composed of a reader domain and an effector domain. Reader domains directly bind epigenetic marks, while effector domains often recruit protein complexes that mediate transcription, chromatin remodeling, and DNA repair. In this chapter, we discuss the role of reader-effectors in driving the outputs of epigenome editing and highlight instances where abnormal and context-specific reader-effectors might impair the effects of epigenome editing. Lastly, we discuss how engineered reader-effectors may complement the epigenome editing toolbox to achieve robust and reliable gene regulation.

Fine-Tuning the Epigenetic Landscape: Chemical Modulation of Epigenome Editors.

Epigenome editing has emerged as a powerful technique for targeted manipulation of the chromatin and transcriptional landscape, employing designer DNA binding domains fused with effector domains, known as epi-editors. However, the constitutive expression of dCas9-based epi-editors presents challenges, including off-target activity and lack of temporal resolution. Recent advancements of dCas9-based epi-editors have addressed these limitations by introducing innovative switch systems that enable temporal control of their activity. These systems allow precise modulation of gene expression over time and offer a means to deactivate epi-editors, thereby reducing off-target effects associated with prolonged expression. The development of novel dCas9 effectors regulated by exogenous chemical signals has revolutionized temporal control in epigenome editing, significantly expanding the researcher's toolbox. Here, we provide a comprehensive review of the current state of these cutting-edge systems and specifically discuss their advantages and limitations, offering context to better understand their capabilities.

An Overview of Global, Local, and Base-Resolution Methods for the Detection of 5-Hydroxymethylcytosine in Genomic DNA.

The discovery of 5-hydroxymethylcytosine (5hmC) as a common DNA modification in mammalian genomes has ushered in new areas of inquiry regarding the dynamic epigenome. The balance between 5hmC and its precursor, 5-methylcytosine (5mC), has emerged as a determinant of key processes including cell fate specification, and alterations involving these bases have been implicated in the pathogenesis of various diseases. The identification of 5hmC separately from 5mC initially posed a challenge given that legacy epigenetic sequencing technologies could not discriminate between these two most abundant modifications, a significant blind spot considering their potentially functionally opposing roles. The growing interest in 5hmC, as well as in the Ten-Eleven Translocation (TET) family enzymes that catalyze its generation and further oxidation to 5-formylcytosine (5fC) and 5-carboxycytosine (5caC), has spurred the development of versatile methods for 5hmC detection. These methods enable the quantification and localization of 5hmC in diverse biological samples and, in some cases, at the resolution of individual nucleotides. However, navigating this growing toolbox of methods for 5hmC detection can be challenging. Here, we detail existing and emerging methods for the detection, quantification, and localization of 5hmC at global, locus-specific, and base resolution levels. These methods are discussed in the context of their advantages and limitations, with the goal of providing a framework to help guide researchers in choosing the level of resolution and the associated method that could be most suitable for specific needs.

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics.

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.

BACKGROUND: Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets. OBJECTIVE: This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression. METHODS: This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. RESULTS: The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network. CONCLUSIONS: The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.

Impact of genomic and epigenomic alterations of multigene on a multicancer pedigree.

BACKGROUND: Germline mutations have been identified in a small number of hereditary cancers, but the genetic predisposition for many familial cancers remains to be elucidated. METHODS: This study identified a Chinese pedigree that presented different cancers (breast cancer, BRCA; adenocarcinoma of the esophagogastric junction, AEG; and B-cell acute lymphoblastic leukemia, B-ALL) in each of the three generations. Whole-genome sequencing and whole-exome sequencing were performed on peripheral blood or bone marrow and cancer biopsy samples. Whole-genome bisulfite sequencing was conducted on the monozygotic twin brothers, one of whom developed B-ALL. RESULTS: According to the ACMG guidelines, bioinformatic analysis of the genome sequencing revealed 20 germline mutations, particularly mutations in the DNAH11 (c.9463G > A) and CFH (c.2314G > A) genes that were documented in the COSMIC database and validated by Sanger sequencing. Forty-one common somatic mutated genes were identified in the cancer samples, displaying the same type of single nucleotide substitution Signature 5. Meanwhile, hypomethylation of PLEK2, MRAS, and RXRA as well as hypermethylation of CpG island associated with WT1 was shown in the twin with B-ALL. CONCLUSIONS: These findings reveal genomic alterations in a pedigree with multiple cancers. Mutations found in the DNAH11, CFH genes, and other genes predispose to malignancies in this family. Dysregulated methylation of WT1, PLEK2, MRAS, and RXRA in the twin with B-ALL increases cancer susceptibility. The similarity of the somatic genetic changes among the three cancers indicates a hereditary impact on the pedigree. These familial cancers with germline and somatic mutations, as well as epigenomic alterations, represent a common molecular basis for many multiple cancer pedigrees.

sciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden.

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Accumulated off-target coverage enables genome-wide differentially methylated region (DMR) calling for clusters with as few as 115 cells. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Genomic and Epigenomic Biomarkers of Immune Checkpoint Immunotherapy Response in Melanoma: Current and Future Perspectives.

Immune checkpoint inhibitors (ICIs) demonstrate durable responses, long-term survival benefits, and improved outcomes in cancer patients compared to chemotherapy. However, the majority of cancer patients do not respond to ICIs, and a high proportion of those patients who do respond to ICI therapy develop innate or acquired resistance to ICIs, limiting their clinical utility. The most studied predictive tissue biomarkers for ICI response are PD-L1 immunohistochemical expression, DNA mismatch repair deficiency, and tumour mutation burden, although these are weak predictors of ICI response. The identification of better predictive biomarkers remains an important goal to improve the identification of patients who would benefit from ICIs. Here, we review established and emerging biomarkers of ICI response, focusing on epigenomic and genomic alterations in cancer patients, which have the potential to help guide single-agent ICI immunotherapy or ICI immunotherapy in combination with other ICI immunotherapies or agents. We briefly review the current status of ICI response biomarkers, including investigational biomarkers, and we present insights into several emerging and promising epigenomic biomarker candidates, including current knowledge gaps in the context of ICI immunotherapy response in melanoma patients.

Epigenomic Profiling of B Cell Subsets by CUT&Tag.

Epigenetic programs play a key role in regulating the development and function of immune cells. However, conventional methods for profiling epigenetic mechanisms, such as the post-translational modifications to histones, present several technical challenges that prevent a complete understanding of gene regulation. Here, we provide a detailed protocol of the Cleavage Under Targets and Tagmentation (CUT&Tag) chromatin profiling technique for identifying histone modifications in human and mouse lymphocytes.

Using High-Throughput Measurements to Identify Principles of Transcriptional and Epigenetic Regulators.

To achieve exquisite control over the epigenome, we need a better predictive understanding of how transcription factors, chromatin regulators, and their individual domain's function, both as modular parts and as full proteins. Transcriptional effector domains are one class of protein domains that regulate transcription and chromatin. These effector domains either repress or activate gene expression by interacting with chromatin-modifying enzymes, transcriptional cofactors, and/or general transcriptional machinery. Here, we discuss important design considerations for high-throughput investigations of effector domains, recent advances in discovering new domains in human cells and testing how domain function depends on amino acid sequence. For every effector domain, we would like to know the following: What role does the cell type, signaling state, and targeted context have on activation, silencing, and epigenetic memory? Large-scale measurements of transcriptional activities can help systematically answer these questions and identify general rules for how all these parameters affect effector domain activities. Last, we discuss what steps need to be taken to turn a newly discovered effector domain into a robust, precise epigenome editor. With more carefully considered high-throughput investigations, soon we will have better predictive control over the epigenome.

Designing Epigenome Editors: Considerations of Biochemical and Locus Specificities.

The advent of locus-specific protein recruitment technologies has enabled a new class of studies in chromatin biology. Epigenome editors (EEs) enable biochemical modifications of chromatin at almost any specific endogenous locus. Their locus-specificity unlocks unique information including the functional roles of distinct modifications at specific genomic loci. Given the growing interest in using these tools for biological and translational studies, there are many specific design considerations depending on the scientific question or clinical need. Here, we present and discuss important design considerations and challenges regarding the biochemical and locus specificities of epigenome editors. These include how to: account for the complex biochemical diversity of chromatin; control for potential interdependency of epigenome editors and their resultant modifications; avoid sequestration effects; quantify the locus specificity of epigenome editors; and improve locus-specificity by considering concentration, affinity, avidity, and sequestration effects.

Fluorescent Reporter Systems to Investigate Chromatin Effector Proteins in Living Cells.

Epigenetic research faces the challenge of the high complexity and tight regulation in chromatin modification networks. Although many isolated mechanisms of chromatin-mediated gene regulation have been described, solid approaches for the comprehensive analysis of specific processes as parts of the bigger epigenome network are missing. In order to expand the toolbox of methods by a system that will help to capture and describe the complexity of transcriptional regulation, we describe here a robust protocol for the generation of stable reporter systems for transcriptional activity and summarize their applications. The system allows for the induced recruitment of a chromatin regulator to a fluorescent reporter gene, followed by the detection of transcriptional changes using flow cytometry. The reporter gene is integrated into an endogenous chromatin environment, thus enabling the detection of regulatory dependencies of the investigated chromatin regulator on endogenous cofactors. The system allows for an easy and dynamic readout at the single-cell level and the ability to compensate for cell-to-cell variances of transcription. The modular design of the system enables the simple adjustment of the method for the investigation of different chromatin regulators in a broad panel of cell lines. We also summarize applications of this technology to characterize the silencing velocity of different chromatin effectors, removal of activating histone modifications, analysis of stability and reversibility of epigenome modifications, the investigation of the effects of small molecule on chromatin effectors and of functional effector-coregulator relationships. The presented method allows to investigate the complexity of transcriptional regulation by epigenetic effector proteins in living cells.

Protocol for Allele-Specific Epigenome Editing Using CRISPR/dCas9.

The discovery and adaptation of CRISPR/Cas systema for epigenome editing has allowed for a straightforward design of targeting modules that can direct epigenome editors to virtually any genomic site. This advancement in DNA-targeting technology brings allele-specific epigenome editing into reach, a "super-specific" variation of epigenome editing whose goal is an alteration of chromatin marks at only one selected allele of the genomic target locus. This technology could be useful for the treatment of diseases caused by a mutant allele with a dominant effect, because allele-specific epigenome editing allows the specific silencing of the mutated allele leaving the healthy counterpart expressed. Moreover, it may allow the direct correction of aberrant imprints in imprinting disorders where editing of DNA methylation is required exclusively in a single allele. Here, we describe a basic protocol for the design and application of allele-specific epigenome editing systems using allele-specific DNA methylation at the NARF gene in HEK293 cells as an example. An sgRNA/dCas9 unit is used for allele-specific binding to the target locus containing a SNP in the seed region of the sgRNA or the PAM region. The dCas9 protein is connected to a SunTag allowing to recruit up to 10 DNMT3A/3L units fused to a single-chain Fv fragment, which specifically binds to the SunTag peptide sequence. The plasmids expressing dCas9-10x SunTag, scFv-DNMT3A/3L, and sgRNA, each of them co-expressing a fluorophore, are introduced into cells by co-transfection. Cells containing all three plasmids are enriched by FACS, cultivated, and later the genomic DNA and RNA can be retrieved for DNA methylation and gene expression analysis.

Plasmid Delivery and Single-Cell Plasmid Expression Analysis for CRISPR/dCas9-Based Epigenetic Editing.

To fully exploit the potentials of reprogramming the epigenome through CRISPR/dCas9 systems for epigenetic editing, there is a growing need for improved transfection methods. With the utilization of constructs often with large sizes and the wide array of cell types used to read out the effect of epigenetic editing in different biological applications, it is evident that ongoing optimalization of transfection protocols tailored to each specific experimental setup is essential. Whether the goal is the production of viral particles using human embryonic kidney (HEK) cells or the direct examination of epigenomic modifications in the target cell type, continuous refinement of transfection methods is crucial. In the hereafter outlined protocol, we focus on optimization of transfection protocols by comparing different reagents and methods, creating a streamlined setup for transfection efficiency optimization in cultured mammalian cells. Our protocol provides a comprehensive overview of flow cytometry analysis following transfection not just to improve transfection efficiency but also to assess the expression level of the utilized construct. We showcase our transfection protocol optimization using HEK293T Lenti-X™ and breast cancer MCF-7 cell lines, using a single-guide RNA-containing plasmid. Specifically, we incorporate heat shock treatment for increased transfection efficiency of the MCF-7 cell line. Our detailed optimization protocol for efficient plasmid delivery and measurement of single-cell plasmid expression provides a comprehensive instruction for assessing both transient and sustained effects of epigenetic reprogramming.

Whole-Genome Bisulfite Sequencing Protocol for the Analysis of Genome-Wide DNA Methylation and Hydroxymethylation Patterns at Single-Nucleotide Resolution.

The analysis of genome-wide epigenomic alterations including DNA methylation and hydroxymethylation has become a subject of intensive research for many biological and clinical questions. DNA methylation analysis bears the particular promise to supplement or replace biochemical and imaging-based tests for the next generation of personalized medicine. Whole-genome bisulfite sequencing (WGBS) using next-generation sequencing technologies is currently considered the gold standard for a comprehensive and quantitative analysis of DNA methylation throughout the genome. However, bisulfite conversion does not allow distinguishing between cytosine methylation and hydroxymethylation requiring an additional chemical or enzymatic step to identify hydroxymethylated cytosines. Here, we provide a detailed protocol based on a commercial kit for the preparation of sequencing libraries for the comprehensive whole-genome analysis of DNA methylation and/or hydroxymethylation. The protocol is based on the construction of sequencing libraries from limited amounts of input DNA by ligation of methylated adaptors to the fragmented DNA prior to bisulfite conversion. For analyses requiring a quantitative distinction between 5-methylcytosine and 5-hydroxymethylcytosines levels, an oxidation step is included in the same workflow to perform oxidative bisulfite sequencing (OxBs-Seq). In this case, two sequencing libraries will be generated and sequenced: a classic methylome following bisulfite conversion and analyzing modified cytosines (not distinguishing between methylated and hydroxymethylated cytosines) and a methylome analyzing only methylated cytosines, respectively. Hydroxymethylation levels are deduced from the differences between the two reactions. We also provide a step-by-step description of the data analysis using publicly available bioinformatic tools. The described protocol has been successfully applied to different human and plant samples and yields robust and reproducible results.

Amplicon-Based Bisulfite Conversion-NGS DNA Methylation Analysis Protocol.

DNA methylation is an important epigenetic modification that regulates chromatin structure and the cell-type-specific expression of genes. The association of aberrant DNA methylation with many diseases, as well as the increasing interest in modifying the methylation mark in a directed manner at genomic sites using epigenome editing for research and therapeutic purposes, increases the need for easy and efficient DNA methylation analysis methods. The standard approach to analyze DNA methylation with a single-cytosine resolution is bisulfite conversion of DNA followed by next-generation sequencing (NGS). In this chapter, we describe a robust, powerful, and cost-efficient protocol for the amplification of target regions from bisulfite-converted DNA, followed by a second PCR step to generate libraries for Illumina NGS. In the two consecutive PCR steps, first, barcodes are added to individual amplicons, and in the second PCR, indices and Illumina adapters are added to the samples. Finally, we describe a detailed bioinformatics approach to extract DNA methylation levels of the target regions from the sequencing data. Combining barcodes with indices enables a high level of multiplexing allowing to sequence multiple pooled samples in the same sequencing run. Therefore, this method is a robust, accurate, quantitative, and cheap approach for the readout of DNA methylation patterns at defined genomic regions.

SANTO: a coarse-to-fine alignment and stitching method for spatial omics.

With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extreme time consumption and unsatisfactory accuracy. Here we propose SANTO, a coarse-to-fine method targeting alignment and stitching tasks for spatial omics. SANTO firstly rapidly supplies reasonable spatial positions of two slices and identifies the overlap region. Then, SANTO refines the positions of two slices by considering spatial and omics patterns. Comprehensive experiments demonstrate the superior performance of SANTO over existing methods. Specifically, SANTO stitches cross-platform slices for breast cancer samples, enabling integration of complementary features to synergistically explore tumor microenvironment. SANTO is then applied to 3D-to-3D spatiotemporal alignment to study development of mouse embryo. Furthermore, SANTO enables cross-modality alignment of spatial transcriptomic and epigenomic data to understand complementary interactions.

Unraveling epigenomic signatures and effectiveness of electroconvulsive therapy in treatment-resistant depression patients: a prospective longitudinal study.

BACKGROUND: Electroconvulsive therapy (ECT) benefits patients with treatment-resistant depression (TRD), but the underlying biological processes are unclear. We conducted an epigenome-wide association study in 32 TRD patients undergoing ECT to depict ECT-associated methylation changes. Illness severity and ECT outcomes were assessed with the Montgomery-Åsberg Depression Rating Scale at baseline (T0) and 1 month after its end (T1). Methylation was profiled at T0 and T1 with the Illumina Infinium Methylation EPIC BeadChip array. RESULTS: Longitudinal T0-T1 analyses showed 3 differentially methylated probes (DMPs) with nominal p values ≤ 10-5, with 2 annotated in the genes CYB5B and PVRL4. Including covariates, we found 4 DMPs for symptoms variation, annotated in FAM20C, EPB41, OTUB1 and ADARB1, and 3 DMPs for response status, with 2 annotated in IQCE and FAM20C. Regional analysis revealed 54 differentially methylated regions (DMRs) with nominal p value area ≤ 0.05, with 9 presenting adjusted p-value area ≤ 0.10, annotated in MCF2L, SLC25A24, RUNX3, MIR637, FOXK2, FAM180B, POU6F1, ALS2CL and CCRL2. Considering covariates, we found 21 DMRs for symptoms variation and 26 DMRs for response (nominal p value area ≤ 0.05), with 4 presenting adjusted p-value area ≤ 0.10 for response, annotated in SNORD34, NLRP6, GALNT2 and SFT2D3. None remained significant after false discovery rate correction. Notably, ADARB1 variants are associated with suicide attempt in patients with psychiatric disorders, and SLC25A24 relates to conduct disorder. Several DMPs and DMRs are annotated in genes associated with inflammatory/immune processes. Longitudinal analyses on females (n = 22) revealed statistically significant DMRs (adjusted p value area ≤ 0.05) and trend-significant DMRs (adjusted p value area ≤ 0.07) for symptoms variation and response status, annotated in genes related to psychiatric disorders (ZFP57, POLD4, TRIM10, GAS7, ADORA2A, TOLLIP), trauma exposure (RIPOR2) and inflammatory/immune responses (LAT, DLX4, POLD4, FAM30A, H19). Pathway analysis on females revealed enrichment for transcriptional activity, growth factors, DNA maintenance, and immune pathways including IRF7 and IRF2. CONCLUSION: Although no significant results were found for the whole cohort, the study provides insights into ECT-associated methylation changes, highlighting DMPs and DMRs related to ECT outcomes. Analyses on females revealed significant DMRs and pathways related to psychiatric disorders and inflammatory/immune processes.

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer.

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.