[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
v
Search
Advanced

Publications > Journals > Journal of Clinical and Translational Hepatology > Article Full Text

  • Review Article
  • OPEN ACCESS

Recent Progress and Future Direction for the Application of Multiomics Data in Clinical Liver Transplantation

  • Zhengtao Liu1,2,3,4,#,
  • Jun Xu2,#,
  • Shuping Que5,
  • Lei Geng2,
  • Lin Zhou3,4,
  • Adil Mardinoglu6,7,*  and
  • Shusen Zheng1,2,3,4,* 
 Author information  Cite
Journal of Clinical and Translational Hepatology   2022;10(2):363-373

doi: 10.14218/JCTH.2021.00219

Abstract

Omics data address key issues in liver transplantation (LT) as the most effective therapeutic means for end-stage liver disease. The purpose of this study was to review the current application and future direction for omics in LT. We reviewed the use of multiomics to elucidate the pathogenesis leading to LT and prognostication. Future directions with respect to the use of omics in LT are also described based on perspectives of surgeons with experience in omics. Significant molecules were identified and summarized based on omics, with a focus on post-transplant liver fibrosis, early allograft dysfunction, tumor recurrence, and graft failure. We emphasized the importance omics for clinicians who perform LTs and prioritized the directions that should be established. We also outlined the ideal workflow for omics in LT. In step with advances in technology, the quality of omics data can be guaranteed using an improved algorithm at a lower price. Concerns should be addressed on the translational value of omics for better therapeutic effects in patients undergoing LT.

Graphical Abstract

Keywords

Multiomic analysis, Transcriptomics, Proteomics, Metabolomics, Liver transplantation, EAD

Introduction

As one of the most common causes of death, liver disease severely impairs global health and utilizes abundant health resources in countries worldwide.1 Currently, liver transplantation (LT) is one of the most effective therapeutic approaches for the treatment of end-stage liver disease and acute hepatic failure;2,3 however, the therapeutic effects of LT are not equal among patients and the prognosis of recipients is determined by many factors related to recipients, donors, grafts, surgery, and post-operative treatment.4 In addition, donor-recipient matching is also essential for improving LT quality.5 LT involves systematic engineering that requires cooperation from the medical staff, scientists, engineers, technicians, and patients. The optimal procedure for LT should be developed on the premise of comprehensive collection of information from each step to maximally benefit the patients. Omics data from the donor, recipient, and graft samples can provide potential approaches to depict the panorama of the working environment for “new organs” in the body. Critical biomarkers might facilitate better organ allocation for most suitable patients.6 Indeed, well-organized clinical cohort studies with inclusion of multiomics data can provide an inspiration for better organ utilization and mechanistic studies.

Omics is the suffix applied to a series of disciplines in the science and medicine domains to acquire systems knowledge on the collective characteristics of molecules from the entire genome, transcriptome, proteome, metabolome, and microbiome. Moreover, studies involving whole molecules from subspecies, such as the lipidome or glycome, are also attributed to the domain of omics.7,8 Omics data describe the dynamic variations of molecules from a macroscopic perspective, which might help researchers to screen and thoroughly evaluate candidates. Moreover, integrative multiomics studies have also shown the advantages of a better understanding of the molecular function and investigation of disease prediction with mutual validations at all levels.9,10

Following improvements in assay technology and statistical algorithms, an increasing number of omics-based studies have been devoted to clinical investigations for comprehensive considerations with lower prices than ever before.11 Criteria from authoritative institutions have been legislated to guarantee the high-quality and provide guidance on further clinical trials;12,13 however, unlike laboratory models, enormous heterogeneities have been observed in human phenotypes and the panel of disease features has been defined with a specific terminology (“phenomics”).14 Clinical omics studies aim to demonstrate the inner mechanism of complex phenotypes based on a comprehensive consideration of whole molecule profiles in patients with common features, such as disease and therapy, by construction of a network model from omics data. The objective of clinical omics studies can be summarized into two parts, as follows: 1. identify the key targets for better individualized treatment of patients for disease prevention, diagnosis, therapy, prognostication, and response to medication; and 2. provide inspiration for further mechanistic investigations involving the key features of disease based on the mathematical relationships of basic science.7,15,16 Currently, more and more large sample-based clinical omics data with a focus on different types of patients have been published and deposited in public databases, such as Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx).17–19 These databases provide potential approaches for researchers to initiate additional “omics-wide” investigations of their interests through free data mining via ready-made, bio-informatic workflows.20 Otherwise, well-organized trials with assays of self-prepared omics data from specific samples in patients with relatively rare diseases or treatments are also worthy of mechanistic investigation and individualized therapy. Additionally, integrative software packages, on-line toolkits, and workflows were developed to lower the barriers for analysis, exploration, and explanation of omics data, and the potential for clinical utilization.21,22 Indeed, increased participation by experienced clinicians with critical thinking and clinical reasoning in omics research as a carrier of multidiscipline cooperation by physicians, bioinformaticians, biologists, and engineers might help to substantially improve the quality of clinical omics studies.23

Surgery is usually aimed at the treatment of severe disease, such as cancer, at the expense of resecting partial or entire organs. In contrast, transplantation medicine was a new surgical field in which an exhausted organ was replaced by a new organ from another person with a completely alien genetic background.2,24 More complexity was presented for various potential genetic confounders linked to the donors and recipients involved in the LT process with an impact on surgical quality and post-operative outcomes.25,26

As a crucial tool for improving the quality of clinical LT, the widely used omics analysis provides an approach to acquire systematic knowledge that facilitates construction of the genetic architecture and clarification on the impacts on prognosis (including mortality or cellular rejection) of patients after LT, which might help to improve the treatment of key issues, such as graft selection and donor-recipient matching.6,27–34 Otherwise, the molecular mechanisms underlying key issues, such as ischemic reperfusion injury (IRI) and operational tolerance in LT were also discussed in a previous multiomics study.35–39 A variety of omics studies involving solid organ transplantation have been collectively named transplantomics as a potential approach for the individualized treatment of transplant patients (Fig. 1).40,41

Research strategy for individualized treatment of key issues in LT based on multiomics clinical data.
Fig. 1  Research strategy for individualized treatment of key issues in LT based on multiomics clinical data.

LT, liver transplantation.

Despite the number of studies that have been published, the application of omics data for clinical LT is rarely summarized.31,37–39,42–45 Based on our previous study, the major peri-operative risk factors for LT were divided into four categories (including recipients, donors, grafts, and surgery).39,46 We summarized the prior achievements of clinical omics studies that were categorized according to the above-mentioned factors. More importantly, future directions on the application of omics with positive benefits for patients who underwent LT operations were highlighted based on the perspectives of experienced surgeons.

Current application of omics data in clinical LT investigations

Studies including less than 15 LT cases were excluded for guarantee of data reliability. Studies aimed at the selection of potential molecular targets under an unsupervised model were reviewed and categorized based on sample species (tissues, peripheral sera/plasma from donor, recipient, or graft).

As presented in Tables 1 and 2, a total of eight studies have reported the connections between omics data and phenotypes in clinical LT.31,37–39,42–45 Metabolomics is the most-used assay involved in all omics studies. Most researchers prefer to collect samples from graft tissues and omics data from recipient sera, but such were only assayed in three studies. As a common short-term complication, early allograft dysfunction (EAD) was the focus in most studies. In addition, post-transplant fibrosis, graft failure, tumor recurrence, and marginal organs were also discussed in select studies. Crossomics analysis and data integration were only performed in one study, with non-identical sample origins.42 More details were introduced by sample species, as discussed below.

Table 1

Literature features with original reports of clinical omics for LT

Author, Country, Publication year [Reference]Case number, Indication for LT, Donation typeSampling time, Follow-up durationSample species, ComparisonPlatformMajor findings
Diamond, USA, 20124215, CHC, NA2003–2004, 12 monthsGraft tissue, Fibrosis (F3-F4 vs. F0-F2)MS, ProteomicsOxidative stress in rapid fibrosis progression observed in CHC recipients
60, CHC, NA2004–2005Recipient serum, Fibrosis (F3-F4 vs. F0-F2)LC-MS, Metabolomics
Cortes, Spain, 201438123, NA, DBD2009–2012, 2 weeksGraft tissue, EAD vs. IGFLC-MS, MetabolomicsMetabolomic factors facilitate decision making on accepting or rejecting an organ to improve donor allocation
Xu, UK, 20154356, NA, DBD (38)/ DCD (18)NA, 2 weeksGraft tissue, EAD vs. IGFLC-MS, LipidomicsLysoPC (16:0) and LysoPC (18:0) might be involved in signaling transduction in liver tissue damage due to warm ischemia before transplantation
Faitot, France, 20173742, Mixed, NA2014–2016, 2 weeksGraft tissue, EAD vs. Non-EADNMR, MetabolomicsMetabolites showed lactate >8.3 mmol/g and phosphocholine >0.646 mmol/g were significantly associated with graft dysfunction with excellent accuracy
Cano, Spain, 201731203, CHC, NANA, 1 yearRecipient serum, Fibrosis (F3-F4 vs. F0-F2)LC-MS, MetabolomicsAn algorithm consisting of two sphingomyelins and two phosphatidyl cholines accurately classified rapid and slow fibrosers after transplantation with AUROC on 0.92
Lu, China, 201944199, HCC, DCD2012–2016, 2 yearsRecipient serum, HCC recurrence (HCC vs. LC+HC)LC-MS, MetabolomicsPC (16:0/P-18:1), PC (18:2/OH-16:0), nutriacholic acid were independently related to tumor recurrence with high efficiency to predict HCC recurrence
Xu, UK, 20204547, NA, DBD (27)/ DCD (20)2011–2014, followed till 2019Graft tissue, EAD vs. EGFLC-MS, MetabolomicsCombination of AMP/urate, adenine/urate, hypoxanthine/urate and ALT proved to have higher prediction ability on EAD compared to a combination of conventional liver function and risk markers
Liu, China, 20203982, Mixed, DBD (22)/DBCD (14)/DCD (46)2015–2019, 616 daysGraft tissue, GF vs. non-GF/ MaS vs. non-MaSLC-MS, MetabolomicsDysfunction on glycerophospholipid Metabolism linked the incidence of donor MaS and GF; decrements on PC and PE amplified the fatal effects of MaS on organ failure

AUROC, area under the receiver operating characteristic curve; CHC, chronic hepatitis C; DBCD, donation after brain and cardiac death; DBD, donation after brain death; DCD, donation after cardiac death; EAD, early allograft dysfunction; GF, graft failure; HC, healthy control; HCC, hepatocellular carcinoma; HR-MAS NMR, high-resolution magic-angle-spinning nuclear magnetic resonance; IGF, immediate graft function; IQR, interquartile range; LC, liver cirrhosis; LC-MS, liquid chromatography coupled to mass spectrometry; MaS, macrosteatosis; NA, not available; PC, phosphatidylcholine; PE, phosphatidylethanolamine.

Table 2

Key mechanisms referred in enrolled clinical omics studies

Name, Publication year [Ref]Comparison, sample, assayKey moleculeKey pathway
Diamond, 201242F3-F4 vs. F0-F2 fibrosis, graft tissue, proteomicsPRKAR2A, TCERG1, DGCR8, WBSCR22, MYH11, PCBP1, GSTK1, TPM1, PFDN1, CDC42UP: CTLA4 signaling in cytotoxic T lymphocytes, cytotoxic T lymphocyte–mediated apoptosis of target cells, Allograft rejection signaling, OX40 signaling pathway, graft-versus-host disease signaling
DN: Arylhydrocarbon receptor signaling, glutathione metabolism, Metabolism of xenobiotics by cytochrome P450, NRF2-mediated oxidative stress response, xenobiotic metabolism signaling
F3-F4 vs. F0-F2 fibrosis, recipient serum, metabolomicsUP: methionine, serine, gamma- glutamylglutamate, gamma- glutamylphenylalanine
DN: CysteineDN: Glutathione biosynthesis
Cortes, 201438EAD vs. IGF, graft tissue, metabolomicsNAUP: Phospholipid degradation, histidine metabolism, bile acids biosynthesis
ALTERED: Ammonia recycling, urea cycle
Xu, 201543EAD vs. IGF, graft tissue, lipidomicsLysoPC (16:0), LysoPC (18:0)NA
Faitot, 201737EAD vs. Non-EAD, graft tissue, metabolomicsLactate, phosphocholineNA
Xu, 202045EAD vs. EGF, graft tissue, metabolomicsAMP, urate, adeninePurine metabolism
Liu, 202039GF vs. non-GF, graft tissue, metabolomicsCalcidiol, delta7-avenasterol, presqualene diphosphate, episterol, 5-dehydroepisterol, 4,4-dimethylcholesta-8,14,24-trienolSteroid biosynthesis
MaS vs. non-MaS, graft tissue, metabolomicsPC (20:5/16:0), linoleic acid, PE (20:4/22:6), PE (20:5/18:2), LysoPC (20:3), LysoPC (20:4), LysoPC (22:4), LysoPC (22:5), phosphocholine, 1-phosphatidyl-D-myo-inositolLinoleic acid, glycerophospholipid metabolism
Overlapped MaS+GFGlycerophospholipid metabolism

DN, downregulated; EAD, early allograft dysfunction; GF, graft failure; IGF, immediate graft function; MaS,macrosteatosis; UP, upregulated.

Omics data for assessing organ function after LT

Four studies, all of which involved European cases,37,38,43,45 predesignated EAD as a major post-transplant outcome by mass spectrometry using a similar comparison strategy. After reanalysis of prominently expressed potential metabolites from published studies, we showed that the glycerophospholipid, histidine, and purine metabolism pathways had significant deviations in EAD cases (Fig. 2). It is noteworthy that dysfunctional glycerophospholipid metabolism was also shown to be a major cause of macrosteatosis (MaS) and graft failure (GF) in a prior study from our center.39 We speculated that a derangement in glycerophospholipid might be a link to inferior organ quality, such as MaS, to early graft dysfunction and final organ failure in LT.

Reanalysis of positive metabolites that associated with EAD in prior metabolomic studies.
Fig. 2  Reanalysis of positive metabolites that associated with EAD in prior metabolomic studies.

(A) Pathway analysis based on positive metabolites associated with EAD occurrence in prior metabolomic studies. (B) Details of pathway on glycerophospholipid metabolism and positive metabolites associated with EAD. (C) Details of pathway on histidine metabolism and positive metabolites associated with EAD. (D) Details of pathway on purine metabolism and positive metabolites associated with EAD. EAD, early allograft dysfunction.

Omics data for assessing primary disease recurrence

The predictive value of noninvasive biomarkers from recipient sera was evaluated in cohorts with a specific etiology (hepatocellular carcinoma [HCC] or chronic hepatitis C [CHC]) from three studies.31,42,44 Among LT recipients with chronic hepatitis C, post-transplant fibrotic severity can be differentiated based on metabolite clusters, including sphingomyelins and phosphatidylcholines. Severe liver fibrosis can be followed by down-regulation on glutathione biosynthesis. Oxidative stress might be involved in regulating fibrogenesis after LT.31,42 Our metabolomic data in plasma samples from pretransplant HCC patients also showed the combined application of classical biological biomarkers, such as alpha-fetoprotein (AFP), phosphatidylcholine, and nutriacholic acid, might improve the predictive accuracy of tumor recurrence.44 Additional details are presented in Table 2.

Limitations of current LT-related omics studies

Collectively, current omics data involving LT are scattered, with a lack of a systematic framework and validation testing. Most LT-related omics studies have adopted a retrospective design pattern. Because of the lower price and easy-to-conduct workflow, liquid chromatography-mass spectrometry (LC-MS) is the most commonly used tool for metabolomic assays. Other platforms, such as next-generation sequencing (NGS) and even multiomics studies with mutual validation in a fixed cohort are urgently needed in future LT-related omics studies.

Despite the importance of donor risk on post-transplant outcomes mentioned in previous studies,47,48 we found no reported omics data from donor blood samples to represent the general donor condition and the impact on LT quality. Donor features can be easily regulated as a flexible indicator by short-term intervention on exercise, diet, and drugs. These effects were more apparent in living donor liver transplantation (LDLT).49 The knowledge gap in omics data from donor sera should be filled by future studies, which might lead to novel findings of manageable targets to improve LT quality.

Future directions for omics studies in LT

Many omics studies involved in the process of clinical LT have been conducted over the past decades. The profiles for proteomes and metabolomes from biological samples in the peritransplant period were outlined to determine the interested issues in LT cases from an omics perspective. Key molecules and pathways involved in transcriptomics might provide potential targets for further translational studies.40,41

Despite these advances, limitations and many gaps still exist among the literature for clinical LT studies. Specifically, these limitations can be summarized as follows: 1. fewer multiomics studies with the inclusion of multi-dimensional samples (tissues and sera) in a fixed LT cohort; 2. lack of donor-recipient-matched omics data to show the panorama of LT; 3. lack of dynamic and longitudinal omics data to show the tendency of key molecules before and after LT; 4. insufficient participation of more advanced technologies, such as single-cell RNA sequencing; and 5. lack of deep data-mining by construction of networks with clinical and omics factors. These limitations might affect the translational value of omics data in LT research. Transplantation is the crown in all liver surgical operations. Omics studies are urgently needed to guide clinical LT to maximize the benefits to patients worldwide.50 In contrast, LT is a far more complex systematic engineering process affected by many factors from clinical, environmental, and the genetics of the donor and recipient.6,51–53 Therefore, omics projects in LT need cooperative networks of clinicians, pharmacists, bioinformaticians, and biologists, with comprehensive consideration of the abovementioned potential factors in an appropriate algorithmic model. Thus, actions with considerate concerns on abovementioned factors are warranted in omics studies for LT cases.

Analysis of multiomics and multidimensional systems in LT cases

Undoubtedly, the multiomics analysis which combines data from various high-throughput technologies has provided meaningful approaches to better understand molecular mechanisms and construct predictive models.54–57 Data from our center indicated a high efficiency of integrative omics assays on the prediction of tumor burden in liver cancer patients.58 However, multiomics data of high quality were rarely reported in prior clinical investigations on LT cases.

As discussed in our prior review (part II), clinicians tend to use metabolomic assays to explore the potential mechanisms underlying key issues in LT. The possible explanation might be due to the low quality of samples with severe RNA degradation after removal from donor bodies.59,60 A previous study reported that the intactness of tissues from different organs was inconsistent following the same cold ischemia exposure after surgical resection.61 With respect to the liver, the RNA appears to tolerate long-distance transportation with grafts from donors after cardiac death (DCD). In a cohort of grafts from cadaveric LT, our ongoing project showed that >93% of graft tissues (41 of 44 samples) were suitable for further RNA-seq analysis even through long-term cold ischemia time (CIT). The prerequisites for RNA-seq analysis were as follows: 1. sample freshness and RNA extraction within 3 months; 2. shortened duration between the end of CIT and snap-freezing in liquid nitrogen (<30 m); and 3. avoidance of repeated freezing and thawing (<2 times). Data from our study indicated the feasibility of transcriptomics assays in liver grafts from a citizen-based organ donation system (Fig. 3). Projects with well-designed RNA-seq are desirable in further LT studies.

Matched scatter plot between sample snap freezing time* and RNA quality for transcriptomic analysis.
Fig. 3  Matched scatter plot between sample snap freezing time* and RNA quality for transcriptomic analysis.

*Snap-freezing time indicates the period between the end of cold preservation and snap-freezing in liquid nitrogen. Correlation analysis was performed by Spearman’s test. Insignificant correlation was observed between snap-freezing time and RNA quality.

With the exception of solid organ tissues, omics data from biological fluids, such as peripheral blood, also showed advantages in disease screening and biomarker investigation in LT.62 Specifically, the superiority of omics studies on blood samples is realized in noninvasive and repeated sampling,6 and these features support blood omics as an appropriate tool in dynamic monitoring programs, such as graft rejection, drug responses, and operational tolerance in LT.63,64 An algorithm was developed to predict post-transplant acute rejections by gene expression arrays in USA-based patients.62 Data from our center also confirmed the potential availability of the metabolomics assays on peripheral blood samples for prognostication of patients who underwent LT.44 In spite of the limitations (instability and internal/external heterogeneity) for omics results in blood samples,6 the diversity of omics assays (transcriptomics, proteomics, and metabolomics), and paralleled multi-sample dimensions (tissues/sera from donors/recipients) within the same LT cases is recommended and might provide possible approaches to better uncover the potential biomarkers for individualized treatments of problems occurring in the peritransplant process.

Importance of donor-recipient-matched omics data in clinical transplantation

The importance of donor-recipient-matched genetic heritage has been emphasized in previous studies; however, the availability of relevant data was limited due to their low throughput with inclusion of single-to-several genes or genetic polymorphisms.25,65,66 Omics data provide available approaches to understand the entire genetic background of LT donors and recipients, which might facilitate the development of potential therapeutic targets and better management of LT patients. The effect of donor features might be conditionally exerted on recipient features and vice versa. Therefore, an individually-matched omics study with considerations of the recipient and donor will provide evidence with more reliability and persuasion for precise treatment of LT cases.40,67,68 A challenge is also anticipated in assessing the synergistic effects using an appropriate mathematical model. Key issues in the LT process are influenced by a network of factors from the recipient, donor, surgery, and post-transplant immunotherapy, with various internal and external connections.69 Advanced statistical methodologies with dimensional reductions are necessary in LT studies with the inclusion of integrative high-throughput data.70 Weighted gene coexpression network analysis (WGCNA) is strongly recommended for dimensional reduction to build the connections between omics and clinical traits in LT cases.71 In the matrix of the WGCNA algorithm, omics traits can be clustered into differentiated modules. The mathematical connections between clinical traits in LT, such as primary nonfunction (PNF) or EAD and modules might help for further investigate the inner mechanism of these phenotypes in the LT process. Least absolute shrinkage and selection operator (Lasso) regression is another option in omics studies and key molecules can be screened out by Lasso regression for scale reduction.72,73 More details are shown in Figure 4.

Recommended flowchart for multiomics study in LT cases.
Fig. 4  Recommended flowchart for multiomics study in LT cases.

LT, liver transplantation.

In addition, the involvement of artificial intelligence (AI) and machine learning algorithms might also provide integrative analyses of high-throughput datasets.74–76

Necessity of dynamic and longitudinal omics studies in LT cases

Many phenotypes, such as drug response, might vary across different stages during the entire disease process. Dynamic omics data might help to uncover the mechanisms underlying this development.77,78 Similarly, longitudinal omics data [e.g., before/after cellular rejection] might also help biomarker development and allograft monitoring in LT cases.28 Continuous metabolomics assays on sera samples from recipients were also performed to build the link between metabolite profiles and post-transplant outcomes.34

Researchers tend to conduct dynamic omics by utilizing biological fluid samples for the simplicity of repeated sampling; however, the projects with dynamic omics results are far less often reported than needed in LT. Using graft steatosis as an example, the reversal of graft steatosis was observed in most cases of grafts used for LT; such reversal might have benefits on prolonged post-transplant survival.79,80 Additionally, we found that reversed fat deposition partially determined the fate (i.e. survival or death) of recipient rats after LT.81 Multiomics studies with a focus on dynamic changes of histologic steatosis in vivo might elucidate this mechanism and potential biomarkers. Collectively, longitudinal omics data are worthy, in some circumstances, to fill in the knowledge gap for precise treatment in clinical LT.

Validation of transplantomics studies

The validity of multiomics results should be ensured based on validation practices over multiple steps.12 The importance of validation for omics studies for LT research has also been mentioned in a previous report.6 The validity of data is routinely tested via internal or external validation. Multicenter omics studies, including validation from relatively independent patient cohorts, yield more reliable biomarker panels in predicting post-transplant rejection with higher translational value.62 With the technologic development of omics assays, the cost of a commercial omics kit is lower with complete and individualized analytic platforms. Differentiated with typical design referred in previous literature,12 new concepts should be imported in omics validation in LT studies.

First, potential mechanisms can be verified by mutual validations across multiomics data. Candidate molecules from transcriptomics, proteomics, and metabolomics can be simultaneously mapped in the KEGG database, and can be strengthened by joint omics studies.82 Furthermore, omics validation in model organisms, such as cell lines or rats, might also help to distinguish the roles of omics molecules on key issues in LT. Biases are inevitable for disturbance from confounders in clinical transplant-related studies.83 Integrative omics studies from patients and model organisms with preset targets might help better understand the underlying mechanisms. Therefore, validation in a LT study does not only simply mean an enlarged sample size. We applied more updates via measures on omics assays for the better treatment of LT cases.

Utilization of online platforms for statistics in multiomics studies

Currently, many online tools have been developed for the integration of omics data.84 Usually, a web-based tool has a user-friendly interface with a programmable procedure to run the data using preset algorithms. With the help of these online platforms, a shortened study curve was presented to researchers regarding omics data analysis.85–87 As an example, MetaboAnalyst (https://www.metaboanalyst.ca/ ) is a popular online tool that analyzes metabolomics and multiomics data.88 The website provides online services with coverage from basic statistics, such as quantitative comparisons and principal component analysis, to advanced clustering, enrichment, and pathway analysis. It is noteworthy that joint multiomics analyses can be conducted and visualized in MetaboAnalyst with an entire set of R codes for the retrospective evaluation of potential errors.

More and more online resources have been developed to deal with multiomics data via ready-made sets of protocols prepared by statisticians that promote the translational value of omics results for clinical LT.84,89 Technological issues should not be barriers of clinical LT studies. User-friendly and open access online platforms have been developed by biostatisticians with mature protocols for the treatment of omics data. Clinicians should pay more attention to the association between omics data and clinical traits. In contrast, timely updates on platforms are also needed to adapt to the rapid technological upgrade in omics assays.

Importance of contributions from experienced clinicians

We have come into the omics era, and it has been followed by technological development. Lower unit prices guarantee the feasibility of multiomics studies in clinical LT cases; however, lower costs also cause abuse of omics studies. Low-quality omics studies might produce redundant information to confuse decision-making in LT.90,91 In our opinion, the academic value of omics studies rely on the ability to improve LT therapeutic efficiency. Thus, investment in omics test resources should uncover key traits with significant potential to improve clinical LT quality. The increasing participation of experienced clinicians can also help to express the clinical requests of omics studies.23 A reliable and replicable clinical trait with translational potential is a good beginning for clinical omics studies in LT.

Using MaS grafts as an example, we found inferior prognoses in patients who received MaS organs with poor concurrent initial liver function (IPF) after LT (a phenomenon with clinical significance).46 The validity of this connection was demonstrated in another cohort with similar features (i.e. replicability). A metabolomics assay was performed to investigate the mechanism and biomarker for machine perfusion to improve the quality of marginal grafts (i.e. translational potential).39 Further omics studies are ongoing in a rat LT model (i.e. laboratory investigation),81 and collectively, clinicians are responsible for playing a central role in implementing a meaningful project with the goal of better treatments for LT patients. In contrast, clinical omics in LT also require a well-organized consortium with a cooperative network of pharmacists, bioinfomaticians, scientists, and technicians.

Ideal flowchart for multiomics studies in LT cases

Based on the points discussed above, we suggest that a well-designed omics study in LT should be based on the following: 1. prospective destination with adequate sample storage; and 2. a research topic with adequate translational value. The molecular mechanism of MaS organs on post-transplant prognosis was detailed in our previous study.39 The details of the recommended flowchart are presented in Figure 4. Specifically, omics data (transcriptomics, proteomics, or metabolomics) can be procured from samples (tissues/sera) from the donor and recipient, and classified by graft’s MaS status. First, the mechanism underlying organ MaS on post-transplant outcomes can be deduced by joint omics analysis. Then, WGCNA can be performed to construct the network and evaluate the clinical omics connection, and the impact on post-transplant outcomes. Third, omics data can be used in the construction of a MaS-related prediction model on post-transplant prognosis. It is noteworthy that the complex omics result is complicated and does not provide a clear expression to readers and in data visualization by tools, such as Cytoscape (https://cytoscape.org/ ), MetaboAnalyst, and other platforms, might help to highlight the key results.92

Conclusions

In conclusion, this review retrospectively evaluated the application of multiomics studies on clinical LT in the past decades and assessed the translational values on mechanistic investigations and therapeutic aims. We forecasted the future directions for omics studies in clinical LT and called for more participation by clinicians in the cooperative consortium. Finally, we shared the flowchart for the use of multiomics data on clinical LT cases. In the coming omics era, the quality of omics data can be guaranteed by improved algorithms with a lower cost, followed by technological development. More mention should be raised on the translational value of further mechanistic exploration and better treatment of LT cases.

Abbreviations

AFP: 

alpha-fetoprotein

AI: 

artificial intelligence

CHC: 

chronic hepatitis C

CIT: 

cold ischemia time

DCD: 

donors after cardiac death

EAD: 

early allograft dysfunction

GEO: 

Gene Expression Omnibus

GF: 

graft failure

GTEx: 

Genotype-Tissue Expression

HCC: 

hepatocellular carcinoma

IPF: 

initial liver function

IRI: 

ischemic reperfusion injury

Lasso: 

Least absolute shrinkage and selection operator

LC-MS: 

liquid chromatography-mass spectrometry

LDLT: 

living donor liver transplantation

LT: 

liver transplantation

MaS: 

macrosteatosis

PNF: 

primary nonfunction

TCGA: 

The Cancer Genome Atlas

WGCNA: 

Weighted gene coexpression network analysis

Declarations

Data sharing statement

The data used to support the findings of this study are available from the corresponding author upon reasonable request.

Funding

This study is supported by Innovative Research Groups of National Natural Science Foundation of China (81721091), Major Program of National Natural Science Foundation of China (91542205), National S&T Major Project (2017ZX10203205), National Natural Science Foundation of China (81902813), Zhejiang International Science and Technology Cooperation Project (2016C04003), Zhejiang Provincial Natural Science Foundation of China (LY18H030002), Zhejiang Medical Association (grant no. 2019ZYC-A81), International Youth Exchange Programme by China Association for Science and Technology (2019), Tianqing Liver Diseases Research Fund (TQGB20200114), Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (2021KY145), Organ Transplantation Overseas Training for Youth Talents from Shulan Excellent Talent Project, CSCO (Chinese Society Of Clinical Oncology)-Bayer Tumor Research Funding (Y-bayer202001/zb-0003) and Open Fund of Key laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University) belonged to Ministry of Education.

Conflict of interest

The authors have no conflict of interests related to this publication.

Authors’ contributions

Conception and design of the study (ZL and SZ), data extraction (JX), data analysis (SQ), drafting of the manuscript (ZL, JX), and review of the manuscript for important intellectual content (LG, LZ, AM, SZ). All of authors approved the final version of manuscript for submission.

References

  1. Williams R. Global challenges in liver disease. Hepatology 2006;44(3):521-526 View Article
  2. Ahmed A, Keeffe EB. Current indications and contraindications for liver transplantation. Clin Liver Dis 2007;11(2):227-247 View Article
  3. O’Leary JG, Lepe R, Davis GL. Indications for liver transplantation. Gastroenterology 2008;134(6):1764-1776 View Article
  4. Dutkowski P, Linecker M, DeOliveira ML, Müllhaupt B, Clavien PA. Challenges to liver transplantation and strategies to improve outcomes. Gastroenterology 2015;148(2):307-323 View Article
  5. Angelico M, Cillo U, Fagiuoli S, Gasbarrini A, Gavrila C, Marianelli T, et al. Liver Match, a prospective observational cohort study on liver transplantation in Italy: study design and current practice of donor-recipient matching. Dig Liver Dis 2011;43(2):155-164 View Article
  6. Sarwal MM. Deconvoluting the ‘omics’ for organ transplantation. Curr Opin Organ Transplant 2009;14(5):544-551 View Article
  7. Karczewski KJ, Snyder MP. Integrative omics for health and disease. Nat Rev Genet 2018;19(5):299-310 View Article
  8. Russell C, Rahman A, Mohammed AR. Application of genomics, proteomics and metabolomics in drug discovery, development and clinic. Ther Deliv 2013;4(3):395-413 View Article
  9. Yoo BC, Kim KH, Woo SM, Myung JK. Clinical multi-omics strategies for the effective cancer management. J Proteomics 2018;188:97-106 View Article
  10. Sun YV, Hu YJ. Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases. Adv Genet 2016;93:147-190 View Article
  11. Boja ES, Kinsinger CR, Rodriguez H, Srinivas P. Integration of omics sciences to advance biology and medicine. Clin Proteomics 2014;11(1):45 View Article
  12. Ioannidis JP, Khoury MJ. Improving validation practices in “omics” research. Science 2011;334(6060):1230-1232 View Article
  13. McShane LM, Cavenagh MM, Lively TG, Eberhard DA, Bigbee WL, Williams PM, et al. Criteria for the use of omics-based predictors in clinical trials. Nature 2013;502(7471):317-320 View Article
  14. Houle D, Govindaraju DR, Omholt S. Phenomics: the next challenge. Nat Rev Genet 2010;11(12):855-866 View Article
  15. Wang X. Clinical trans-omics: an integration of clinical phenomes with molecular multiomics. Cell Biol Toxicol 2018;34(3):163-166 View Article
  16. Forshed J. Experimental Design in Clinical ‘Omics Biomarker Discovery. J Proteome Res 2017;16(11):3954-3960 View Article
  17. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002;30(1):207-210 View Article
  18. Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 2015;19(1A):A68-77 View Article
  19. Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, et al. The Genotype-Tissue Expression (GTEx) project. Nat Genet 2013;45(6):580-585 View Article
  20. Schneider MV, Orchard S. Omics technologies, data and bioinformatics principles. Methods Mol Biol 2011;719:3-30 View Article
  21. Zheng CL, Ratnakar V, Gil Y, McWeeney SK. Use of semantic workflows to enhance transparency and reproducibility in clinical omics. Genome Med 2015;7(1):73 View Article
  22. Canuel V, Rance B, Avillach P, Degoulet P, Burgun A. Translational research platforms integrating clinical and omics data: a review of publicly available solutions. Brief Bioinform 2015;16(2):280-290 View Article
  23. van Karnebeek CDM, Wortmann SB, Tarailo-Graovac M, Langeveld M, Ferreira CR, van de Kamp JM, et al. The role of the clinician in the multi-omics era: are you ready?. J Inherit Metab Dis 2018;41(3):571-582 View Article
  24. Mahmud N. Selection for Liver Transplantation: Indications and Evaluation. Curr Hepatol Rep 2020;19:203-212 View Article
  25. Kelava T, Turcic P, Markotic A, Ostojic A, Sisl D, Mrzljak A. Importance of genetic polymorphisms in liver transplantation outcomes. World J Gastroenterol 2020;26(12):1273-1285 View Article
  26. Ali MA, Elshobari MM, Salah T, Kandeel AR, Sultan AM, Elghawalby AN, et al. Impact of donor-recipient genetic relationship on outcome of living donor liver transplantation. Liver Transpl 2017;23(1):43-49 View Article
  27. Attard JA, Dunn WB, Mergental H, Mirza DF, Afford SC, Perera MTPR. Systematic Review: Clinical Metabolomics to Forecast Outcomes in Liver Transplantation Surgery. OMICS 2019;23(10):463-476 View Article
  28. Kohut TJ, Barandiaran JF, Keating BJ. Genomics and Liver Transplantation: Genomic Biomarkers for the Diagnosis of Acute Cellular Rejection. Liver Transpl 2020;26(10):1337-1350 View Article
  29. Hrydziuszko O, Silva MA, Perera MT, Richards DA, Murphy N, Mirza D, et al. Application of metabolomics to investigate the process of human orthotopic liver transplantation: a proof-of-principle study. OMICS 2010;14(2):143-150 View Article
  30. Vionnet J, Sánchez-Fueyo A. Biomarkers of immune tolerance in liver transplantation. Hum Immunol 2018;79(5):388-394 View Article
  31. Cano A, Mariño Z, Millet O, Martínez-Arranz I, Navasa M, Falcón-Pérez JM, et al. A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients. Sci Rep 2017;7(1):10497 View Article
  32. Kriss M, Verna EC, Rosen HR, Lozupone CA. Functional Microbiomics in Liver Transplantation: Identifying Novel Targets for Improving Allograft Outcomes. Transplantation 2019;103(4):668-678 View Article
  33. Serkova NJ, Zhang Y, Coatney JL, Hunter L, Wachs ME, Niemann CU, et al. Early detection of graft failure using the blood metabolic profile of a liver recipient. Transplantation 2007;83(4):517-521 View Article
  34. Sui W, Gan Q, Liu F, Ou M, Wang B, Liao S, et al. Dynamic Metabolomics Study of the Bile Acid Pathway During Perioperative Primary Hepatic Carcinoma Following Liver Transplantation. Ann Transplant 2020;25:e921844 View Article
  35. Huang S, Ju W, Zhu Z, Han M, Sun C, Tang Y, et al. Comprehensive and combined omics analysis reveals factors of ischemia-reperfusion injury in liver transplantation. Epigenomics 2019;11(5):527-542 View Article
  36. Gehrau RC, Mas VR, Dumur CI, Ladie DE, Suh JL, Luebbert S, et al. Regulation of molecular pathways in ischemia-reperfusion injury after liver transplantation. Transplantation 2013;96(10):926-934 View Article
  37. Faitot F, Besch C, Battini S, Ruhland E, Onea M, Addeo P, et al. Impact of real-time metabolomics in liver transplantation: Graft evaluation and donor-recipient matching. J Hepatol 2018;68(4):699-706 View Article
  38. Cortes M, Pareja E, García-Cañaveras JC, Donato MT, Montero S, Mir J, et al. Metabolomics discloses donor liver biomarkers associated with early allograft dysfunction. J Hepatol 2014;61(3):564-574 View Article
  39. Liu Z, Zhu H, Wang W, Xu J, Que S, Zhuang L, et al. Metabonomic Profile of Macrosteatotic Allografts for Orthotopic Liver Transplantation in Patients With Initial Poor Function: Mechanistic Investigation and Prognostic Prediction. Front Cell Dev Biol 2020;8:826 View Article
  40. Sirota M, Sarwal MM. Transplantomics: Toward Precision Medicine in Transplantation Research. Transplantation 2017;101(8):1777-1782 View Article
  41. Sarwal MM, Benjamin J, Butte AJ, Davis MM, Wood K, Chapman J. Transplantomics and biomarkers in organ transplantation: a report from the first international conference. Transplantation 2011;91(4):379-382 View Article
  42. Diamond DL, Krasnoselsky AL, Burnum KE, Monroe ME, Webb-Robertson BJ, McDermott JE, et al. Proteome and computational analyses reveal new insights into the mechanisms of hepatitis C virus-mediated liver disease posttransplantation. Hepatology 2012;56(1):28-38 View Article
  43. Xu J, Casas-Ferreira AM, Ma Y, Sen A, Kim M, Proitsi P, et al. Lipidomics comparing DCD and DBD liver allografts uncovers lysophospholipids elevated in recipients undergoing early allograft dysfunction. Sci Rep 2015;5:17737 View Article
  44. Lu D, Yang F, Lin Z, Zhuo J, Liu P, Cen B, et al. A prognostic fingerprint in liver transplantation for hepatocellular carcinoma based on plasma metabolomics profiling. Eur J Surg Oncol 2019;45(12):2347-2352 View Article
  45. Xu J, Hassan-Ally M, Casas-Ferreira AM, Suvitaival T, Ma Y, Vilca-Melendez H, et al. Deregulation of the Purine Pathway in Pre-Transplant Liver Biopsies Is Associated with Graft Function and Survival after Transplantation. J Clin Med 2020;9(3):711 View Article
  46. Liu Z, Wang W, Zhuang L, Liu J, Que S, Zhu D, et al. Clear mortality gap caused by graft macrosteatosis in Chinese patients after cadaveric liver transplantation. Hepatobiliary Surg Nutr 2020;9(6):739-758 View Article
  47. Feng S, Goodrich NP, Bragg-Gresham JL, Dykstra DM, Punch JD, DebRoy MA, et al. Characteristics associated with liver graft failure: the concept of a donor risk index. Am J Transplant 2006;6(4):783-790 View Article
  48. Flores A, Asrani SK. The donor risk index: A decade of experience. Liver Transpl 2017;23(9):1216-1225 View Article
  49. Nakamuta M, Morizono S, Soejima Y, Yoshizumi T, Aishima S, Takasugi S, et al. Short-term intensive treatment for donors with hepatic steatosis in living-donor liver transplantation. Transplantation 2005;80(5):608-612 View Article
  50. Ling Q, Zheng SS, Xu X. Strategic thinking to improve the basic research in liver transplantation in the new era. Zhonghua Wai Ke Za Zhi 2020;58(10):737-740.Chinese View Article
  51. Nacif LS, Kim V, Galvão F, Ono SK, Pinheiro RS, Carrilho FJ, et al. Translational medical research and liver transplantation: systematic review. Transl Gastroenterol Hepatol 2018;3:91 View Article
  52. Naesens M, Anglicheau D. Precision Transplant Medicine: Biomarkers to the Rescue. J Am Soc Nephrol 2018;29(1):24-34 View Article
  53. Kurian SM, Whisenant TC, Marsh CL. Systems biology approaches in solid organ transplantation. Curr Opin Organ Transplant 2021;26(1):37-42 View Article
  54. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol 2017;18(1):83 View Article
  55. Meng C, Kuster B, Culhane AC, Gholami AM. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics 2014;15:162 View Article
  56. Huang S, Chaudhary K, Garmire LX. More Is Better: Recent Progress in Multi-Omics Data Integration Methods. Front Genet 2017;8:84 View Article
  57. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics Data Integration, Interpretation, and Its Application. Bioinform Biol Insights 2020;14:1-24 View Article
  58. Huang H, Ren Z, Gao X, Hu X, Zhou Y, Jiang J, et al. Integrated analysis of microbiome and host transcriptome reveals correlations between gut microbiota and clinical outcomes in HBV-related hepatocellular carcinoma. Genome Med 2020;12(1):102 View Article
  59. Gallego Romero I, Pai AA, Tung J, Gilad Y. RNA-seq: impact of RNA degradation on transcript quantification. BMC Biol 2014;12:42 View Article
  60. Davila JI, Fadra NM, Wang X, McDonald AM, Nair AA, Crusan BR, et al. Impact of RNA degradation on fusion detection by RNA-seq. BMC Genomics 2016;17(1):814 View Article
  61. Viana CR, Neto CS, Kerr LM, Palmero EI, Marques MM, Colaiacovo T, et al. The interference of cold ischemia time in the quality of total RNA from frozen tumor samples. Cell Tissue Bank 2013;14(2):167-173 View Article
  62. Levitsky J, Asrani SK, Schiano T, Moss A, Chavin K, Miller C, et al. Discovery and validation of a novel blood-based molecular biomarker of rejection following liver transplantation. Am J Transplant 2020;20(8):2173-2183 View Article
  63. Brouard S, Mansfield E, Braud C, Li L, Giral M, Hsieh SC, et al. Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance. Proc Natl Acad Sci U S A 2007;104(39):15448-15453 View Article
  64. Sarwal M, Pascual J. Immunosuppression minimization in pediatric transplantation. Am J Transplant 2007;7(10):2227-2235 View Article
  65. Arthur VL, Guan W, Loza BL, Keating B, Chen J. Joint testing of donor and recipient genetic matching scores and recipient genotype has robust power for finding genes associated with transplant outcomes. Genet Epidemiol 2020;44(8):893-907 View Article
  66. Watt KD, Dierkhising R, Fan C, Heimbach JK, Tillman H, Goldstein D, et al. Investigation of PNPLA3 and IL28B genotypes on diabetes and obesity after liver transplantation: insight into mechanisms of disease. Am J Transplant 2013;13(9):2450-2457 View Article
  67. Roedder S, Vitalone M, Khatri P, Sarwal MM. Biomarkers in solid organ transplantation: establishing personalized transplantation medicine. Genome Med 2011;3(6):37 View Article
  68. Yang JY, Sarwal MM. Transplant genetics and genomics. Nat Rev Genet 2017;18(5):309-326 View Article
  69. Liu ZT, Chen TC, Lu XX, Cheng J, Xie HY, Zhou L, et al. PNPLA3 I148M variant affects non-alcoholic fatty liver disease in liver transplant recipients. World J Gastroenterol 2015;21(34):10054-10056 View Article
  70. Meng C, Zeleznik OA, Thallinger GG, Kuster B, Gholami AM, Culhane AC. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief Bioinform 2016;17(4):628-641 View Article
  71. Li J, Zhou D, Qiu W, Shi Y, Yang JJ, Chen S, et al. Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design. Sci Rep 2018;8(1):622 View Article
  72. Liu C, Wang X, Genchev GZ, Lu H. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction. Methods 2017;124:100-107 View Article
  73. Pineda S, Real FX, Kogevinas M, Carrato A, Chanock SJ, Malats N, et al. Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer. PLoS Genet 2015;11(12):e1005689 View Article
  74. Ramon C, Gollub MG, Stelling J. Integrating -omics data into genome-scale metabolic network models: principles and challenges. Essays Biochem 2018;62(4):563-574 View Article
  75. Zeng ISL, Lumley T. Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science). Bioinform Biol Insights 2018;12:1-16 View Article
  76. He T, Fong JN, Moore LW, Ezeana CF, Victor D, Divatia M, et al. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer. Comput Med Imaging Graph 2021;89:101894 View Article
  77. Selevsek N, Caiment F, Nudischer R, Gmuender H, Agarkova I, Atkinson FL, et al. Network integration and modelling of dynamic drug responses at multi-omics levels. Commun Biol 2020;3(1):573 View Article
  78. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Chen R, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012;148(6):1293-1307 View Article
  79. Croome KP, Livingston D, Croome S, Keaveny AP, Taner CB, Nakhleh R. Sequential Protocol Biopsies Post-Liver Transplant From Donors With Moderate Macrosteatosis: What Happens to the Fat?. Liver Transpl 2021;27(2):248-256 View Article
  80. Li J, Liu B, Yan LN, Zuo YX, Li B, Zeng Y, et al. Reversal of graft steatosis after liver transplantation: prospective study. Transplant Proc 2009;41(9):3560-3563 View Article
  81. Liu Z, Wang W, Que S, He Y, Zheng S. Presence of Macrosteatosis In Vivo Determined the Survival Status of Rats After Liver Transplantation. Liver Transpl 2021;27(3):459-460 View Article
  82. Chong J, Xia J. Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data. Methods Mol Biol 2020;2104:337-360 View Article
  83. Volk ML, Roney M, Merion RM. Systematic bias in surgeons’ predictions of the donor-specific risk of liver transplant graft failure. Liver Transpl 2013;19(9):987-990 View Article
  84. Misra BB, Langefeld CD, Olivier M, Cox LA. Integrated Omics: Tools, Advances, and Future Approaches. J Mol Endocrinol 2019;62(1):R21-R45 View Article
  85. Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci 2019;20(19):4781 View Article
  86. Graw S, Chappell K, Washam CL, Gies A, Bird J, Robeson MS, et al. Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics 2021;17(2):170-185 View Article
  87. Urbanski AH, Araujo JD, Creighton R, Nakaya HI. Integrative Biology Approaches Applied to Human Diseases. In: Husi H, editor. Computational Biology [Internet]. Brisbane (AU): Codon Publications; 2019; Chapter 2
  88. Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 2018;46(W1):W486-W494 View Article
  89. Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res 2013;41(W1):W77-W83 View Article
  90. Prohaska SJ, Stadler PF. The use and abuse of -omes. Methods Mol Biol 2011;719:173-196 View Article
  91. Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials; Board on Health Care Services; Board on Health Sciences Policy; Institute of Medicine; Micheel CM, Nass SJ, Omenn GS, editors. Evolution of Translational Omics: Lessons Learned and the Path Forward. Washington (DC): National Academies Press (US); 2012 Mar 23. COMMITTEE ON THE REVIEW OF OMICS-BASED TESTS FOR PREDICTING PATIENT OUTCOMES IN CLINICAL TRIALS. Available from: https://www.ncbi.nlm.nih.gov/books/NBK202160/
  92. Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol 2011;696:291-303 View Article
Copyright © 2022 Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 License (CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Journal of Clinical and Translational Hepatology
  • pISSN 2225-0719
  • eISSN 2310-8819

Article Options

Metrics

Cite this Article

  • © 2024 Xia & He Publishing Inc.
  • Total visits : 2748529
  • Visits today : 2232
Back to Top

Recent Progress and Future Direction for the Application of Multiomics Data in Clinical Liver Transplantation

Zhengtao Liu, Jun Xu, Shuping Que, Lei Geng, Lin Zhou, Adil Mardinoglu, Shusen Zheng
  • Reset Zoom
  • Download TIFF