Dataset Information

Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection.

ABSTRACT: Acute cellular rejection (ACR) is a prevalent postoperative complication following liver transplantation (LT), exhibiting an increasing incidence of morbidity and mortality. However, the molecular mechanisms of ACR following LT remain unclear. To explore the genetic pathogenesis and identify biomarkers of ACR following LT, three relevant Gene Expression Omnibus (GEO) datasets consisting of data on ACR or non-ACR patients after LT were comprehensively investigated by computational analysis. A total of 349 upregulated and 260 downregulated differentially expressed genes (DEGs) and eight hub genes (ISG15, HELZ2, HNRNPK, TIAL1, SKIV2L2, PABPC1, SIRT1, and PPARA) were identified. Notably, HNRNPK, TIAL1, and PABPC1 exhibited the highest predictive potential for ACR with AUCs of 0.706, 0.798, and 0.801, respectively. KEGG analysis of hub genes revealed that ACR following LT was predominately associated with ferroptosis, protein processing in the endoplasmic reticulum, complement and coagulation pathways, and RIG-I/NOD/Toll-like receptor signaling pathway. According to the immune cell infiltration analysis, γδT cells, NK cells, Tregs, and M1/M2-like macrophages had the highest levels of infiltration. Compared to SIRT1, ISG15 was positively correlated with γδT cells and M1-like macrophages but negatively correlated with NK cells, CD4⁺ memory T cells, and Tregs. In conclusion, this study identified eight hub genes and their potential pathways, as well as the immune cells involved in ACR following LT with the greatest levels of infiltration. These findings provide a new direction for future research on the underlying mechanism of ACR following LT.

SUBMITTER: Zhang C

PROVIDER: S-EPMC11336371 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection.

Zhang Cheng C Chen Jun-Ze JZ Dong Kun K Jian Yong-Yuan YY Huang Kai-Yong KY Su Rui-Ling RL Tan Xue-Lin XL Yuan Guan-Dou GD Lan Yu-Yan YY He Song-Qing SQ Dong Chun-Qiang CQ

Heliyon 20240622 15

Acute cellular rejection (ACR) is a prevalent postoperative complication following liver transplantation (LT), exhibiting an increasing incidence of morbidity and mortality. However, the molecular mechanisms of ACR following LT remain unclear. To explore the genetic pathogenesis and identify biomarkers of ACR following LT, three relevant Gene Expression Omnibus (GEO) datasets consisting of data on ACR or non-ACR patients after LT were comprehensively investigated by computational analysis. A tot ...[more]

PMID: 39170115

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Project description:Acute rejection (AR) after kidney transplant is one of the major obstacles to obtain ideal graft survival. Reliable molecular biomarkers for AR and renal allograft loss are lacking. This study was performed to identify novel long noncoding RNAs (lncRNAs) for diagnosing AR and predicting the risk of graft loss. The several microarray datasets with AR and nonrejection specimens of renal allograft downloaded from Gene Expression Omnibus database were analyzed to screen differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). Univariate and multivariate Cox regression analyses were used to identify optimal prognosis-related DElncRNAs for constructing a risk score model. 39 common DElncRNAs and 185 common DEmRNAs were identified to construct a lncRNA-mRNA regulatory relationship network. DElncRNAs were revealed to regulate immune cell activation and proliferation. Then, 4 optimal DElncRNAs, ATP1A1-AS1, CTD-3080P12.3, EMX2OS, and LINC00645, were selected from 17 prognostic DElncRNAs to establish the 4-lncRNA risk score model. In the training set, the high-risk patients were more inclined to graft loss than the low-risk patients. Time-dependent receiver operating characteristics analysis revealed the model had good sensitivity and specificity in prediction of 1-, 2-, and 3-year graft survival after biopsy (AUC = 0.891, 0.836, and 0.733, respectively). The internal testing set verified the result well. Gene set enrichment analysis which expounded NOD-like receptor, the Toll-like receptor signaling pathways, and other else playing important role in immune response was enriched by the 4 lncRNAs. Allograft-infiltrating immune cells analysis elucidated the expression of 4 lncRNAs correlated with gamma delta T cells and eosinophils, etc. Our study identified 4 novel lncRNAs as potential biomarkers for AR of renal allograft and constructed a lncRNA-based model for predicting the risk of graft loss, which would provide new insights into mechanisms of AR.

Project description:Early transplant dysfunction and failure because of immunological and nonimmunological factors still presents a significant clinical problem for transplant recipients. A critical unmet need is the noninvasive detection and prediction of immune injury such that acute injury can be reversed by proactive immunosuppression titration. In this study, we used iTRAQ -based proteomic discovery and targeted ELISA validation to discover and validate candidate urine protein biomarkers from 262 renal allograft recipients with biopsy-confirmed allograft injury. Urine samples were randomly split into a training set of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection (AR) (n = 74), stable graft (STA) (n = 74), chronic allograft injury (CAI) (n = 58), BK virus nephritis (BKVN) (n = 38), nephrotic syndrome (NS) (n = 8), and healthy, normal control (HC) (n = 10). A total of 389 proteins were measured that displayed differential abundances across urine specimens of the injury types (p < 0.05) with a significant finding that SUMO2 (small ubiquitin-related modifier 2) was identified as a "hub" protein for graft injury irrespective of causation. Sixty-nine urine proteins had differences in abundance (p < 0.01) in AR compared with stable graft, of which 12 proteins were up-regulated in AR with a mean fold increase of 2.8. Nine urine proteins were highly specific for AR because of their significant differences (p < 0.01; fold increase >1.5) from all other transplant categories (HLA class II protein HLA-DRB1, KRT14, HIST1H4B, FGG, ACTB, FGB, FGA, KRT7, DPP4). Increased levels of three of these proteins, fibrinogen beta (FGB; p = 0.04), fibrinogen gamma (FGG; p = 0.03), and HLA DRB1 (p = 0.003) were validated by ELISA in AR using an independent sample set. The fibrinogen proteins further segregated AR from BK virus nephritis (FGB p = 0.03, FGG p = 0.02), a finding that supports the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Dataset Information

Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection.

Publications

Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection.

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