Five-gene signature predicts acute kidney injury in early kidney transplant patients

Results

Identification of AKI-related co-expression modules by weighted co-expression analysis

To better screen AKI-related genes and miRNAs, Median Absolute Deviation (MAD) >25% was selected using gene expression profiles and miRNA expression profiles. Weighted co-expression networks were constructed using WGCNA, and for mRNA expression profiles, power of β = 5 (scale-free R^2 = 0.85) was the soft threshold to ensure a scale-free network (Figure 1A–1B). A total of 22 modules were identified (Figure 1C). Similarly, for miRNA expression profiles, the power of β = 3 (scale-free R^2 = 0.9) was a soft threshold to ensure a scale-free network (Figure 1D–1E). A total of 6 modules were identified (Figure 1F). Furthermore, the correlation between each gene module and AKI was analyzed according to the feature vectors of the modules. It was found that pale turquoise module was significantly negatively correlated with AKI but positively correlated with zero-hour, and that cyan module was significantly negatively correlated with PBx but positively correlated with zero-hour, moreover, brown and dark magenta module were significantly negatively correlated with zero-hour in the co-expression modules (Figure 1G). Among the six miRNA co-expression modules, three modules were closely correlated with zero-hour, among which the brown module was significantly negatively correlated with AKI (Figure 1H). These results indicated that abnormal transcriptome changes after kidney transplantation may lead to different clinical outcomes of kidney transplant patients.

Figure 1. Identification of AKI-related co-expression modules. (A) Analysis of the scale-free fit index for various soft-thresholding powers (β) in mRNA expression profiles. (B) Analysis of the mean connectivity for various soft-thresholding powers in mRNA expression profiles. (C) Dendrogram of genes clustered based on a dissimilarity measure (1-TOM). (D) Analysis of the scale-free fit index for various soft-thresholding powers (β) in miRNA expression profiles. (E) Analysis of the mean connectivity for various soft-thresholding powers in mRNA expression profiles. (F) Dendrogram of miRNA clustered based on a dissimilarity measure (1-TOM). (G) Heat map of correlation between gene co-expression module and AKI. (H) Heat map of correlation between miRNA co-expression module and AKI.

Functional analysis of phenotypic related gene co-expression modules

To observe the function of the three phenotype-related modules zero-hour, PBx and AKI, we extracted the most module-related genes (correlation coefficient > 0.8) from the four modules brown, dark magenta, pale turquoise and cyan, respectively. This study detected 136 genes in the brown module, 34 genes in the dark magenta module, 30 genes in the cyan module, and 30 genes in the pale turquoise module. After subjecting these genes to KEGG Pathway enrichment analysis, specifically, the pale turquoise module was enriched to seven KEGG Pathways, which were mainly related to the PI3K-Akt signaling pathway, ECM-receptor interaction, Fatty acid biosynthesis, and Renin-angiotensin system (Figure 2A); the cyan module was enriched to four Pathways, which were mainly related to Axon guidance, Pathogenic Escherichia coli infection (Figure 2B); the brown module was mainly enriched in immune-related pathways such as Toll-like receptor signaling pathway, Chemokine signaling pathway, Cytokine-cytokine receptor interaction (Figure 2C); the dark magenta module was enriched to the Purine metabolism and Nitrogen metabolism pathways (Figure 2D). Among the pathways enriched by these four modules, only Proteoglycans in cancer was enriched by dark magenta and cyan at the same time, and other pathways did show overlaps, indicating that different co-expression modules may participate in different biological pathways. For example, the brown module was enriched to a variety of immune-related pathways, and the genes in the brown module were significantly negatively correlated with kidney transplantation. This showed that the expression of genes in the brown module was mainly related to mid- and long-term immunosuppressive therapy in kidney transplant patients.

Figure 2. Functional enrichment analysis of phenotype-related gene co-expression modules. (A) 7 KEGG Pathway enriched by pale turquoise module. (B) 4 KEGG Pathway enriched by cyan module. (C) 21 KEGG Pathway enriched by brown module. (D) 2 KEGG Pathway enriched by dark magenta module. The dots are the number of genes enriched in the pathway; the color represents the significance of enrichment, the horizontal axis represents the enrichment factor, and the vertical axis represents the KEGG Pathway.

MiRNA and mRNA specifically expressed in AKI

We selected the most relevant gene modules and miRNA modules of AKI for further analysis to identify miRNAs and mRNAs specifically expressed in AKI. Here, 215 genes were included in the most relevant gene module of AKI (pale turquoise), which showed a double-peak distribution of correlations with AKI (Figure 3A). A total of 164 miRNAs were included in the most relevant miRNA module of AKI (brown), and the correlation of these miRNAs with AKI also showed a double-peak distribution (Figure 3B), indicating that the genes and miRNAs in the module presented two expression patterns in the AKI samples. We selected genes and miRNAs with expression correlation coefficients greater than 0.8 with the modules and greater than 0.4 with AKI as the genes and miRNAs specifically expressed in AKI. Here, we obtained 4 miRNAs and 29 genes. Fourteen of these 29 genes were significantly under-expressed in AKI and PBx samples, 15 genes were significantly overexpressed in AKI and PBx samples, and four miRNAs were significantly under-expressed in both AKI and PBx samples (Figure 3C). Furthermore, after comparing the expression differences of these genes in AKI and PBx samples, we found that 17(58.6%) of the genes showed significant expression differences (Figure 3D), indicating that these genes may serve as specific biomarkers for AKI. Interestingly, no significant difference in the expression of the four miRNAs was detected in the samples of AKI and PBx (Figure 3E). However, KEGG Pathway enrichment analysis of four miRNA target genes showed a total of 87 enriched pathways, which had the most intersections with the KEGG Pathway enriched by the brown and pale turquoise modules of co-expressed genes (Supplementary Figure 1A), suggesting that these four miRNAs may be involved in both AKI and immunomodulatory processes.

Figure 3. miRNA and mRNA specifically expressed in AKI. (A) Relationship between gene expression in pale turquoise module and module/AKI. The horizontal axis represents the correlation coefficient between gene expression and pale turquoise module, and the vertical axis represents the correlation coefficient between gene expression and AKI. (B) Relationship between miRNA expression in brown module and module/AKI. The horizontal axis represents the correlation coefficient between miRNA expression and brown module, and the vertical axis represents the correlation coefficient between miRNA expression and AKI. (C) heatmap of genes and miRNAs specifically expressed in the module. (D, E): Differences between genes and miRNAs specifically expressed in the module in AKI samples and PBx samples. Statistical p-values were obtained using the t-test, with “*” indicating p < 0.05 and “**” indicating p < 0.01, “.” indicates p < 0.1, “-” indicates p > 0.1.

Identification of key mRNA markers in AKI

To further screen gene markers in AKI, we selected 17 genes (10 specifically high-expressed and 7 genes specifically low-expressed) differences in the three types of samples (Figure 4A). Support vector machines were used to establish an AKI prediction model based on the expression of each gene, and ROC was used to analyze the prediction performance of each gene (Figure 4B). The results showed that AUC of 9(53%) genes was greater than 0.9 and AUC of 15 (88%) genes was greater than 0.8, showing a high performance in predicting AKI samples based on the expression of these genes. Subsequently, the top five AUC genes (HAS2, MYOF, PLPPR1, QDPR, SFXN1) were subjected to GO enrichment analysis, with FDR <0.01 as the threshold. Four genes were found to be enriched to 41 GO terms (Figure 4C), which were mainly related to transmembrane transport, amino acid metabolism, renal absorption and positive regulation of urine volume, suggesting that these four genes may play an important role in kidney injury.

Figure 4. Key mRNA biomarkers in AKI. (A) The expression differences of the 17 genes in AKI, PBX and zero-hour, among which “*” means p < 0.05; “**” said p < 0.01; “***” means p < 0.001, “****” means p < 0.0001. (B) ROC curve of 17 genes. (C) The enrichment results of GO function of the five largest AUC genes. Different colors on the right side of the circle represent the GO Term, different colors on the left side of the circle represent genes, and the lines represent gene enrichment to the corresponding GO Term.

Construction of diagnostic model

Studies have increasingly shown that combining multiple genes is more predictive than the use of a single gene, therefore the five genes with the highest AUC were selected. The gene expression profiling of 36 samples was used as the training set (AKI = 8, PBx = 10, zero-hour = 18) to construct the classification model with support vector machine. The model was tested by ten-fold cross validation method. The classification accuracy of AKI was 100%, that of PBx was 90%, and that of zero-hour was 94.7% (Figure 5A). Among the 36 samples, 35 were correctly classified, and the AUC of the 3 samples was 1 (Figure 5B), indicating that these 5 genes could accurately predict AKI, and distinguish AKI from other chronic kidney diseases. Next, the model was applied to the external verification data set GSE30718 and GSE30718. The expression patterns of the five genes in the verification set were found to be highly consistent with the training set. MYOF and HAS2 were high-expressed in AKI, and SFXN1, PLPPR1, and QDPR were low-expressed in AKI (Figure 5C). 8 nephrectomy samples and randomly selected 8 AKI samples repeated for 1000 times were used as a verification set and substituted into the model for further verifying the prediction accuracy of the model. The prediction accuracy rate was 100% and 98% when repeated for 965 times and 35 times, respectively (Figure 5D). In addition, we selected 11 samples of stable kidney transplantation and randomly selected 1000 AKI samples as a verification set to verify the model performance, the prediction accuracy rate of which was 100%, 98.7%, and 97.4% when repeated for 950 times (95%), 49 times (4.9 %), and 1 time (0.1%), respectively (Figure 5E). Moreover, GSE37838 dataset with 70 samples [12] containing 12 AKI samples and 58 immediate graft function (IGF) samples were obtained for validation. The expression profiles of HAS2, MYOF, PLPPR1, QDPR, and SFXN1 genes were extracted from GSE37838, and we found that HAS2 and MYOF expression was upregulated in AKI, and that PLPPR1, QDPR, and SFXN1 genes were significantly downregulated in AKI, which was consistent with our results (Supplementary Figure 1B–1C). These results indicated that the five-gene model had a high prediction performance.

Figure 5. Construction of diagnostic model. (A) Prediction of AKI, PBx, and zero-hour in a diagnostic model constructed from 5 genes. (B) ROC curves for the prediction of AKI, PBx, and zero-hour in a diagnostic model constructed by 5 genes. (C) Expression patterns of the five genes in the training set and external test sets. (D) A thousand randomized validations of the distribution of predictive accuracy for nephrectomy samples and AKI samples in the validation set. (E) Thousands of random verifications in the verification set for the prediction accuracy distribution of stable kidney transplant samples and AKI samples.

Potential regulatory pathways of the five-gene diagnosis model

To observe the potential regulatory patterns of these genes, we first separately predicted the miRNAs targeting these genes. In combination with the phenotype-associated miRNA co-expression module, it was observed that hsa-mir-29b-2 targeted HAS2 and SFXN1 in the four core miRNAs of the miRNA co-expression module, and interestingly hsa-mir-29b-2 was significantly negatively associated with HAS2 (Figure 6A) but positively correlated with SFXN1 (Figure 6B). This indicated that mir-29b-2 may be involved in the occurrence and development of AKI. Furthermore, based on the expression of these five genes, unsupervised clustering was conducted on zero-hour samples, and the results showed that these five genes could be divided into two groups (Cluster1 and Cluster2) (Figure 6C), where HAS2 and MYOF were high-expressed in Cluster1 and PLPPR1, QDPR, and SFXN1 were low-expressed in Cluster1. After analyzing the differences in the KEGG pathways between the two samples with GSEA (Figure 6D), three pathways, namely, LYSINE DEGRADATION, INOSITOL PHOSPHATE METABOLISM and oxidation, were observed to be significantly enriched in Cluster1, suggesting that there may be phosphorylation abnormalities in AKI-like samples, which were indirectly or directly involved in the regulation of AKI.

Figure 6. Functional analysis of 5 genes in the model. (A) The expression of hsa-mir-29b-2 was correlated with HAS2. (B) The expression of hsa-mir-29b-2 was correlated with SFXN1. (C) Unsupervised clustering of five genes in the model. (D) GSEA enrichment analysis results of two samples with different expression patterns.

Materials and Methods

Data collection

In this study, we screened two sets of gene expression data and one set of miRNA expression data from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). miRNA expression data was from the GPL16384 platform ([miRNA-3] Affymetrix Multispecies miRNA-3 Array, dataset ID GSE53771) [27, 28]. Gene expression profiling data were collected from two platforms. The platform for the dataset GSE53769 [27, 28] was the GPL16686 platform ([HuGene-2_0-st] Affymetrix Human Gene 2.0 ST Array [transcript (gene) version]), and that for dataset GSE30718 was the GPL570 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). Sample distribution for each dataset was shown in Table 1. The GSE53771 and GSE53769 datasets were derived from preoperative and postoperative samples of 18 patients treated with immunosuppressive therapy, and postoperative samples were follow-up samples with kidney biopsies performed within 12 days of transplantation. Specifically, eight patients with acute tubular necrosis without rejection were defined as AKI, while ten protocol samples with biopsies but without pathology were in control group (primary graft function). Patients with AKI were identified according to the Banff 2012 criteria [29]. AKI kidney biopsies were indicated by more than one dialysis session during the first week after transplantation, with serum sarcosine higher than 4 mg/dL the first week after surgery. The GSE30718 dataset was derived from 39 samples of post-transplant of kidney, all the patients were treated with immunosuppressive therapy, and biopsies were obtained one year after transplantation (other clinical therapies were administered as symptomatic treatment, detailed clinical information was shown in Supplementary Table 1). The work flow chart was shown in Figure 7.

Table 1. Sample distribution of each data set.

GEO Accession	No. of AKI	No. of PBx	No. of control	No. of Nephrectomy
GSE53771	8	10	18	10
GSE53769	8	10	18	9
GSE30718	28	0	11	8

Figure 7. Work flow chart.

Data processing

For gene expression data, the probes of the standardized chip data downloaded were annotate by the R package hgu133plus2.db. The probes that were matched to multiple genes were removed, while the median of those matched to a gene was regarded as the expression of the modified genes. Finally, expression profiles of 8675 genes and 36 samples were extracted from the GSE53769 data set, and 47 samples of 20,549 genes were obtained from the GSE30718 data set. For miRNA expression data, we downloaded the standardized microarray data and obtained miRNA expression data of 1239 miRNAs and 36 samples from other human species.

Co-expression network construction

Firstly, mRNA/miRNA data profile of top75% MADs was validated for sample and miRNA/ mRNA quality. Then, the WGCNA [30] package in R was used to construct a scale-free co-expression network for the mRNA/miRNAs. First, the Pearson’s correlation matrices and average linkage method were performed for all pair-wise mRNA/miRNAs. Then, a weighted adjacency matrix was constructed using a power function A_mn = |C_mn|^β (C_mn = Pearson’s correlation between mRNA/miRNA m and mRNA/miRNA n; A_mn = adjacency between mRNA/miRNA m and mRNA/miRNA n). β was a soft-thresholding parameter emphasizing strong correlations between mRNA/miRNAs and penalizing weak correlations. After choosing the power of β, the adjacency was transformed into a topological overlap matrix (TOM) to measure the network connectivity of an mRNA/miRNA, which was defined as the sum of its adjacency with all other mRNA/miRNAs, and the corresponding dissimilarity (1-TOM) was calculated. To classify mRNA/miRNAs with similar expression profiles into mRNA/miRNA modules, average linkage hierarchical clustering was conducted for the mRNA/miRNAs dendrogram, according to the TOM-based dissimilarity measured with a minimum size (mRNA/miRNA group) of 30. To further analyze the module, we calculated the dissimilarity of module mRNA/miRNAs to determine a cut line for module dendrogram and merged some module.

Gene set enrichment analysis

Gene Set Enrichment Analysis (GSEA) [31] was performed by the JAVA program (http://software.broadinstitute.org/gsea/downloads.jsp) using the MSigDB [32] on C2 Canonical pathways gene set collection containing 1320 gene sets. Gene sets with an FDR less than 0.01 after performing 1000 permutations were considered to be significantly enriched.

Functional enrichment analyses

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted in the R package clusterProfiler [33] to identify over-represented GO terms in three categories (biological processes, molecular function and cellular component), and KEGG pathway. Here, a p < 0.05 was considered to denote statistical significance.

Correlation analysis between module and phenotype, and identification of hub gene

To examine the correlation between co-expression modules and phenotypes, we defined a 0-1 matrix of phenotypes. Specifically, for any phenotype such as AKI, a correct detection of AKI from the samples was marked as 1, otherwise it is marked as 0. In this way, the numeric matrix of each phenotype was established, and the correlation between the feature vector of each module and the numeric matrix of the phenotype was calculated using the Spielman rank correlation coefficient, with p < 0.05 indicating a significant phenotype in the module. Two methods were used to determine hub genes. Firstly, the correlation between the expression of each gene and the module feature vector was calculated with correlation coefficient was greater than 0.8.

Prediction of miRNA target genes

Experimentally verified miRNA target gene databases miRecords [34], miRTarBase [35] and starBase [36] were applied to identify reliable miRNA target genes.

Construction of diagnostic prediction model and evaluation of model performance

Feature genes were used to construct a diagnostic prediction model based on SVM [37] classification to predict AKI and non-AKI samples. Support vector machine (SVM), which is a supervised learning model in machine learning algorithm, analyzes data and identifies patterns. A support vector mechanism creates a hyperplane in a high or infinite dimensional space for classification and regression. Given a set of training samples, each mark belongs to two classes. A SVM training algorithm establishes a model and assigns new instances to one class or another, making it a binary linear classification. We built the model in the training data set and used the ten-fold cross validation method to verify the classification ability of the model. The established model was then used to predict the samples in the validation data set. The predictive power of the model was evaluated using the area under the ROC curve (AUC), and the sensitivity and specificity of the model to AKI were analyzed.

The model prediction ability in an external data set

GSE30718 served as an independent external validation dataset, and we downloaded the normalized data, extracted the expression levels of the trait genes and proxied the model against the samples to verify the accuracy of the model prediction.

Statistical analysis

The R package pROC was used for AUC analysis, and the R package ComplexHeatmap was used for heat map drawing. All analyses, USES default parameters, and data visualization were performed using ggplot2 in version 3.4.3 of R software, if not specially instructed.

Author Contributions

Conception and design of the research: Hongqiang Lou. Acquisition of data: Jing Hu. Statistical analysis, Analysis and interpretation of data: Hongqiang Lou, Jing Hu. Drafting the manuscript: Xia Zhai. Revision of manuscript for important intellectual content: Xia Zhai.

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Funding

This study was supported by Science and Technology Planning Project of Jinhua City (2020-3-068), China.

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