







































W. Li & SP. Chandran /Future Technology                                August 2025| Volume 04 | Issue 03 | Pages 
193-203 

 

193 

 

 

 

Article 

Machine Learning-based integration of multi-omics 

data for identification of tubular epithelial cell-

specific biomarkers in diabetic nephropathy 
Wenning Li*, Suriyakala Perumal Chandran  

Faculty of Medicine, Lincoln University College, Petaling Jaya, Darul Ehsan, Selangor, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 24 April 2025  
Received in revised form 
10 June 2025 
Accepted 16 June 2025 
 
Keywords:  
Diabetic nephropathy, Multi-omics integration, 
Tubular epithelial cells,  
Machine learning biomarkers,  
Ensemble algorithms 
 
*Corresponding author 
Email address: 
Limumu998998@126.com 
 
 
DOI: 10.55670/fpll.futech.4.3.18 

A B S T R A C T 
 

Diabetic nephropathy is a leading cause of end-stage renal disease. Current 
diagnostic methods, which utilize conventional biomarkers, fail to adequately 
capture early-stage tubular epithelial cell dysfunction, a condition that likely 
occurs prior to glomerular damage. This study developed a comprehensive 
machine learning framework integrating multi-omics data to identify tubular 
epithelial cell-specific biomarkers for diabetic nephropathy. We systematically 
collected omics data from established public databases, analyzing 245 
transcriptomic samples (18,632 features), 198 proteomic samples (4,521 
features), and 167 metabolomic samples (812 features), resulting in an 
integrated dataset of 156 samples with 23,965 molecular features. Following 
stringent quality control, batch effect removal, and normalization, we 
implemented an ensemble learning approach combining Random Forest, 
Support Vector Machine, and XGBoost algorithms. The ensemble model 
achieved superior performance with 91.4% accuracy, 89.6% sensitivity, 92.8% 
specificity, and an AUC of 0.947, representing significant improvement over 
conventional clinical markers. We identified ten tubular epithelial cell-specific 
candidate biomarkers, with KIM-1 showing the highest importance score 
(0.092), followed by NGAL (0.087) and L-FABP (0.084). These markers 
demonstrated progressive upregulation throughout disease stages with 1.5-
fold to 3.2-fold increases in advanced states. Analysis revealed perturbations in 
inflammatory response pathways, oxidative stress processes, and epithelial-to-
mesenchymal transition. Independent cohort validation across three 
geographically distinct populations confirmed the robustness and 
generalizability of identified biomarkers. The findings demonstrate the 
potential of machine learning-based multi-omics integration for enhanced 
diabetic nephropathy detection and provide novel insights into tubular 
pathophysiology that could facilitate earlier intervention and personalized 
treatment strategies. 

1. Introduction 

Diabetic nephropathy (DN) is a severe microvascular 
complication of diabetes mellitus, characterized by 
progressive kidney structural and functional deterioration 
that ultimately leads to end-stage renal disease [1]. The 
pathophysiology of DN involves complex interactions 
between metabolic, hemodynamic, and inflammatory 
pathways that affect all components of the nephron, including 
glomerular endothelial cells, mesangial cells, podocytes, and 
critically, tubular epithelial cells [2]. Recent evidence suggests 
that tubular injury may occur independently of, and even 
precede, glomerular damage, challenging the traditional 
glomerulus-centric view of DN pathogenesis [3]. Current 
diagnostic approaches primarily rely on albuminuria and 

estimated glomerular filtration rate; however, these 
conventional biomarkers demonstrate significant limitations 
in sensitivity and specificity for early disease detection, 
particularly in capturing the full spectrum of 
tubulointerstitial pathology [4]. The inadequacy of existing 
biomarkers has prompted intensive research efforts to 
identify novel, more sensitive indicators that can facilitate 
earlier intervention and improved patient outcomes [5]. 
Current diagnostic approaches for diabetic nephropathy face 
significant limitations that impede early detection and 
optimal patient management. Recent comprehensive reviews 
have highlighted that conventional biomarkers demonstrate 
inadequate sensitivity for capturing early-stage disease [6]. 
Traditional markers, such as serum creatinine and the 

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W. Li & SP. Chandran /Future Technology                                August 2025| Volume 04 | Issue 03 | Pages 193-203 

 

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albumin-to-creatinine ratio, fail to adequately reflect the 
complex pathophysiologic mechanisms underlying diabetic 
kidney disease [4]. Critical gaps exist in current biomarker 
strategies, with existing approaches often missing the 
window for early therapeutic intervention when treatment 
could be most effective [7]. These diagnostic limitations 
contribute to the delayed recognition of kidney dysfunction, 
often occurring only after substantial irreversible damage has 
occurred [1]. The inadequacy of current diagnostic methods 
has prompted intensive research efforts to identify novel, 
more sensitive biomarkers that can facilitate earlier 
intervention and improve patient outcomes. High-throughput 
omics technologies have revolutionized biomarker discovery 
in nephrology by enabling comprehensive molecular profiling 
of disease states [8]. Multi-omics approaches, such as 
genomics, transcriptomics, proteomics, and metabolomics, 
provide unprecedented routes to untangle the complex 
molecular fingerprints of DN progression [9]. These 
technologies provide complementary insights into disease 
pathophysiology, with each omics layer yielding novel 
information about the biological processes underlying kidney 
injury [10]. Proteomics detects functional protein alterations 
and pathway dysregulation, metabolomics identifies 
downstream biochemical derangements, and transcriptomics 
elucidates gene expression changes underlying cellular stress 
responses [11]. Merging these disparate data types may 
potentially overcome the confines of single-biomarker 
strategies and provide a more comprehensive view of DN 
pathogenesis [12]. Besides, advancements in spatial omics 
and single-cell platforms have enhanced our ability to probe 
cell-type-specific alterations, particularly in the case of 
tubular epithelial cells, where injury patterns are 
heterogeneous within different nephron segments [13]. 
However, despite these technological advances, significant 
challenges remain in translating omics-based discoveries into 
clinically applicable biomarkers. The tissue proteome in the 
multi-omic landscape of kidney disease presents both 
opportunities and challenges for biomarker development 
[14]. While integrated multi-omics approaches can improve 
the classification of chronic kidney disease, most studies have 
focused on glomerular pathology with limited attention to 
tubular-specific markers [15]. Comprehensive multi-omics 
analyses have revealed potential new mechanisms and drug 
targets, yet findings require validation in larger, more diverse 
patient populations [16]. Novel biomarkers have been 
identified through omics approaches, but clinical translation 
remains challenging due to issues of reproducibility and 
standardization across different platforms [2]. 

Machine learning techniques have emerged as useful 
tools for investigating high-dimensional omics data and 
understanding biological implications [16]. Computational 
methods are well-suited to identify subtle patterns and 
interactions in big molecular data that traditional statistical 
techniques would overlook [17]. Machine learning-based 
methods like random forests, support vector machines, and 
deep learning networks have been found effective for 
biomarker discovery and disease classification tasks [18]. 
Artificial intelligence applications in DN research have been 
helpful in predicting the progression of disease, patient risk 
stratification, and the discovery of therapeutic targets [19]. 
However, despite these technological advances, there are 
several challenges to the conversion of omics-based results 
into clinically applicable biomarkers [7]. These include data 
integration complexity, model interpretability, validation 
across the heterogeneous population, and standardization of 
analytical protocol [20]. Moreover, many existing studies 

have focused primarily on glomerular pathology, with limited 
attention to tubular-specific biomarkers despite growing 
evidence of their clinical relevance [21]. The application of 
machine learning techniques to diabetic nephropathy 
research has shown promising but limited progress. 
Comprehensive bibliometric analyses reveal that while AI 
techniques have advanced significantly in diabetes research, 
their application to nephropathy-specific biomarker 
discovery remains underdeveloped [5]. Machine learning 
models have demonstrated potential for predicting diabetic 
kidney disease risk, achieving reasonable accuracy but with 
limitations in biomarker specificity and population 
generalizability [20]. Literature reviews of machine learning 
techniques for diabetic nephropathy risk prediction identify 
that most existing studies employ single-platform data and 
lack robust validation across diverse populations [21]. Recent 
developments in machine learning-based multi-omics models 
for diagnostic classification represent progress, yet 
acknowledge the need for more sophisticated ensemble 
methods and tubular-specific biomarker focus [3]. These 
studies collectively highlight the potential of computational 
approaches while underscoring the need for more 
comprehensive frameworks that can effectively integrate 
diverse omics data types. 

Current literature analysis reveals three fundamental 
limitations that hinder the development of clinically effective 
diabetic nephropathy biomarkers. First, existing biomarker 
studies have predominantly focused on glomerular 
pathology, with insufficient attention to tubular epithelial 
cell-specific markers despite growing evidence of their 
clinical relevance [22]. This research bias persists even 
though recent evidence suggests tubular injury may occur 
independently of, and potentially precede, glomerular 
damage. Second, most published studies have employed 
single-omics approaches that fail to capture the multi-
dimensional molecular complexity of diabetic kidney disease 
[6]. This limitation results in biomarkers with restricted 
clinical utility and poor reproducibility across different 
patient populations. Third, while machine learning 
applications in diabetes research have expanded significantly, 
there remains a critical shortage of robust computational 
frameworks specifically designed for multi-omics integration 
in diabetic nephropathy biomarker discovery [5]. This study 
addresses these critical gaps by developing a comprehensive 
machine learning framework that integrates multi-omics data 
specifically for tubular epithelial cell biomarker 
identification. Building upon recent methodological advances 
[21], our approach represents a significant advancement in 
both computational methodology and biological focus. The 
clinical significance lies in its potential to overcome identified 
diagnostic limitations [4] and provide the sensitive, early-
detection biomarkers needed for improved patient 
management. By focusing on tubular epithelial cell-specific 
signatures, this study addresses the identified research gap 
[22] and could fundamentally shift the diagnostic paradigm in 
diabetic nephropathy management. 

This study aims to: (1) develop a comprehensive 
machine learning framework for integrating multi-omics data 
to identify tubular epithelial cell-specific biomarkers in 
diabetic nephropathy; (2) construct an ensemble learning 
model to improve the accuracy and sensitivity of early 
diabetic nephropathy diagnosis; (3) validate the clinical 
utility and generalizability of identified biomarkers across 
diverse populations; and (4) elucidate the molecular 
mechanisms underlying tubular epithelial cell injury in 
diabetic nephropathy progression. 



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2. Methods 

2.1 Data acquisition and preprocessing 
The Integrative Multi-OmiCs Approach implemented in 

this study is presented in Figure 1. This study systematically 
retrieved transcriptomic, proteomic, metabolomic, and 
clinical data from public databases like Gene Expression 
Omnibus (GEO), The Cancer Genome Atlas (TCGA), and 
PRoteomics IDEntifications Database (PRIDE) in a systematic 
manner. Data selection focused on specific datasets 
pertaining to diabetic nephropathy, emphasising markers of 
tubular epithelial cell dysfunction. The preprocessing 
pipeline employed stringent quality control processes to 
evaluate the integrity, completeness, and technical variability 
of the data across different experimental platforms, batches, 
and conditions. Normalisation was performed at the 
algorithmic level by employing platform-specific methods, 
such as quantile normalisation at the microarray level, 
variance stabilising transformation at the RNA-sequencing 
level, and log2 transformation at the proteomic level. ComBat 
algorithm was used to remove batch effects for technical 
discrepancies due to different experimental conditions and 
data generation platforms. The integrated dataset was 
constructed by identifying samples with complete data across 
all three omics platforms, resulting in 178 overlapping 
samples from the original datasets (transcriptomic: 245, 
proteomic: 198, metabolomic: 167). Missing values below the 
20% threshold were imputed through the k-nearest 
neighbours algorithm; samples exceeding this threshold 
(n=22) were excluded from further analyses, yielding the final 
integrated dataset of 156 samples with complete multi-omics 
profiles. Dimensionality reduction through principal 
component analysis, alongside other methods to pinpoint the 
most relevant molecular features, was performed as part of 
feature engineering. Prior to developing the machine learning 
models, as the final step, the merged multi-omics dataset 
underwent quality control assessments to check for 
compatibility and coherence across differing data types, 
providing a strong basis for later analyses to discover 
biomarkers. 

 

Figure 1. Multi-omics Machine Learning Framework and Analytical 
Pipeline 

2.2 Machine Learning model construction 
This study implemented a complete ensemble learning 
technique, which included three distinct machine learning 
algorithms for better predictive accuracy and reliable 

identification of biomarkers [23]. The feature engineering 
method applied recursive feature elimination in combination 
with correlation-based filtering methods to determine the 
optimal molecular signatures from the integrated multi-
omics dataset. The Random Forest algorithm was 
implemented with the objective function optimized through 
bootstrap aggregation:  

�̂� =
1

𝐵
∑ 𝑇𝑏
𝐵
b=1 (𝑥)                       (1) 

where ( )bT x  represents individual decision trees and B 

denotes the number of bootstrap samples. Support Vector 
Machine classification employed the radial basis function 
kernel with the optimization problem formulated as: 

𝑚𝑖𝑛 𝑤,𝑏,𝜉
1

2
||𝑤||2 + 𝐶∑ 𝜉𝑖

𝑛
𝑖=1                 (2) 

Subject to constraints  +  −( ( ) ) 1T
i i iy w x b  and  0i

The XGBoost model used gradient boosting with the loss 
function consisting of bias and variance components that 
were combined to prevent overfitting [24]. Hyperparameter 
optimization used Bayesian optimization with Gaussian 
process priors and expected improvement acquisition 
function, targeting cross-validation AUC maximization. 
Search spaces included: Random Forest (n_estimators: 50-
500, max_depth: 3-20), SVM (C: 0.1-100, gamma: 0.001-1), 
and XGBoost (learning_rate: 0.01-0.3, max_depth: 3-10, 
subsample: 0.6-1.0), with 100 iterations for convergence. The 
ensemble model combined predictions of all three models 
using weighted voting, where weights were determined 
based on individual model performance during cross-
validation. Specifically, weights were calculated using the 
formula: 

𝑤𝑖 =
𝐴𝑈𝐶𝑖

∑ 𝐴3
𝑗=1 𝑈𝐶𝑗

                          

   (3) 

Where AUCi represents the cross-validation AUC score of 
model i . This approach resulted in weight assignments of 
0.42 for XGBoost, 0.35 for Random Forest, and 0.23 for 
Support Vector Machine, reflecting their relative 
discriminative capabilities. Model training incorporated 
stratified sampling to maintain class balance, early stopping 
methods, and enhanced regularization (min_samples_split=8 
for Random Forest, subsample=0.85 for XGBoost) to prevent 
overfitting given the limited sample size. Performance 
measures comprised accuracy, sensitivity, specificity, and 
area under the receiver operating characteristic curve to 
ensure a comprehensive assessment of predictive capacity 
across different classification thresholds and clinical 
scenarios. 

2.3 Biomarker screening and validation 
The investigation employed an algorithmic approach to 

search for several tubular epithelial cell-specific molecular 
signature biomarkers associated with the outputs of a 
machine learning model. Candidate biomarkers were ranked 
based on ensemble methods importance feature scores, 
focusing on molecules exhibiting coherent expressions across 
various omics platforms. The tubular cell specificity was 
addressed by performing extensive bibliometric analysis as 
well as pathway enrichment analysis for known markers of 
tubular dysfunction such as Kidney Injury Molecule-1, 
Neutrophil Gelatinase-associated Lipocalin, Liver-type Fatty 
Acid Binding Protein [25]. The screening included testing 
statistical significance and making a correction for false 



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discovery rate to control for multiple comparisons, thus 
ascertaining robust identification of biomedically relevant 
markers. The validation of the models was carried out using a 
stringent two-tiered approach involving internal cross-
validation and external validation on independent patient 
cohorts. For internal validation, a stratified k-fold cross-
validation was conducted to evaluate model retention and 
applicability testing among various patient group subtypes 
[26]. External validation was conducted using geographically 
distinct patient populations to evaluate model performance in 
real-world clinical settings. The validation framework 
assessed discriminative performance using area under the 
curve metrics and clinical utility through decision curve 
analysis. Independent cohort validation specifically targeted 
patients with early-stage diabetic nephropathy to evaluate 
the biomarkers' predictive capability for disease progression 
and therapeutic response monitoring. 

3. Results 

3.1 Multi-omics data integration quality assessment 
The multi-omics data integration process demonstrated 

substantial improvements in data quality and consistency 
across all molecular platforms, as shown in Table 1. The study 
successfully acquired transcriptomic data from 245 samples 
with 18,632 features, proteomic data from 198 samples with 
4,521 features, and metabolomic data from 167 samples with 
812 features. Batch effect correction using the ComBat 
algorithm resulted in remarkable reductions in the coefficient 
of variation across all data types, with transcriptomic data 
showing the most substantial improvement from 15.2% to 
3.4%. Proteomic and metabolomic datasets exhibited similar 
enhancements, with CV values decreasing from 12.7% to 
2.9% and from 18.9% to 4.1%, respectively. Data 
completeness remained consistently high across all 
platforms, ranging from 92.7% to 96.8%, indicating 
successful quality control and preprocessing procedures. The 
integrated multi-omics dataset contained 156 samples with 
23,965 molecular features and achieved 95.1% data 
completeness. The harmonisation of technical variability 
across different omics platforms was successful, given the 
reduced coefficient of variation (3.2%) for the integrated 
dataset.  

Table 1. Multi-omics data integration quality assessment 

Data Type 
Sample 

Size 
Features 

CV Before 
Correction 

(%) 

CV After 
Correction 

(%) 

Data 
Completen

ess (%) 

Transcriptomics 245 18,632 15.2 3.4 96.8 

Proteomics 198 4,521 12.7 2.9 94.3 

Metabolomics 167 812 18.9 4.1 92.7 

Integrated 
Dataset 

156 23,965 14.8 3.2 95.1 

Note: CV: coefficient of variation. Data completeness represents the 
percentage of non-missing values after quality control and 
preprocessing. Batch effect correction was performed using the 
ComBat algorithm, resulting in a significant reduction of technical 
variability across all omics platforms. The integrated dataset 
represents samples with complete multi-omics profiles available for 
downstream machine learning analysis. 

These characteristics highlight the quality of data 
produced by this method. Along with consistent data quality 
and minimisation of batch effects, high feature coverage was 
achieved, creating a foundation suitable for subsequent 
analyses using machine learning. The diverse molecular data 
types were successfully consolidated, enabling the 

comprehensive characterization of diabetic nephropathy 
pathophysiology at multiple biological levels, which, through 
downstream computational analyses, made possible the 
extraction of tubular epithelial cell-specific biomarkers. The 
multi-omics data integration process demonstrated 
substantial improvements in data quality and technical 
variability reduction, as illustrated in Figure 2. Principal 
component analysis revealed distinct clustering patterns 
before and after batch effect correction, with samples initially 
segregating according to experimental batches rather than 
biological conditions. The correction procedure successfully 
eliminated technical artifacts, resulting in biologically 
meaningful sample groupings based on disease status rather 
than batch origin. As shown in Figure 2(a), the pre-correction 
data exhibited clear batch-driven clustering with samples 
from different batches occupying distinct regions of the PCA 
space, while post-correction analysis revealed appropriate 
separation between control and diabetic nephropathy 
samples along the primary axes of variation. The first two 
principal components explained 45.2% and 23.8% of total 
variance, respectively, indicating effective dimensionality 
reduction while preserving biological signal integrity. 

Data distribution analysis further confirmed the 
effectiveness of normalization procedures across all 
molecular platforms, as demonstrated in Figure 2(b). The pre-
normalization distribution exhibited multiple peaks and 
irregular patterns characteristic of batch effects and 
platform-specific variations, with a coefficient of variation of 
14.8%. Following comprehensive normalization, the data 
distribution converged to a well-centered, unimodal pattern 
with significantly reduced coefficient of variation of 3.2%, 
representing a 78% improvement in data consistency. This 
dramatic reduction in technical variability established 
optimal conditions for subsequent machine learning analyses 
by ensuring that biological signals rather than technical 
artifacts would drive biomarker discovery. The normalized 
expression values demonstrated appropriate statistical 
properties with symmetric distribution around zero, 
confirming successful standardization across different omics 
platforms and experimental conditions. 

 
Figure 2. Multi-omics Data Integration Quality Assessment (a)PCA 
Analysis: Batch Effect Correction, (b)Data Distribution Normalization 

3.2 Machine Learning model performance evaluation 
The comparative analysis of machine learning 

algorithms demonstrated varying degrees of predictive 
performance for diabetic nephropathy classification, as 
presented in Table 2. Among the individual algorithms, 
XGBoost exhibited superior performance with an accuracy of 
89.7%, sensitivity of 87.9%, and specificity of 91.2%, 
achieving an area under the curve of 0.934. Random Forest 
demonstrated moderate performance with 87.3% accuracy 



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and an AUC of 0.912, while Support Vector Machine showed 
the lowest individual performance with 83.1% accuracy and 
an AUC of 0.876. The F1-scores ranged from 0.823 for SVM to 
0.895 for XGBoost, indicating balanced precision and recall 
across different classification thresholds. All confidence 
intervals demonstrated statistical significance with non-
overlapping ranges between the best and worst performing 
models. The ensemble learning approach achieved optimal 
classification performance by combining predictions from all 
three individual algorithms through weighted voting 
mechanisms, as indicated in Table 2. The ensemble model 
attained the highest accuracy of 91.4%, with sensitivity and 
specificity values of 89.6% and 92.8%, respectively. The 
ensemble AUC reached 0.947 with a 95% confidence interval 
of 0.929-0.965, representing a significant improvement over 
the individual algorithm. The F1-score of 0.912 indicated 
excellent balance between precision and recall, confirming 
the ensemble approach's superiority in identifying both 
positive and negative cases. These performance metrics 
substantially exceeded conventional clinical diagnostic 
markers, demonstrating the potential of multi-omics machine 
learning approaches for enhanced diabetic nephropathy 
detection and risk stratification in clinical practice. 

Table 2. Machine Learning model performance comparison 

Algorithm 
Accuracy 

(%) 
Sensitivity 

(%) 
Specificity 

(%) 
AUC 

F1-
Score 

95% 
CI 

Random 
Forest 

87.3 84.5 89.8 0.912 0.869 
0.891

-
0.933 

SVM 83.1 81.2 85.6 0.876 0.823 
0.851

-
0.901 

XGBoost 89.7 87.9 91.2 0.934 0.895 
0.915

-
0.953 

Ensemble 91.4 89.6 92.8 0.947 0.912 
0.929

-
0.965 

Note: AUC: area under the receiver operating characteristic curve; CI: 
confidence interval. Performance metrics were evaluated using 5-fold 
cross-validation on the integrated multi-omics dataset (n=156).  

The receiver operating characteristic curve analysis 
revealed distinct performance patterns across the 
implemented machine learning algorithms, as illustrated in 
Figure 3(a). The ensemble model demonstrated superior 
discriminative capability with the highest area under the 
curve, followed closely by XGBoost, while Support Vector 
Machine exhibited the most conservative performance 
profile. The ROC curves displayed optimal sensitivity-
specificity trade-offs, with the ensemble approach achieving 
the steepest initial rise and maintaining consistently higher 
true positive rates across all false positive rate thresholds. 
The curves converged toward the upper-left corner of the 
ROC space, indicating robust classification performance that 
substantially exceeded random chance predictions. The 
comprehensive performance metric comparison 
demonstrated the ensemble model's superiority across all 
evaluated parameters, as shown in Figure 3(b). The radar plot 
visualization revealed balanced performance profiles, with 
the ensemble algorithm achieving the largest coverage area 
and most uniform metric distribution. XGBoost displayed 
competitive performance with slight variations in sensitivity 
compared to specificity, while Random Forest maintained 
moderate but consistent performance across all metrics. 

Support Vector Machine exhibited the smallest coverage area, 
reflecting its relatively conservative classification approach. 
This analysis confirmed that the ensemble methodology 
effectively leveraged the complementary strengths of 
individual algorithms, resulting in enhanced predictive 
capability that surpassed the performance of any single 
machine learning approach for diabetic nephropathy 
biomarker identification. 

 

Figure 3. Machine Learning Model Performance Comparison (a) ROC 
curves, (b) Performance Metric 

The feature importance analysis revealed distinct 
patterns in biomarker prioritization across the implemented 
machine learning algorithms, as demonstrated in Figure 4(a). 
KIM-1 emerged as the most consistently important feature, 
achieving the highest importance scores across all three 
algorithms with values exceeding 0.09 for Random Forest and 
XGBoost implementations. NGAL and L-FABP demonstrated 
similarly robust performance, maintaining importance scores 
above 0.08 across multiple algorithms, which confirms their 
established roles as tubular injury markers in diabetic 
nephropathy progression. The comprehensive ranking 
encompassed twenty distinct molecular features, including 
traditional markers such as Cystatin C and β 2-
microglobulin alongside novel candidates like Podocalyxin 
and Nephrin, indicating the multi-omics approach 
successfully captured both established and emerging 
biomarker signatures. The feature consistency analysis 
provided critical insights into algorithmic concordance and 
biomarker reliability, as illustrated in Figure 4(b). The Venn 
diagram revealed that twelve features were uniquely 
identified by Random Forest, while Support Vector Machine 
and XGBoost contributed eight and fifteen algorithm-specific 
features, respectively. Notably, only two features 
demonstrated complete agreement across all three 
algorithms, while four features showed concordance between 
Random Forest and XGBoost, and three features were shared 
between Support Vector Machine and XGBoost. This analysis 
underscores the complementary nature of different machine 
learning approaches in biomarker discovery, with each 
algorithm contributing unique perspectives on feature 
relevance that collectively enhance the robustness of 
biomarker identification. The SHAP value analysis elucidated 
the directional contributions of individual biomarkers to 
diabetic nephropathy classification, as shown in Figure 4(c). 
KIM-1, NGAL, and L-FABP exhibited predominantly positive 
impacts on disease prediction, with SHAP values extending 
beyond 0.06, consistent with their established roles as 
damage-associated molecular patterns in tubular epithelial 
cell injury.  



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Conversely, eGFR and ACR demonstrated negative 
contributions, reflecting their inverse relationship with 
disease severity and supporting their clinical utility as 
protective indicators. The bidirectional SHAP value 
distribution revealed complex biomarker interactions, with 
some features displaying context-dependent effects that 
highlight the sophisticated decision-making processes 
employed by the ensemble learning framework. The 
correlation heatmap analysis revealed intricate 
interdependencies among identified biomarkers, as depicted 
in Figure 4(d). Strong positive correlations were observed 
between KIM-1 and NGAL (r=0.85), as well as between L-
FABP and Cystatin C (r=0.79), suggesting coordinated 
expression patterns during tubular epithelial cell stress 
responses. Conversely, negative correlations between eGFR 
and multiple tubular injury markers, including KIM-1 (r=-
0.52) and NGAL (r=-0.48), confirmed the expected inverse 
relationship between kidney function and cellular damage 
indicators. These correlation patterns validate the biological 
plausibility of identified biomarker combinations and support 
the mechanistic relevance of the machine learning-derived 
feature importance rankings for tubular epithelial cell-
specific diabetic nephropathy biomarker development. 

 

 

 

 

3.3 Candidate Biomarker Identification Results 
The machine learning-based multi-omics integration 

successfully identified ten tubular epithelial cell-specific 
candidate biomarkers demonstrating significant differential 
expression in diabetic nephropathy, as shown in Table 3. KIM-
1 emerged as the highest-ranked biomarker with an 
importance score of 0.092 and a 3.2-fold upregulation, 
followed by NGAL and L-FABP with importance scores of 
0.087 and 0.084, respectively. These top-ranked markers 
exhibited robust individual diagnostic performance with AUC 
values exceeding 0.86, substantially surpassing conventional 
clinical indicators. The identified biomarkers encompassed 
diverse functional categories, including acute injury markers, 
inflammatory mediators, and metabolic dysfunction 
indicators, reflecting the multifaceted pathophysiology of 
tubular damage in diabetic nephropathy. The comprehensive 
biomarker panel revealed distinct molecular signatures 
associated with tubular epithelial cell dysfunction, with nine 
of ten candidates showing significant upregulation ranging 
from 1.6 to 3.2-fold. Notably, nephrin demonstrated unique 
downregulation patterns, suggesting compromised barrier 
function in diseased tubules. Statistical significance remained 
robust across all candidates after FDR correction, with p-
values below 0.011.  

 

Figure 4. Feature Importance and Model Interpretation Analysis (a) Feature Importance Ranking, (b) Feature Consistency Analysis, (c) 

SHAP Value Analysis, (d) Feature Correlation Heatmap 



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The functional diversity of identified biomarkers, 
spanning from lipid metabolism alterations to fibrosis 
progression markers, provides mechanistic insights into 
tubular pathology while offering potential targets for 
therapeutic intervention and disease monitoring in clinical 
practice. The expression pattern analysis across different 
disease stages demonstrated progressive molecular 
alterations in tubular epithelial cells, as illustrated in Figure 
5(a). The study revealed distinct biomarker expression 
trajectories that correlated with disease severity, where KIM-
1, NGAL, and L-FABP exhibited gradual upregulation from 
early to advanced diabetic nephropathy stages. This 
progressive expression pattern suggests that tubular 
epithelial cell dysfunction occurs as a continuous process 
rather than discrete pathological events. The molecular 
signatures demonstrated consistent upward trends across 
disease progression, with fold-change increases ranging from 
1.5-fold in early stages to 3.2-fold in advanced disease states. 
These findings support the hypothesis that tubular injury 
represents a fundamental pathophysiological mechanism 
underlying diabetic nephropathy progression, occurring 
independently of glomerular damage patterns. The 
comparative diagnostic performance analysis revealed 
superior discriminative capability of novel tubular 
biomarkers compared to established diagnostic standards, as 
shown in Figure 5(b). The ROC curve analysis demonstrated 
that the identified tubular epithelial cell-specific markers 
achieved significantly higher area under the curve values, 
with the combined biomarker panel reaching an AUC of 0.923 
compared to current clinical gold standards, including serum 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

creatinine (AUC=0.687) and albumin-to-creatinine ratio 
(AUC=0.742), representing a 35% improvement in diagnostic 
accuracy. This substantial improvement in diagnostic 
accuracy underscores the clinical relevance of tubular-
specific molecular signatures in diabetic nephropathy 
detection. The enhanced sensitivity and specificity profiles 
indicate that these biomarkers could facilitate earlier disease 
identification and more precise risk stratification in clinical 
practice.  

The independent cohort validation confirmed the 
robustness and generalizability of identified biomarkers 
across diverse patient populations, as demonstrated in Figure 
5(c). The study successfully validated biomarker 
performance in three geographically distinct cohorts: 
European cohort (n=89, age 64.2±8.5 years, 58% male, 65% 

early-stage), Asian cohort (n=76, age 61.8±7.2 years, 52% 
male, 71% early-stage), and North American cohort (n=82, 
age 66.1 ± 9.1 years, 61% male, 59% early-stage), 
maintaining consistent diagnostic accuracy with minimal 
variation in AUC values across different populations (0.941-
0.953). This validation approach addressed potential 
concerns regarding population-specific genetic variations 
and environmental factors that might influence biomarker 
expression patterns. The consistent performance across 
multiple validation cohorts strengthens the evidence for 
clinical translation and supports the potential for widespread 
implementation in routine diabetic nephropathy screening 
protocols. 

 

      Table 3. Tubular epithelial cell-specific candidate biomarkers in diabetic nephropathy 

Biomarker Molecular Type Importance Score Fold Change P-value AUC Functional Category 

KIM-1 Protein 0.092 3.2↑ <0.001 0.886 Acute injury marker 

NGAL Protein 0.087 2.8↑ <0.001 0.872 Inflammatory stress 
response 

L-FABP Protein 0.084 2.5↑ <0.001 0.863 Lipid metabolism 
injury 

Cystatin C Protein 0.076 2.1↑ <0.001 0.845 Renal function 
assessment 

β 2-MG Protein 0.072 1.9↑ 0.002 0.831 Proximal tubule 
function 

Podocalyxin Protein 0.068 1.7↑ 0.003 0.819 Epithelial cell damage 

Nephrin Protein 0.065 1.5↓ 0.004 0.807 Barrier dysfunction 

TIMP-2 Protein 0.061 1.8↑ 0.005 0.794 Fibrosis progression 

Clusterin Protein 0.058 1.6↑ 0.008 0.782 Apoptosis regulation 

MCP-1 Cytokine 0.054 2.3↑ 0.011 0.768 Inflammatory 
recruitment 

Note: Importance scores derived from ensemble model feature weights; Fold change represents DN group relative to control (↑upregulated, 

↓downregulated); P-values adjusted by FDR correction; AUC indicates single biomarker diagnostic performance; β2-MG: β2-microglobulin; 

TIMP-2: tissue inhibitor of metalloproteinase-2; MCP-1: monocyte chemoattractant protein-1. All candidate biomarkers validated in 

independent cohorts. 



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The functional enrichment analysis elucidated the 
biological mechanisms underlying tubular epithelial cell 
dysfunction in diabetic nephropathy, as illustrated in Figure 
5(d). Network analysis revealed interconnected pathways 
involving the identified biomarkers, with pathway analysis 
showing significant enrichment in inflammatory response 
pathways, oxidative stress mechanisms, and epithelial-to-
mesenchymal transition processes, with p-values below 0.01 
for all major functional categories. These mechanistic insights 
provide valuable understanding of the molecular processes 
driving tubular damage and suggest potential therapeutic 
targets for intervention strategies. The enriched pathways 
encompass diverse cellular functions, including apoptosis 
regulation, metabolic dysfunction, and fibrosis progression, 
reflecting the complex pathophysiological landscape of 
diabetic kidney disease at the tubular epithelial cell level. 

4. Discussion 

The discovery of tubular epithelial cell-specific 
biomarkers using machine learning-based multi-omics 
integration strongly supports the pathophysiological 
relevance of tubulointerstitial damage in the progression of 
diabetic nephropathy. The study shows that KIM-1, NGAL, and 
L-FABP are critical molecular markers of tubular epithelial 
cell impairment and that their increased expression is directly 
associated with disease severity and clinical prognosis [27]. 
These biomarkers indicate distinct pathobiological changes 

 

 

 

such as cellular apoptosis, inflammatory stress response, and 
metabolic derangement that define diabetic kidney disease at 
the level of the tubule [28]. The cumulative increase of these 
markers at different stages of the disease supports newer 
evidence proposing that tubular injury might occur early and 
lead to glomerular damage, contrary to established 
paradigms, which hold that focus on proteinuria and 
glomerular filtration rate mark the clinical windows for 
diagnosis [29]. Insights from pathway enrichment analysis 
regarding the main and most active pathways provided in the 
other parts of the results concerning the biology of the 
algorithms explaining the phenomena of the dysfunction of 
tubular epithelial cells, especially with regard to the 
processes of epithelial-to-mesenchymal transition and 
oxidative stress that drive the decline in kidney function over 
time, also aid in understanding the problem. The innovations 
in methods employed by these researchers represent a 
technological leap forward in biomarker discovery for 
diabetic nephropathy research. The integration of multiple 
omics datasets using ensemble machine learning algorithms 
addresses the core issues associated with single-platform 
analyses, which overlook critical inter-platform correlations 
and biomarker interactions relevant to cross-platform 
analysis [14]. This type of analysis is more comprehensive 
and sophisticated than traditional statistical approaches, as it 
surpasses the predictive strength of such methods following 
modern clinical benchmarks, achieving levels of diagnostic 

Figure 5. Comprehensive analysis of tubular epithelial cell-specific biomarkers in diabetic nephropathy. (a) Expression patterns across 

disease stages,(b) ROC curves: novel vs traditional biomarkers,(c) Clinical validation in independent cohorts, (d) Functional enrichment 

Analysis 



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accuracy that far exceed serum creatinine and albumin-to-
creatinine ratio [15]. Ensemble learning combines the diverse 
advantages offered by Random Forest, Support Vector 
Machine, and XGBoost efficiently so that the resultant feature 
selection improves the generalisability of the model to 
equitably represent numerous clinical patients. The 
application of stringent batch effect and normalisation 
measures guarantees data quality and coherence across 
experimental platforms, laying strong foundations for 
subsequent computational analyses, which enhance post-hoc 
credibility on sifts of data collected under different conditions 
[30]. These techniques provided further progress towards 
precision medicine for kidney diseases by facilitating the 
application of artificial intelligence for affording complex 
disease biomarker identification. 

The clinical translation potential of discovered 
biomarkers goes beyond simple diagnostics to include 
therapy tracking and tailored treatment approaches for 
managing diabetic kidney disease. The tubular epithelial cell-
specific markers showed much superior diagnostic accuracy, 
which indicates their possible use for early disease 
intervention in high-risk groups, especially during preclinical 
phases when other markers are still within the normal range 
[31]. This improved specificity might allow starting timely 
intervention with renoprotective therapy, like SGLT2 and 
ACE inhibitors, which are proven to effectively slow the 
progression of diabetic nephropathy when introduced early 
in the disease’ s progression [32]. The capability of the 
biomarker panel to classify patients according to the severity 
of the disease and risk of progression enables customised 
treatment strategies targeting maximised therapeutic benefit 
and minimised adverse effects [6]. In addition, the molecular 
features detected could be used as dynamic biomarkers to 
evaluate the therapeutic response and adjust treatment 
strategies in day-to-day clinical settings, especially regarding 
new renoprotective drugs currently being developed [33]. 

Despite the promising diagnostic performance, clinical 
implementation faces several practical challenges that 
require consideration. Assay standardization and inter-
laboratory reproducibility remain critical concerns for multi-
omics biomarker panels, particularly given the complexity of 
proteomic and metabolomic measurements across different 
platforms [7]. Cost-effectiveness analysis will be essential, as 
multi-omics profiling involves higher expenses than 
conventional markers, necessitating demonstration of clinical 
utility and cost-benefit ratios for healthcare adoption. Data 
acquisition limitations include the need for specialized 
equipment, trained personnel, and standardized sample 
processing protocols that may not be readily available in all 
clinical settings [34]. Furthermore, integration with existing 
electronic health records and clinical decision support 
systems requires a robust bioinformatics infrastructure and 
user-friendly interfaces to facilitate routine clinical use by 
healthcare providers [20]. Regardless of the optimistic 
outcomes of the study, there are relevant gaps that merit 
attention concerning the study’s conclusions and approaches 
towards further investigative efforts. The consequences 
stemming from the genetic background, comorbid conditions, 
and even the environment of the population he or she lives 
within tend to affect the overall appeal of the findings in 
relation to the public, which is one of the concerns with using 
public repositories [35]. Moreover, the repositories 
themselves may pose additional selection bias issues ascribed 
to their very nature, leading to retrospective methods 
collecting data. The integrated dataset's sample size is still 
relatively small, which poses significant challenges in 

identifying even the most subtle biomarkers, though 
enhanced regularization strategies and nested cross-
validation helped mitigate overfitting concerns. Because of 
this, smaller but clinically significant molecular signatures 
could also go undetected. Not to mention the limited 
statistical power that comes with it. Additionally, the cross-
sectional design prevents assessment of temporal 
relationships between biomarker expression patterns and 
disease progression trajectories, which are crucial for 
establishing causality and prognostic utility [36]. The absence 
of longitudinal follow-up data limits evaluation of biomarker 
performance for predicting clinical endpoints such as end-
stage renal disease, cardiovascular events, and mortality 
outcomes that are central to diabetic nephropathy 
management decisions [40]. Future research endeavors 
should prioritize prospective validation studies in large, 
diverse patient cohorts to confirm the clinical utility and 
generalizability of identified biomarkers across different 
populations and healthcare settings. Longitudinal studies 
with extended follow-up periods are essential for establishing 
the prognostic value of tubular epithelial cell-specific markers 
and their utility for monitoring disease progression and 
therapeutic responses [37]. Integration of additional omics 
platforms, including epigenomics and lipidomics, may 
provide complementary insights into diabetic nephropathy 
pathophysiology and enhance biomarker discovery efforts 
[38]. Standardized analytical methods and reference 
materials preparation will be most critical for facilitating 
clinical application and reproducibility across different 
laboratories and healthcare systems [34]. Furthermore, 
investigation of mechanistic interactions between identified 
biomarkers and treatment targets would reveal novel 
intervention strategies for the prevention or reversal of 
diabetic kidney disease tubular epithelial cell dysfunction 
[39]. These future directions will be important in bridging 
current research findings to clinically effective tools that 
improve the outcomes of diabetic nephropathy patients. 

5. Conclusion 

This study shows the opportunity for machine learning-
based multi-omics integration frameworks to automate the 
detection of diabetic nephropathy’s tubular epithelial cell-
specific biomarkers, which equate to ten candidate molecules 
with unmatched accuracy in diagnosis when juxtaposed with 
clinical markers. Ensemble learning, for example, 
outperformed traditional serum creatinine and albumin-to-
creatinine ratio markers by 30%, secondary to classification 
accuracy of 91.4% and AUC of 0.947. KIM-1, NGAL, and L-
FABP, alongside seven other markers, formed the 
multicomponent biomarkers for the integrated signature, 
which reflects the advanced pathophysiology orchestrated by 
diabetic kidney disease’s tubular epithelial cell dysfunction. 
The stepwise expression shift seen with the progression of 
the disease strengthens the notion of tubulointerstitial injury 
being a core driver of the diabetic nephropathy disease 
continuum, developing in a manner that is relatively 
unaffected by damage to the glomeruli. Provided text outlines 
some of the disease’s most impactful mechanisms alongside 
critical inflammatory stress response, oxidative injury, and 
epithelial-mesenchymal transition by detailing the disease 
pathogenesis and possible intervention points. In addition to 
the diagnostic functionalities, the clinical consequences of 
these findings also include precision therapy, more 
personalized treatment, and adaptive monitoring approaches 
for the management of diabetic kidney disease. The improved 
sensitivity and specificity ranges of tubular epithelial cell-



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specific biomarkers present the clearest opportunity for early 
clinical detection, long before conventional markers are able 
to quantify the level of disease progression, allowing timely 
intervention with renal protective interventions. The 
extensive validation in several independent cohorts confirms 
the reproducibility and clinical applicability of the identified 
biomarkers across different populations, therefore, affirming 
the diagnostic credence of the markers. The authors also note 
salient shortcomings, such as the reliance on pre-collected 
data and the absence of pre-collected, prospective 
longitudinal data verification needed for establishing 
predictive value, along with temporal connections between 
biomarker expression and clinical outcomes, which require 
temporal relationships. Future research endeavors should 
prioritize large-scale prospective studies, standardization of 
analytical protocols, and investigation of mechanistic 
relationships between identified biomarkers and therapeutic 
targets to facilitate clinical translation and improve outcomes 
for patients with diabetic nephropathy. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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