Abstract

   Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening
   complication of sepsis, characterized by high mortality and prolonged
   hospitalization. Early diagnosis and effective therapy remain difficult
   despite extensive investigation. To address this, we developed an
   AI-driven integrative framework that combines a Transformer-based deep
   learning model with established machine learning techniques (LASSO,
   SVM-RFE, Random Forest and neural networks) to uncover complex,
   nonlinear interactions among gene-expression biomarkers. Analysis of
   normalized microarray data from GEO ([36]GSE95233 and [37]GSE69063)
   identified differentially expressed genes (DEGs), and KEGG/GO
   enrichment via clusterProfiler revealed key pathways in immune
   response, protein synthesis, and antigen presentation. By integrating
   multiple transcriptomic cohorts, we pinpointed 617 SA-AKI-associated
   DEGs—21 of which overlapped between sepsis and AKI datasets. Our
   Transformer-based classifier ranked five genes (MYL12B, RPL10, PTBP1,
   PPIA, and TOMM7) as top diagnostic markers, with AUC values ranging
   from 0.9395 to 0.9996 (MYL12B yielding 0.9996). Drug–gene interaction
   mining using DGIdb (FDR < 0.05) nominated 19 candidate therapeutics for
   SA-AKI. Together, these findings demonstrate that melding deep learning
   with classical machine learning not only sharpens early SA-AKI
   detection but also systematically uncovers actionable drug targets,
   laying groundwork for precision intervention in critical care settings.

   Keywords: sepsis, acute kidney injury, transformer, machine learning,
   diagnostic model

1. Introduction

   Sepsis-associated acute kidney injury (SA-AKI) is a critical
   complication of sepsis, carrying substantial mortality risk and
   frequently requiring prolonged intensive care [[38]1,[39]2]. Sepsis,
   defined as life-threatening organ dysfunction caused by a dysregulated
   host response to infection, triggers both inflammatory and
   immunosuppressive pathways that contribute to multi-organ failure
   [[40]3]. Improving Global Outcomes (KDIGO) criteria define acute kidney
   injury (AKI) based on increases in serum creatinine or decreases in
   urine output [[41]4]. Specifically, Stage 1 AKI is characterized by an
   increase in serum creatinine to 1.5–1.9 times baseline or an increase
   of ≥0.3 mg/dL (≥26.5 µmol/L), or a reduction in urine output to <0.5
   mL/kg/h for 6–12 h [[42]5]. Despite these criteria, SA-AKI diagnosis
   remains challenging due to heterogeneous pathophysiology and delayed
   biomarker specificity. Identifying novel biomarkers and integrating
   them with machine-learning-driven analytics (e.g., electronic health
   records or proteomic/transcriptomic data) could enable automated
   diagnosis, prognostication, and personalized therapeutic targeting.

   Recent advances in biomarker discovery have led to the identification
   of novel candidates, including neutrophil gelatinase-associated
   lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and the combination
   of tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like
   growth factor-binding protein 7 (IGFBP7). For example, NGAL and
   cystatin C demonstrate utility in early risk stratification, with
   cystatin C (cutoff ≥15.1 mg/L) showing exceptional accuracy in
   predicting persistent SA-AKI (AUROC 0.977) [[43]6]. Importantly,
   emerging evidence has demonstrated that the inflammatory response
   induced by sepsis amplifies kidney damage via mechanisms like cytokine
   activation [[44]7]. Pro-inflammatory cytokines such as IL-6 (>35 pg/mL)
   and TNF-
   [MATH: <mrow><mi>α</mi></mrow> :MATH]
   (>20 pg/mL) not only amplify systemic inflammation but directly
   compromise glomerular integrity through NF-
   [MATH: <mrow><mi>κ</mi></mrow> :MATH]
   B-mediated endothelial activation, increasing renal vascular
   permeability. These mediators orchestrate immunometabolic crosstalk,
   with IL-6 shown to suppress renal tubular fatty acid oxidation while
   TNF-
   [MATH: <mrow><mi>α</mi></mrow> :MATH]
   upregulates mitochondrial ROS generation, establishing a
   self-perpetuating cycle of injury [[45]8,[46]9]. In recent years, the
   development of precision medicine has offered new perspectives for
   personalized treatment, where understanding the key genetic pathways
   involved in SA-AKI is essential to developing more effective
   therapeutic strategies with fewer side effects [[47]10]. Precision
   medicine advances have revolutionized SA-AKI management by identifying
   critical pathogenic pathways and biomarkers, enabling targeted
   therapies.

   Based on 403 screened studies focusing on AKI prediction models,
   machine learning algorithms have been increasingly applied to SA-AKI,
   with traditional logistic regression (LR) being the most commonly used
   (44% of 25 studies), followed by extreme gradient boosting (XGBoost,
   20%), support vector machines (SVMs), light gradient boosting
   (LightGBM), recurrent neural network-long short-term memory (RNN-LSTM),
   and categorical boosting (CatBoost)
   [[48]11,[49]12,[50]13,[51]14,[52]15,[53]16]. These models primarily
   focus on the early identification (60%), prognostic prediction (32%),
   and subtype identification (8%) of SA-AKI. XGBoost demonstrated
   superior performance in multiple studies, achieving AUROCs of
   0.821–0.954 for early AKI detection and mortality prediction, while
   RNN-LSTM showed exceptional accuracy (AUROC 1.000) in predicting acute
   kidney disease progression [[54]17]. Key predictors included serum
   creatinine, lactate, blood urea nitrogen, and diabetes mellitus, with
   biomarker-enhanced models performing comparably to clinical
   variable-based models [[55]18]. Despite these advancements, significant
   limitations persist. Over 80% of studies were retrospective and
   single-center, predominantly using databases like MIMIC-III/IV,
   limiting generalizability [[56]13,[57]16]. While biomarker integration
   (e.g., urinary peptides, gene signatures) could enhance
   interpretability and pathophysiological relevance, only 16% of studies
   incorporated such approaches [[58]19]. Model interpretability remained
   challenging, with only 24% employing methods like SHAP or LIME to
   address the “black box” nature of machine learning [[59]15].
   Additionally, studies neglected long-term outcomes (e.g., CKD
   transition) and non-ICU populations, while overreliance on late
   biomarkers like serum creatinine hindered early detection
   [[60]1,[61]20]. Future research should prioritize multicenter
   prospective validation, biomarker-augmented interpretability
   frameworks, and clinical integration to improve model robustness and
   therapeutic targeting interventions for SA-AKI [[62]19,[63]20,[64]21].

   Sepsis-associated acute kidney injury (SA-AKI) remains a critical
   challenge in clinical settings, with limited early diagnostic tools and
   therapeutic options despite its significant impact on patient outcomes.
   In this study, we hypothesize that our machine learning models,
   particularly those leveraging a Transformer-based deep learning
   approach, can predict SA-AKI with greater accuracy by identifying key
   gene biomarkers from complex gene-expression profiles. The objectives
   of this research are threefold: (1) to develop an integrated AI-driven
   framework combining Transformer-based models with classical machine
   learning techniques to detect gene biomarkers associated with SA-AKI,
   (2) to assess the diagnostic potential of these biomarkers through
   rigorous feature selection and validation, and (3) to explore potential
   therapeutic targets by analyzing gene–drug interactions, thereby paving
   the way for precision interventions in SA-AKI management.

2. Materials and Methods

2.1. Diagram of Study Flow and Data Collection

   Relevant datasets were selected from the NCBI Gene Expression Omnibus
   (GEO, [65]https://www.ncbi.nlm.nih.gov/gds/) (accessed on 15 October
   2024). Search keywords included “Sepsis” and “Homo sapiens”, and the
   data type was set to “Expression profiling by array” to obtain
   gene-expression data. After screening, the dataset [66]GSE95233 was
   chosen as the primary dataset, and [67]GSE69063 was selected as the
   validation dataset. The [68]GSE95233 dataset is based on the [69]GPL570
   [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array and
   includes 124 samples, consisting of 102 sepsis samples and 22 healthy
   control samples. The [70]GSE69063 dataset includes four disease
   phenotypes: allergic reaction, sepsis, trauma, and healthy controls. To
   meet the research requirements, 57 samples from septic patients and 33
   samples from healthy controls were extracted, totaling 90 samples. The
   top 1000 genes highly related to acute kidney injury were selected from
   the GTEx ([71]https://www.gtexportal.org/home/ (accessed on 15 October
   2024)) [[72]22], Malacards ([73]https://www.malacards.org/ (accessed on
   15 October 2024)) [[74]23], Phenopedia
   ([75]https://phgkb.cdc.gov/PHGKB/startPagePhenoPedia.action (accessed
   on 15 October 2024)) [[76]24], and DisGeNET ([77]https://disgenet.com/
   (accessed on 15 October 2024)) [[78]25] databases (accessed on 24
   October 2024) and downloaded for further research analysis [[79]26].
   The whole workflow of this study in [80]Figure 1.

Figure 1.

   [81]Figure 1
   [82]Open in a new tab

   The whole workflow of this study.

2.2. Sepsis and AKI-Related Gene Analysis

   Gene-expression data were normalized and annotated. Considering that
   multiple probes may map to the same gene symbol in the dataset, these
   probes were converted into gene symbols based on the annotation files
   of the platform. For probes mapping to the same gene symbol, the
   average expression value of these probes was selected as the gene’s
   expression level, thus obtaining the expression levels of each gene
   across different samples.

   Differential expression analysis was performed using the limma package
   [[83]27] in R (version 2024.12.1+563). This method employs a linear
   model to assess the expression differences of each gene between the
   sepsis group and healthy control group. During the analysis, a
   significance threshold was set as p-value < 0.05 and fold change (FC)
   greater than 1.2, i.e., FC > 1.2, as the criteria for selecting
   differentially expressed genes (DEGs). Using these criteria, genes with
   differential expression between the sepsis group and healthy control
   group were identified.

   The DEGs were intersected with AKI-associated genes from public
   databases to identify shared sepsis-AKI disease genes. The overlapping
   genes between sepsis and AKI were identified and termed “disease
   genes”. The identification of these disease genes provides strong
   evidence for the relationship between sepsis and acute kidney injury,
   supporting the molecular correlation between them.

2.3. Gene Function Analysis

   Gene Ontology (GO) functional analysis, which includes the terms
   Cellular Component (CC), Biological Process (BP), and Molecular
   Function (MF), is a powerful bioinformatics tool used to categorize
   gene expressions and their characteristics. KEGG pathway analysis is
   employed to identify the cellular pathways that may be involved in the
   changes in differentially expressed genes (DEGs). GO and KEGG pathway
   enrichment analyses of AKI and septic shock DEGs were conducted using
   the R package clusterProfiler [[84]28]. A p-value of <0.05 was
   considered statistically significant.

2.4. Diagnostic Signature Machine Learning Analysis

   To identify key diagnostic genes associated with SA-AKI, machine
   learning algorithms were employed to construct multiple classification
   models and perform feature selection using the DEGs identified in
   previous analyses. The disease-related genes identified in previous
   analyses were used as features, and corresponding gene-expression data
   were extracted for model construction. The dataset was randomly divided
   into a training set (70%) and a test set (30%) to ensure reliable model
   training and evaluation.

   The glmnet R package [[85]29] was used to implement the LASSO (Least
   Absolute Shrinkage and Selection Operator) algorithm for feature
   selection. LASSO applies L1 regularization to the model parameters,
   causing some coefficients to become zero, thus effectively selecting
   the most predictive gene features.

   The SVM-RFE (Support Vector Machine Recursive Feature Elimination)
   algorithm was employed, using the SVM R package [[86]30] for feature
   selection. This method recursively trains the SVM model, eliminating
   less important features and retaining those that have a greater impact
   on classification performance.

   The randomForest R package [[87]31] was used to implement the Random
   Forest (RF) algorithm. The RF algorithm constructs multiple decision
   trees and combines their results through voting, effectively
   identifying key gene features and providing an importance score for
   each gene.

   The nnet R package [[88]32] was applied to implement the artificial
   neural network (NNET) algorithm. The NNET model uses a multi-layer
   perceptron architecture to capture complex non-linear relationships
   between gene features and the target variable.

2.5. Diagnostic Signature Transformer Analysis

   Building upon the machine learning approaches, the study also utilized
   the Transformer model to further enhance model performance and capture
   higher-order feature interactions. Unlike traditional machine learning
   algorithms, the Transformer model employs self-attention mechanisms to
   learn complex relationships between features, enabling it to capture
   dependencies that might be difficult to detect through conventional
   methods. The Transformer model, implemented using the
   transformer-pytorch library, was trained using the entire dataset,
   which included continuous features. This approach provided a deeper
   understanding of the genetic interactions involved in SA-AKI and
   facilitated more accurate identification of important diagnostic
   biomarkers.

   For each of the four machine learning models (LASSO, SVM-RFE, RF, and
   NNET), the top 12 most important genes were selected. The intersection
   of these top genes from all four methods was then determined. These
   intersecting genes were subsequently input into the Transformer model,
   which prioritized the five most important genes for SA-AKI diagnosis.
   This comprehensive process led to the identification of a robust set of
   key biomarkers, enhancing the accuracy of SA-AKI diagnosis.

2.6. Model Construction and Evaluation

   After selecting the key feature genes associated with SA-AKI, this
   study proceeded to construct diagnostic models based on the selected
   genes. These models aimed to distinguish between sepsis, acute kidney
   injury (AKI), and healthy control groups using the expression data of
   the selected genes.

   To assess the diagnostic capability of the models, we calculated the
   AUC (Area Under the Curve) for the model and each feature gene. The AUC
   is a commonly used metric to evaluate the performance of binary
   classification models, with values closer to 1 indicating better
   diagnostic performance [[89]33]. The AUC values for each gene were
   compared, and the accuracy and stability of the models were evaluated
   based on the combined results. To validate the clinical applicability
   of the constructed diagnostic models, we utilized the publicly
   available dataset [90]GSE69063 as a validation set. This allowed us to
   assess the performance of the models on an independent sample set. By
   analyzing the prediction results from the validation set, we further
   confirmed the model’s ability to distinguish between sepsis and healthy
   controls, as well as between sepsis and AKI [[91]34,[92]35,[93]36].

2.7. Drug Target Prediction

   To identify potential drug targets, particularly for the treatment of
   SA-AKI, this study retrieved drug–gene interaction information related
   to the feature genes from the Drug Gene Interaction Database (DGIdb,
   [94]https://www.dgidb.org/ (accessed on 15 October 2024)) [[95]37].

   We extracted the key feature genes and performed drug–gene interaction
   searches using the DGIdb database. DGIdb is a comprehensive resource
   for drug–gene information, offering detailed data on known and
   potential drugs, including drug–gene interactions, known targets, and
   mechanisms of drug action. These drugs include those already approved
   for other diseases as well as those showing potential efficacy in
   preclinical studies. Through drug–gene interaction analysis, we
   identified potential drugs targeting the key genes associated with
   sepsis and acute kidney injury, providing new directions for clinical
   treatment [[96]38].

3. Results

3.1. Analysis of Genes Associated with Sepsis and AKI

   Through differentially expressed gene (DEG) analysis of the
   [97]GSE95233 dataset, we first outlined the gene screening process
   associated with both sepsis and AKI ([98]Figure 2A). UMAP (Uniform
   Manifold Approximation and Projection) analysis was then used to
   visualize the distribution of samples ([99]Figure 2B), revealing a
   clear separation between sepsis and control groups without obvious
   outliers. A total of 617 DEGs were identified, including 248
   upregulated and 369 downregulated genes ([100]Figure 2C). The top 30
   DEGs were further visualized using a heatmap to illustrate their
   expression patterns across samples ([101]Figure 2D). Venn analysis
   showed that 21 genes were shared between sepsis DEGs and key
   AKI-related genes, defined as “disease genes” ([102]Figure 2E). The
   expression profiles of these 21 disease genes between sepsis and
   control groups were subsequently displayed in a heatmap ([103]Figure
   2F).

Figure 2.

   [104]Figure 2
   [105]Open in a new tab

   Analysis of genes associated with sepsis and AKI. (A) Identification
   process of key genes in sepsis and AKI. (B) UMAP plot of the
   [106]GSE95233 Dataset. The UMAP analysis displays the distribution of
   sepsis (red circles) and control (blue circles) samples, with the
   x-axis representing UMAP1 and the y-axis representing UMAP2. The clear
   separation between the two groups indicates significant differences in
   gene-expression profiles. (C) Distribution of differentially expressed
   genes between the sepsis and control groups. The x-axis represents log2
   fold change, and the y-axis represents −log10 FDR. Red dots indicate
   upregulated genes (248), and green dots indicate downregulated genes
   (369), with the threshold set at |log2FoldChange| > 1 and FDR < 0.05.
   (D) Heatmap of the top 30 DEGs between the sepsis and control groups.
   The x-axis represents samples (left: sepsis group; right: control
   group), and the y-axis lists the gene names of the top 30 DEGs. Red
   indicates upregulation, and blue indicates downregulation, with color
   intensity reflecting the magnitude of expression changes. (E)
   Overlapping genes between sepsis DEGs and key AKI genes, termed
   “disease genes”. The Venn diagram shows the overlap between sepsis DEGs
   (617 genes) and key AKI-related genes, identifying 21 shared genes
   defined as “disease genes”. (F) Heatmap of the 21 disease genes between
   the sepsis and control groups. The x-axis represents samples (left:
   sepsis group; right: control group), and the y-axis lists the 21
   disease genes. Red indicates upregulation, and blue indicates
   downregulation, highlighting the expression differences between the two
   groups.

3.2. Gene Function Analysis

   KEGG analysis showed that DEGs in SA-AKI were mainly enriched in
   pathways like “ribosome”, “antigen processing and presentation”, and
   “proteasome”. Other pathways such as “oxidative amino acid metabolism”
   and “Atoplasmosis” were also significantly enriched, suggesting
   involvement in immune response, protein degradation, and metabolic
   abnormalities ([107]Figure 3A). GO enrichment analysis revealed that
   DEGs were enriched in “cytoplasmic translation”, “viral process”, and
   “protein insertion into mitochondrial outer membrane” in biological
   processes. In Molecular Function, genes were enriched in “structural
   constituent of ribosome” and “T cell receptor binding”. Cellular
   Component analysis showed enrichment in “outer mitochondrial membrane
   transport complex” and “cytoplasmic ribosomal subunit” ([108]Figure
   3B). Metascape network analysis classified 21 disease genes into
   functions related to “Nop56p-related prerRNA complex”, “response to
   virus”, “mitochondrial transport”, and “negative regulation of
   apoptotic signaling”, indicating their role in immune response,
   cellular stress, and cell death regulation ([109]Figure 3C). Cell type
   signature analysis identified “adult kidney C9 thin ascending limb” and
   “adult kidney C10 thin ascending limb” cells as key in kidney repair
   and immune responses, with certain gut and lung cells also implicated
   ([110]Figure 3D).

Figure 3.

   [111]Figure 3
   [112]Open in a new tab

   Enrichment analysis of disease genes. (A) KEGG pathway enrichment
   analysis of disease genes. (B) Top 10 GO terms, including Biological
   Process (BP), Cellular Component (CC), and Molecular Function (MF) of
   disease genes. (C) Enrichment network of disease genes obtained using
   the Metascape database, represented by cluster membership
   relationships. (D) summary of enrichment analysis in cell type
   signatures.

3.3. Diagnostic Signature Identification Analysis

   To identify diagnostic biomarkers for SA-AKI, four machine learning
   algorithms—LASSO, SVM-RFE, Random Forest (RF), and neural network
   (NNET)—were applied. The optimal
   [MATH: <mrow><mi>λ</mi></mrow> :MATH]
   parameter for LASSO regression was determined using cross-validation
   ([113]Figure 4A). The performance of each algorithm during training was
   assessed through the reverse cumulative distribution of residuals
   ([114]Figure 4B). Each algorithm selected the top 12 most informative
   genes based on their importance scores, as measured by dropout loss
   (where a smaller value indicates greater contribution to the model’s
   predictive power). This cutoff of 12 genes was chosen because an
   analysis of the dropout loss distribution across all models revealed a
   significant decline in predictive value beyond the 12th gene
   ([115]Table S1). A sensitivity analysis, testing configurations of 10,
   12, and 15 genes, further confirmed that 12 genes optimized model
   performance, as assessed by the Area Under the Curve (AUC) on the
   validation set [116]GSE69063 ([117]Figure S1). Each algorithm selected
   the top 12 most informative genes, and overlapping genes among the four
   methods were identified ([118]Figure 4C). These candidate genes were
   subsequently used to construct a diagnostic model based on a
   Transformer architecture, and SHAP (Shapley Additive Explanations)
   analysis was performed to assess feature importance. Five genes with
   the highest SHAP values were identified as key diagnostic markers
   ([119]Figure 4D). The expression levels of these five genes in sepsis
   and control groups are shown in [120]Figure 4E, highlighting their
   differential expression and diagnostic relevance.

Figure 4.

   [121]Figure 4
   [122]Open in a new tab

   Analysis for identifying diagnostic signature. (A) Gene selection for
   diagnostic model construction via LASSO regression. The x-axis
   represents the
   [MATH: <mrow><mi>λ</mi></mrow> :MATH]
   value, and the y-axis represents cross-validation error. (B) Reverse
   cumulative distribution of residuals during the training process of the
   four machine learning models. (C) Overlapping genes selected by LASSO
   regression, SVM algorithm, random forest model and the artificial
   neural network. (D) SHAP values for feature importance in Transformer
   model for diagnosis. (E) Expression Levels of the 5 selected feature
   genes in the sepsis and control groups (**** indicates
   [MATH: <mrow><mrow><mi>p</mi><mo><</mo><mn>0.0001</mn></mrow></mrow>
   :MATH]
   ).

3.4. Diagnostic Model Validation

   The diagnostic capability of the five feature genes in distinguishing
   sepsis and healthy samples demonstrated good diagnostic value
   ([123]Table S1). In our model, the AUC was 1.0, with MYL12B having an
   AUC of 0.9996, RPL10 an AUC of 0.9902, PTBP1 an AUC of 0.9890, PPIA an
   AUC of 0.9662, and TOMM7 an AUC of 0.9395 ([124]Figure 5A). A nomogram
   was constructed based on the five feature genes, providing a visual
   representation of the diagnostic model. This nomogram facilitates the
   prediction of sepsis and AKI by summing the individual contributions of
   each gene ([125]Figure 5B). A diagnostic model constructed based on
   these five genes was validated using the independent dataset
   [126]GSE69063. This external validation yielded an AUC of 0.973
   ([127]Figure 5C). The corresponding ROC curve confirmed the model’s
   ability to accurately distinguish SA-AKI samples from healthy kidney
   samples, supporting the potential clinical utility of the identified
   biomarkers.

Figure 5.

   [128]Figure 5
   [129]Open in a new tab

   Validation of model for the diagnosis sepsis and AKI. (A) The ROC curve
   of the diagnostic signal showing each signature gene based on the
   datasets of [130]GSE95233. (B) Norman plot constructed based on the 8
   feature genes. (C) ROC curves of diagnostic signals showing the
   sensitivity and specificity of our models based on [131]GSE69063.

3.5. Drug–Gene Interaction Analysis

   To explore potential drug–gene interactions relevant to SA-AKI, we
   queried the DGIdb database using the 12 key genes identified by our
   machine learning models as critical to SA-AKI pathogenesis. This
   analysis identified 19 drugs with documented interactions with these
   genes, based on interaction confidence scores (>0.7) and established
   pharmacological data ([132]Figure 6). These drugs include various
   immunosuppressants, antiviral agents, and some anticancer drugs,
   suggesting that they may play significant roles in regulating immune
   responses, inflammation, and apoptosis. After excluding the unapproved
   drugs from the initial list, 11 drugs remained, which may serve as
   potential treatments for sepsis and acute kidney injury ([133]Table
   S3). Notably, some of these drugs, such as cyclosporine (associated
   with the PPIA gene), are well-documented nephrotoxins capable of
   causing AKI [[134]39,[135]40]. For example, cyclosporine interacts with
   PPIA (cyclophilin A), a protein involved in inflammatory pathways
   [[136]40], yet its nephrotoxic profile, characterized by acute renal
   vasoconstriction and chronic structural damage [[137]41], precludes its
   consideration as a therapeutic option for SA-AKI. Similarly,
   fluorouracil, linked to PTBP1 (an RNA-binding protein), emerged due to
   its regulatory effects on gene expression, though its clinical use is
   primarily in oncology and it has been associated with nephrotoxicity
   [[138]42,[139]43]. These associations, derived from computational
   analysis using machine learning models, highlight the complexity of
   drug–gene interactions, where such models can uncover connections
   reflecting both potential biological relevance and adverse effects,
   depending on the clinical context [[140]44,[141]45].

Figure 6.

   [142]Figure 6
   [143]Open in a new tab

   Drug–gene interaction diagram, blue circles represent signature genes
   and the yellow circles represent drugs.

4. Discussion

   This study introduces an innovative approach to identifying key
   diagnostic biomarkers for SA-AKI by integrating gene-expression
   analysis with advanced machine learning techniques, including the
   application of Transformer-based models in conjunction with traditional
   machine learning methods. Through a comprehensive multi-step process,
   we successfully identified pivotal feature genes that differentiate
   SA-AKI from healthy controls.

   SA-AKI was interrogated via differential expression analysis, which
   uncovered 617 DEGs—including 248 upregulated and 369 downregulated
   genes. Subsequent Venn analysis identified 21 genes common to both
   sepsis and acute kidney injury, positioning them as potential
   biomarkers for SA-AKI. KEGG and GO pathway enrichment analyses further
   underscored the key biological processes involved: KEGG analysis
   demonstrated significant enrichment in immune response, protein
   synthesis, and antigen presentation pathways, while GO analysis
   revealed that these genes principally participate in immune response,
   cellular translation, and viral processes—pathways intimately linked to
   the pathogenesis of SA-AKI. To refine these biomarkers, an integrative
   feature selection strategy employing four machine learning algorithms
   (LASSO, SVM-RFE, RF, and NNET) was implemented, yielding eight
   consensus genes. Further enhancing model performance, a
   Transformer-based deep learning model was incorporated to capture
   complex nonlinear interactions, ultimately prioritizing five key genes
   as critical diagnostic biomarkers. The resultant diagnostic framework
   exhibited excellent performance in the validation set, affirming its
   reliability and predictive power. Moreover, drug–gene interaction
   mining via DGIdb prioritized 19 therapeutic candidates, offering novel
   avenues for intervention in SA-AKI.

   The results of this study align with findings in the existing
   literature while also offering new perspectives. Key genes we
   identified, such as RPL10, MYL12B, and PPIA, have been shown to play
   significant roles in the mechanisms associated with sepsis and AKI.
   Specifically, RPL10, as part of the ribosome, plays a crucial role in
   translation elongation and the maturation of ribosomal subunits
   [[144]46], a function that is supported by our findings linking immune
   response and protein synthesis pathways. Similarly, MYL12B is involved
   in regulating dynamic changes in the cytoskeleton and influences the
   cell division process by modulating the activity of myosin II
   [[145]47], which correlates with the relationship between cell division
   and immune regulation observed in our study. PPIA promotes inflammation
   and cell signaling, playing a role in kidney injury [[146]40], a
   finding supported by this study, suggesting its potential as a
   therapeutic target for sepsis and AKI.

   In this study, we introduce a novel AI-driven framework that
   synergistically integrates Transformer models with machine learning
   algorithms (LASSO, SVM-RFE, Random Forest, and NNET) to identify
   diagnostic biomarkers and therapeutic targets for SA-AKI. This
   innovative methodology not only offers fresh insights into disease
   mechanisms but also combines differential gene-expression analysis,
   KEGG/GO pathway enrichment, machine-learning-based feature selection,
   and drug–gene interaction analysis to provide a comprehensive view of
   SA-AKI. The identification of five key genes and 19 potential
   therapeutic candidates furnishes valuable references for early