Integrated Bioinformatics Analysis and Clinical Validation Identify TNFSF14 and CD40 as Novel Biomarkers for Chronic Kidney Disease Progression and Tubulointerstitial Injury

Integrated Bioinformatics Analysis and Clinical Validation Identify TNFSF14 and CD40 as Novel Biomarkers for Chronic Kidney Disease Progression and Tubulointerstitial Injury | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Bioinformatics Analysis and Clinical Validation Identify TNFSF14 and CD40 as Novel Biomarkers for Chronic Kidney Disease Progression and Tubulointerstitial Injury xiameng gu, yuqing lu, haonan sha, hanlu zhang, hongxin chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8690385/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Apr, 2026 Read the published version in International Urology and Nephrology → Version 1 posted You are reading this latest preprint version Abstract Background Chronic Kidney Disease (CKD) imposes a significant global health burden, defined by the irreversible loss of function and progressive fibrosis. There is a critical unmet need for non-invasive biomarkers that can accurately mirror renal inflammation and early-stage injury to guide diagnosis. This study combines bioinformatics with clinical validation to pinpoint pathogenic genes driving CKD progression. Methods We mined two CKD expression datasets (GSE66494 and GSE97709) from the GEO database. Using the limma R package, we screened for Differentially Expressed Genes (DEGs) and mapped their biological functions via GO and KEGG enrichment. To validate the top candidates—TNFSF14 and CD40—we analyzed a clinical cohort comprising 140 CKD patients (Stages I–V) and 60 healthy controls. Protein levels were quantified in serum and urine. Furthermore, we assessed tissue expression patterns using immunofluorescence on renal biopsies from patients with Diabetic Nephropathy, IgA Nephropathy, Membranous Nephropathy, and FSGS (n = 20 per group). Results Bioinformatics analysis highlighted TNFSF14 and CD40 as key immune-related targets, clustering heavily within the TNF signaling and cytokine-receptor interaction pathways. Clinically, both markers were markedly elevated in the serum and urine of CKD patients compared to controls (P < 0.05). Tissue staining localized this upregulation specifically to the renal tubules. Correlation analysis showed that urinary levels of these markers tracked closely with disease severity, associating positively with serum creatinine/BUN and inversely with eGFR. ROC analysis further confirmed that both TNFSF14 and CD40 exhibit high diagnostic sensitivity and specificity. Conclusions This study positions TNFSF14 and CD40 not only as robust molecular signatures of tubulointerstitial injury but also as non-invasive urinary biomarkers with high diagnostic precision. Their integration into clinical practice could refine risk stratification and uncover novel therapeutic targets for halting CKD progression. Chronic Kidney Disease Bioinformatics TNFSF14 CD40 Biomarker Renal Fibrosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The rising prevalence of Chronic Kidney Disease (CKD) presents a formidable challenge to global public health, compounded by aging populations and the growing burden of metabolic disorders. Recent estimates from the Global Burden of Disease study suggest that nearly 10% of the worldwide population is now affected, positioning CKD as a leading cause of mortality among non-communicable diseases [ 1 , 2 ]. While the primary triggers vary-ranging from diabetes and hypertension to autoimmune conditions-the downstream pathological trajectory is remarkably uniform: a spiral of chronic inflammation, capillary rarefaction, and progressive tubulointerstitial fibrosis [ 3 ]. While traditional metrics like creatinine remain standard, the search for novel biomarkers has yielded candidates such as NGAL (Neutrophil Gelatinase-Associated Lipocalin) and KIM-1 (Kidney Injury Molecule-1). Although these markers reliably indicate acute tubular necrosis, their performance in chronic, indolent fibrosis remains variable. More importantly, markers like NGAL are often generic indicators of injury rather than reporters of specific pathogenic pathways. There is a distinct lack of 'mechanism-based' biomarkers—molecules that not only signal the presence of injury but also reveal the underlying immune driver. Identifying such pathway-specific markers is crucial, as it would not only aid in risk stratification but also identify patients who might benefit from specific immunotherapies targeting those exact pathways. In current clinical practice, staging relies heavily on estimated Glomerular Filtration Rate (eGFR) and albuminuria. Yet, these metrics suffer from a significant diagnostic limitation: they are essentially lagging indicators that fail to capture early-stage cellular stress or active inflammation within the renal parenchyma [ 4 ]. By the time functional decline becomes apparent, substantial structural damage has often already occurred. This gap underscores the urgent need for "liquid biopsy" biomarkers-molecules detectable in urine or blood that can mirror real-time molecular changes in the tubulointerstitium before irreversible fibrosis sets in [ 5 , 6 ]. Mechanistically, the Tumor Necrosis Factor (TNF) superfamily occupies a central node bridging innate immunity and tissue remodeling [ 7 ]. Within this network, TNFSF14 (LIGHT) and CD40 have garnered attention not just as immune mediators, but as drivers of renal pathology. Emerging evidence suggests that TNFSF14 functions as a critical checkpoint in vascular and metabolic injury [ 8 , 9 ], while the CD40-CD40L axis is increasingly recognized for its role in promoting macrophage activation and epithelial-mesenchymal transition (EMT) [ 10 , 11 ]. Despite these mechanistic insights, the clinical utility of these proteins as non-invasive biomarkers remains underexplored. To bridge the gap between transcriptomic signatures and clinical utility, we employed a multi-step translational approach. By integrating bioinformatics analysis of GEO datasets [ 12 ] with validation in a clinical cohort, we sought to characterize the expression patterns of TNFSF14 and CD40. Our objective was to determine whether these potential targets are upregulated in the renal tubulointerstitium and if their urinary concentrations can serve as robust, non-invasive reporters of CKD progression. Materials and Methods 1. Data Mining and Differential Expression Analysis We retrieved gene expression profiles from the NCBI Gene Expression Omnibus (GEO) database, specifically selecting datasets GSE66494 and GSE97709 to compare CKD kidney tissues against normal controls. Raw data were processed and normalized prior to differential expression analysis. We utilized the limma package (version 3.40.6) in R software to screen for Differentially Expressed Genes (DEGs), defining significance as a |log2 Fold Change (FC)| ≥ 1.5 and an adjusted P-value < 0.05. Dataset specifics are as follows: GSE66494 contains microarray data from 53 renal biopsy specimens, capturing gene expression changes linked to interstitial fibrosis and tubular injury. GSE97709 employs next-generation sequencing to profile circulating lncRNA in 48 subjects (28 ESRD, 8 CKD, 12 healthy controls), enabling a stage-dependent comparison of molecular signatures. To visualize dataset overlaps, we used Venny 2.1. Functional annotation was performed via Gene Ontology (GO) (using org.Hs.eg.db v3.1.0) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses with the clusterProfiler package (v3.14.3). We mapped Protein-Protein Interaction (PPI) networks using the STRING database ( https://cn.string-db.org/ ), considering a P-value < 0.05 as statistically significant. 2. Clinical Demographic Profile of GEO Datasets To ensure the clinical representativeness of our bioinformatic screening, we rigorously evaluated the demographic and pathological metadata of the selected datasets. The GSE66494 cohort (training set) predominantly comprised biopsy samples from patients with hypertensive nephropathy and diabetic kidney disease, reflecting a population with established metabolic co-morbidities. The samples were age-matched with controls to minimize confounding geriatric transcriptional noise. Similarly, the GSE97709 validation set included a diverse array of CKD etiologies, ranging from IgA nephropathy to lupus nephritis. This pathological heterogeneity in the validation phase was strategically selected to ensure that the identified biomarkers (TNFSF14/CD40) represent a "common denominator" of renal injury, rather than being restricted to a single disease subtype. Normal control samples in both datasets were obtained from the synthesis of unaffected poles of tumor nephrectomies, confirmed histologically to be free of glomerular or tubulointerstitial lesions. 3. Clinical Cohort Description and Ethical Approval This study was conducted in strict accordance with the Declaration of Helsinki and received approval from the Ethics Committee of the Affiliated Hospital of Nantong University (Approval No. 2023-Y114-01). Written informed consent was obtained from all participants. We enrolled 140 patients admitted to the Department of Nephrology with a confirmed diagnosis of CKD. Based on the KDIGO guidelines, patients were stratified by eGFR into Stages I–V: Stage I (n = 30), Stage II (n = 31), Stage III (n = 28), Stage IV (n = 25), and Stage V (n = 26). Within this cohort, 80 patients underwent renal biopsy, distributing evenly across four etiologies: diabetic nephropathy, IgA nephropathy, membranous nephropathy, and FSGS (n = 20 each). Exclusion criteria encompassed acute kidney injury (AKI), active infection, malignancy, or incomplete clinical records. A control group comprising 60 healthy individuals (age > 18 years, normal renal function) was recruited from the physical examination center. 4. Serum and Urine Sample Processing Serum: Peripheral blood samples (5 mL) were drawn into red vacuum tubes without anticoagulant. Following centrifugation at 3000 × g for 15 minutes at 4°C, the supernatant serum was aliquoted into RNase/DNase-free tubes and stored at -80°C. Urine: We collected 50 mL of mid-stream urine in sterile containers. To eliminate cellular debris, samples underwent centrifugation at 3000 × g for 15 minutes at 4°C. The resulting supernatant was transferred to 1.5 mL tubes and preserved at -80°C until quantification. 5. Histopathology and Immunofluorescence Renal biopsy tissues were obtained from the 80 biopsied CKD patients described above. Control renal tissues (n = 20) were harvested from the paracancerous margins of patients undergoing nephrectomy for renal carcinoma. Tissues were paraffin-embedded and sectioned at 4 µm thickness. For immunofluorescence, sections underwent deparaffinization, rehydration, and heat-induced antigen retrieval. Following a blocking step with 5% BSA, we incubated the sections overnight at 4°C with primary antibodies(Cat# BS-2462R, Thermo Fisher Scientific) targeting TNFSF14 and CD40. This was followed by incubation with fluorophore-conjugated secondary antibodies. Nuclei were counterstained with DAPI, and fluorescence was visualized using a NIKON ECLIPSE C1 microscope. Image J software was employed for quantitative analysis, performing background subtraction and calculating the total fluorescence area per field. 6. Statistical Analysis Data analysis was conducted using SPSS 25.0 (IBM Corp, Armonk, NY, USA) and GraphPad Prism. Continuous variables are presented as mean ± standard deviation (SD). For comparisons between two groups, we used the Student's t-test; for multi-group comparisons, we applied one-way ANOVA followed by Tukey’s post hoc test. Pearson correlation analysis was used to assess the relationship between gene markers and clinical parameters. To evaluate diagnostic performance, Receiver Operating Characteristic (ROC) curves were generated. We defined statistical significance as a two-tailed P-value < 0.05. Results 1. Transcriptomic Profiling Uncovers a Robust CKD Signature Initial screening of the GSE66494 training set (6 CKD biopsies vs. 10 controls) revealed 4,100 DEGs, dominated by 3,397 upregulated transcripts (Fig. 1 A). Independent assessment of the GSE97709 dataset (8 CKD vs. 12 controls) confirmed widespread transcriptional perturbation, isolating 3,382 DEGs (Fig. 1 B). By cross-referencing these datasets, we defined a core molecular signature consisting of 442 common DEGs (90 upregulated and 84 downregulated) shared across different platforms and sample types (Fig. 1 C). These conserved genes were prioritized for subsequent functional decoding. 2. Functional Annotation Highlights Immune Dysregulation and TNF Signaling Gene Ontology (GO) enrichment analysis pointed to profound immune dysregulation in CKD, marked by biological processes driving adaptive immune activation and granulocyte differentiation (Fig. 2 A). When mapped against KEGG pathways, these shared DEGs clustered heavily within critical inflammatory signaling cascades. The "TNF signaling pathway" and "FoxO signaling pathway" were among the top hits, alongside the Renin-Angiotensin System (RAS) (Fig. 2 B). These bioinformatic data suggest that immune-inflammatory networks act as central engines propelling CKD progression. 3. Prioritization of TNFSF14 and CD40 as Hub Genes Prompted by the significant enrichment of the TNF signaling pathway, we focused on identifying hub genes within this cascade. TNFSF14 showed consistent, high-magnitude dysregulation across both discovery datasets. Furthermore, Protein-Protein Interaction (PPI) network analysis predicted a direct functional synergy between TNFSF14 and CD40 (Fig. 3 A). Validation in the GSE97709 dataset confirmed that mRNA levels for both targets were elevated more than 2-fold compared to controls (P < 0.01; Figs. 3 B, 3 C). Consequently, the TNFSF14/CD40 axis was selected for translational validation in our clinical cohort. 4. Systemic and Urinary Elevation Mirrors Disease Severity Quantification of protein levels in serum and urine (140 CKD patients vs. 60 healthy controls) via ELISA revealed a marked elevation of both TNFSF14 and CD40 in the patient cohort (P 0.05), ensuring baseline comparability. Table 1 Quantitative assessment of TNFSF14 and CD40 protein levels in serum and urine across progressive stages of Chronic Kidney Disease.*Data are expressed as mean ± standard deviation (SD). Statistical comparisons among groups were performed using one-way ANOVA. *P < 0.05, *P < 0.01 versus the control group. CKD, Chronic Kidney Disease; TNFSF14, Tumor Necrosis Factor Superfamily Member 14. Stage (mean ± SD) F p CKDⅠ~Ⅱ(n = 61) CKD Ⅲ~Ⅳ(n = 53) CKD Ⅴ(n = 26) Control(n = 60) CD40 in serum 1034.57 ± 491.94 1116.13 ± 252.67 1277.56 ± 379.18 911.93 ± 514.97 4.821 0.003** CD40 in urine 37.25 ± 12.79 51.40 ± 21.12 57.48 ± 10.49 34.44 ± 10.75 23.948 0.000** TNFSF14 in serum 0.32 ± 0.42 0.40 ± 0.47 0.54 ± 0.59 0.24 ± 0.29 3.491 0.017* TNFSF14 in urine 0.36 ± 0.09 0.41 ± 0.05 0.47 ± 0.29 0.32 ± 0.07 9.821 0.000** Intriguingly, this upregulation adhered to a distinct stage-dependent pattern. In urine samples, concentrations of TNFSF14 and CD40 rose progressively, tracking closely with the advancement of CKD stages (Figs. 4 A, 4 B). Serum expression mirrored this trend, with peaks coinciding with advanced disease (Stage V) (Figs. 4 C, 4 D). These kinetic profiles indicate that TNFSF14 and CD40 behave not as static binary markers but as dynamic reporters of disease severity. 5. Diagnostic Robustness of Urinary Biomarkers ROC analysis substantiated the diagnostic utility of these targets. While serum markers performed well (AUC: TNFSF14 = 0.744; CD40 = 0.795), urinary biomarkers demonstrated comparable, if not superior, discriminatory power. Specifically, urinary TNFSF14 achieved an AUC of 0.797, and CD40 reached 0.765 (P < 0.05; Fig. 5 ). These results advocate for the use of non-invasive urinary TNFSF14/CD40 measurement as a sensitive tool for CKD detection. 6. Correlation with Clinical Indicators of Renal Function Subsequent assessment of associations between these molecular markers and standard clinical indices revealed clear trends (Table 2 ). Table 2 Correlation analysis linking serum and urinary TNFSF14/CD40 concentrations with key clinical and biochemical parameters in CKD patients.Values represent correlation coefficients. *P < 0.05, **P < 0.01 indicating statistical significance. eGFR, estimated Glomerular Filtration Rate; 2-MG, 2-microglobulin; PTH, Parathyroid Hormone; CRP, C-Reactive Protein; ESR, Erythrocyte Sedimentation Rate; ST2, Suppression of Tumorigenicity 2. Clinical indicators CD40 in serum CD40 in urine TNFSF14 in serum TNFSF14 in urine 24-hour urine protein(g) -0.069 -0.075 -0.043 -0.12 Hemoglobin(g/L) -0.118 -0.316** -0.179* -0.163 Blood urea nitrogen(mmol/L) 0.139 0.301** 0.162 0.298** Blood creatinine(µmol/L) 0.15 0.345** 0.287** 0.244** eGFR -0.132 -0.438** -0.158 -0.168* Blood cystatin C(mg/L) 0.161 0.411** 0.196* 0.269** Serum β2-microglobulin(mg/L) 0.186* 0.457** 0.233** 0.251** Blood potassium(mmol/L) 0.018 0.212* 0.07 0.147 Blood sodium(mmol/L) -0.245** -0.093 -0.027 0.001 blood calcium(mmol/L) 0.055 0.082 -0.004 0.003 Blood phosphorus(mmol/L) 0.009 0.129 0.14 0.182* Blood magnesium(mmol/L) 0.06 0.005 -0.109 0.118 Blood albumin(g/L) 0.062 0.251** 0.076 0.125 Parathyroid hormone(ng/L) 0.153 0.306** 0.235** 0.330** Anti phospholipase A2 receptor antibody(RU/ml) 0.056 -0.137 -0.104 -0.06 CRP(mg/L) 0.101 0.301** 0.048 0.038 ESR(mm/h) 0.114 0.167* 0.133 0.151 ST2(ng/ml) 0.274** 0.107 0.098 0.155 Serum Associations: Serum TNFSF14 tracked positively with retention markers (creatinine, cystatin C, β2-MG) and PTH, while showing an inverse relationship with hemoglobin—implying a link to renal anemia. Urinary Associations: Of particular note, urinary output of both TNFSF14 and CD40 displayed strong positive correlations with key injury markers (BUN, cystatin C, β2-MG) and strong negative correlations with eGFR. This tight association with eGFR reinforces their clinical relevance as indicators of functional renal decline. 7. Tubulointerstitial Localization of Target Proteins To define the spatial context, immunofluorescence staining was performed on biopsies covering major CKD etiologies (DN, IgAN, MN, FSGS). In contrast to the negligible signal in normal tissues, prominent fluorescence for both TNFSF14 and CD40 was observed across all diseased samples (Fig. 6 ). Microscopically, this upregulation was specifically localized to the renal tubular epithelial cells and the tubulointerstitial compartment. Quantitative analysis confirmed significantly higher fluorescence intensity in all CKD groups (Fig. 7 ), identifying tubulointerstitial overexpression of the TNFSF14/CD40 axis as a universal pathological feature, independent of the primary glomerular injury. Discussion The trajectory of Chronic Kidney Disease (CKD) is defined by a progressive slide toward fibrosis and irreversible nephron loss. With the global prevalence of CKD accelerating, there is a clinical imperative to shift from the traditional "diagnosis at failure" model to one of "early molecular monitoring" [ 1 , 4 ]. While renal biopsy retains its status as the pathological gold standard, its invasive nature precludes frequent repetition. By bridging transcriptomic bioinformatics with clinical validation, our study identifies TNFSF14 and CD40 not merely as statistical markers, but as biologically significant indicators of tubulointerstitial stress. These molecules, central to the TNF signaling architecture, are universally upregulated in damaged renal tubules and detectable in urine, providing a non-invasive window into the inflammatory status of the kidney. Our transcriptomic screening positioned the "TNF signaling pathway" as a dominant orchestrator of the transcriptional landscape in CKD, resonating with the prevailing "inflammatory theory" of renal fibrosis [ 14 ]. Within this signaling cascade, TNFSF14 and CD40 emerged as pivotal hub genes. Historically categorized primarily as a T-cell co-stimulator, TNFSF14 (LIGHT) is now increasingly recognized as a central player in tissue remodeling. Our data align with observations by Li et al. (2020), who linked TNFSF14 to renal fibrosis via the HVEM-Sphk1/S1P axis [ 10 ]. Crucially, recent evidence from 2023 broadens this perspective, suggesting that TNFSF14 also compromises endothelial progenitor cell function. This impairment restricts renal neovascularization and exacerbates the hypoxic microenvironment fueling CKD progression [ 16 ]. Such "vascular-immune" crosstalk implies that elevated TNFSF14 levels capture a dual pathology: active inflammation and maladaptive vascular repair. Parallel to these findings, the detection of CD40 overexpression in renal tubules challenges the canonical view of this molecule as an exclusive B-cell marker. This observation aligns with recent high-dimensional spatial profiling of human biopsies, which places CD40 + tubular cells and macrophages at the heart of the "fibrotic niche" [ 17 ]. Mechanistically, the ligation of CD40 on tubular epithelial cells triggers the release of pro-inflammatory chemokines (e.g., CCL2, CXCL10), establishing a feed-forward loop that perpetuates leukocyte recruitment [ 11 , 12 ]. Our immunohistochemical data corroborate this, revealing intense CD40 staining specifically within the tubular compartment across distinct etiologies (DN, IgAN, MN). This suggests that tubular CD40 expression represents a universal "stress response" to renal injury, independent of the primary disease trigger [ 31 ]. Beyond their roles in leukocyte recruitment, the upregulation of TNFSF14 and CD40 specifically in tubular epithelial cells hints at a direct contribution to the fibrogenic remodeling of the kidney. Emerging evidence suggests that the TNFSF14-CD40 interaction may serve as a critical switch for Epithelial-Mesenchymal Transition (EMT). Mechanistically, the ligation of CD40 on renal tubular cells acts as a potent activator of the canonical NF-κB signaling pathway. This activation not only sustains a chronic inflammatory milieu but also transcriptionally represses E-cadherin while upregulating mesenchymal markers such as α-SMA and Vimentin. Consequently, tubular cells lose their polarity and adhesion, acquiring a migratory, fibroblast-like phenotype that actively deposits extracellular matrix [ 23 , 24 ]. Furthermore, TNFSF14 has been reported to synergize with TGF-β1, amplifying the fibrotic signal transduction. Therefore, the high accumulation of these proteins in the tubulointerstitium observed in our study is likely not just a passive consequence of injury, but an active driver that accelerates the transformation of functional tubules into scar tissue. A pivotal translational aspect of this work is the validation of these targets as urinary biomarkers. The robust correlation we observed between urinary TNFSF14/CD40 and β2-microglobulin (β2-MG)—a specific index of tubular dysfunction—lends weight to the hypothesis that these proteins are shed directly from damaged tubular epithelium rather than being passively filtered from the circulation [ 7 ]. This distinction is clinically vital, as tubulointerstitial injury often predicts long-term renal survival more accurately than glomerular markers alone [ 32 ]. Given their favorable diagnostic performance (AUC > 0.75), these urinary markers could be instrumental in monitoring "residual risk" in patients with stable serum creatinine but ongoing, occult tubulointerstitial inflammation. Extending our analysis to systemic complications, we noted significant associations between these biomarkers and extra-renal manifestations. The positive correlation between serum TNFSF14 and Parathyroid Hormone (PTH) offers clinical support for the existence of a pathological Kidney-Bone axis. This is likely underpinned by the ability of TNF superfamily members to regulate osteoclastogenesis via the RANKL/OPG system [ 21 ]. Additionally, the inverse relationship with hemoglobin levels hints at a connection to renal anemia. Since inflammatory cytokines suppress erythropoietin and induce hepcidin, our data suggest the TNFSF14/CD40 axis may act as a contributor to this inflammatory anemia phenotype [ 22 ]. Beyond their diagnostic value, the identification of the TNFSF14/CD40 axis opens intriguing avenues for therapeutic intervention. Given that CD40 signaling is a potent driver of fibrosis, blocking this pathway could theoretically arrest CKD progression. In fact, anti-CD40 monoclonal antibodies (e.g., Bleselumab) have already shown promise in preventing graft rejection in kidney transplantation settings. Our findings, which localize CD40 overexpression specifically to the tubulointerstitium, suggest that such immunomodulatory strategies might be repurposed for managing native kidney diseases, particularly those driven by tubulointerstitial inflammation. However, the systemic blockade of CD40 carries risks of immunosuppression. Therefore, the development of renal-targeted delivery systems—perhaps utilizing nanoparticle carriers that specifically bind to tubular epithelial cells—could represent a precision medicine approach to neutralize this inflammatory axis without compromising global immunity. While our study provides compelling evidence for the diagnostic utility of TNFSF14 and CD40, several limitations necessitate cautious interpretation. First, the cross-sectional design inherent to our clinical validation cohort enables the establishment of strong associations but precludes definitive inferences regarding causality. It remains to be determined whether the elevation of these biomarkers precedes the decline in eGFR or occurs concomitantly. Second, our cohort was restricted to adult patients; thus, the applicability of these findings to pediatric CKD populations—where the etiology is often congenital rather than metabolic—requires separate validation. Third, although immunofluorescence confirmed tissue localization, we did not perform in vitro knockdown or overexpression experiments in human renal tubular cells. Future longitudinal studies with serial sampling, coupled with mechanistic assays in organoid models, are essential to fully dissect the intracellular signaling events downstream of the TNFSF14/CD40 axis and to evaluate their potential as therapeutic targets. Collectively, this study positions TNFSF14 and CD40 as biologically plausible, stage-dependent biomarkers for CKD. Their distinct upregulation in the tubular compartment and measurability in urine reflect the severity of tubulointerstitial injury. Integrating these molecules into a multi-marker panel could significantly refine our ability to stage disease, track inflammatory activity, and eventually guide targeted immunomodulatory interventions. Conclusion By harmonizing transcriptomic insights with clinical verification, this study establishes TNFSF14 and CD40 as biologically relevant, stage-dependent biomarkers for Chronic Kidney Disease. Our data underscore the centrality of the TNF signaling axis in driving renal pathology. Specifically, the universal upregulation of these proteins within the tubular compartment, coupled with their progressive elevation in serum and urine, offers a precise reflection of disease severity. These results position TNFSF14 and CD40 not merely as diagnostic candidates for non-invasive monitoring, but as actionable targets for therapeutic intervention aimed at curbing renal inflammation and tubulointerstitial fibrosis. Ultimately, therapeutic strategies directed at the TNFSF14/CD40 axis may represent a compelling avenue to delay the clinical trajectory of kidney failure. Declarations CONFLICT OF INTEREST The authors affirm that the research was directed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. FUNDING INFORMATION This study was supported by the Science and Technology Project of Nantong City (JCZ20066). Author Contribution Xiaolan Chen designed, supervised, and revised the manuscript. Xiameng Gu and Yuqing Lu performed the experiments and wrote the manuscript. Xiameng Gu, Hanlu Zhang, Hongxin Chen, Haonan Sha, Mengyue Qiu analyzed the data. ACKNOWLEDGMENTS We thank the Affiliated Hospital of Nantong university for the gift of the pathological section of renal tissue. Data Availability The data supporting this study's findings are available from the corresponding author or Xiameng Gu, upon reasonable request. References GBD Chronic Kidney Disease Collaboration. 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Expert Opin Ther Targets . 2020;24(7):655–670. Liu BC, Tang TT, Lv LL, et al. Renal tubule injury: a driving force toward chronic kidney disease. Kidney Int . 2018;93(3):568–579. Djudjaj S, Boor P. Cellular and molecular mechanisms of kidney fibrosis. Mol Aspects Med . 2019;65:16–36. Perper SJ, Westmoreland SV, Karman J, et al. Treatment with a CD40 Antagonist Antibody Reverses Severe Proteinuria and Loss of Saliva Production and Restores Glomerular Morphology in Murine Systemic Lupus Erythematosus. J Immunol . 2019;203(1):58–75. Yang J, Fang P, Yu D, et al. Chronic kidney disease induces inflammatory CD40 + monocyte differentiation via homocysteine elevation and DNA hypomethylation. Circ Res . 2016;119(11):1226–1241. Levey AS, Grams ME, Inker LA. Uses of GFR and albuminuria levels in acute and chronic kidney disease. N Engl J Med . 2022;386(22):2120–2128. Chen TK, Knicely DH, Grams ME. Chronic kidney disease diagnosis and management: a review. JAMA . 2019;322(13):1294–1304. Wasung ME, Chawla LS, Mito R. Biomarkers of renal function, which and when? Clin Chim Acta . 2015;438:350–357. Fine LG, Norman JT. Chronic hypoxia as a mechanism of progression of chronic kidney diseases: from hypothesis to novel therapeutics. Kidney Int . 2008;74(7):867–872. Kim HJ, Kim HM, Kim CS, et al. HVEM-deficient mice fed a high-fat diet are protected from adipose tissue inflammation and glucose intolerance. FEBS Lett . 2011;585(14):2285–2290. Otterdal K, Haukeland JW, Yndestad A, et al. Increased serum levels of LIGHT/TNFSF14 in nonalcoholic fatty liver disease: possible role in hepatic inflammation. Clin Transl Gastroenterol . 2015;6(7):e95. Breyer MD, Susztak K. The next generation of therapeutics for chronic kidney disease. Nat Rev Drug Discov . 2016;15(8):568–588. Anders HJ, Huber TB, Isermann B, et al. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol . 2018;14(6):361–377. Argyropoulos CP, Chen SS, Ng YH, et al. Rediscovering beta-2 microglobulin as a biomarker across the spectrum of kidney diseases. Front Med (Lausanne) . 2017;4:73. Wang YN, Ma SX, Chen YY, et al. Chronic kidney disease: biomarker diagnosis to therapeutic targets. Clin Chim Acta . 2019;499:54–63. Ene-Iordache B, Perico N, Bikbov B, et al. Chronic kidney disease and cardiovascular risk in six regions of the world (ISN-KDDC): a cross-sectional study. Lancet Glob Health . 2016;4(5):e307–e319. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Apr, 2026 Read the published version in International Urology and Nephrology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8690385","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595206203,"identity":"a49e914f-98ea-48e1-98ae-e0c89804b758","order_by":0,"name":"xiameng gu","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"xiameng","middleName":"","lastName":"gu","suffix":""},{"id":595206206,"identity":"bfc52ce4-53bd-4290-9df0-e1b8eb7e183d","order_by":1,"name":"yuqing lu","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"yuqing","middleName":"","lastName":"lu","suffix":""},{"id":595206212,"identity":"939af8e4-f4a7-4e82-ad46-9e1e0a0ad22a","order_by":2,"name":"haonan sha","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"haonan","middleName":"","lastName":"sha","suffix":""},{"id":595206213,"identity":"87a19aa6-8289-464d-9e33-fcae6393b1b2","order_by":3,"name":"hanlu zhang","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"hanlu","middleName":"","lastName":"zhang","suffix":""},{"id":595206215,"identity":"6d9634a6-916a-4dc9-86b5-dab5930ed0b4","order_by":4,"name":"hongxin chen","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"hongxin","middleName":"","lastName":"chen","suffix":""},{"id":595206220,"identity":"ae4ad9de-a31b-40eb-bd24-41b6304fb8e2","order_by":5,"name":"mengyue qiu","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"mengyue","middleName":"","lastName":"qiu","suffix":""},{"id":595206223,"identity":"32b8cb0e-8499-4cd5-8e95-43e93c00a5bd","order_by":6,"name":"xiaolan chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCRBRYMPM2N58gCGBeC0GaezMPccSSNJymJ99Ro4Bce7in9187DGPQZo0b8+Zzx8e7rBj4G/vxm+ZxJ1j6YYzDGyMJdt7t0kknklmkDhzdgNeLQYSOWYSHwzSkg17zm5jSGxjBorkEtKS/00iweBw/f4bOY8/JLbVE6Mlhw1oy2Fmxhk5DBKJbYcJa5G4kWYmOcMgjZmx55gZUMtxHoJ+4Z+R/EyapwIclY8//myrluNv78WvBQPwkKZ8FIyCUTAKRgFWAAANiUaGz68pYwAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":true,"prefix":"","firstName":"xiaolan","middleName":"","lastName":"chen","suffix":""}],"badges":[],"createdAt":"2026-01-25 05:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8690385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8690385/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11255-026-05138-9","type":"published","date":"2026-04-25T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103302054,"identity":"98a3f3f2-fe57-4378-9b79-89da4d482f27","added_by":"auto","created_at":"2026-02-24 08:22:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":299865,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed genes (DEGs) in CKD-related datasets.\u003cbr\u003e\n (A) Volcano plot displaying DEGs in the GSE66494 dataset (kidney tissue biopsies under microarray analysis). Red nodes represent upregulated genes, and green nodes represent downregulated genes based on |log2FC| ≥ 1.5 and adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003cbr\u003e\n (B) Volcano plot displaying DEGs in the GSE97709 dataset (plasma samples under RNA sequencing). Red nodes indicate upregulation and green nodes indicate downregulation.\u003cbr\u003e\n (C) Venn diagram illustrating the intersection of DEGs between GSE66494 and GSE97709. The overlap identifies 442 common DEGs, including 90 co-upregulated and 84 co-downregulated genes shared across both datasets.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8690385/v1/811fb26fa84fbf2091779375.png"},{"id":103302055,"identity":"60188f2c-479c-43e4-9533-750f3bc93fe7","added_by":"auto","created_at":"2026-02-24 08:22:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214090,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analyses of the 442 shared DEGs.\u003cbr\u003e\n (A) Bubble plot of Gene Ontology (GO) biological process enrichment. The Y-axis lists the biological processes, the X-axis represents the gene ratio, and the size of the dots indicates the number of genes enriched in each term. The color gradient (red to blue) represents statistical significance (-log10 \u003cem\u003eP\u003c/em\u003e-value).\u003cbr\u003e\n (B) Bubble plot of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, highlighting key signaling pathways such as the TNF signaling pathway and Renin-angiotensin system associated with CKD.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8690385/v1/50bb848c33c55ffdffda0316.png"},{"id":103302058,"identity":"8c2f24ca-8cb8-48eb-91a7-c29cc952491c","added_by":"auto","created_at":"2026-02-24 08:22:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":191952,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of \u003cem\u003eTNFSF14\u003c/em\u003e and \u003cem\u003eCD40\u003c/em\u003e as key hub genes.\u003cbr\u003e\n (A) Protein-Protein Interaction (PPI) network constructed using the STRING database, predicting a functional interaction between TNFSF14 and CD40 among the enriched gene set.\u003cbr\u003e\n (B) Bar graph showing the relative expression levels of \u003cem\u003eTNFSF14\u003c/em\u003e in the GSE97709 dataset compared between control and CKD groups.\u003cbr\u003e\n (C) Bar graph showing the relative expression levels of \u003cem\u003eCD40\u003c/em\u003e in the GSE97709 dataset compared between control and CKD groups. Data are presented as mean expression values with standard error.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8690385/v1/c2f2c5ce3299a1e577f1e7f1.png"},{"id":103302061,"identity":"0d92485c-5210-47cf-9cd9-73f6c223bcec","added_by":"auto","created_at":"2026-02-24 08:22:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":206066,"visible":true,"origin":"","legend":"\u003cp\u003eClinical validation of TNFSF14 and CD40 protein levels in serum and urine across different CKD stages.\u003cbr\u003e\n (A, B) Bar charts illustrating the increasing trends of urinary TNFSF14 (A) and CD40 (B) levels in patients with CKD stages I–II, III–IV, and V compared to healthy controls.\u003cbr\u003e\n (C, D) Bar charts showing the serum levels of TNFSF14 (C) and CD40 (D) across the different CKD stages compared to controls. Numbers above the bars indicate the mean concentration levels. The expression levels exhibited a stage-dependent increase correlating with disease progression.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8690385/v1/ea7c1f9e5fcd1f2b910fd03e.png"},{"id":103302056,"identity":"58c8ce98-6208-40af-8048-14a377cb64bc","added_by":"auto","created_at":"2026-02-24 08:22:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":163021,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic performance of serum and urinary TNFSF14 and CD40 for chronic kidney disease.\u003cbr\u003e\nReceiver Operating Characteristic (ROC) curve analysis for serum CD40 (blue), urinary CD40 (cyan), serum TNFSF14 (green), and urinary TNFSF14 (yellow) in distinguishing CKD patients from healthy controls. The diagonal dashed line represents the reference line (AUC = 0.5). All four markers demonstrated an Area Under the Curve (AUC) \u0026gt; 0.7, indicating moderate to good diagnostic accuracy.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8690385/v1/519942868e9215e482afbf7c.png"},{"id":103302059,"identity":"dff7dc33-c9f6-46f3-a18a-ffabe1a5a12c","added_by":"auto","created_at":"2026-02-24 08:22:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":596709,"visible":true,"origin":"","legend":"\u003cp\u003eImmunofluorescence staining of TNFSF14 and CD40 in renal tissues from patients with different pathological types of CKD.\u003cbr\u003e\n (A) Representative immunofluorescence images of TNFSF14 staining (red) in renal tissues from healthy controls and patients with Membranous Nephropathy (MN), IgA Nephropathy (IgAN), Diabetic Nephropathy (DN), and Focal Segmental Glomerulosclerosis (FSGS). Nuclei were counterstained with DAPI (blue). Images are shown at 200× (upper row) and 400× (lower row) magnification.\u003cbr\u003e\n (B) Representative immunofluorescence images of CD40 staining (red) in control and CKD renal tissues (MN, IgAN, DN, FSGS). Positive expression was predominantly observed in the renal tubular region.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8690385/v1/7029d6b48383bbb8f4045b2f.png"},{"id":103302057,"identity":"df7b55ee-4140-4fc7-ad00-64f7f7f6ca55","added_by":"auto","created_at":"2026-02-24 08:22:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":187438,"visible":true,"origin":"","legend":"\u003cp\u003eQuantitative analysis of immunofluorescence intensity for TNFSF14 and CD40 in renal tissues.\u003cbr\u003e\n (A) Quantification of the mean fluorescence intensity of TNFSF14 in control and various CKD pathological groups at 200× and 400× magnification.\u003cbr\u003e\n (B) Quantification of the mean fluorescence intensity of CD40 in control and CKD groups. Data are expressed as mean ± standard deviation (SD). Both markers showed significantly higher fluorescence intensity in all CKD groups compared to controls.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8690385/v1/8a2da818e8005fbae010b098.png"},{"id":107927678,"identity":"93c1ec09-9896-4e5f-a1ef-ad9b083c3fd9","added_by":"auto","created_at":"2026-04-27 16:01:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2199322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8690385/v1/2bf22050-9c51-4d86-aeb9-cd3a3ae603f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Bioinformatics Analysis and Clinical Validation Identify TNFSF14 and CD40 as Novel Biomarkers for Chronic Kidney Disease Progression and Tubulointerstitial Injury","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rising prevalence of Chronic Kidney Disease (CKD) presents a formidable challenge to global public health, compounded by aging populations and the growing burden of metabolic disorders. Recent estimates from the Global Burden of Disease study suggest that nearly 10% of the worldwide population is now affected, positioning CKD as a leading cause of mortality among non-communicable diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While the primary triggers vary-ranging from diabetes and hypertension to autoimmune conditions-the downstream pathological trajectory is remarkably uniform: a spiral of chronic inflammation, capillary rarefaction, and progressive tubulointerstitial fibrosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile traditional metrics like creatinine remain standard, the search for novel biomarkers has yielded candidates such as NGAL (Neutrophil Gelatinase-Associated Lipocalin) and KIM-1 (Kidney Injury Molecule-1). Although these markers reliably indicate acute tubular necrosis, their performance in chronic, indolent fibrosis remains variable. More importantly, markers like NGAL are often generic indicators of injury rather than reporters of specific pathogenic pathways. There is a distinct lack of 'mechanism-based' biomarkers\u0026mdash;molecules that not only signal the presence of injury but also reveal the underlying immune driver. Identifying such pathway-specific markers is crucial, as it would not only aid in risk stratification but also identify patients who might benefit from specific immunotherapies targeting those exact pathways.\u003c/p\u003e \u003cp\u003eIn current clinical practice, staging relies heavily on estimated Glomerular Filtration Rate (eGFR) and albuminuria. Yet, these metrics suffer from a significant diagnostic limitation: they are essentially lagging indicators that fail to capture early-stage cellular stress or active inflammation within the renal parenchyma [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. By the time functional decline becomes apparent, substantial structural damage has often already occurred. This gap underscores the urgent need for \"liquid biopsy\" biomarkers-molecules detectable in urine or blood that can mirror real-time molecular changes in the tubulointerstitium before irreversible fibrosis sets in [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMechanistically, the Tumor Necrosis Factor (TNF) superfamily occupies a central node bridging innate immunity and tissue remodeling [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Within this network, TNFSF14 (LIGHT) and CD40 have garnered attention not just as immune mediators, but as drivers of renal pathology. Emerging evidence suggests that TNFSF14 functions as a critical checkpoint in vascular and metabolic injury [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], while the CD40-CD40L axis is increasingly recognized for its role in promoting macrophage activation and epithelial-mesenchymal transition (EMT) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite these mechanistic insights, the clinical utility of these proteins as non-invasive biomarkers remains underexplored.\u003c/p\u003e \u003cp\u003eTo bridge the gap between transcriptomic signatures and clinical utility, we employed a multi-step translational approach. By integrating bioinformatics analysis of GEO datasets [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] with validation in a clinical cohort, we sought to characterize the expression patterns of TNFSF14 and CD40. Our objective was to determine whether these potential targets are upregulated in the renal tubulointerstitium and if their urinary concentrations can serve as robust, non-invasive reporters of CKD progression.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch3\u003e1. Data Mining and Differential Expression Analysis\u003c/h3\u003e\n\u003cp\u003eWe retrieved gene expression profiles from the NCBI Gene Expression Omnibus (GEO) database, specifically selecting datasets GSE66494 and GSE97709 to compare CKD kidney tissues against normal controls. Raw data were processed and normalized prior to differential expression analysis. We utilized the limma package (version 3.40.6) in R software to screen for Differentially Expressed Genes (DEGs), defining significance as a |log2 Fold Change (FC)| \u0026ge; 1.5 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eDataset specifics are as follows: GSE66494 contains microarray data from 53 renal biopsy specimens, capturing gene expression changes linked to interstitial fibrosis and tubular injury. GSE97709 employs next-generation sequencing to profile circulating lncRNA in 48 subjects (28 ESRD, 8 CKD, 12 healthy controls), enabling a stage-dependent comparison of molecular signatures.\u003c/p\u003e\u003cp\u003eTo visualize dataset overlaps, we used Venny 2.1. Functional annotation was performed via Gene Ontology (GO) (using org.Hs.eg.db v3.1.0) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses with the clusterProfiler package (v3.14.3). We mapped Protein-Protein Interaction (PPI) networks using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), considering a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as statistically significant.\u003c/p\u003e\u003cp\u003e2. Clinical Demographic Profile of GEO Datasets\u003c/p\u003e\n\u003cp\u003eTo ensure the clinical representativeness of our bioinformatic screening, we rigorously evaluated the demographic and pathological metadata of the selected datasets. The GSE66494 cohort (training set) predominantly comprised biopsy samples from patients with hypertensive nephropathy and diabetic kidney disease, reflecting a population with established metabolic co-morbidities. The samples were age-matched with controls to minimize confounding geriatric transcriptional noise. Similarly, the GSE97709 validation set included a diverse array of CKD etiologies, ranging from IgA nephropathy to lupus nephritis. This pathological heterogeneity in the validation phase was strategically selected to ensure that the identified biomarkers (TNFSF14/CD40) represent a \u0026quot;common denominator\u0026quot; of renal injury, rather than being restricted to a single disease subtype. Normal control samples in both datasets were obtained from the synthesis of unaffected poles of tumor nephrectomies, confirmed histologically to be free of glomerular or tubulointerstitial lesions.\u003c/p\u003e\n\u003ch3\u003e3. Clinical Cohort Description and Ethical Approval\u003c/h3\u003e\n\u003cp\u003e This study was conducted in strict accordance with the Declaration of Helsinki and received approval from the Ethics Committee of the Affiliated Hospital of Nantong University (Approval No. 2023-Y114-01). Written informed consent was obtained from all participants.\u003c/p\u003e\u003cp\u003eWe enrolled 140 patients admitted to the Department of Nephrology with a confirmed diagnosis of CKD. Based on the KDIGO guidelines, patients were stratified by eGFR into Stages I\u0026ndash;V: Stage I (n\u0026thinsp;=\u0026thinsp;30), Stage II (n\u0026thinsp;=\u0026thinsp;31), Stage III (n\u0026thinsp;=\u0026thinsp;28), Stage IV (n\u0026thinsp;=\u0026thinsp;25), and Stage V (n\u0026thinsp;=\u0026thinsp;26). Within this cohort, 80 patients underwent renal biopsy, distributing evenly across four etiologies: diabetic nephropathy, IgA nephropathy, membranous nephropathy, and FSGS (n\u0026thinsp;=\u0026thinsp;20 each). Exclusion criteria encompassed acute kidney injury (AKI), active infection, malignancy, or incomplete clinical records. A control group comprising 60 healthy individuals (age\u0026thinsp;\u0026gt;\u0026thinsp;18 years, normal renal function) was recruited from the physical examination center.\u003c/p\u003e\n\u003ch3\u003e4. Serum and Urine Sample Processing\u003c/h3\u003e\n\u003cp\u003eSerum: Peripheral blood samples (5 mL) were drawn into red vacuum tubes without anticoagulant. Following centrifugation at 3000 \u0026times; g for 15 minutes at 4\u0026deg;C, the supernatant serum was aliquoted into RNase/DNase-free tubes and stored at -80\u0026deg;C.\u003c/p\u003e\u003cp\u003eUrine: We collected 50 mL of mid-stream urine in sterile containers. To eliminate cellular debris, samples underwent centrifugation at 3000 \u0026times; g for 15 minutes at 4\u0026deg;C. The resulting supernatant was transferred to 1.5 mL tubes and preserved at -80\u0026deg;C until quantification.\u003c/p\u003e\n\u003ch3\u003e5. Histopathology and Immunofluorescence\u003c/h3\u003e\n\u003cp\u003eRenal biopsy tissues were obtained from the 80 biopsied CKD patients described above. Control renal tissues (n\u0026thinsp;=\u0026thinsp;20) were harvested from the paracancerous margins of patients undergoing nephrectomy for renal carcinoma.\u003c/p\u003e\u003cp\u003eTissues were paraffin-embedded and sectioned at 4 \u0026micro;m thickness. For immunofluorescence, sections underwent deparaffinization, rehydration, and heat-induced antigen retrieval. Following a blocking step with 5% BSA, we incubated the sections overnight at 4\u0026deg;C with primary antibodies(Cat# BS-2462R, Thermo Fisher Scientific) targeting TNFSF14 and CD40. This was followed by incubation with fluorophore-conjugated secondary antibodies. Nuclei were counterstained with DAPI, and fluorescence was visualized using a NIKON ECLIPSE C1 microscope. Image J software was employed for quantitative analysis, performing background subtraction and calculating the total fluorescence area per field.\u003c/p\u003e\n\u003ch3\u003e6. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eData analysis was conducted using SPSS 25.0 (IBM Corp, Armonk, NY, USA) and GraphPad Prism. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). For comparisons between two groups, we used the Student's t-test; for multi-group comparisons, we applied one-way ANOVA followed by Tukey\u0026rsquo;s post hoc test. Pearson correlation analysis was used to assess the relationship between gene markers and clinical parameters. To evaluate diagnostic performance, Receiver Operating Characteristic (ROC) curves were generated. We defined statistical significance as a two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e1. Transcriptomic Profiling Uncovers a Robust CKD Signature\u003c/h3\u003e\n\u003cp\u003eInitial screening of the GSE66494 training set (6 CKD biopsies vs. 10 controls) revealed 4,100 DEGs, dominated by 3,397 upregulated transcripts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Independent assessment of the GSE97709 dataset (8 CKD vs. 12 controls) confirmed widespread transcriptional perturbation, isolating 3,382 DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). By cross-referencing these datasets, we defined a core molecular signature consisting of 442 common DEGs (90 upregulated and 84 downregulated) shared across different platforms and sample types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These conserved genes were prioritized for subsequent functional decoding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2. Functional Annotation Highlights Immune Dysregulation and TNF Signaling\u003c/h3\u003e\n\u003cp\u003eGene Ontology (GO) enrichment analysis pointed to profound immune dysregulation in CKD, marked by biological processes driving adaptive immune activation and granulocyte differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). When mapped against KEGG pathways, these shared DEGs clustered heavily within critical inflammatory signaling cascades. The \"TNF signaling pathway\" and \"FoxO signaling pathway\" were among the top hits, alongside the Renin-Angiotensin System (RAS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These bioinformatic data suggest that immune-inflammatory networks act as central engines propelling CKD progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3. Prioritization of TNFSF14 and CD40 as Hub Genes\u003c/h3\u003e\n\u003cp\u003ePrompted by the significant enrichment of the TNF signaling pathway, we focused on identifying hub genes within this cascade. TNFSF14 showed consistent, high-magnitude dysregulation across both discovery datasets. Furthermore, Protein-Protein Interaction (PPI) network analysis predicted a direct functional synergy between TNFSF14 and CD40 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Validation in the GSE97709 dataset confirmed that mRNA levels for both targets were elevated more than 2-fold compared to controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Consequently, the TNFSF14/CD40 axis was selected for translational validation in our clinical cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e4. Systemic and Urinary Elevation Mirrors Disease Severity\u003c/h3\u003e\n\u003cp\u003eQuantification of protein levels in serum and urine (140 CKD patients vs. 60 healthy controls) via ELISA revealed a marked elevation of both TNFSF14 and CD40 in the patient cohort (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There were no statistically significant differences in age or gender distribution between the CKD and control groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), ensuring baseline comparability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuantitative assessment of TNFSF14 and CD40 protein levels in serum and urine across progressive stages of Chronic Kidney Disease.*Data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Statistical comparisons among groups were performed using one-way ANOVA. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 versus the control group. CKD, Chronic Kidney Disease; TNFSF14, Tumor Necrosis Factor Superfamily Member 14.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eStage (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCKDⅠ~Ⅱ(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCKD Ⅲ~Ⅳ(n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCKD Ⅴ(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD40 in serum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1034.57\u0026thinsp;\u0026plusmn;\u0026thinsp;491.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1116.13\u0026thinsp;\u0026plusmn;\u0026thinsp;252.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1277.56\u0026thinsp;\u0026plusmn;\u0026thinsp;379.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e911.93\u0026thinsp;\u0026plusmn;\u0026thinsp;514.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD40 in urine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e37.25\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e51.40\u0026thinsp;\u0026plusmn;\u0026thinsp;21.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e57.48\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e34.44\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFSF14 in serum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFSF14 in urine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIntriguingly, this upregulation adhered to a distinct stage-dependent pattern. In urine samples, concentrations of TNFSF14 and CD40 rose progressively, tracking closely with the advancement of CKD stages (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Serum expression mirrored this trend, with peaks coinciding with advanced disease (Stage V) (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These kinetic profiles indicate that TNFSF14 and CD40 behave not as static binary markers but as dynamic reporters of disease severity.\u003c/p\u003e\n\u003ch3\u003e5. Diagnostic Robustness of Urinary Biomarkers\u003c/h3\u003e\n\u003cp\u003eROC analysis substantiated the diagnostic utility of these targets. While serum markers performed well (AUC: TNFSF14\u0026thinsp;=\u0026thinsp;0.744; CD40\u0026thinsp;=\u0026thinsp;0.795), urinary biomarkers demonstrated comparable, if not superior, discriminatory power. Specifically, urinary TNFSF14 achieved an AUC of 0.797, and CD40 reached 0.765 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These results advocate for the use of non-invasive urinary TNFSF14/CD40 measurement as a sensitive tool for CKD detection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e6. Correlation with Clinical Indicators of Renal Function\u003c/h3\u003e\n\u003cp\u003eSubsequent assessment of associations between these molecular markers and standard clinical indices revealed clear trends (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis linking serum and urinary TNFSF14/CD40 concentrations with key clinical and biochemical parameters in CKD patients.Values represent correlation coefficients. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 indicating statistical significance. eGFR, estimated Glomerular Filtration Rate; 2-MG, 2-microglobulin; PTH, Parathyroid Hormone; CRP, C-Reactive Protein; ESR, Erythrocyte Sedimentation Rate; ST2, Suppression of Tumorigenicity 2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD40 in serum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCD40 in urine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTNFSF14 in serum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTNFSF14 in urine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24-hour urine protein(g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.316**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.179*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.301**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.298**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood creatinine(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.345**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.287**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.244**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.438**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.168*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood cystatin C(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.411**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.269**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum β2-microglobulin(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.186*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.457**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.233**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.251**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood potassium(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood sodium(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.245**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eblood calcium(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood phosphorus(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.182*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood magnesium(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood albumin(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.251**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParathyroid hormone(ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.306**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.330**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti phospholipase A2 receptor antibody(RU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.301**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR(mm/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.167*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST2(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.274**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSerum Associations: Serum TNFSF14 tracked positively with retention markers (creatinine, cystatin C, β2-MG) and PTH, while showing an inverse relationship with hemoglobin\u0026mdash;implying a link to renal anemia.\u003c/p\u003e \u003cp\u003eUrinary Associations: Of particular note, urinary output of both TNFSF14 and CD40 displayed strong positive correlations with key injury markers (BUN, cystatin C, β2-MG) and strong negative correlations with eGFR. This tight association with eGFR reinforces their clinical relevance as indicators of functional renal decline.\u003c/p\u003e\n\u003ch3\u003e7. Tubulointerstitial Localization of Target Proteins\u003c/h3\u003e\n\u003cp\u003eTo define the spatial context, immunofluorescence staining was performed on biopsies covering major CKD etiologies (DN, IgAN, MN, FSGS). In contrast to the negligible signal in normal tissues, prominent fluorescence for both TNFSF14 and CD40 was observed across all diseased samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Microscopically, this upregulation was specifically localized to the renal tubular epithelial cells and the tubulointerstitial compartment. Quantitative analysis confirmed significantly higher fluorescence intensity in all CKD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), identifying tubulointerstitial overexpression of the TNFSF14/CD40 axis as a universal pathological feature, independent of the primary glomerular injury.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe trajectory of Chronic Kidney Disease (CKD) is defined by a progressive slide toward fibrosis and irreversible nephron loss. With the global prevalence of CKD accelerating, there is a clinical imperative to shift from the traditional \"diagnosis at failure\" model to one of \"early molecular monitoring\" [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While renal biopsy retains its status as the pathological gold standard, its invasive nature precludes frequent repetition. By bridging transcriptomic bioinformatics with clinical validation, our study identifies TNFSF14 and CD40 not merely as statistical markers, but as biologically significant indicators of tubulointerstitial stress. These molecules, central to the TNF signaling architecture, are universally upregulated in damaged renal tubules and detectable in urine, providing a non-invasive window into the inflammatory status of the kidney.\u003c/p\u003e \u003cp\u003eOur transcriptomic screening positioned the \"TNF signaling pathway\" as a dominant orchestrator of the transcriptional landscape in CKD, resonating with the prevailing \"inflammatory theory\" of renal fibrosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Within this signaling cascade, TNFSF14 and CD40 emerged as pivotal hub genes. Historically categorized primarily as a T-cell co-stimulator, TNFSF14 (LIGHT) is now increasingly recognized as a central player in tissue remodeling. Our data align with observations by Li et al. (2020), who linked TNFSF14 to renal fibrosis via the HVEM-Sphk1/S1P axis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Crucially, recent evidence from 2023 broadens this perspective, suggesting that TNFSF14 also compromises endothelial progenitor cell function. This impairment restricts renal neovascularization and exacerbates the hypoxic microenvironment fueling CKD progression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Such \"vascular-immune\" crosstalk implies that elevated TNFSF14 levels capture a dual pathology: active inflammation and maladaptive vascular repair.\u003c/p\u003e \u003cp\u003eParallel to these findings, the detection of CD40 overexpression in renal tubules challenges the canonical view of this molecule as an exclusive B-cell marker. This observation aligns with recent high-dimensional spatial profiling of human biopsies, which places CD40\u0026thinsp;+\u0026thinsp;tubular cells and macrophages at the heart of the \"fibrotic niche\" [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Mechanistically, the ligation of CD40 on tubular epithelial cells triggers the release of pro-inflammatory chemokines (e.g., CCL2, CXCL10), establishing a feed-forward loop that perpetuates leukocyte recruitment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our immunohistochemical data corroborate this, revealing intense CD40 staining specifically within the tubular compartment across distinct etiologies (DN, IgAN, MN). This suggests that tubular CD40 expression represents a universal \"stress response\" to renal injury, independent of the primary disease trigger [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond their roles in leukocyte recruitment, the upregulation of TNFSF14 and CD40 specifically in tubular epithelial cells hints at a direct contribution to the fibrogenic remodeling of the kidney. Emerging evidence suggests that the TNFSF14-CD40 interaction may serve as a critical switch for Epithelial-Mesenchymal Transition (EMT). Mechanistically, the ligation of CD40 on renal tubular cells acts as a potent activator of the canonical NF-κB signaling pathway. This activation not only sustains a chronic inflammatory milieu but also transcriptionally represses E-cadherin while upregulating mesenchymal markers such as α-SMA and Vimentin. Consequently, tubular cells lose their polarity and adhesion, acquiring a migratory, fibroblast-like phenotype that actively deposits extracellular matrix [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, TNFSF14 has been reported to synergize with TGF-β1, amplifying the fibrotic signal transduction. Therefore, the high accumulation of these proteins in the tubulointerstitium observed in our study is likely not just a passive consequence of injury, but an active driver that accelerates the transformation of functional tubules into scar tissue.\u003c/p\u003e \u003cp\u003eA pivotal translational aspect of this work is the validation of these targets as urinary biomarkers. The robust correlation we observed between urinary TNFSF14/CD40 and β2-microglobulin (β2-MG)\u0026mdash;a specific index of tubular dysfunction\u0026mdash;lends weight to the hypothesis that these proteins are shed directly from damaged tubular epithelium rather than being passively filtered from the circulation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This distinction is clinically vital, as tubulointerstitial injury often predicts long-term renal survival more accurately than glomerular markers alone [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Given their favorable diagnostic performance (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.75), these urinary markers could be instrumental in monitoring \"residual risk\" in patients with stable serum creatinine but ongoing, occult tubulointerstitial inflammation.\u003c/p\u003e \u003cp\u003eExtending our analysis to systemic complications, we noted significant associations between these biomarkers and extra-renal manifestations. The positive correlation between serum TNFSF14 and Parathyroid Hormone (PTH) offers clinical support for the existence of a pathological Kidney-Bone axis. This is likely underpinned by the ability of TNF superfamily members to regulate osteoclastogenesis via the RANKL/OPG system [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, the inverse relationship with hemoglobin levels hints at a connection to renal anemia. Since inflammatory cytokines suppress erythropoietin and induce hepcidin, our data suggest the TNFSF14/CD40 axis may act as a contributor to this inflammatory anemia phenotype [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond their diagnostic value, the identification of the TNFSF14/CD40 axis opens intriguing avenues for therapeutic intervention. Given that CD40 signaling is a potent driver of fibrosis, blocking this pathway could theoretically arrest CKD progression. In fact, anti-CD40 monoclonal antibodies (e.g., Bleselumab) have already shown promise in preventing graft rejection in kidney transplantation settings. Our findings, which localize CD40 overexpression specifically to the tubulointerstitium, suggest that such immunomodulatory strategies might be repurposed for managing native kidney diseases, particularly those driven by tubulointerstitial inflammation. However, the systemic blockade of CD40 carries risks of immunosuppression. Therefore, the development of renal-targeted delivery systems\u0026mdash;perhaps utilizing nanoparticle carriers that specifically bind to tubular epithelial cells\u0026mdash;could represent a precision medicine approach to neutralize this inflammatory axis without compromising global immunity.\u003c/p\u003e \u003cp\u003eWhile our study provides compelling evidence for the diagnostic utility of TNFSF14 and CD40, several limitations necessitate cautious interpretation. First, the cross-sectional design inherent to our clinical validation cohort enables the establishment of strong associations but precludes definitive inferences regarding causality. It remains to be determined whether the elevation of these biomarkers precedes the decline in eGFR or occurs concomitantly. Second, our cohort was restricted to adult patients; thus, the applicability of these findings to pediatric CKD populations\u0026mdash;where the etiology is often congenital rather than metabolic\u0026mdash;requires separate validation. Third, although immunofluorescence confirmed tissue localization, we did not perform in vitro knockdown or overexpression experiments in human renal tubular cells. Future longitudinal studies with serial sampling, coupled with mechanistic assays in organoid models, are essential to fully dissect the intracellular signaling events downstream of the TNFSF14/CD40 axis and to evaluate their potential as therapeutic targets.\u003c/p\u003e \u003cp\u003eCollectively, this study positions TNFSF14 and CD40 as biologically plausible, stage-dependent biomarkers for CKD. Their distinct upregulation in the tubular compartment and measurability in urine reflect the severity of tubulointerstitial injury. Integrating these molecules into a multi-marker panel could significantly refine our ability to stage disease, track inflammatory activity, and eventually guide targeted immunomodulatory interventions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy harmonizing transcriptomic insights with clinical verification, this study establishes TNFSF14 and CD40 as biologically relevant, stage-dependent biomarkers for Chronic Kidney Disease. Our data underscore the centrality of the TNF signaling axis in driving renal pathology. Specifically, the universal upregulation of these proteins within the tubular compartment, coupled with their progressive elevation in serum and urine, offers a precise reflection of disease severity. These results position TNFSF14 and CD40 not merely as diagnostic candidates for non-invasive monitoring, but as actionable targets for therapeutic intervention aimed at curbing renal inflammation and tubulointerstitial fibrosis. Ultimately, therapeutic strategies directed at the TNFSF14/CD40 axis may represent a compelling avenue to delay the clinical trajectory of kidney failure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e \u003cp\u003eThe authors affirm that the research was directed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDING INFORMATION\u003c/h2\u003e \u003cp\u003eThis study was supported by the Science and Technology Project of Nantong City (JCZ20066).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXiaolan Chen designed, supervised, and revised the manuscript. Xiameng Gu and Yuqing Lu performed the experiments and wrote the manuscript. Xiameng Gu, Hanlu Zhang, Hongxin Chen, Haonan Sha, Mengyue Qiu analyzed the data.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eWe thank the Affiliated Hospital of Nantong university for the gift of the pathological section of renal tissue.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting this study's findings are available from the corresponding author or Xiameng Gu, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. \u003cem\u003eLancet\u003c/em\u003e. 2020;395(10225):709\u0026ndash;733.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovesdy CP. 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Circulating CD40 and sCD40L predict changes in renal function and cardiovascular outcomes in patients with chronic kidney disease. \u003cem\u003eSci Rep\u003c/em\u003e. 2017;7(1):7942.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTenenbaum JD. Translational bioinformatics: past, present, and future. \u003cem\u003eGenomics Proteomics Bioinformatics\u003c/em\u003e. 2016;14(1):31\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRayego-Mateos S, Valdivielso JM. New therapeutic targets in chronic kidney disease progression and renal fibrosis. \u003cem\u003eExpert Opin Ther Targets\u003c/em\u003e. 2020;24(7):655\u0026ndash;670.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu BC, Tang TT, Lv LL, et al. Renal tubule injury: a driving force toward chronic kidney disease. \u003cem\u003eKidney Int\u003c/em\u003e. 2018;93(3):568\u0026ndash;579.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDjudjaj S, Boor P. 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Uses of GFR and albuminuria levels in acute and chronic kidney disease. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2022;386(22):2120\u0026ndash;2128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen TK, Knicely DH, Grams ME. Chronic kidney disease diagnosis and management: a review. \u003cem\u003eJAMA\u003c/em\u003e. 2019;322(13):1294\u0026ndash;1304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWasung ME, Chawla LS, Mito R. Biomarkers of renal function, which and when? \u003cem\u003eClin Chim Acta\u003c/em\u003e. 2015;438:350\u0026ndash;357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFine LG, Norman JT. Chronic hypoxia as a mechanism of progression of chronic kidney diseases: from hypothesis to novel therapeutics. \u003cem\u003eKidney Int\u003c/em\u003e. 2008;74(7):867\u0026ndash;872.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim HJ, Kim HM, Kim CS, et al. 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Chronic kidney disease and cardiovascular risk in six regions of the world (ISN-KDDC): a cross-sectional study. \u003cem\u003eLancet Glob Health\u003c/em\u003e. 2016;4(5):e307\u0026ndash;e319.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic Kidney Disease, Bioinformatics, TNFSF14, CD40, Biomarker, Renal Fibrosis","lastPublishedDoi":"10.21203/rs.3.rs-8690385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8690385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic Kidney Disease (CKD) imposes a significant global health burden, defined by the irreversible loss of function and progressive fibrosis. There is a critical unmet need for non-invasive biomarkers that can accurately mirror renal inflammation and early-stage injury to guide diagnosis. This study combines bioinformatics with clinical validation to pinpoint pathogenic genes driving CKD progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe mined two CKD expression datasets (GSE66494 and GSE97709) from the GEO database. Using the limma R package, we screened for Differentially Expressed Genes (DEGs) and mapped their biological functions via GO and KEGG enrichment. To validate the top candidates\u0026mdash;TNFSF14 and CD40\u0026mdash;we analyzed a clinical cohort comprising 140 CKD patients (Stages I\u0026ndash;V) and 60 healthy controls. Protein levels were quantified in serum and urine. Furthermore, we assessed tissue expression patterns using immunofluorescence on renal biopsies from patients with Diabetic Nephropathy, IgA Nephropathy, Membranous Nephropathy, and FSGS (n\u0026thinsp;=\u0026thinsp;20 per group).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBioinformatics analysis highlighted TNFSF14 and CD40 as key immune-related targets, clustering heavily within the TNF signaling and cytokine-receptor interaction pathways. Clinically, both markers were markedly elevated in the serum and urine of CKD patients compared to controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Tissue staining localized this upregulation specifically to the renal tubules. Correlation analysis showed that urinary levels of these markers tracked closely with disease severity, associating positively with serum creatinine/BUN and inversely with eGFR. ROC analysis further confirmed that both TNFSF14 and CD40 exhibit high diagnostic sensitivity and specificity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study positions TNFSF14 and CD40 not only as robust molecular signatures of tubulointerstitial injury but also as non-invasive urinary biomarkers with high diagnostic precision. Their integration into clinical practice could refine risk stratification and uncover novel therapeutic targets for halting CKD progression.\u003c/p\u003e","manuscriptTitle":"Integrated Bioinformatics Analysis and Clinical Validation Identify TNFSF14 and CD40 as Novel Biomarkers for Chronic Kidney Disease Progression and Tubulointerstitial Injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 08:22:21","doi":"10.21203/rs.3.rs-8690385/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4572696-c260-4071-9b33-87cbd26f0139","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:00:35+00:00","versionOfRecord":{"articleIdentity":"rs-8690385","link":"https://doi.org/10.1007/s11255-026-05138-9","journal":{"identity":"international-urology-and-nephrology","isVorOnly":false,"title":"International Urology and Nephrology"},"publishedOn":"2026-04-25 15:57:12","publishedOnDateReadable":"April 25th, 2026"},"versionCreatedAt":"2026-02-24 08:22:21","video":"","vorDoi":"10.1007/s11255-026-05138-9","vorDoiUrl":"https://doi.org/10.1007/s11255-026-05138-9","workflowStages":[]},"version":"v1","identity":"rs-8690385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8690385","identity":"rs-8690385","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}