This study collected hospitalized patients diagnosed with type 2 DN through pathological biopsy at Sichuan Provincial People’s Hospital, Department of Nephrology, from January 2014 to December 2022 (40 cases) as the experimental group. The inclusion criteria followed the diagnostic standards for T2DM established by the American Diabetes Association and the diagnostic criteria for DN set by the Kidney Disease Outcomes Quality Initiative (14). Based on pathological biopsy staging, DN was classified into early-stage DN (stages I and II, n=20) and late-stage DN (stages III and IV, n=20) (15). Additionally, 6 patients with benign renal tumors were selected as the normal control group, with adjacent kidney tissue confirmed by renal biopsy to be free from abnormalities. The exclusion criteria were: (1) age <18 years; (2) non-type 2 diabetes patients; (3) patients with other primary or secondary kidney diseases; (4) patients with severe acute or chronic infections, malignant tumors, cardiovascular, cerebrovascular, immune, hematological, or other systemic diseases; (5) patients with acute diabetic complications or pregnancy; (6) patients who had undergone dialysis treatment. This study was approved by the Ethics Committee of Sichuan Provincial People’s Hospital (No. Lun Shen (Yan) 2024, No. 59) and informed consent was obtained from all enrolled subjects.

We collected baseline clinical data, including gender (male/female), age (years), body mass index (BMI, kg/m²), duration of type 2 diabetes (years), history of hypertension (yes/no), systolic blood pressure (SBP, mmHg), and diastolic blood pressure (DBP, mmHg) measured at admission. Laboratory data included serum creatinine (Scr, umol/L), serum uric acid (SUA, umol/L), estimated glomerular filtration rate (eGFR, mL/min/1.73m²), blood urea nitrogen (BUN, mmol/L), serum albumin (Alb, g/L), hemoglobin (Hb, g/L), total cholesterol (TC, mmol/L), triglycerides (TG, mmol/L), high-density lipoprotein cholesterol (HDL-C, mmol/L), low-density lipoprotein cholesterol (LDL-C, mmol/L), fibrinogen (Fib, g/L), glycated hemoglobin (HbA1c, %), fasting blood glucose (FBG, mmol/L), urinary albumin/creatinine ratio (UACR, ug/mg), and 24-hour urinary protein (24hUpro, g/24h). All values were measured by routine clinical laboratories following standard procedures.

Renal biopsy specimens from DN patients were collected to assess the proportion of interstitial fibrosis and renal tubular atrophy (IFTA) and the degree of inflammatory cell infiltration (evaluated by at least two nephropathologists). Hematoxylin-eosin (HE) staining was used to observe and assess renal tissue pathological damage (evaluated by at least two nephropathologists), and Masson staining was used to stain collagen fibers. Through microscopic observation, multiple random fields were selected, and the percentage of collagen-positive area relative to the total tissue area was analyzed and calculated using ImageJ software (16).

Renal tissue specimens were sectioned, dewaxed, hydrated, and subjected to antigen retrieval. After endogenous peroxidase (A8850, Solarbio, Beijing, China) blocking for 30 minutes, appropriate primary antibodies NONO antibody(ab50411, abcam, Cambridge, UK, 1:200,), MMP-9 antibody (A2095, abclonal, Wuhan, China, 1:200), Col-I antibody (ab34710, abcam, Cambridge, UK, 1:200), or Col-III antibody (ab7778, abcam, Cambridge, UK, 1:200) were added and incubated overnight at 4°C in a humidified box. The next day, an appropriate secondary antibody goat anti-mouse/rabbit IgG polymer (RS0011, Immunoway, California, USA) was added and incubated at room temperature for 20 minutes. After DAB (ZLI-9017, ZSGB-Bio, Beijing, China) color development, the sections were mounted with neutral balsam. The samples were observed under a microscope (DP73, Olympus, Tokyo, Japan), and multiple fields were randomly captured. Image J software was used to analyze and calculate the percentage of positive staining area in the total tissue area (16).

Renal tissue sections were dewaxed, hydrated, and processed for antigen retrieval as in immunohistochemistry. After blocking with BSA (A8850, Solarbio, Beijing, China) for 30 minutes, NONO antibody (ab50411, abcam, Cambridge, UK, 1:100) and APQ1 (A15030, abclonal, Wuhan, China, 1:100) antibody or WT-1 antibody (A16298, abclonal, Wuhan, China, 1:100) diluted in PBST were added and incubated overnight at 4°C in a humidified box. The next day, secondary antibodies Alexa Fluor 488 (A32814, Jackson ImmunoResearch, Pennsylvania, USA)-labeled NONO antibody, Alexa Fluor 594 (A32754, Jackson ImmunoResearch, Pennsylvania, USA)-labeled APQ1 or WT-1 antibody were added (dilution 1:500) and incubated at room temperature for 1 hour in the dark. After DAPI (ab104139, Abcam, Cambridge, UK) staining of the nuclei, the sections were mounted with anti-fade mounting medium. Fluorescent images were collected using a confocal fluorescence microscope (LSM710, Leica, Wetzlar, Germany), and Image J software was used to analyze and calculate the average fluorescence intensity of NONO expression (17).

The kidney outcome time was defined as the occurrence of a 50% reduction in eGFR from baseline or the initiation of renal replacement therapy (hemodialysis, peritoneal dialysis, or kidney transplantation) within 24 months of follow-up (18). Outpatient and inpatient case systems were used for follow-up tracking, and for patients not regularly followed up at our hospital, follow-up data were obtained through phone interviews. The time from enrollment to the occurrence of a renal endpoint event was recorded as the kidney progression-free survival time, measured in months. Patients who experienced renal endpoint events during follow-up were categorized into the poor prognosis group. The remaining patients were divided into the good prognosis group (n=20) and the poor prognosis group (n=15). Five patients lost to follow-up were excluded.

SPSS statistical software was used for all data analysis. All clinical and pathological data were tested for normality. Data conforming to a normal distribution are expressed as mean ± standard deviation (Mean ± SD), while non-normally distributed data are expressed as median and interquartile range (M (QL, QU)). Categorical data are expressed as percentages. For data that followed a normal distribution, independent sample t-tests or one-way analysis of variance (ANOVA) were used for inter-group comparisons. For categorical variables, the χ² test or Fisher’s exact test was used for comparison between groups. Non-normally distributed data were analyzed by the rank-sum test. Pearson correlation analysis was used for normally distributed quantitative data, and Spearman correlation analysis was used for non-normally distributed quantitative data, with correlation coefficients (r) and p-values reported. Multivariate linear regression analysis was performed to evaluate independent influencing factors of NONO expression in DN. Cox regression analysis was used to investigate the relationship between renal tissue NONO expression and renal prognosis. ROC curves were used to assess the ability of renal tissue NONO expression to distinguish patients with poor renal prognosis. Kaplan-Meier survival analysis was used to evaluate the relationship between NONO expression levels and DN prognosis. Image J software was used for quantitative analysis of pathological staining (Masson staining, immunohistochemistry, immunofluorescence). Statistical charts were plotted using GraphPad Prism 8.0.2. A p-value of <0.05 was considered statistically significant.

A total of 40 patients were enrolled in this study, with 20 in the early DN group and 20 in the late DN group. Among them, 28 were male (70%) and 12 were female (30%), with an average age of 54.53 ± 8.47 years and an average BMI of 25.36 ± 3.30 kg/m². We analyzed and compared the differences in general data and research indicators between the two groups. The results showed significant statistical differences between the early and late DN groups in gender, UACR, 24hUpro, BUN, Scr, eGFR, Hb, Alb, Fib, TC, HDL-C, the proportion of IFTA involvement, and the degree of inflammatory cell infiltration (P<0.05). However, there were no statistically significant differences between the two groups in terms of age, BMI, duration of diabetes, history of hypertension, SBP, DBP, SUA, HbA1c, FBG, TG, and LDL-C (P > 0.05) ( Table 1 ).

HE staining results showed that in the control group, the kidney structure was clear, and the cell morphology was normal, with no obvious mesangial proliferation or other pathological changes. In the early DN group, kidney tissue showed glomerular hypertrophy, mild diffuse proliferation of mesangial cells and matrix, renal tubular damage, and thickening of the glomerular basement membrane. In the late DN group, the renal lesions were more severe than in the early DN group, with severe diffuse proliferation of glomeruli and matrix, and typical pathological manifestations such as segmental glomerulosclerosis, microangiomas, and K-W nodules ( Figure 1A ). Semi-quantitative analysis of pathological damage revealed that the degree of pathological injury in the early DN group was significantly more severe compared to the control group (P<0.05). Furthermore, the extent of renal injury in the late DN group was markedly greater than that in the early DN group, with a statistically significant difference (P<0.05) ( Figure 1B ).

Masson staining results showed a small amount of blue collagen deposition in the renal interstitium of the control group, with no obvious interstitial fibrosis. Compared with the control group, both the early and late DN groups showed a large amount of collagen fiber deposition in the renal interstitium, with the late DN group exhibiting more significant changes than the early DN group ( Figure 1A ). Further semi-quantitative analysis of the Masson staining results using Image J software showed an increase in the collagen volume percentage compared to the control group, with statistical significance (P<0.05). The late DN group also exhibited a higher collagen volume percentage than the early DN group, and the difference was statistically significant (P<0.05) ( Figure 1C ).

Immunohistochemical staining results showed that in the control group, NONO was expressed in the nuclei of some renal tubular epithelial cells, mesangial cells of the glomeruli, and interstitial cells, appearing brownish yellow or dark brown under the microscope. MMP-9 was expressed in some mesangial cells of the glomeruli, renal tubular epithelial cells, and renal interstitium. Col-I and Col-III were mainly expressed in mesangial cells of the glomeruli and renal interstitium, showing brownish yellow cytoplasm with blue cell nuclei. Compared with the control group, the expression of NONO, MMP-9, Col-I, and Col-III was increased in both the early and late DN groups, with more pronounced expression in the late DN group ( Figure 1D ). Additionally, further semi-quantitative analysis of the protein expression levels of NONO, MMP-9, Col-I, and Col-III revealed that their expression levels were higher in both the early and late DN groups compared to the control group (P<0.05), with the late DN group showing higher expression levels than the early DN group (P<0.05) ( Figure 1E-H ).

Wilms’ Tumor 1 (WT-1) is specifically expressed in podocytes in glomerular tissue and can be used as a biomarker for glomerular localization, while Aquaporin 1 (AQP1) is specifically expressed in proximal convoluted tubules and can be used as a biomarker for tubular localization (19). By using immunofluorescence, we conducted double staining for NONO with WT1 or Aquaporin 1 in renal tissue. The results showed that in glomerular tissue, NONO fluorescence (green) could be observed within the WT1 fluorescence (red) region ( Figure 2A ). In renal tubules, NONO fluorescence (green) was seen within the AQP1 fluorescence (red) region ( Figure 2B ), confirming the expression of NONO in both glomeruli and renal tubules.

Further quantitative analysis of the immunofluorescence results showed that compared with the control group, the expression of NONO in renal tissue was higher in both the early and late DN groups (P<0.05). The expression in the late DN group was significantly higher than in the early DN group (P<0.05) ( Figures 2C, D ).

A multiple linear regression model was constructed with the immunohistochemical detection of NONO expression in renal tissue as the dependent variable and other clinical and pathological indicators as independent variables. Correlation analysis revealed that NONO expression levels were positively correlated with clinical indicators such as SBP, DBP, UACR, 24hUpro, BUN, Fib, TC, and HDL-C (r values were 0.335, 0.343, 0.367, 0.468, 0.479, 0.322, 0.358, and 0.376, respectively, P<0.05), and negatively correlated with Hb and Alb (r values were -0.529 and -0.435, respectively, P<0.05). NONO expression was also positively correlated with the expression levels of MMP-9, Col-I, Col-III, collagen volume percentage, IFTA involvement ratio, and degree of inflammatory cell infiltration (r values were 0.552, 0.623, 0.490, 0.648, 0.418, and 0.341, respectively, P<0.05). No significant correlation was found between NONO expression and age, BMI, diabetes duration, SUA, Scr, eGFR, HbA1c, FBG, TG, and LDL-C (P > 0.05) ( Table 2 ).

A similar correlation analysis was performed for MMP-9 expression, with results showing that MMP-9 expression was positively correlated with clinical indicators such as SBP, DBP, UACR, 24hUpro, BUN, Scr, TC, and HDL-C (r values were 0.401, 0.344, 0.380, 0.361, 0.424, 0.387, 0.335, and 0.356, respectively, P<0.05), and negatively correlated with eGFR, Alb, and Hb (r values were -0.410, -0.487, and -0.575, respectively, P<0.05). MMP-9 expression was also positively correlated with NONO, Col-I, Col-III expression levels, collagen volume percentage, IFTA involvement ratio, and degree of inflammatory cell infiltration (r values were 0.552, 0.583, 0.552, 0.658, 0.456, and 0.343, respectively, P<0.05). No significant correlation was found between MMP-9 expression and age, BMI, diabetes duration, SUA, HbA1c, FBG, Fib, TG, and LDL-C (P > 0.05) ( Table 2 ).

A multivariate linear regression analysis was performed using NONO expression in renal tissue as the dependent variable and the indicators that were correlated with NONO expression as independent variables. The final optimal model showed that UACR and Col-I expression levels were independent influencing factors for NONO (F=15.854, P<0.001). This indicates that as UACR and Col-I expression levels increase, the expression of NONO also increases ( Table 3 ).

Univariate Cox regression was used to identify risk factors for poor prognosis in DN patients by evaluating clinical and pathological indicators, including gender (coded as: female=0, male=1), hypertension history (coded as: none=0, yes=1), and other continuous variables. The analysis revealed that gender, UACR, 24hUpro, Hb, Alb, TC, HDL-C, IFTA, NONO expression, MMP-9 expression, Col-I expression, Col-III expression, and collagen volume percentage were all factors influencing the prognosis of DN patients (P<0.05) ( Table 4 ).

To study the diagnostic and predictive values of NONO expression for predicting poor prognosis in DN patients, ROC curve analysis was performed. The results showed that the area under the AUC curve for NONO expression in predicting composite renal outcomes was 0.877 (95% CI: 0.756-0.997, P<0.05). The optimal cutoff value for NONO expression was 1.734, with a sensitivity of 86.7% and specificity of 90.0% ( Figure 3A ).

To compared kidney progression-free survival in DN patients followed for 24 months, patients were divided into two groups based on the optimal cutoff value of NONO expression (≤1.734 and >1.734). Kaplan-Meier survival analysis showed that the kidney progression-free survival rates at 15, 20, and 24 months were 95.0%, 90.0%, and 90.0% in the NONO ≤ 1.734 group, respectively, while the rates were 53.3%, 26.7%, and 13.3% in the NONO > 1.734 group. Log-Rank test analysis revealed a significant difference in kidney progression-free survival between the two groups (P<0.001) ( Figure 3B ).