Correlation between serum miR-154-5p and urinary albumin excretion rates in patients with type 2 diabetes mellitus: a cross-sectional cohort study

Abstract

This study aimed to investigate the correlation between serum miR-154-5p and urinary albumin to creatinine ratio (UACR) in patients with type 2 diabetes mellitus (T2DM) and the association with biomarkers of inflammation and fibrosis in diabetic kidney disease (DKD). A total of 390 patients with T2DM were divided into three groups: normal albuminuria (UACR<30 mg/g, n=136, NA), microalbuminuria (UACR at 30–300 mg/g, n=132, MA), and clinical albuminuria (UACR>300 mg/g, n=122, CA). Circulating miR-154-5p, inflammatory (C-reactive protein (CRP); erythrocyte sedimentation rate (ESR); and tumor necrosis factor-α (TNF-α) and fibrotic markers (vascular endothelial growth factor (VEGF); transforming growth factor-β1 (TGF-β1); and fibronectin (FN)), and other biochemical indicators were assessed via real-time PCR, enzyme-linked immunosorbent assay, and chemiluminescence assay in patients with T2DM and 138 control subjects (NC). UACR, miR-154-5p, glycated hemoglobin (HbA1c), serum creatinine (sCr), blood urea nitrogen (BUN), ESR, CRP, VEGF, TNF-α, TGF-β1, and FN were significantly higher and the estimated glomerular filtration rate (eGFR) was significantly lower in NA, MA, and CA groups than in NC subjects (P<0.05). Elevated levels of UACR and miR-154-5p were directly correlated with HbA1c, sCr, BUN, ESR, CRP, VEGF, TNF-α, TGF-β1, and FN and negatively correlated with eGFR (P<0.05). miR-154-5p, HbA1c, sCr, BUN, eGFR, ESR, CRP, VEGF, TNF-α, TGF-β1, and FN were important factors affecting UACR. These findings indicated that elevated serum miR-154-5p is significantly correlated with high UACR in patients with T2DM and may offer a novel reference for the early diagnosis of DKD.

Keywords

type 2 diabetes mellitus / diabetic kidney disease / miR-154-5p / urinary albumin to creatinine ratio

Introduction

Diabetic kidney disease (DKD), one of the complications of type 2 diabetes mellitus (T2DM), results from metabolic disorders. Its pathogenesis includes defects in the inflammatory response, immune system activation, and microvascular changes [¹], which may eventually progress to end-stage renal disease (ESRD) [²–⁴]. However, the mechanisms underlying the occurrence and development of DKD remain unclear [⁵]. Therefore, the search for detectable serum biomarkers has become a hot topic in the scientific and medical communities.

MicroRNAs (miRNAs) are highly conserved non-coding RNAs of 18–25 nucleotides that regulate gene expression through incomplete complementarity to base sequences of the target mRNA 3′ untranslated terminal region [⁶]. Previous studies have confirmed that miRNAs can play decisive roles in the pathogenesis of diseases, including DKD, by regulating fibrosis, inflammation, oxidative stress, and other related pathological processes [⁷].

miR-154, a recently discovered miRNA, is a serum miRNA with anti-cancer property and is rich in the classic binding site of Smad/TGF-β1. It can resist carcinogenic effects by regulating the progression of pulmonary fibrosis in patients [⁸]. This study aimed to explore the levels of serum miR-154-5p with different urinary albumin to creatinine ratios (UACRs) in patients with T2DM and biochemical indicators in the context of DKD, one of the microvascular complications of T2DM, to provide new serological indicators for the early prediction of DKD.

Materials and methods

Subjects

A total of 528 cases were included in this study. 390 patients with T2DM were initially diagnosed and treated in the endocrinology department of our hospital from November 2017 to October 2018 (Fig. 1) [⁹]. The normal control group (138 cases, NC) was from the Health Examination Center. The exclusion criteria were as follows: (1) age<18 years, diabetic hyperosmolar coma, diabetic ketoacidosis, or other acute diabetic complications in the last 3 months; (2) intentional cerebrovascular disease, liver function damage, infectious disease, and history of malignant tumor; (3) recent stress, such as infection, surgery, trauma, or special physical conditions, such as pregnancy and lactation; (4) patients using angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers that may affect the excretion rate of urinary albumin. This study was approved by the Ethics Committee of China Medical University. All participants signed informed consent.

Patients with T2DM were further divided into normal albuminuria (136 cases, UACR<30 mg/g, NA), microalbuminuria (132 cases, UACR 30–300 mg/g, MA) and clinical albuminuria groups (122 cases, UACR>300 mg/g, CA) on the basis of UACR [¹⁰]. Fasting blood samples were collected from patients and healthy volunteers via standard venipuncture [¹¹]. Two samples of 5 mL of venous blood were collected without anticoagulant and kept at room temperature for 30 min; the first sample was centrifuged for 15 min (1000 g, 4 °C), whereas the second sample was centrifuged for 5 min (2000 g, 4 °C). Both serum samples were collected, sealed, and stored at −80 °C until further use.

Anthropometric and analytic measurements

All volunteers were estimated by age, course, height (H), weight (W), and blood pressure (systolic blood pressure (SBP), and diastolic blood pressure (DBP)) in accordance with a standard protocol [¹²], and body mass index (BMI) was calculated as W (kg)/H² (m²). Fasting blood samples was collected from all volunteers via standard venipuncture and from moderate morning urine. The serum was separated via double centrifugation with a Beckman J-6M Induction Drive Centrifuge (Beckman Coulter, Inc., Brea, CA, USA). All urine, serum, and plasma samples were stored at −80 °C until the final analysis.

Glucose oxidase and fasting plasma insulin (FINS) were examined via double-antibody radioimmunoassay and used to measure fasting plasma glucose (FPG). The homeostasis model assessment of insulin resistance (HOMA-IR) was used to estimate insulin resistance as follows: FINS (mU/L) × FPG (mmol/L)/22.5. Glycated hemoglobin (HbA1c) was detected using an automated HbA1c analyzer (Bio-Rad, Hercules, CA, USA). Urinary albumin, urinary creatinine (uCr), serum levels of total triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), uric acid (UA), serum creatinine (sCr), blood urea nitrogen (BUN), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were measured using automated picric colorimetry (Beckman Coulter, Inc., Brea, CA, USA). UACR was calculated to estimate the urinary proteins [¹³]. Serum creatinine values were used to calculate the estimated glomerular filtration rate (eGFR) with the Modification of Diet in Renal Disease [¹⁴].

miRNA isolation and quantitative real-time PCR analysis (qPCR)

The target genes of miR-154-5p were predicted using the Sanger microRNA sequence database (miRBase, 18.0, http://www.mirbase.org/) and hsa-miR-154-5p primers (Sequence: 5′-UAG GUU AUC CGU GUU GCC UUC G-3′, Accession: MIMAT0000452, Beijing Tiangen miRNA primer library). RNA was extracted from 400 µL serum samples by using a miRcute miRNA isolation kit (DP501, Beijing Tiangen) [¹⁵]. Before extraction, the Caenorhabditis elegans synthetic miRNA mimic (cel-miR-39; Qiagen, Hilden, Germany) was added to each serum sample to correct for extraction errors. The cDNA strand modified via tail reverse transcription was synthesized using miRcute miRNA First-Strand cDNA Synthesis Kit (Tiangen, Beijing, China) [¹⁶]. Real-time PCR detection of miR-154-5p in 3.0 µL cDNA template was performed using the miRcute miRNA qPCR Detection Kit (FP401, SYBR Green, Tiangen, Beijing, China). PCR amplification was performed under the following conditions: initial denaturation for 2 min at 94 °C and 45 cycles of denaturation for 5 s at 95 °C. Annealing and extension were continued for 40 s at 60 °C on a Takara Thermal Cycler Dice Real Time System. The relative expression of miR-154-5p in each sample was calculated using the 2^−∆∆Ct method [⁹,¹⁷]. The spiked-in cel-miR-39 served as an endogenous control for data normalization to generate DCt values.

Enzyme-linked immunosorbent assay (ELISA)

Sandwich ELISA was used to measure the vascular endothelial growth factor (VEGF), tumor necrosis factor-α (TNF-α), fibronectin (FN), and transforming growth factor-β1 (TGF-β1) (product nos.: CSB-E11718h, CSB-E04740h, CSB-E04551h, and CSB-E04725h, respectively; CUSABIO, Wuhan, China) in serum samples in accordance with the manufacturers’ instructions. Inter- and intra-assay coefficients of variation were<8% and<10%, respectively.

Statistical analysis

IBM SPSS Statistics (V.20.0, IBM Corp., Armonk, NY, USA) was used for data analysis. The normality of each group was judged in accordance with a previous literature and normality test. All values were expressed as the mean±standard deviation (SD) for normally distributed values and as median (interquartile range) for nonparametric values. Difference between groups was analyzed using one-way ANOVA for normally distributed values and Kruskal–Wallis H test for nonparametric values. The least significant difference t-test or Mann–Whitney U test was used for pairwise comparison of differences among multiple groups. After logarithmic transformation was used for nonparametric values, the clinical parameters related to Ln UACR (natural logarithmic UACR) and Ln miR-154-5p were analyzed using Pearson correlation and multiple linear regression analysis. When collinearity existed, ridge regression analysis was performed to determine the association between clinical parameters and Ln UACR. All P-values were two-tailed, and P<0.05 was considered statistically significant.

Results

Clinical characteristics of patients and controls

The baseline anthropometric and biochemical characteristics of the study populations are shown in Table 1. There was no significant difference in age, sex, course, BMI, or blood pressure between the T2DM patient groups with different urinary albumin levels and the NC group (respectively, P>0.05). HOMA-IR, HDL-C, LDL-C, TC, TG, and UA levels were significantly higher in the T2DM group than in the NC group, but no significant difference was observed among the different UACR groups. Moreover, UACR, HbA1c, sCr, BUN, ESR, and CRP levels were successively elevated, whereas eGFR was decreased with different urinary albumin levels (P<0.05).

Serum miR-154-5p, VEGF, TNF-α, TGF-β1, and FN levels

Table 1 shows the differences in serum markers between the NC and the T2DM groups (NA, MA, and CA) in accordance with the urinary albumin levels. Patients with T2DM had significantly increased miR-154-5p, VEGF, TNF-α, TGF-β1, and FN levels compared with the NC group. miR-154-5p, VEGF, TNF-α, TGF-β1, and FN levels increased successively with UACR. All levels of urinary albumin were statistically different (P<0.05).

Clinical parameters and serum markers related to the elevated serum miR-154-5p in patients

Table 2 lists the correlation of serum miR-154-5p with clinical parameters in the NA, MA, and CA groups. Ln miR-154-5p in serum was positively correlated with HbA1c, sCr, BUN, Ln UACR, Ln ESR, CRP, VEGF, TNF-α, TGF-β1, and FN and negatively correlated with Ln eGFR (P<0.05).

Clinical parameters and serum markers related to elevated serum UACR in patients

Correlation analysis indicated that Ln UACR values were directly correlated with Ln miR-154-5p, HbA1c, sCr, BUN, Ln ESR, CRP, VEGF, TNF-α, TGF-β1, and FN and negatively correlated with Ln eGFR in patients with T2DM (P<0.05, Table 3).

Analysis of the relationship among UACR, clinical parameters, and other serum markers in patients

Exploratory multiple regression analysis was conducted by taking the indicators significantly related to Ln UACR (HbA1c, CRP, sCr, BUN, VEGF, TNF-α, TGF-β1, Ln miR-154-5p, Ln eGFR, Ln ESR, and FN) as independent variables and Ln UACR as dependent variables. The results showed that a significant correlation existed between the respective variables. Given the severe collinearity revealed by common linear regression analysis (collinearity diagnosis indicated that the maximum conditional index>30, variance expansion factor>10, and variance component>0.5), we adopted a ridge regression analysis.

The ridge trace shown in Fig. 2 (x1−x11 referred to HbA1c, CRP, sCr, BUN, VEGF, TNF-α, TGF-β1, Ln miR-154-5p, Ln eGFR, Ln ESR, and FN) with Ln UACR as the dependent variable indicated that when the ridge parameter was K=0.5, the standardized regression coefficients of each independent variable tended to be stable. Therefore, parameter estimation of the ridge regression was performed with K=0.5. The results showed that Ln UACR was affected by HbA1c, CRP, sCr, BUN, VEGF, TNF-α, TGF-β1, Ln miR-154-5p, Ln eGFR, Ln ESR, and FN (Table 4). Combined with the regression coefficients, the increase in HbA1c, CRP, sCr, BUN, VEGF, TNF-α, TGF-β1, Ln miR-154-5p, Ln ESR, and FN was positively correlated with Ln UACR, whereas an increase in Ln eGFR resulted in a decrease in Ln UACR (negative correlation).

Discussion

Some miRNAs have been proven to be associated with UACR in the process of DKD in our previous studies [¹⁸–²⁰]. miR-154, a recently discovered miRNA with anti-cancer effect, is involved in the pathophysiological process of non-small cell lung cancer, prostate stroma, hepatocellular carcinoma, colorectal cancer, and other diseases through various pathways [²¹–²⁵]. miR-154 is located on a single strand of chromosome 14q32 [²⁶], which is reported to be the differential methylated region of approximately 200 kb upstream of the miRNA cluster [²⁷–²⁹]. The chromosome itself is enriched for Smad binding sites [⁸,³⁰], which are also classic binding sites of the fibrosis biomarker, TGF-β1 [³¹].

Previous studies on lung tissues from patients with idiopathic fibrosis have implicated miR-154 in the mechanism of TGF-β1- and Smad3-mediated pulmonary fibrosis [⁸,³⁰]. TGF-β/Smad3 signal is a classical pathway associated with fibrosis in DKD [³²]. The mature sequence of miR-154-5p is also present in human serum [³³–³⁵]. On the basis of the above findings, we hypothesized that serum miR-154-5p may be associated with the fibrotic mechanisms of DKD in patients with T2DM.

Given the insufficient input or unbalanced allocation of China’s urban and rural public health resources, the progression of healthcare in first-class hospitals is faster relative to that in rural town and county hospitals, which are still relatively backward due to the shortage of specialized doctors with extensive experience [³⁶]. Hence, most patients with T2DM suffer from the lack of standard hospital treatment until serious complications affect their quality of life [³⁷]. Given the apparent disparity in China’s medical and healthcare systems, this study included only 390 patients with T2DM that were initially diagnosed and treated in the outpatient or inpatient departments of our hospital as the experimental subjects, and recruited 138 age- and sex-matched healthy volunteers from physical examination centers in the same period as controls.

The results showed that circulating miR-154-5p in patients with T2DM in the NA, MA, and CA groups was higher than that of the NC group, indicating the likelihood of its correlation to UACR. In addition, Pearson correlation and multiple linear regression analysis indicated that HbA1c, UACR, sCr, BUN, and eGFR are independent risk factors affecting miR-154-5p in DKD.

DKD is one of the prevalent T2DM-associated microvascular complications leading to ESRD [³⁸]. The main clinical features of DKD are persistent albuminuria and progressive decrease in eGFR [³⁹]. Both UACR and eGFR are recommended for identifying and monitoring by the American Diabetes Association [⁴⁰,⁴¹], and Kidney Disease: Improving Global Outcomes suggests cause–GFR–albuminuria as the basic classification standard of DKD [¹⁴]. We found that UACR was positively correlated with HbA1c, sCr, and BUN but negatively correlated with eGFR. As such, hyperglycemia enhanced proteinuria, and urine proteins played important roles in kidney disorders of patients with T2DM.

The occurrence and development of DKD are the consequences of defects in the following pathways: glycolipid metabolism, renal hemodynamic changes including various vasoactive substances, angiogenesis, inflammatory cell infiltration, and renal fibrosis [⁵,⁴²]. Previous studies have shown that evaluating the changes in fibrotic factors, such as VEGF, TGF-β1, and FN, can effectively measure the degree of renal damage and guide timely clinical treatment for DKD in patients with T2DM [³²,⁴³–⁴⁵]. Changes in inflammatory cytokines, such as CRP, ESR, and TNF-α, are positively associated with the risk of chronic kidney disease in patients with T2DM [⁴⁶–⁴⁹].

The positive correlation between UACR and ESR, CRP, VEGF, TNF-α, TGF-β1, and FN observed in the present study indicated that these factors may play a guiding role in predicting the severity of disease in T2DM patients with different urinary albumin excretion rates.

We found that patients with T2DM in the NA, MA, and CA groups showed significant increase in inflammation (TNF-α, CRP, and ESR) and fibrosis index (VEGF, TGF-β1, and FN) compared with the NC group. Moreover, serum miR-154-5p was positively correlated with VEGF, CRP, ESR, TNF-α, TGF-β1, and FN levels, suggesting that miR-154-5p may regulate angiogenesis, inflammation, and fibrosis by regulating the excretion of urinary proteins in DKD. However, serum miR-154-5p was not correlated with other indicators, such as age, sex, course, BMI, HOMA-IR, SBP, DBP, TC, TG, HLD-C, LDL-C, and UA. Thus, miR-154-5p was not involved with insulin resistance, lipids, or blood pressure in patients with T2DM.

In summary, increased miR-154-5p, HbA1c, sCr, BUN, ESR, CRP, VEGF, TNF-α, TGF-β1, and FN and decreased eGFR may be associated with diabetic renal disorders. Circulating miR-154-5p could be a sensitive indicator of DKD inflammation and fibrosis for early diagnosis in patients with T2DM.

However, this study has several limitations. A cross-sectional cohort study with no follow-up only indicates the correlation between serum miR-154-5p levels and DKD and does not provide specific molecular mechanisms of DKD pathology. Future research on the molecular mechanisms may enhance our understanding of the pathological processes of DKD.

References

[1] Sandeep V. Type 2 diabetes. Ann Intern Med 2015; 162(5): 231–242

[2] Quiroga B, Arroyo D, de Arriba G. Present and future in the treatment of diabetic kidney disease. J Diabetes Res 2015; 2015: 801348

[3] Toth-Manikowski S, Atta MG. Diabetic kidney disease: pathophysiology and therapeutic targets. J Diabetes Res 2015; 2015: 697010

[4] Chamberlain JJ, Herman WH, Leal S, Rhinehart AS, Shubrook JH, Skolnik N, Kalyani RR. Pharmacologic therapy for type 2 diabetes: synopsis of the 2017 American Diabetes Association Standards of Medical Care in Diabetes. Ann Intern Med 2017; 166(8): 572–578

[5] Brownlee M, Aiello LP, Cooper ME, Vinik AI, Plutzky J, Boulton AJM. Chapter 33—Complications of Diabetes Mellitus. Elsevier Inc., 2016

[6] Bagga S, Bracht J, Hunter S, Massirer K, Holtz J, Eachus R, Pasquinelli AE. Regulation by let-7 and lin-4 miRNAs results in target mRNA degradation. Cell 2005; 122(4): 553–563

[7] Saito D, Maeshima Y, Nasu T, Yamasaki H, Tanabe K, Sugiyama H, Sonoda H, Sato Y, Makino H. Amelioration of renal alterations in obese type 2 diabetic mice by vasohibin-1, a negative feedback regulator of angiogenesis. Am J Physiol Renal Physiol 2011; 300(4): F873–F886

[8] Milosevic J, Pandit K, Magister M, Rabinovich E, Ellwanger DC, Yu G, Vuga LJ, Weksler B, Benos PV, Gibson KF, McMillan M, Kahn M, Kaminski N. Profibrotic role of miR-154 in pulmonary fibrosis. Am J Respir Cell Mol Biol 2012; 47(6): 879–887

[9] American Diabetes Association. Standards of medical care in diabetes—2014. Diabetes Care 2014; 37(Suppl 1): S14–S80

[10] Chalmers J. The 1999 WHO-ISH Guidelines for the Management of Hypertension. Med J Aust 1999; 171(9): 458–459

[11] Affara M, Sanders D, Araki H, Tamada Y, Dunmore BJ, Humphreys S, Imoto S, Savoie C, Miyano S, Kuhara S, Jeffries D, Print C, Charnock-Jones DS. Vasohibin-1 is identified as a master-regulator of endothelial cell apoptosis using gene network analysis. BMC Genomics 2013; 14(1): 23

[12] Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, Jones DW, Materson BJ, Oparil S, Wright JT Jr, Roccella EJ; National Heart, Lung, and Blood Institute Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; National High Blood Pressure Education Program Coordinating Committee. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 2003; 289(19): 2560–2572

[13] Yu R, Yang Y, Tian Y, Zhang Y, Lyu G, Zhu J, Xiao L, Zhu J. The mechanism played by 1,25-dihydroxyvitamin D3 in treating renal fibrosis in diabetic nephropathy. Chin J Endocrinol Metab (Zhonghua Nei Fen Mi Dai Xie Za Zhi) 2015; 9: 793–799 (in Chinese)

[14] Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013; 3: 1–150

[15] Wang YC, Li Y, Wang XY, Zhang D, Zhang H, Wu Q, He YQ, Wang JY, Zhang L, Xia H, Yan J, Li X, Ying H. Circulating miR-130b mediates metabolic crosstalk between fat and muscle in overweight/obesity. Diabetologia 2013; 56(10): 2275–2285

[16] Ng EK, Chong WW, Jin H, Lam EK, Shin VY, Yu J, Poon TC, Ng SS, Sung JJ. Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening. Gut 2009; 58(10): 1375–1381

[17] Ortega FJ, Mercader JM, Moreno-Navarrete JM, Rovira O, Guerra E, Esteve E, Xifra G, Martínez C, Ricart W, Rieusset J, Rome S, Karczewska-Kupczewska M, Straczkowski M, Fernández-Real JM. Profiling of circulating microRNAs reveals common microRNAs linked to type 2 diabetes that change with insulin sensitization. Diabetes Care 2014; 37(5): 1375–1383

[18] Shao Y, Ren H, Lv C, Ma X, Wu C, Wang Q. Changes of serum miR-217 and the correlation with the severity in type 2 diabetes patients with different stages of diabetic kidney disease. Endocrine 2017; 55(1): 130–138

[19] Ma X, Lu C, Lv C, Wu C, Wang Q. The expression of miR-192 and its significance in diabetic nephropathy patients with different urine albumin creatinine ratio. J Diabetes Res 2016; 2016: 6789402

[20] Lv C, Zhou YH, Wu C, Shao Y, Lu CL, Wang QY. The changes in miR-130b levels in human serum and the correlation with the severity of diabetic nephropathy. Diabetes Metab Res Rev 2015; 31(7): 717–724

[21] Lin X, Yang Z, Zhang P, Liu Y, Shao G. miR-154 inhibits migration and invasion of human non-small cell lung cancer by targeting ZEB2. Oncol Lett 2016; 12(1): 301–306

[22] Dambal S, Giangreco AA, Acosta AM, Fairchild A, Richards Z, Deaton R, Wagner D, Vieth R, Gann PH, Kajdacsy-Balla A, Van der Kwast T, Nonn L. MicroRNAs and DICER1 are regulated by 1,25-dihydroxyvitamin D in prostate stroma. J Steroid Biochem Mol Biol 2017; 167: 192–202

[23] Luk JM, Burchard J, Zhang C, Liu AM, Wong KF, Shek FH, Lee NP, Fan ST, Poon RT, Ivanovska I, Philippar U, Cleary MA, Buser CA, Shaw PM, Lee CN, Tenen DG, Dai H, Mao M. DLK1-DIO3 genomic imprinted microRNA cluster at 14q32.2 defines a stemlike subtype of hepatocellular carcinoma associated with poor survival. J Biol Chem 2011; 286(35): 30706–30713

[24] Xin C, Zhang H, Liu Z. miR-154 suppresses colorectal cancer cell growth and motility by targeting TLR2. Mol Cell Biochem 2014; 387(1-2): 271–277

[25] Gardiner E, Beveridge NJ, Wu JQ, Carr V, Scott RJ, Tooney PA, Cairns MJ. Imprinted DLK1-DIO3 region of 14q32 defines a schizophrenia-associated miRNA signature in peripheral blood mononuclear cells. Mol Psychiatry 2012; 17(8): 827–840

[26] Formosa A, Markert EK, Lena AM, Italiano D, Finazzi-Agro’ E, Levine AJ, Bernardini S, Garabadgiu AV, Melino G, Candi E. MicroRNAs, miR-154, miR-299-5p, miR-376a, miR-376c, miR-377, miR-381, miR-487b, miR-485-3p, miR-495 and miR-654-3p, mapped to the 14q32.31 locus, regulate proliferation, apoptosis, migration and invasion in metastatic prostate cancer cells. Oncogene 2014; 33(44): 5173–5182

[27] Seitz H, Royo H, Bortolin ML, Lin SP, Ferguson-Smith AC, Cavaillé J. A large imprinted microRNA gene cluster at the mouse Dlk1-Gtl2 domain. Genome Res 2004; 14(9): 1741–1748

[28] Dixon-McIver A, East P, Mein CA, Cazier JB, Molloy G, Chaplin T, Andrew Lister T, Young BD, Debernardi S. Distinctive patterns of microRNA expression associated with karyotype in acute myeloid leukaemia. PLoS One 2008; 3(5): e2141

[29] Altuvia Y, Landgraf P, Lithwick G, Elefant N, Pfeffer S, Aravin A, Brownstein MJ, Tuschl T, Margalit H. Clustering and conservation patterns of human microRNAs. Nucleic Acids Res 2005; 33(8): 2697–2706

[30] Kaminski N, Benos P, Corcoran D, Pandit KV, Milosevic J, Yousef H. MicroRNAs In Idiopathic Pulmonary Fibrosis. Mosby, Inc., 2012. 191–199

[31] Yang H, Wang L, Zhao J, Chen Y, Lei Z, Liu X, Xia W, Guo L, Zhang HT. TGF-b-activated SMAD3/4 complex transcriptionally upregulates N-cadherin expression in non-small cell lung cancer. Lung Cancer 2015; 87(3): 249–257

[32] Li Y, Hu F, Xue M, Jia YJ, Zheng ZJ, Wang L, Guan MP, Xue YM. Klotho down-regulates Egr-1 by inhibiting TGF-b1/Smad3 signaling in high glucose treated human mesangial cells. Biochem Biophys Res Commun 2017; 487(2): 216–222

[33] Huang J, Wu J, Li Y, Li X, Yang T, Yang Q, Jiang Y. Deregulation of serum microRNA expression is associated with cigarette smoking and lung cancer. BioMed Res Int 2014; 2014: 364316

[34] Zheng Y, Zhu C, Ma L, Shao P, Qin C, Li P, Cao Q, Ju X, Cheng G, Zhu Q, Gu X, Hua L. miRNA-154-5p inhibits proliferation, migration and invasion by targeting E2F5 in prostate cancer cell lines. Urol Int 2017; 98(1): 102–110

[35] Ding J, Li JL, Yu MK. Expression of miRNA-154 in astrocytomas and its clinical significance. Chin Clin Oncol (Lin Chuang Zhong Liu Xue Za Zhi) 2017; 22(4): 314–318 (in Chinese)

[36] Liu HY, Zhang CH. China’s urban and rural public health resources insufficiency input or unbalanced allocation. Chin Health Econ (Zhongguo Wei Sheng Jing Ji) 2012; 31(8): 12–15 (in Chinese)

[37] Feng Z. Chinese health care in rural areas. BMJ 2010; 341: c5254

[38] KDOQI. KDOQI clinical practice guidelines and clinical practice recommendations for diabetes and chronic disease. Am J Kidney Dis 2007; 49(2 Suppl 2): S12–S154

[39] Tuttle KR, Bakris GL, Bilous RW, Chiang JL, de Boer IH, Goldstein-Fuchs J, Hirsch IB, Kalantar-Zadeh K, Narva AS, Navaneethan SD, Neumiller JJ, Patel UD, Ratner RE, Whaley-Connell AT, Molitch ME. Diabetic kidney disease: a report from an ADA Consensus Conference. Am J Kidney Dis 2014; 64(4): 510–533

[40] National Clinical Guideline Centre (UK). Chronic Kidney Disease (Partial Update): Early Identification and Management of Chronic Kidney Disease in Adults in Primary and Secondary Care. London: National Institute for Health and Care Excellence (UK). 2014

[41] Zitkus BS. Update on the American Diabetes Association Standards of Medical Care. Nurse Pract 2014; 39(8): 22–32

[42] Kanwar YS, Sun L, Xie P, Liu FY, Chen S. A glimpse of various pathogenetic mechanisms of diabetic nephropathy. Annu Rev Pathol 2011; 6(1): 395–423

[43] Lan HY, Chung ACK. Transforming growth factor-b and Smads. Contrib Nephrol 2011; 170: 75–82

[44] Sato Y. The vasohibin family: a novel family for angiogenesis regulation. J Biochem 2013; 153(1): 5–11

[45] Yao XF, Cai D, Quan JJ. Levels and clinical significances of IGF-1, TGF-β and VEGF in patients with type 2 diabetic nephropathy. Med Pharm J Chin PLA (Jie Fang Jun Yi Yao Za Zhi) 2017; 29(6): 78–81 (in Chinese)

[46] Fathy SA, Mohamed MR, Ali M A M, El-Helaly AE, Alattar AT. Influence of IL-6, IL-10, IFN-γ and TNF-α genetic variants on susceptibility to diabetic kidney disease in type 2 diabetes mellitus patients. Biomarkers 2019; 24(1): 43–55

[47] Lu C, Han HD, Mangala LS, Ali-Fehmi R, Newton CS, Ozbun L, Armaiz-Pena GN, Hu W, Stone RL, Munkarah A, Ravoori MK, Shahzad MM, Lee JW, Mora E, Langley RR, Carroll AR, Matsuo K, Spannuth WA, Schmandt R, Jennings NB, Goodman BW, Jaffe RB, Nick AM, Kim HS, Guven EO, Chen YH, Li LY, Hsu MC, Coleman RL, Calin GA, Denkbas EB, Lim JY, Lee JS, Kundra V, Birrer MJ, Hung MC, Lopez-Berestein G, Sood AK. Regulation of tumor angiogenesis by EZH2. Cancer Cell 2010; 18(2): 185–197

[48] Yeo ES, Hwang JY, Park JE, Choi YJ, Huh KB, Kim WY. Tumor necrosis factor (TNF-α) and C-reactive protein (CRP) are positively associated with the risk of chronic kidney disease in patients with type 2 diabetes. Yonsei Med J 2010; 51(4): 519–525

[49] Magri CJ, Calleja N, Buhagiar G, Fava S, Vassallo J. Factors associated with diabetic nephropathy in subjects with proliferative retinopathy. Int Urol Nephrol 2012; 44(1): 197–206

RIGHTS & PERMISSIONS

Higher Education Press