A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients

Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients.Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed.Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel.Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

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Development and Validation of a Lncrnas-Based Nomogram for Survival Prediction in Neuroblastoma Patients

10.21203/rs.3.rs-448031/v1 ◽

2021 ◽

Author(s):

Yong Lv ◽

ShuGuang Jin ◽

Bo Xiang

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Validation Cohort ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment

Abstract BackgroundTreatment of neuroblastoma is evolving toward precision medicine. LncRNAs can be used as prognostic biomarkers in many types of cancer.MethodsBased on the RNA-seq data from GSE49710, we built a lncRNAs-based risk score using the least absolute shrinkage and selection operation (LASSO) regression. Cox regression, receiver operating characteristic curves were used to evaluate the association of the LASSO risk score with overall survival. Nomograms were created and then validated in an external cohort from TARGET database. Gene set enrichment analysis was performed to identify the significantly changed biological pathways. ResultsThe 16-lncRNAs-based LASSO risk score was used to separate patients into high-risk and low-risk groups. In GSE49710 cohort, the high-risk group exhibited a poorer OS than those in the low-risk group (P<0.001). Moreover, multivariate Cox regression analysis demonstrated that LASSO risk score was an independent risk factor (HR=6.201;95%CI:2.536-15.16). The similar prognostic powers of the 16-lncRNAs were also achieved in the external cohort and in stratified analysis. In addition, a nomogram was established and worked well both in the internal validation cohort (C-index=0.831) and external validation cohort (C-index=0.773). The calibration plot indicated the good clinical utility of the nomogram. Gene set enrichment analysis (GSEA) indicated that high-risk group was related with cancer recurrence, metastasis and inflammatory associated pathways.ConclusionThe lncRNA-based LASSO risk score is a promising and potential prognostic tool in predicting the survival of patients with neuroblastoma. The nomogram combined the lncRNAs and clinical parameters allows for accurate risk assessment in guiding clinical management.

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A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

10.21203/rs.3.rs-335140/v1 ◽

2021 ◽

Author(s):

Yanjia Hu ◽

Jing Zhang ◽

Jing Chen

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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An immune-related lncRNA signature as a prognostic immunotherapy target for colon cancer

10.21203/rs.3.rs-35120/v1 ◽

2020 ◽

Author(s):

Bin Wu ◽

Yi Yao ◽

Yi Dong ◽

Si Qi Yang ◽

Deng Jing Zhou ◽

...

Keyword(s):

Colon Cancer ◽

High Risk ◽

Risk Score ◽

Enrichment Analysis ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Gene Set Enrichment ◽

Immunotherapy Target

Abstract Background：We aimed to investigate an immune-related long non-coding RNA (lncRNA) signature that may be exploited as a potential immunotherapy target in colon cancer. Materials and methods: Colon cancer samples from The Cancer Genome Atlas (TCGA) containing available clinical information and complete genomic mRNA expression data were used in our study. We then constructed immune-related lncRNA co-expression networks to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high-risk and low-risk groups were separated on the basis of the median risk score, which served as the cutoff value. An overall survival analysis was then performed to confirm that the risk score developed from screened immune-related lncRNAs could predict colon cancer prognosis. The prediction reliability was further evaluated in the independent prognostic analysis and receiver operating characteristic curve (ROC). A principal component analysis (PCA) and gene set enrichment analysis (GSEA) were performed for functional annotation. Results: Information for a total of 514 patients was included in our study. After multiplex analysis, 12 immune-related lncRNAs were confirmed as a signature to evaluate the risk scores for each patient with cancer. Patients in the low-risk group exhibited a longer overall survival (OS) than those in the high-risk group. Additionally, the risk scores were an independent factor, and the Area Under Curve (AUC) of ROC for accuracy prediction was 0.726. Moreover, the low-risk and high-risk groups displayed different immune statuses based on principal components and gene set enrichment analysis.Conclusions: Our study suggested that the signature consisting of 12 immune-related lncRNAs can provide an accessible approach to measuring the prognosis of colon cancer and may serve as a valuable antitumor immunotherapy.

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Hypoxia-Related Long Non-Coding RNA Signature Predicts the Prognosis of Breast Cancer

10.21203/rs.3.rs-1084000/v1 ◽

2021 ◽

Author(s):

Jinlong Huo ◽

Shuang Shen ◽

Chen Chen ◽

Rui Qu ◽

Youming Guo ◽

...

Keyword(s):

Breast Cancer ◽

T Cell ◽

High Risk ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Cytolytic Activity ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas

Abstract Background: Breast cancer(BC) is the most common tumour in women. Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis.Methods: We screened prognostic-related lncRNAs(Long Non-Coding RNAs) from the Cancer Genome Atlas (TCGA) data and constructed a prognostic signature based on hypoxia-related lncRNAs in BC.Results: We identified 21 differentially expressed lncRNAs associated with BC prognosis. Kaplan Meier survival analysis indicated a significantly worse prognosis for the high-risk group(P<0.001). Moreover, the ROC-curve (AUC) of the lncRNAs signature was 0.700, a performance superior to other traditional clinicopathological characteristics. Gene set enrichment analysis (GSEA) showed many immune and cancer-related pathways and in the low-risk group patients. Moreover, TCGA revealed that functions including activated protein C (APC)co-inhibition, Cinnamoyl CoA reductase(CCR),check-point pathways, cytolytic activity, human leukocyte antigen (HLA), inflammation-promotion, major histocompatibility complex(MHC) class1, para-inflammation, T cell co-inhibition, T cell co-stimulation, and Type Ⅰ and Ⅱ Interferons (IFN) responses were significantly different in the low-risk and high-risk groups. Immune checkpoint molecules such as ICOS, IDO1, TIGIT, CD200R1, CD28, PDCD1(PD-1), were also expressed differently between the two risk groups. The expression of m6A-related mRNA indicated that YTHDC1, RBM15, METTL3, and FTO were significantly between the high and low-risk groups.Additionally, immunotherapy in patients with BC from the low-risk group yielded a higher frequency of clinical responses to anti-PD-1/PD-L1 therapy or a combination of anti-PD-1/PD-L1and anti-CTLA4 therapies.Except for lapatinib, the results also show that a high-risk score is related to a higher half-maximal inhibitory concentration (IC50) of chemotherapy drugs.Conclusion: A novel hypoxia-related lncRNAs signature may serve as a prognostic model for BC.

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A Risk Signature Established From Three Coagulation-fibrinolysis Genes Predicts Prognosis in Digestive System Pancancer

10.21203/rs.3.rs-1157925/v1 ◽

2022 ◽

Author(s):

Xingyun Wang ◽

Jinli Ji ◽

Ying Jiang ◽

Yiyang Zhao ◽

Zheyao Song ◽

...

Keyword(s):

High Risk ◽

Poor Prognosis ◽

Digestive System ◽

Risk Group ◽

Epigenetic Modification ◽

Enrichment Analysis ◽

Risk Groups ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Cox Analysis

Abstract Venous thromboembolism (VTE) is one of the major complications of digestive system cancer, and coagulation-fibrinolysis genes play an important role in VTE. We used univariate Cox analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analysis to construct 3-PCFGs (prognostic coagulation-fibrinolysis genes) model based on six prognostic coagulation-fibrinolysis genes. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the high- and low-risk groups. In addition, we classified digestive system pancancer patients into three clusters A, B, and C based on 3-PCFGs by K means. High-risk group and cluster C were associated with poor prognosis in digestive system pancancer. The m6A-related genes ALKBH5, FTO, RBM15, YTHDC1, and YTHDC2 (P<0.001) were highly expressed in the high-risk group and cluster C. The risk score was positively correlated with cancer-associated fibroblasts and endothelial cells. Cluster C had the highest immune score and stromal score. The poor prognosis in the high-risk group and cluster C may be affected by m6A epigenetic modification and immune microenvironment components in the digestive system pancancer.

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Construction of a Ferroptosis-Related Nine-lncRNA Signature for Predicting Prognosis and Immune Response in Hepatocellular Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.719175 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhijie Xu ◽

Bi Peng ◽

Qiuju Liang ◽

Xi Chen ◽

Yuan Cai ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

T Cells ◽

High Risk ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Prognostic Signature

Ferroptosis is an iron-dependent cell death process that plays important regulatory roles in the occurrence and development of cancers, including hepatocellular carcinoma (HCC). Moreover, the molecular events surrounding aberrantly expressed long non-coding RNAs (lncRNAs) that drive HCC initiation and progression have attracted increasing attention. However, research on ferroptosis-related lncRNA prognostic signature in patients with HCC is still lacking. In this study, the association between differentially expressed lncRNAs and ferroptosis-related genes, in 374 HCC and 50 normal hepatic samples obtained from The Cancer Genome Atlas (TCGA), was evaluated using Pearson’s test, thereby identifying 24 ferroptosis-related differentially expressed lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model were used to construct and validate a prognostic risk score model from both TCGA training dataset and GEO testing dataset (GSE40144). A nine-lncRNA-based signature (CTD-2033A16.3, CTD-2116N20.1, CTD-2510F5.4, DDX11-AS1, LINC00942, LINC01224, LINC01231, LINC01508, and ZFPM2-AS1) was identified as the ferroptosis-related prognostic model for HCC, independent of multiple clinicopathological parameters. In addition, the HCC patients were divided into high-risk and low-risk groups according to the nine-lncRNA prognostic signature. The gene set enrichment analysis enrichment analysis revealed that the lncRNA-based signature might regulate the HCC immune microenvironment by interfering with tumor necrosis factor α/nuclear factor kappa-B, interleukin 2/signal transducers and activators of transcription 5, and cytokine/cytokine receptor signaling pathways. The infiltrating immune cell subtypes, such as resting memory CD4(+) T cells, follicular helper T cells, regulatory T cells, and M0 macrophages, were all significantly different between the high-risk group and the low-risk group as indicated in Spearman’s correlation analysis. Moreover, a substantial increase in the expression of B7H3 immune checkpoint molecule was found in the high-risk group. Our findings provided a promising insight into ferroptosis-related lncRNAs in HCC and a personalized prediction tool for prognosis and immune responses in patients.

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An Inflammatory Response-Related Gene Signature Can Impact the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.644416 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhuo Lin ◽

Qian Xu ◽

Dan Miao ◽

Fujun Yu

Keyword(s):

Inflammatory Response ◽

Immune Status ◽

Risk Group ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Signature ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Prognostic Prediction ◽

Cox Analysis

BackgroundHepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. As part of the active cross-talk between the tumor and the host, inflammatory response in the tumor or its microenvironment could affect prognosis. However, the prognostic value of inflammatory response-related genes in HCC remains to be further elucidated.MethodsIn this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from the public database. The least absolute shrinkage and selection operator Cox analysis was utilized to construct a multigene prognostic signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Kaplan Meier analysis was used to compare the overall survival (OS) between high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Single-sample gene set enrichment analysis was utilized to calculate the immune cell infiltration score and immune related pathway activity. Gene set enrichment analysis was implemented to conduct GO terms and KEGG pathways. The qRT-PCR and immunohistochemistry were utilized to perform the mRNA and protein expression of prognostic genes between HCC tissues and normal liver tissues respectively.ResultsAn inflammatory response-related gene signature model was constructed by LASSO Cox regression analysis. Compared with the low-risk group, patients in the high-risk group showed significantly reduced OS. Receiver operating characteristic curve analysis confirmed the predictive capacity of the prognostic gene signature. Multivariate Cox analysis revealed that the risk score was an independent predictor for OS. Functional analysis indicated that immune status was definitely different between two risk groups, and cancer-related pathways were enriched in high-risk group. The risk score was significantly correlated with tumor grade, tumor stage and immune infiltrate types. The expression levels of prognostic genes were significantly correlated with sensitivity of cancer cells to anti-tumor drugs. Furthermore, the expression of prognostic genes showed significant difference between HCC tissues and adjacent non-tumorous tissues in the separate sample cohort.ConclusionA novel signature constructed with eight inflammatory response-related genes can be used for prognostic prediction and impact the immune status in HCC. Moreover, inhibition of these genes may be a therapeutic alternative.

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Development of a novel three-lncRNA immune-related signature as a prognostic indicator for hepatocellular carcinoma

10.21203/rs.2.23831/v1 ◽

2020 ◽

Author(s):

Bo Hu ◽

Xiao-Bo Yang ◽

Xinting Sang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Risk Group ◽

Principal Component ◽

Enrichment Analysis ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Testing Set

Abstract Background: Hepatocellular carcinoma (HCC) is one of the deadliest malignancies. Currently, there is still a lack of effective treatment. Our purpose was to develop an immune-related prognosis lncRNA signature with regard to HCC.Methods: A total of 14,142 lncRNAs and 331 immune genes were obtained from The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database to construct the immune-related lncRNAs co-expression networks. Moreover, the tumor samples collected from TCGA were randomized as training set and testing set, among which, the testing set and the entire set were used for verification. Subsequently, gene set enrichment analysis (GSEA) and principal component analysis (PCA) were employed for functional annotation.Results: An immune-related signature consisting of AC015908.3, AC068987.4 and AL365203.2 was identified among HCC patients. Under different conditions, patients in low-risk group exhibited longer overall survival (OS) than those in high-risk group (P < 0.001). Moreover, the as-constructed signature was an independent factor, which showed marked association with patient OS (P < 0.001, hazard ratio (HR) = 1.407). These findings were further validated in testing set and the entire set. Additionally, GSEA results revealed the different immune states between low-risk and high-risk groups. On the other hand, lncRNA-related mRNAs were also extracted to depict the networks.Conclusion: Our findings indicate that the three-lncRNA immune-related signature shows prognostic value for HCC.

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Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

10.21203/rs.3.rs-49217/v1 ◽

2020 ◽

Author(s):

Jianfeng Zheng ◽

Jinyi Tong ◽

Benben Cao ◽

Xia Zhang ◽

Zheng Niu

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Risk Score ◽

Immune Cell ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Training Set ◽

Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

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MO048MULTICENTRE PROSPECTIVE VALIDATION OF THE URINARY PEPTIDOME-BASED CLASSIFIER CKD273 AS A PREDICTOR OF RENAL FUNCTION DECLINE IN SUBJECTS WITH TYPE 2 DIABETES

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa140.mo048 ◽

2020 ◽

Vol 35 (Supplement_3) ◽

Author(s):

Morten Lindhardt ◽

Nete Tofte ◽

Gemma Currie ◽

Marie Frimodt-Moeller ◽

Heiko Von der Leyen ◽

...

Keyword(s):

Type 2 Diabetes ◽

Renal Function ◽

High Risk ◽

Risk Group ◽

Cox Model ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Renal Function Decline

Abstract Background and Aims In the PRIORITY study, it was recently demonstrated that the urinary peptidome-based classifier CKD273 was associated with increased risk for progression to microalbuminuria. As a prespecified secondary outcome, we aim to evaluate the classifier CKD273 as a determinant of relative reductions in eGFR (CKD-EPI) of 30% and 40% from baseline, at one timepoint without requirements of confirmation. Method The ‘Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial’ (PRIORITY) is the first prospective observational study to evaluate the early detection of diabetic kidney disease in subjects with type 2 diabetes (T2D) and normoalbuminuria using the CKD273 classifier. Setting 1775 subjects from 15 European sites with a mean follow-up time of 2.6 years (minimum of 7 days and a maximum of 4.3 years). Patients Subjects with T2D, normoalbuminuria and estimated glomerular filtration rate (eGFR) ≥ 45 ml/min/1.73m2. Participants were stratified into high- or low-risk groups based on their CKD273 score in a urine sample at screening (high-risk defined as score > 0.154). Results In total, 12 % (n = 216) of the subjects had a high-risk proteomic pattern. Mean (SD) baseline eGFR was 88 (15) ml/min/1.73m2 in the low-risk group and 81 (17) ml/min/1.73m2 in the high-risk group (p < 0.01). Baseline median (interquartile range) urinary albumin to creatinine ratio (UACR) was 5 (3-8) mg/g and 7 (4-12) mg/g in the low-risk and high-risk groups, respectively (p < 0.01). A 30 % reduction in eGFR from baseline was seen in 42 (19.4 %) subjects in the high-risk group as compared to 62 (3.9 %) in the low-risk group (p < 0.0001). In an unadjusted Cox-model the hazard ratio (HR) for the high-risk group was 5.7, 95 % confidence interval (CI) (3.9 to 8.5; p<0.0001). After adjustment for baseline eGFR and UACR, the HR was 5.2, 95 % CI (3.4 to 7.8; p<0.0001). A 40 % reduction in eGFR was seen in 15 (6.9 %) subjects in the high-risk group whereas 22 (1.4 %) in the low-risk group developed this endpoint (p<0.0001). In an unadjusted Cox-model the HR for the high-risk group was 5.0, 95 % CI (2.6 to 9.6; p<0.0001). After adjustment for baseline eGFR and UACR, the HR was 4.8, 95 % CI (2.4 to 9.7; p<0.0001). Conclusion In normoalbuminuric subjects with T2D, the urinary proteomic classifier CKD273 predicts renal function decline of 30 % and 40 %, independent of baseline eGFR and albuminuria.

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