Modeling survival of arteriovenous accesses for hemodialysis: semiparametric versus parametric methods

Development of a prognostic prediction model based on microRNA-1269a in esophageal cancer

BACKGROUND

Esophageal cancer (ESCA) is a heterogeneous cancer with variable outcomes that are challenging to predict. MicroRNA (miR)-1269a is a newly discovered non-coding RNA that shows promising prognostic prediction in other cancers, but its clinical value in ESCA remains unclear.

AIM

To explore the relationship between miR-1269a and its clinical value and to develop a nomogram to succinctly display this relationship.

METHODS

We analyzed the expression of miR-1269a in 125 ESCA tissue samples with complete clinical data and 52 normal tissue samples. We determined the prognostic value of miR-1269a for overall survival (OS) and cancer-specific survival (CSS) and evaluated the association between miR-1269a and clinical variables including tumor location, histologic grade, metastatic stage, and American Joint Committee on Cancer (AJCC) stage using multivariate Cox analysis. Additionally, we developed a nomogram for OS and CSS based on miR-1269a expression using age and AJCC stage and assessed its prognostic performance. Using Gene Ontology and Kyoto Encyclopedia of Gene and Genomes analyses, we predicted the target genes of miR-1269a and analyzed their potential function in caner development.

RESULTS

The expression of miR-1269a was significantly higher in ESCA patients than healthy controls. Patients with high expression of miR-1269a showed poor prognosis in OS and CSS, suffered increased rates of low differentiation and metastasis, and exhibited tumor stage T3 + T4, positive lymph stage, and AJCC stage III + IV. The area under the receiver operating characteristic curve of miR-1269a was 0.716 for OS and 0.764 for CSS. Multivariate Cox analysis revealed that AJCC stage and miR-1269a were independent factors for OS and CSS. Combing with age, we constructed a nomogram for prognostic prediction. Additionally, our nomogram showed excellent predictive performance for OS and CSS after 3 years and 5 years and was easy to use. Ultimately, the functional analysis suggested that miR-1269a was mostly involved in the PI3K-AKT signaling pathway.

CONCLUSION

miR-1269a can be used as a potential indicator for the prognosis of ESCA patients. We developed an easy-to-use nomogram with excellent ESCA prognostic prediction for clinical use.

Five long non-coding RNAs establish a prognostic nomogram and construct a competing endogenous RNA network in the progression of non-small cell lung cancer

BACKGROUND

Accumulating evidence has revealed that long non-coding RNAs (lncRNAs) play vital roles in the progression of non-small cell lung cancer (NSCLC). But the relationship between lncRNAs and survival outcome of NSCLC remains to be explored. Therefore, we attempt to figure out their survival roles and molecular connection in NSCLC.

METHODS

By analyzing the transcriptome profiling of NSCLC from TCGA databases, we divided patients into three groups, and identified differentially expressed lncRNAs (DELs) of each group. Next, we explored the prognostic roles of common DELs by univariate and multivariate Cox analysis, Lasson, and Kaplan-Meier analysis. Additionally, we assessed and compared the prognostic accuracy of 5 lncRNAs through ROC curves and AUC values. Ultimately, we detected their potential function by enrichment analysis and molecular connection through establishing a competing endogenous RNA (ceRNA) network.

RESULTS

One hundred ninety-seven common DELs were spotted. And we successfully screened out 5 lncRNAs related to the patient's survival, including LINC01833, AC112206.2, FAM83A-AS1, BANCR, and HOTAIR. Combing with age and AJCC stage, we constructed a nomogram that prognostic prediction was superior to the traditional parameters. Furthermore, 275 qualified mRNAs related to 5 lncRNAs were spotted. Functional analysis indicates that these lncRNAs act key roles in the progression of NSCLC, such as P53 and cell cycle signaling pathway. And ceRNA network also suggests that these lncRNAs are tightly connected with tumor progression.

CONCLUSIONS

A nomogram and ceRNA network based on 5 lncRNAs indicate that there can effectively predict the overall survival of NSCLC and potentially serve as a therapeutic guide for NSCLC.

Complications of the Arteriovenous Fistula: A Systematic Review

The implementation of patient-centered care requires an individualized approach to hemodialysis vascular access, on the basis of each patient's unique balance of risks and benefits. This systematic review aimed to summarize current literature on fistula risks, including rates of complications, to assist with patient-centered decision making. We searched Medline from 2000 to 2014 for English-language studies with prospectively captured data on ≥100 fistulas. We assessed study quality and extracted data on study design, patient characteristics, and outcomes. After screening 2292 citations, 43 articles met our inclusion criteria (61 unique cohorts; n>11,374 fistulas). Median complication rates per 1000 patient days were as follows: 0.04 aneurysms (14 unique cohorts; n=1827 fistulas), 0.11 infections (16 cohorts; n>6439 fistulas), 0.05 steal events (15 cohorts; n>2543 fistulas), 0.24 thrombotic events (26 cohorts; n=4232 fistulas), and 0.03 venous hypertensive events (1 cohort; n=350 fistulas). Risk of bias was high in many studies and event rates were variable, thus we could not present pooled results. Studies generally did not report variables associated with fistula complications, patient comorbidities, vessel characteristics, surgeon experience, or nursing cannulation skill. Overall, we found marked variability in complication rates, partly due to poor quality studies, significant heterogeneity of study populations, and inconsistent definitions. There is an urgent need to standardize reporting of methods and definitions of vascular access complications in future clinical studies to better inform patient and provider decision making.

Khan F, Schmitz U, Wolkenhauer O, Gupta SK. MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression

Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches.

Application of Parametric Models to a Survival Analysis of Hemodialysis Patients

Background

Hemodialysis is the most common renal replacement therapy in patients with end stage renal disease (ESRD).

Objectives

The present study compared the performance of various parametric models in a survival analysis of hemodialysis patients.

Methods

This study consisted of 270 hemodialysis patients who were referred to Imam Khomeini and Fatima Zahra hospitals between November 2007 and November 2012. The Akaike information criterion (AIC) and residuals review were used to compare the performance of the parametric models. The computations were done using STATA Software, with significance accepted at a level of 0.05.

Results

The results of a multivariate analysis of the variables in the parametric models showed that the mean serum albumin and the clinic attended were the most important predictors in the survival of the hemodialysis patients (P < 0.05). Among the parametric models tested, the results indicated that the performance of the Weibull model was the highest.

Conclusions

Parametric models may provide complementary data for clinicians and researchers about how risks vary over time. The Weibull model seemed to show the best fit among the parametric models of the survival of hemodialysis patients.

Temporal risk profile for infectious and noninfectious complications of hemodialysis access

Vascular access complications are a major cause of morbidity in patients undergoing hemodialysis, and determining how the risks of different complications vary over the life of an access may benefit the design of prevention strategies. We used data from the Dialysis Outcomes and Practice Patterns Study (DOPPS) to assess the temporal profiles of risks for infectious and noninfectious complications of fistulas, grafts, and tunneled catheters in incident hemodialysis patients. We used longitudinal data to model time from access placement or successful treatment of a previous complication to subsequent complication and considered multiple accesses per patient and repeated access complications using baseline and time-varying covariates to obtain adjusted estimates. Of the 7769 incident patients identified, 7140 received at least one permanent access. During a median follow-up of 14 months (interquartile range, 7-22 months), 10,452 noninfectious and 1131 infectious events (including 551 hospitalizations for sepsis) occurred in 112,085 patient-months. The hazards for both complication types declined over time in all access types: They were 5-10 times greater in the first 3-6 months than in later periods after access placement or a remedial access-related procedure. The hazards declined more quickly with fistulas than with grafts and catheters (P<0.001; Weibull regression). These data indicate that risks for noninfectious and infectious complications of the hemodialysis access decline over time with all access types and suggest that prevention strategies should target the first 6 months after access placement or a remedial access-related procedure.

Optimizing renal replacement therapy in older adults: a framework for making individualized decisions

It is often difficult to synthesize information about the risks and benefits of recommended management strategies in older patients with end-stage renal disease since they may have more comorbidity and lower life expectancy than patients described in clinical trials or practice guidelines. In this review, we outline a framework for individualizing end-stage renal disease management decisions in older patients. The framework considers three factors: life expectancy, the risks and benefits of competing treatment strategies, and patient preferences. We illustrate the use of this framework by applying it to three key end-stage renal disease decisions in older patients with varying life expectancy: choice of dialysis modality, choice of vascular access for hemodialysis, and referral for kidney transplantation. In several instances, this approach might provide support for treatment decisions that directly contradict available practice guidelines, illustrating circumstances when strict application of guidelines may be inappropriate for certain patients. By combining quantitative estimates of benefits and harms with qualitative assessments of patient preferences, clinicians may be better able to tailor treatment recommendations to individual older patients, thereby improving the overall quality of end-stage renal disease care.