The maraca plot: A novel visualization of hierarchical composite endpoints

BACKGROUND

Hierarchical composite endpoints are complex endpoints combining outcomes of different types and different clinical importance into an ordinal outcome that prioritizes the clinically most important (e.g. most severe) event of a patient. Hierarchical composite endpoint can be analysed with the win odds, an adaptation of win ratio to include ties. One of the difficulties in interpreting hierarchical composite endpoint is the lack of proper tools for visualizing the treatment effect captured by hierarchical composite endpoint, given the complex nature of the endpoint which combines events of different types.

METHODS

Hierarchical composite endpoints usually combine time-to-event outcomes and continuous outcomes into a composite; hence, it is important to capture not only the shift from more severe categories to less severe categories in the active group in comparison to the control group (as in any ordinal endpoint), but also changes occurring within each category. We introduce the novel maraca plot which combines violin plots (with nested box plots) to visualize the density of the distribution of the continuous outcome and Kaplan-Meier plots for time-to-event outcomes into a comprehensive visualization.

CONCLUSION

The novel maraca plot is suggested for visualization of hierarchical composite endpoints consisting of several time-to-event outcomes and a continuous outcome. It has a very simple structure and therefore easily communicates both the overall treatment effect and the effect on individual components.

Survival analysis: A primer for the clinician scientists

Survival analysis is a collection of statistical procedures employed on time-to-event data. The outcome variable of interest is time until an event occurs. Conventionally, it dealt with death as the event, but it can handle any event occurring in an individual like disease, relapse from remission, and recovery. Survival data describe the length of time from a time of origin to an endpoint of interest. By time, we mean years, months, weeks, or days from the beginning of being enrolled in the study. The major limitation of time-to-event data is the possibility of an event not occurring in all the subjects during a specific study period. In addition, some of the study subjects may leave the study prematurely. Such situations lead to what is called censored observations as complete information is not available for these subjects. Life table and Kaplan-Meier techniques are employed to obtain the descriptive measures of survival times. The main objectives of survival analysis include analysis of patterns of time-to-event data, evaluating reasons why data may be censored, comparing the survival curves, and assessing the relationship of explanatory variables to survival time. Survival analysis also offers different regression models that accommodate any number of covariates (categorical or continuous) and produces adjusted hazard ratios for individual factor.

CCTs as new biomarkers for the prognosis of head and neck squamous cancer

The chaperonin-containing T-complex protein 1 (CCT) subunits participate in diverse diseases. However, little is known about their expression and prognostic values in human head and neck squamous cancer (HNSC). This article aims to evaluate the effects of CCT subunits regarding their prognostic values for HNSC. We mined the transcriptional and survival data of CCTs in HNSC patients from online databases. A protein-protein interaction network was constructed and a functional enrichment analysis of target genes was performed. We observed that the mRNA expression levels of CCT1/2/3/4/5/6/7/8 were higher in HNSC tissues than in normal tissues. Survival analysis revealed that the high mRNA transcriptional levels of CCT3/4/5/6/7/8 were associated with a low overall survival. The expression levels of CCT4/7 were correlated with advanced tumor stage. And the overexpression of CCT4 was associated with higher N stage of patients. Validation of CCTs' differential expression and prognostic values was achieved by the Human Protein Atlas and GEO datasets. Mechanistic exploration of CCT subunits by the functional enrichment analysis suggests that these genes may influence the HNSC prognosis by regulating PI3K-Akt and other pathways. This study implies that CCT3/4/6/7/8 are promising biomarkers for the prognosis of HNSC.

Prognostic Significance of Sodium Iodide Symporter and Deiodinase Enzymes mRNA Expression in Gastric Cancer

Context

Thyroid hormones (THs) are critically important for development, homeostasis, and metabolic regulation in mammals. Iodine, one of the constituents of TH, is actively supplied by sodium iodide symporter (NIS) into the thyroid gland. TH is subsequently transported to distant organs where its activation and deactivation is catalyzed by isoforms of deiodinases (DIOs). NIS protein has been known to overexpress in cancer cases of the breast and gastrointestinal organs. Recent studies show a possible role of DIOs in various cancers.

Aims

In the present investigation, the prognostic significance of NIS and DIO-1, 2 and 3 was studied in gastric cancer using a data mining bioinformatic approach.

Methods

"The Kaplan-Meier plotter" database was used for direct in silico validation in clinically relevant 876 gastric cancer patients with >15 years of follow-up information. After obtaining KM survival plots, hazard ratio and log-rank P value were calculated.

Results

Increased expression of NIS and DIO 1-3 is significantly associated with worsen overall survival of gastric cancer patients followed for 20 years. Prognostic roles of NIS and individual DIOs were assessed in different types of gastric cancer classified based on morphologies, human epidermal growth factor receptor-2 receptor status, treatment choices, and different clinicopathological features.

Conclusions

Based on these analyses, the present study found the indication of prognostic values of these genes. This information will contribute to better understanding of managing complex and heterogeneous gastric cancer. Further, these findings may be beneficial as a companion diagnostic tool predicting more accurate gastric cancer prognosis.

Expressions and prognostic values of the E2F transcription factors in human breast carcinoma

E2F transcription factors (E2Fs) are a family of transcription factors involved in cell proliferation, differentiation, and apoptosis. Their important roles in the development and metastasis of breast carcinoma (BC) have been discovered by previous in vitro and in vivo studies. Yet, expressions and distinct prognostic values of these eight E2Fs in human BC remain unclear in many respects. In this study, we aimed to reveal their roles in BC through analyzing the transcription and survival data of the E2Fs in BC patients from four online databases including ONCOMINE, Breast Cancer Gene-Expression Miner v4.1, cBioPortal for Cancer Genomics, and Kaplan–Meier Plotter. We found the overexpression of E2Fs in BC tissues compared with normal breast tissues, except for E2F4. Higher expression levels of E2Fs, except for E2F4 and E2F6, were associated with higher levels of Scarff–Bloom–Richardson grade of BC. Alterations of E2Fs were found to be significantly correlated with poorer overall survival of BC patients. Through plotting the survival curve in the Kaplan–Meier Plotter, it was found that higher mRNA levels of E2F1, E2F3, E2F7, and E2F8 were associated with poorer relapse-free survival in all BC patients, indicating that they are potential targets for individualized treatments of BC patients. Conversely, higher mRNA expression level of E2F4 predicted better RFS in BC patients, suggesting E2F4 as a new biomarker for BC prognosis. Considering currently available limited evidence, further studies need to be performed to investigate the roles of E2Fs in BC.