Knockdown of MON1B Exerts Anti-Tumor Effects in Colon Cancer In Vitro

miRNAs as predictive biomarkers of response to treatment in pediatric patients with acute lymphoblastic leukemia

MicroRNAs (miRNAs/miRs) are promising prognostic biomarkers in pediatric acute lymphoblastic leukemia (ALL). The present study aimed to identify miRNAs that could serve as prognostic biomarkers or as novel therapeutic targets in ALL. The expression levels of 84 miRNAs were assessed in the bone marrow aspirates of 10 pediatric patients with newly diagnosed ALL at diagnosis and on day 33 of induction of the ALL Intercontinental Berlin-Frankfurt-Münster 2009 protocol, and associations with established prognostic factors were evaluated. The levels at diagnosis of 25 miRNAs were associated with ≥2 prognostic factors. Higher expression levels of let-7c-5p, miR-106b-5p, miR-26a-5p, miR-155-5p, miR-191-5p, miR-30b-5p and miR-31-5p were significantly associated with a good prednisone response. The expression levels of miR-125b-5p, miR-150-5p and miR-99a-5p were significantly higher in standard- or intermediate-risk patients compared with those in high-risk patients (P=0.017, P=0.033 and P=0.017, respectively), as well as in those with a complete response at the end of induction (P=0.044 for all three miRNAs). The change in expression levels between diagnosis and the end of induction differed significantly between risk groups for three miRNAs: miR-206, miR-210 and miR-99a (P=0.033, P=0.047 and P=0.008, respectively), with the post induction levels of miR-206 increased in high-risk patients, whilst miR-210 and miR-99a levels were increased in intermediate/standard risk patients. Therefore, miRNAs that could be integrated into the risk stratification of pediatric ALL after further evaluation in larger patient cohorts were identified.

Gene Screening in High-Throughput Right-Censored Lung Cancer Data

Background

Advances in sequencing technologies have allowed collection of massive genome-wide information that substantially advances lung cancer diagnosis and prognosis. Identifying influential markers for clinical endpoints of interest has been an indispensable and critical component of the statistical analysis pipeline. However, classical variable selection methods are not feasible or reliable for high-throughput genetic data. Our objective is to propose a model-free gene screening procedure for high-throughput right-censored data, and to develop a predictive gene signature for lung squamous cell carcinoma (LUSC) with the proposed procedure.

Methods

A gene screening procedure was developed based on a recently proposed independence measure. The Cancer Genome Atlas (TCGA) data on LUSC was then studied. The screening procedure was conducted to narrow down the set of influential genes to 378 candidates. A penalized Cox model was then fitted to the reduced set, which further identified a 6-gene signature for LUSC prognosis. The 6-gene signature was validated on datasets from the Gene Expression Omnibus.

Results

Both model-fitting and validation results reveal that our method selected influential genes that lead to biologically sensible findings as well as better predictive performance, compared to existing alternatives. According to our multivariable Cox regression analysis, the 6-gene signature was indeed a significant prognostic factor (p-value < 0.001) while controlling for clinical covariates.

Conclusions

Gene screening as a fast dimension reduction technique plays an important role in analyzing high-throughput data. The main contribution of this paper is to introduce a fundamental yet pragmatic model-free gene screening approach that aids statistical analysis of right-censored cancer data, and provide a lateral comparison with other available methods in the context of LUSC.