Dataset Information

Establishing a detection method for CCNY: a potentially significant clinical investigative marker in NSCLC patients.

ABSTRACT:

Background

CCNY, a novel cyclin family member, plays an increasingly important role in the progression of tumor invasion and metastasis, including lung cancer. However, the clinical significance of CCNY in non-small-cell lung cancer (NSCLC) patients is unknown.

Patients and methods

We prepared CCNY monoclonal antibodies, validated specific peptides by a peptide array, and established a double-antibody sandwich ELISA detection method. Then, we measured CCNY levels in 100 NSCLC patients and 50 healthy controls. A blinded validation was subsequently performed in 399 NSCLC patients and 150 healthy controls.

Results

We successfully prepared two specific mouse anti-human CCNY monoclonal antibodies and established a reliable and stable detection method. In the training set, serum CCNY was markedly increased in the NSCLC patients (P<0.05) with an integrated area under the curve of 0.751. With further analysis of the CCNY levels, there were no differences in age, sex, smoking status, tumor location, histologic subtype, or tumor size, but differences were observed in lymphatic (P<0.001) and distant (P<0.001) metastases in NSCLC patients. The CCNY[+] patients had a shorter survival time and progression-free survival than CCNY[-] patients at 3-year follow-up (P<0.001). The results were confirmed by the validation set.

Conclusion

Our study suggests that CCNY may be useful as a latent tumor marker to facilitate diagnosis and may be an effective indicator of tumor aggressiveness, playing an important role in the prognosis of NSCLC patients.

SUBMITTER: Ma L

PROVIDER: S-EPMC6357874 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Establishing a detection method for CCNY: a potentially significant clinical investigative marker in NSCLC patients.

Ma Li L Gu Meng M Teng Yu Y Li Weiying W

OncoTargets and therapy 20190129

<h4>Background</h4>CCNY, a novel cyclin family member, plays an increasingly important role in the progression of tumor invasion and metastasis, including lung cancer. However, the clinical significance of CCNY in non-small-cell lung cancer (NSCLC) patients is unknown.<h4>Patients and methods</h4>We prepared CCNY monoclonal antibodies, validated specific peptides by a peptide array, and established a double-antibody sandwich ELISA detection method. Then, we measured CCNY levels in 100 NSCLC pati ...[more]

PMID: 30774378

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Project description:BACKGROUND: Presence of interaction between a genotype and certain factor in determination of a trait's value, it is expected that the trait's variance is increased in the group of subjects having this genotype. Thus, test of heterogeneity of variances can be used as a test to screen for potentially interacting single-nucleotide polymorphisms (SNPs). In this work, we evaluated statistical properties of variance heterogeneity analysis in respect to the detection of potentially interacting SNPs in a case when an interaction variable is unknown. RESULTS: Through simulations, we investigated type I error for Bartlett's test, Bartlett's test with prior rank transformation of a trait to normality, and Levene's test for different genetic models. Additionally, we derived an analytical expression for power estimation. We showed that Bartlett's test has acceptable type I error in the case of trait following a normal distribution, whereas Levene's test kept nominal Type I error under all scenarios investigated. For the power of variance homogeneity test, we showed (as opposed to the power of direct test which uses information about known interacting factor) that, given the same interaction effect, the power can vary widely depending on the non-estimable direct effect of the unobserved interacting variable. Thus, for a given interaction effect, only very wide limits of power of the variance homogeneity test can be estimated. Also we applied Levene's approach to test genome-wide homogeneity of variances of the C-reactive protein in the Rotterdam Study population (n = 5959). In this analysis, we replicate previous results of Pare and colleagues (2010) for the SNP rs12753193 (n = 21,799). CONCLUSIONS: Screening for differences in variances among genotypes of a SNP is a promising approach as a number of biologically interesting models may lead to the heterogeneity of variances. However, it should be kept in mind that the absence of variance heterogeneity for a SNP can not be interpreted as the absence of involvement of the SNP in the interaction network.

Project description:BackgroundLeprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type.MethodsTwo sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors.ResultsFor both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: -1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14-2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08-2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review.ConclusionsThe log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.

Dataset Information

Establishing a detection method for CCNY: a potentially significant clinical investigative marker in NSCLC patients.

Background

Patients and methods

Results

Conclusion

Publications

Establishing a detection method for CCNY: a potentially significant clinical investigative marker in NSCLC patients.

Similar Datasets

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