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Programmed cell death 4 (PDCD4) as a novel prognostic marker for papillary thyroid carcinoma.

ABSTRACT: Background:The primary goal of papillary thyroid cancer (PTC) management was to stratify patients at pre- and post-surgical level to identify the small proportion of cases with potentially aggressive disease. Purpose:The aim of our study is to evaluate the possible role of programmed cell death 4 (PDCD4) and BRAF status as prognostic markers in PTC. Patients and methods:We investigate programmed cell death 4 (PDCD4) immunohistochemical expression in 125 consecutive PTCs with median follow-up of 75.3 months (range, 15-98 months) to verify the possible correlation between BRAF status and correlate the classical clinicopathological prognostic factors and PTC outcome with PDCD4 expression. To further support the data, miR-21 expression was tested (by quantitative real-time PCR and in situ hybridization) in a different series of 30 cases (15 PTCs BRAFwt and 15 PTCs BRAFV600E). Moreover, we validated our results using TGCA thyroid carcinoma dataset. Results:We found that 59.8% of the patients showed low-grade PDCD4 nuclear expression and low-grade expression correlated with BRAF V600E. Compared with BRAF 15 wild-type tissue samples, a significant miR-21 up-regulation was associated with BRAF V600E mutations. Low-grade PDCD4 resulted, and was associated with aggressive histological variants, higher cancer size, extra-thyroidal extension, multifocality, lymph-node metastasis and lymph nodal ratio at the diagnosis. Concerning the outcome, the low-grade PDCD4 expression correlated at univariate and multivariate analysis, with lower levels of recurrence-free survival rate (RFS) and with poor outcome. Moreover, there was significant association between BRAF V600E patients with PDCD4 nuclear loss and lower RFS, whilet here was significant association between BRAF wild-type patients with PDCD4 nuclear expression and better outcome. Conclusion:These results showed that PDCD4 could predict PTC outcome and that the sum of PDCD4 and BRAF alterations increases the prognostic power of BRAF mutation alone.

SUBMITTER: Galuppini F

PROVIDER: S-EPMC6708393 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Programmed cell death 4 (PDCD4) as a novel prognostic marker for papillary thyroid carcinoma.

Galuppini Francesca F Fassan Matteo M Bertazza Loris L Barollo Susi S Cascione Luciano L Watutantrige-Fernando Sara S Lazzarin Vanni V Simonato Paolo P Vianello Federica F Rugge Massimo M Mian Caterina C Pennelli Gianmaria G

Cancer management and research 20190820

<h4>Background</h4>The primary goal of papillary thyroid cancer (PTC) management was to stratify patients at pre- and post-surgical level to identify the small proportion of cases with potentially aggressive disease.<h4>Purpose</h4>The aim of our study is to evaluate the possible role of programmed cell death 4 (PDCD4) and BRAF status as prognostic markers in PTC.<h4>Patients and methods</h4>We investigate programmed cell death 4 (PDCD4) immunohistochemical expression in 125 consecutive PTCs wit ...[more]

PMID: 31692513

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Project description:BACKGROUND:We present a functional gene association network of the CLIP2 gene, generated by de-novo reconstruction from transcriptomic microarray data. CLIP2 was previously identified as a potential marker for radiation induced papillary thyroid carcinoma (PTC) of young patients in the aftermath of the Chernobyl reactor accident. Considering the rising thyroid cancer incidence rates in western societies, potentially related to medical radiation exposure, the functional characterization of CLIP2 is of relevance and contributes to the knowledge about radiation-induced thyroid malignancies. METHODS:We generated a transcriptomic mRNA expression data set from a CLIP2-perturbed thyroid cancer cell line (TPC-1) with induced CLIP2 mRNA overexpression and siRNA knockdown, respectively, followed by gene-association network reconstruction using the partial correlation-based approach GeneNet. Furthermore, we investigated different approaches for prioritizing differentially expressed genes for network reconstruction and compared the resulting networks with existing functional interaction networks from the Reactome, Biogrid and STRING databases. The derived CLIP2 interaction partners were validated on transcript and protein level. RESULTS:The best reconstructed network with regard to selection parameters contained a set of 20 genes in the 1st neighborhood of CLIP2 and suggests involvement of CLIP2 in the biological processes DNA repair/maintenance, chromosomal instability, promotion of proliferation and metastasis. Peptidylprolyl Isomerase Like 3 (PPIL3), previously identified as a potential direct interaction partner of CLIP2, was confirmed in this study by co-expression at the transcript and protein level. CONCLUSION:In our study we present an optimized preselection approach for genes subjected to gene-association network reconstruction, which was applied to CLIP2 perturbation transcriptome data of a thyroid cancer cell culture model. Our data support the potential carcinogenic role of CLIP2 overexpression in radiation-induced PTC and further suggest potential interaction partners of the gene.

Project description:BackgroundOsteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.MethodWe retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.ResultIn our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.ConclusionThe five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

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Programmed cell death 4 (PDCD4) as a novel prognostic marker for papillary thyroid carcinoma.

Publications

Programmed cell death 4 (PDCD4) as a novel prognostic marker for papillary thyroid carcinoma.

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