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      MicroRNA‑21 contributes to renal cell carcinoma cell invasiveness and angiogenesis via the PDCD4/c‑Jun (AP‑1) signalling pathway

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          Abstract

          <p class="first" id="d1144488e267">Accumulating evidence has demonstrated that microRNAs are associated with malignant biological behaviour, including tumorigenesis, cancer progression and metastasis via the regulation of target gene expression. Our previous study demonstrated that programmed cell death protein 4 (PDCD4), which is a tumour suppressor gene, is a target of microRNA-21 (miR-21), which affects the proliferation and transformation capabilities of renal cell carcinoma (RCC) cells. However, the role of miR-21 in the molecular mechanism underlying the migration, invasion and angiogenesis of RCC remains poorly understood. The effects of miR-21 on the invasion, migra tion and angiogenesis of RCC cells was determined through meta-analysis and regulation of miR-21 expression <i>in vitro</i>. After searching several databases, 6 articles including a total of 473 patients met the eligibility criteria for this analysis. The combined results of the meta-analysis revealed that increased miR-21 expression was significantly associated with adverse prognosis in patients with RCC, with a pooled hazard ratio estimate of 1.740. In <i>in vitro</i> experiments, we demonstrated that a miR-21 inhibitor decreased the number of migrating and invading A498 and 786-O RCC cells, along with a decrease in PDCD4, c-Jun, matrix metalloproteinase (MMP)2 and MMP9 expression. Additionally, inhibition of miR-21 was revealed to reduce tube formation and tube junctions in the endothelial cell line HMEC-1 by affecting the expression of angiotensin-1 and vascular endothelial growth factor A, whereas PDCD4 small interfering RNA exerted opposite effects on the same cells. Overall, these findings, along with evidence-based molecular biology, demonstrated that miR-21 expression promoted the migration, invasion and angiogenic abilities of RCC cells by directly targeting the PDCD4/c-Jun signalling pathway. The results may help elucidate the molecular mechanism under lying the development and progression of RCC and provide a promising target for microRNA-based therapy. </p>

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Cancer statistics, 2018

            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2014, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2015, were collected by the National Center for Health Statistics. In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2005-2014) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2006-2015) declined by about 1.5% annually in both men and women. The combined cancer death rate dropped continuously from 1991 to 2015 by a total of 26%, translating to approximately 2,378,600 fewer cancer deaths than would have been expected if death rates had remained at their peak. Of the 10 leading causes of death, only cancer declined from 2014 to 2015. In 2015, the cancer death rate was 14% higher in non-Hispanic blacks (NHBs) than non-Hispanic whites (NHWs) overall (death rate ratio [DRR], 1.14; 95% confidence interval [95% CI], 1.13-1.15), but the racial disparity was much larger for individuals aged <65 years (DRR, 1.31; 95% CI, 1.29-1.32) compared with those aged ≥65 years (DRR, 1.07; 95% CI, 1.06-1.09) and varied substantially by state. For example, the cancer death rate was lower in NHBs than NHWs in Massachusetts for all ages and in New York for individuals aged ≥65 years, whereas for those aged <65 years, it was 3 times higher in NHBs in the District of Columbia (DRR, 2.89; 95% CI, 2.16-3.91) and about 50% higher in Wisconsin (DRR, 1.78; 95% CI, 1.56-2.02), Kansas (DRR, 1.51; 95% CI, 1.25-1.81), Louisiana (DRR, 1.49; 95% CI, 1.38-1.60), Illinois (DRR, 1.48; 95% CI, 1.39-1.57), and California (DRR, 1.45; 95% CI, 1.38-1.54). Larger racial inequalities in young and middle-aged adults probably partly reflect less access to high-quality health care. CA Cancer J Clin 2018;68:7-30. © 2018 American Cancer Society.
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              Is Open Access

              Practical methods for incorporating summary time-to-event data into meta-analysis

              Background In systematic reviews and meta-analyses, time-to-event outcomes are most appropriately analysed using hazard ratios (HRs). In the absence of individual patient data (IPD), methods are available to obtain HRs and/or associated statistics by carefully manipulating published or other summary data. Awareness and adoption of these methods is somewhat limited, perhaps because they are published in the statistical literature using statistical notation. Methods This paper aims to 'translate' the methods for estimating a HR and associated statistics from published time-to-event-analyses into less statistical and more practical guidance and provide a corresponding, easy-to-use calculations spreadsheet, to facilitate the computational aspects. Results A wider audience should be able to understand published time-to-event data in individual trial reports and use it more appropriately in meta-analysis. When faced with particular circumstances, readers can refer to the relevant sections of the paper. The spreadsheet can be used to assist them in carrying out the calculations. Conclusion The methods cannot circumvent the potential biases associated with relying on published data for systematic reviews and meta-analysis. However, this practical guide should improve the quality of the analysis and subsequent interpretation of systematic reviews and meta-analyses that include time-to-event outcomes.
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                Author and article information

                Journal
                International Journal of Oncology
                Int J Oncol
                Spandidos Publications
                1019-6439
                1791-2423
                December 02 2019
                December 02 2019
                Affiliations
                [1 ]Department of Urology, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, P.R. China
                [2 ]Department of Biochemistry, Institute of Glycobiology, Dalian Medical University, Dalian, Liaoning 116044, P.R. China
                [3 ]Department of Pharmacy, Zhongshan College of Dalian Medical University, Dalian, Liaoning 116011, P.R. China
                [4 ]Department of Clinical Medicine, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, P.R. China
                [5 ]Department of Anatomy, College of Basic Medicine, Dalian Medical University, Dalian, Liaoning 116044, P.R. China
                [6 ]Department of Oral Pathology, College of Stomatology of Dalian Medical University, Dalian, Liaoning 116044, P.R. China
                Article
                10.3892/ijo.2019.4928
                6910186
                31789394
                57fb839d-5812-4eab-a373-1cef1dc5b686
                © 2019
                History

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