2024MAY10: Our hosting provider is experiencing intermittent networking issues. We apologize for any inconvenience.

Searching across hundreds of databases

Our searching services are busy right now. Your search will reload in five seconds.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.

Search

Type in a keyword to search

On page 1 showing 1 ~ 3 papers out of 3 papers

Soluble Serum αKlotho Is a Potential Predictive Marker of Disease Progression in Clear Cell Renal Cell Carcinoma.

  • Margherita Gigante‎ et al.
  • Medicine‎
  • 2015‎

Renal cell carcinoma (RCC) accounts for approximately 3% of adult malignancies, and clear cell RCC (ccRCC), that has a high metastatic index and high relapse rate, is the most common histological subtype. The identification of new biomarkers in ccRCC is fundamental for stratifying patients into prognostic risk groups and to guide therapy. The renoprotective antiaging gene, αKlotho, has recently been found to work as a tumor suppressor in different human cancers. Here, we evaluated αKlotho expression in tissue and serum of ccRCC patients and correlated it with disease progression. Tissue αKlotho expression was studied by quantitative RT-PCR and immunohistochemistry. In addition, soluble serum αKlotho levels were preoperatively measured in 160 patients who underwent nephrectomy for RCC with ELISA. Estimates of cancer-specific (CSS) and progression-free survival (PFS) were calculated according to the Kaplan-Meier method. Multivariate analysis was performed to identify the most significant variables for predicting CSS and PFS. αKlotho protein levels were significantly decreased in RCC tissues compared with normal tissues (P < 0.01) and the more advanced the disease, the more evident the down-regulation. This trend was also observed in serum samples. Statistically significant differences resulted between serum αKlotho levels and tumor size (P = 0.003), Fuhrman grade (P = 0.007), and clinical stage (P = 0.0004). CSS and PFS were significantly shorter in patients with lower levels of αKlotho (P < 0.0001 and P = 0.0004, respectively). At multivariate analysis low serum levels of αKlotho were independent adverse prognostic factors for CSS (HR = 2.11; P = 0.03) and PFS (HR = 2.18; P = 0.03).These results indicate that a decreased αKlotho expression is correlated with RCC progression, and suggest a key role of declining αKlotho in the onset of cancer metastasis.


Increased Expression of the Autocrine Motility Factor is Associated With Poor Prognosis in Patients With Clear Cell-Renal Cell Carcinoma.

  • Giuseppe Lucarelli‎ et al.
  • Medicine‎
  • 2015‎

Glucose-6-phosphate isomerase (GPI), also known as phosphoglucose isomerase, was initially identified as the second glycolytic enzyme that catalyzes the interconversion of glucose-6-phosphate to fructose-6-phosphate. Later studies demonstrated that GPI was the same as the autocrine motility factor (AMF), and that it mediates its biological effects through the interaction with its surface receptor (AMFR/gp78). In this study, we assessed the role of GPI/AMF as a prognostic factor for clear cell renal cell carcinoma (ccRCC) cancer-specific (CSS) and progression-free survival (PFS). In addition, we evaluated the expression and localization of GPI/AMF and AMFR, using tissue microarray-based immunohistochemistry (TMA-IHC), indirect immunofluorescence (IF), and confocal microscopy analysis.Primary renal tumor and nonneoplastic tissues were collected from 180 patients who underwent nephrectomy for ccRCC. TMA-IHC and IF staining showed an increased signal for both GPI and AMFR in cancer cells, and their colocalization on plasma membrane. Kaplan-Meier curves showed significant differences in CSS and PFS among groups of patients with high versus low GPI expression. In particular, patients with high tissue levels of GPI had a 5-year survival rate of 58.8%, as compared to 92.1% for subjects with low levels (P < 0.0001). Similar findings were observed for PFS (56.8% vs 93.3% at 5 years). At multivariate analysis, GPI was an independent adverse prognostic factor for CSS (HR = 1.26; P = 0.001), and PFS (HR = 1.16; P = 0.01).In conclusion, our data suggest that GPI could serve as a marker of ccRCC aggressiveness and a prognostic factor for CSS and PFS.


Pre-existing type 2 diabetes mellitus is an independent risk factor for mortality and progression in patients with renal cell carcinoma.

  • Antonio Vavallo‎ et al.
  • Medicine‎
  • 2014‎

Malignancies are one of the main causes of mortality in diabetic patients; however, to date, very limited data have been reported on the specific influence of type 2 diabetes mellitus (T2DM) on the survival of patients with renal cell carcinoma (RCC). In the present long-term retrospective study, we investigated whether T2DM may influence the overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) in patients with surgically treated RCC. Medical records of 924 patients treated by radical or partial nephrectomy for sporadic, unilateral RCC were reviewed. Patients with type-1 DM and with T2 DM receiving insulin treatment were excluded. Survival estimates were calculated according to the Kaplan-Meier method and compared with the log-rank test. Univariate and multivariate analyses were performed using the Cox regression model.Of the 924 RCC patients, 152 (16.5%) had T2DM. Mean follow-up was 68.5 months. Mean OS was 41.3 and 96.3 months in T2DM and non-T2DM patients, respectively (P < 0.0001).The estimated CSS rates at 1, 3, and 5 years in T2DM versus non-T2DM patients were 63.4% versus 76.7%, 30.4% versus 56.6%, and 16.3% versus 48.6%, respectively (P = 0.001). Mean PFS was significantly lower (31.5 vs 96.3 months; P < 0.0001) in the T2DM group. At multivariate analysis, T2DM was an independent adverse prognostic factor for OS (hazard ratio [HR]  = 3.44; 95% confidence interval [CI]:2.40-4.92), CSS (HR = 6.39; 95% CI: 3.78-10.79), and PFS (HR = 4.71; 95% CI: 3.11-7.15). In conclusion, our findings suggest that patients with RCC and pre-existing T2DM have a shorter OS, increased risk of recurrence, and higher risk for kidney cancer mortality than those without diabetes.


  1. SciCrunch.org Resources

    Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.

  2. Navigation

    You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.

  3. Logging in and Registering

    If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.

  4. Searching

    Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:

    1. Use quotes around phrases you want to match exactly
    2. You can manually AND and OR terms to change how we search between words
    3. You can add "-" to terms to make sure no results return with that term in them (ex. Cerebellum -CA1)
    4. You can add "+" to terms to require they be in the data
    5. Using autocomplete specifies which branch of our semantics you with to search and can help refine your search
  5. Save Your Search

    You can save any searches you perform for quick access to later from here.

  6. Query Expansion

    We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.

  7. Collections

    If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.

  8. Facets

    Here are the facets that you can filter your papers by.

  9. Options

    From here we'll present any options for the literature, such as exporting your current results.

  10. Further Questions

    If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.

Publications Per Year

X

Year:

Count: