Igf-Ii Mrna Expression in Breast Cancer: Predictive Value and Relationship to Other Prognostic Factors

Fiore, Emilio; Campani, Daniela; Muller, Ilaria; Belardi, V.; Giustarini, E.; Rossi, Giovanni; Pinchera, Aldo; Giani, Carlotta

doi:10.1177/172460081002500305

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…To our knowledge there are no additional reports of these three markers in combination (or pair-wise combinations) in the breast cancer literature. Turning to single marker studies, our major low mortality group defined by p53 ≤ 75 th percentile and TGF β receptor II ≤ 66 th percentile is consistent with literature results for p53, as increased p53 expression has been implicated in poor breast cancer prognosis [42], but potentially inconsistent with results for TGF β receptor II since loss of TGF β receptor II function has been implicated in breast cancer metastasis [43]. There is limited breast cancer literature on Na-KATPase-β1, but elevated levels were found in African American breast cancer cells in comparison to Caucasian, where the former cancer is typically more aggressive [44].…”

Section: Discussionsupporting

confidence: 84%

Protein expression based multimarker analysis of breast cancer samples

et al. 2011

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BackgroundTissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.MethodsWe apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.ResultsWe find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.ConclusionsWe show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.

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Section: Discussionsupporting

confidence: 84%

Protein expression based multimarker analysis of breast cancer samples

et al. 2011

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“…The mRNA levels of Hsp90-beta and annexin A1 exhibited positive staining in the cytoplasm. A specific scoring method for ISH was performed according to a previously published report [12]. The scoring method was as follows: according to the signal intensity, the signals were divided into 4 groups, namely, absent (0), low (+), moderate (++), and high (+++).…”

Section: Methodsmentioning

confidence: 99%

Upregulation of Hsp90-beta and annexin A1 correlates with poor survival and lymphatic metastasis in lung cancer patients

Biaoxue

Jiang

Yang

et al. 2012

J Exp Clin Cancer Res

100

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BackgroundHsp90-beta and annexin A1 were investigated as prognostic factors because of their apparent association with tumorigenesis. However, the effect of Hsp90-beta and annexin A1 in lung cancer remains poorly understood. The expressions of Hsp90-beta and annexin A1 in lung cancer and normal lung specimens were examined, and the relationships with respect to the clinico-pathological features and patient survival in lung cancer were analyzed.MethodsThe expression levels of Hsp90-beta and annexin A1 were examined using immunohistochemistry, in-situ hybridization, and Western blot.ResultsLung cancer tissues exhibited higher expression levels of Hsp90-beta and annexin A1 than the normal tissues (p < 0.05), and the expression levels of the markers were significantly associated with the pathological grade and lymphatic invasion of lung cancer (p < 0.05). Moreover, the upregulation of Hsp90-beta and annexin A1 correlated with decreased survival (p < 0.05).ConclusionThe upregulation of Hsp90-beta and annexin A1 were associated with poor post-surgical survival time and lymphatic metastasis of lung cancer patients. Moreover, the high expression of the markers was an independent predictor of poor outcomes.

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“…Type II diabetes mellitus is associated with an increased risk to develop many different types of cancer, including BC [185]. The precise mechanisms involved are unknown, however increased insulin levels and IGF signaling likely play a role [186][187][188], as well as the dysregulation of ovarian steroid hormones and chronic inflammation [189].…”

Section: Risk Factors For Bcmentioning

confidence: 99%

The antigenic link between thyroid autoimmunity and breast cancer

Muller

Barrett-Lee

2020

Seminars in Cancer Biology

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The association between breast cancer and benign thyroid disorders, in particular thyroid autoimmunity, has been debated for decades. Autoantibodies to thyroid peroxidase, the hallmark of thyroid autoimmunity, have a higher prevalence among patients with breast cancer compared with the general population. Furthermore a correlation between their positivity and a better prognosis of breast cancer was found in several independent small-scale studies, even if such observation was not confirmed in a subsequent retrospective study conducted on the largest patient cohort to date. The thyroid and mammary glands present several biological similarities, therefore the hypothesis of an immune response to shared thyroid/breast antigens could in part explain the association between thyroid autoimmunity and breast cancer. The sodium iodide symporter is expressed in both glands, however it seems unlikely to be the key common antigen, considering that autoantibodies targeting it are rare. Instead thyroid peroxidase, one of the major thyroid autoantigens, is also expressed in breast tissue and therefore represents the main antigenic link between thyroid autoimmunity and breast cancer. Furthermore lactoperoxidase, an enzyme of the same family that shares structural similarities with thyroid peroxidase, is expressed in neoplastic breast cells and is responsible for the cross-reactivity with some autoantibodies to thyroid peroxidase. Novel strategies for the diagnosis and treatment of breast cancer might take advantage of the antigenic link between thyroid and breast tissues.

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Igf-Ii Mrna Expression in Breast Cancer: Predictive Value and Relationship to Other Prognostic Factors

Abstract: IGF-II mRNA expression per se is not an independent predictive factor in breast cancer but may be a marker of poor prognosis when associated with other prognostic factors such as Ki-67 index and p53 expression.

Cited by 8 publications

References 20 publications

Protein expression based multimarker analysis of breast cancer samples

Protein expression based multimarker analysis of breast cancer samples

Upregulation of Hsp90-beta and annexin A1 correlates with poor survival and lymphatic metastasis in lung cancer patients

The antigenic link between thyroid autoimmunity and breast cancer

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