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Expression of pre-selected TMEMs with predicted ER localization as potential classifiers of ccRCC tumors

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ABSTRACT

Background: VHL inactivation is the most established molecular characteristic of clear cell renal cell carcinoma (ccRCC), with only a few additional genes implicated in development of this kidney tumor. In recently published ccRCC gene expression meta-analysis study we identified a number of deregulated genes with limited information available concerning their biological role, represented by gene transcripts belonging to transmembrane proteins family (TMEMs). TMEMs are predicted to be components of cellular membranes, such as mitochondrial membranes, ER, lysosomes and Golgi apparatus. Interestingly, the function of majority of TMEMs remains unclear. Here, we analyzed expression of ten TMEM genes in the context of ccRCC progression and development, and characterized these proteins bioinformatically.

Methods: The expression of ten TMEMs (RTP3, SLC35G2, TMEM30B, TMEM45A, TMEM45B, TMEM61, TMEM72, TMEM116, TMEM207 and TMEM213) was measured by qPCR. T-test, Pearson correlation, univariate and multivariate logistic and Cox regression were used in statistical analysis. The topology of studied proteins was predicted with Metaserver, together with PSORTII, Pfam and Localizome tools.

Results: We observed significant deregulation of expression of 10 analyzed TMEMs in ccRCC tumors. Cluster analysis of expression data suggested the down-regulation of all tested TMEMs to be a descriptor of the most advanced tumors. Logistic and Cox regression potentially linked TMEM expression to clinical parameters such as: metastasis, Fuhrman grade and overall survival. Topology predictions classified majority of analyzed TMEMs as type 3 and type 1 transmembrane proteins, with predicted localization mainly in ER.

Conclusions: The massive down-regulation of expression of TMEM family members suggests their importance in the pathogenesis of ccRCC and the bioinformatic analysis of TMEM topology implies a significant involvement of ER proteins in ccRCC pathology.

Electronic supplementary material: The online version of this article (doi:10.1186/s12885-015-1530-4) contains supplementary material, which is available to authorized users.

No MeSH data available.


A comparison of TMEM expression in PBMCs. Average log2 relative expression data in each sample group ± standard error of mean is shown in each chart. FC – fold-change. C – control samples (PBMCs from healthy volunteers). T0 – PBMCs obtained from patients before nephrectomy. T2 – PBMCs obtained from patients 12 months after surgery. n – number of samples
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Fig4: A comparison of TMEM expression in PBMCs. Average log2 relative expression data in each sample group ± standard error of mean is shown in each chart. FC – fold-change. C – control samples (PBMCs from healthy volunteers). T0 – PBMCs obtained from patients before nephrectomy. T2 – PBMCs obtained from patients 12 months after surgery. n – number of samples

Mentions: To assess the utility of TMEM expression as potential biomarkers we analyzed their expression in PBMCs of ccRCC patients (see Table 1). According to the Illumina Human Body Map 2.0 project (NCBI Gene Expression Omnibus accession no. GSE30611) six out of ten selected TMEMs were found to be expressed in PBMCs and those were included in further analysis (i.e., SLC35G2, TMEM30B, TMEM45A, TMEM45B, TMEM116 and TMEM213). Although a comparison between expression of TMEMs in PBMCs obtained from patients before nephrectomy (T0) and from healthy volunteers did not reveal statistically significant differences, we observed significant down-regulation of TMEM213 (fold-change = −12.24, q < 0.0001, n = 17), down-regulation of TMEM45B (fold-change = −2.47, q < 0.1, n = 26) and up-regulation of SLC35G2 (fold-change = 2.57, q < 0.1, n = 25) between PBMCs of healthy volunteers and PBMCs obtained from patients one year post nephrectomy (T2, Fig. 4). Furthermore, we found statistically significant down-regulation of TMEM213 and up-regulation of SLC35G2, as comparing time points before (T0) and 12 months after the surgery (T2), suggesting fluctuation of their expression in PBMCs post-nephrectomy (Fig. 4). No correlation between TMEM expression in tumors and PBMCs was observed (data not shown). Logistic regression did not reveal the dependence of clinical parameters (i.e., tumor size, Fuhrman grade, metastases and progression status) on TMEM expression in PBMCs (data not shown). Univariate Cox regression did not suggest TMEM’s expression to be involved in disease progression or survival.Fig. 4


Expression of pre-selected TMEMs with predicted ER localization as potential classifiers of ccRCC tumors
A comparison of TMEM expression in PBMCs. Average log2 relative expression data in each sample group ± standard error of mean is shown in each chart. FC – fold-change. C – control samples (PBMCs from healthy volunteers). T0 – PBMCs obtained from patients before nephrectomy. T2 – PBMCs obtained from patients 12 months after surgery. n – number of samples
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Related In: Results  -  Collection

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Fig4: A comparison of TMEM expression in PBMCs. Average log2 relative expression data in each sample group ± standard error of mean is shown in each chart. FC – fold-change. C – control samples (PBMCs from healthy volunteers). T0 – PBMCs obtained from patients before nephrectomy. T2 – PBMCs obtained from patients 12 months after surgery. n – number of samples
Mentions: To assess the utility of TMEM expression as potential biomarkers we analyzed their expression in PBMCs of ccRCC patients (see Table 1). According to the Illumina Human Body Map 2.0 project (NCBI Gene Expression Omnibus accession no. GSE30611) six out of ten selected TMEMs were found to be expressed in PBMCs and those were included in further analysis (i.e., SLC35G2, TMEM30B, TMEM45A, TMEM45B, TMEM116 and TMEM213). Although a comparison between expression of TMEMs in PBMCs obtained from patients before nephrectomy (T0) and from healthy volunteers did not reveal statistically significant differences, we observed significant down-regulation of TMEM213 (fold-change = −12.24, q < 0.0001, n = 17), down-regulation of TMEM45B (fold-change = −2.47, q < 0.1, n = 26) and up-regulation of SLC35G2 (fold-change = 2.57, q < 0.1, n = 25) between PBMCs of healthy volunteers and PBMCs obtained from patients one year post nephrectomy (T2, Fig. 4). Furthermore, we found statistically significant down-regulation of TMEM213 and up-regulation of SLC35G2, as comparing time points before (T0) and 12 months after the surgery (T2), suggesting fluctuation of their expression in PBMCs post-nephrectomy (Fig. 4). No correlation between TMEM expression in tumors and PBMCs was observed (data not shown). Logistic regression did not reveal the dependence of clinical parameters (i.e., tumor size, Fuhrman grade, metastases and progression status) on TMEM expression in PBMCs (data not shown). Univariate Cox regression did not suggest TMEM’s expression to be involved in disease progression or survival.Fig. 4

View Article: PubMed Central - PubMed

ABSTRACT

Background: VHL inactivation is the most established molecular characteristic of clear cell renal cell carcinoma (ccRCC), with only a few additional genes implicated in development of this kidney tumor. In recently published ccRCC gene expression meta-analysis study we identified a number of deregulated genes with limited information available concerning their biological role, represented by gene transcripts belonging to transmembrane proteins family (TMEMs). TMEMs are predicted to be components of cellular membranes, such as mitochondrial membranes, ER, lysosomes and Golgi apparatus. Interestingly, the function of majority of TMEMs remains unclear. Here, we analyzed expression of ten TMEM genes in the context of ccRCC progression and development, and characterized these proteins bioinformatically.

Methods: The expression of ten TMEMs (RTP3, SLC35G2, TMEM30B, TMEM45A, TMEM45B, TMEM61, TMEM72, TMEM116, TMEM207 and TMEM213)&nbsp;was measured by qPCR. T-test, Pearson correlation, univariate and multivariate logistic and Cox regression were used in statistical analysis. The topology of studied proteins was predicted with Metaserver, together with PSORTII, Pfam and Localizome tools.

Results: We observed significant deregulation of expression of 10 analyzed TMEMs in ccRCC tumors. Cluster analysis of expression data suggested the down-regulation of all tested TMEMs to be a descriptor of the most advanced tumors. Logistic and Cox regression potentially linked TMEM expression to clinical parameters such as: metastasis, Fuhrman grade and overall survival. Topology predictions classified majority of analyzed TMEMs as type 3 and type 1 transmembrane proteins, with predicted localization mainly in ER.

Conclusions: The massive down-regulation of expression of TMEM family members suggests their importance in the pathogenesis of ccRCC and the bioinformatic analysis of TMEM topology implies a significant involvement of ER proteins in ccRCC pathology.

Electronic supplementary material: The online version of this article (doi:10.1186/s12885-015-1530-4) contains supplementary material, which is available to authorized users.

No MeSH data available.