Open Access

Comprehensive evaluation of FKBP10 expression and its prognostic potential in gastric cancer

  • Authors:
    • Liang Liang
    • Kun Zhao
    • Jin‑Hui Zhu
    • Gang Chen
    • Xin‑Gan Qin
    • Jun‑Qiang Chen
  • View Affiliations

  • Published online on: June 11, 2019     https://doi.org/10.3892/or.2019.7195
  • Pages: 615-628
  • Copyright: © Liang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

FK506 binding protein 10 (FKBP10) has been reported to be dysregulated in numerous types of cancer; however, few reports have investigated FKBP10 in gastric cancer (GC). The aim of the present study was to investigate FKBP10 expression in GC and to analyze its association with the prognosis of patients with GC. FKBP10 mRNA expression was evaluated using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The standardized mean differences of the meta‑analysis were comprehensively evaluated for FKBP10 expression from a series of GEO datasets. Kaplan‑Meier survival and Cox regression analyses were applied to predict the prognostic value of FKBP10 in patients with GC. Additionally, the protein expression levels of FKBP10 were validated by immunohistochemistry (IHC) in 40 GC and adjacent tissues. FKBP10 co‑expression network and bioinformatics analyses were then used to explore the potential functional mechanisms of FKBP10. The results revealed that the mRNA expression levels of FKBP10 were significantly increased in GC within the TCGA and GEO databases. Survival analysis revealed that high FKBP10 expression results in poorer overall survival and disease‑free survival (P<0.05). Multivariate cox regression analysis indicate FKBP10 as a dependent prognostic factor. The results of IHC indicated that the protein expression levels of FKBP10 were higher in GC tissues than in adjacent non‑GC tissues (P<0.001). Co‑expression networks and functional enrichment analysis suggested that FKBP10 may be involved in the development of GC via cell adhesion molecules and extracellular matrix‑receptor interaction pathways. Therefore, the findings of the present study indicated that FKBP10 is upregulated in GC tissues, and suggests its potential prognostic value. Therefore FKBP10 may be a potential therapeutic target for the treatment of GC.

Introduction

Gastric cancer (GC) is a common malignancy worldwide. The incidence of GC is highest in East Asian countries, including Korea, Mongolia, Japan and China, with 40–60 cases per 100,000 individuals, followed by Eastern Europe (~35 cases per 100,000 people) (1,2). According to a statistical report, there were ~679,000 new cases of GC and 498,000 associated mortalities in China in 2015 (3). Current treatments for GC include surgery, chemotherapy, radiation and immunotherapy, all of which can be administered alone or in combination (4). Adjuvant treatment has been shown to be beneficial for GC (4,5). In Japan, early diagnosis via endoscopy and early tumor resection are used to improve the 5-year survival rate of patients with GC (5,6). Those with GC often present symptoms only in the later stages; however, the majority of patients do not receive medical attention until symptoms present. At the time of definitive diagnosis, many patients with GC are of an advanced stage of disease, at which point treatment is less effective. Despite advancements in treatment, no significant improvement in the prognosis of patients with GC have been reported; the 5-year overall survival rate was determined to be 30–35% (7). Thus, highly sensitive biomarkers to increase the sensitivity of early diagnostic methods for GC are of great interest for the development high-specificity drugs.

FK506 binding protein (FKBP65) is a 65-kDa protein and highly conserved; almost all FKBP family members have peptide precursor cis-trans isomerase activity (8). FKBP prolyl isomerase 10 (FKBP10) is a gene encoding FKBP65, and is a member of a group of proteins termed the immunophilins, belonging to the FKBP-type peptidylprolyl cis/trans isomerase family (9,10). It is located in the endoplasmic reticulum, and is a molecular chaperone that interacts with collagen (11); FKBP10 has been reported to directly interact with collagen I (11). As an important intracellular regulatory factor for extracellular matrix (ECM) reconstruction, FKBP10 is an important potential target for the treatment of idiopathic pulmonary fibrosis (12,13). In addition, it is increasingly apparent that FKBP members serve a very important role in the formation of tumors and may be considered as novel biomarkers of cancer (14,15). For example, FKBP10 is associated with ovarian cancer (16,17), lung cancer (18), prostate cancer (19), leukemia (20), renal cell carcinoma (21) and colorectal cancer (22).

In recent years, numerous studies have identified genes related to the prognosis of GC (2325). Some of these genes can act as prognostic factors for GC, yet the prognostic potential of these genes as biomarkers in GC remains unknown. The importance of differentially expressed FKBP10 in GC and its prognostic value in patients with GC require further investigation.

In the present study, the differential expression levels of FKBP10 mRNA in GC and normal tissues were compared using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases; the removal of batch effects on same type platforms was performed in our GEO analysis. In addition, Kaplan-Meier survival and cox regression analyses were conducted to explore the potential prognostic value of FKBP10 expression in patients with GC.

Materials and methods

Data extraction of FKBP10 expression from GEO and TCGA databases

The data were limited to microarray and RNA sequencing uploaded before August 2018. Mesh-terms and free words were used for increasing the search parameters in the GEO databases (https://www.ncbi.nlm.nih.gov/geo/). The search terms were: ‘Cancer’, ‘tumor’, ‘carcinoma’ or ‘neoplasm’, and ‘gastric’ or ‘stomach’. ‘Homo sapiens’ was used to limit the search range. In total, 20 microarrays containing 957 samples of GC and 536 samples of paracancer tissues with FKBP10 expression information were downloaded (Table I). The normalized expression value and the median expression value were obtained from multiple probes of FKBP10. The data of FKBP10 expression from the TCGA database (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) were downloaded with University of Santa Cruz Xena (https://xena.ucsc.edu/), which provided a normalized count of gene-level transcription.

Table I.

Information of elected Gene Expression Omnibus series dataset.

Table I.

Information of elected Gene Expression Omnibus series dataset.

Case

IDTypeCountryGCnon-GC
GSE29272GPL96USA134134
GSE37023GPL96Singapore11239
GSE54129GPL570China11121
GSE64951GPL570USA6331
GSE13911GPL570Italy3831
GSE19826GPL570China1215
GSE79973GPL570China1010
GSE51725GPL570Japan  8  2
GSE27342GPL5175USA8080
GSE63089GPL5175China4545
GSE13195GPL5175China2525
GSE33335GPL5175China2525
GSE56807GPL5175China5  5
GSE26899GPL6947USA9612
GSE13861GPL6884USA6519
GSE65801GPL14550China3232
GSE103236GPL4133Romania10  9
GSE84787GPL17077China1010

[i] GC, gastric cancer.

Detection of FKBP10 mRNA expression levels in gastric cancer

To investigate the expression of FKBP10, Gene Expression Profiling Interactive Analysis (GEPIA) (26) was used to retrieve expression data of GC tissues. In addition, data on FKBP10 expression in ~1,000 cell lines were provided by The Cancer Cell Line Encyclopedia (https://portals.broadinstitute.org/ccle). Except for the TCGA data, FKBP10 expression was obtained from a microarray series GEO dataset. After comparing FKBP10 expression in single series datasets, same-type microarray platforms were combined to expand sample capacity, in order to comprehensively analyze the expression of FKBP10. The removal of batch effects across platforms was performed using the ‘sva’ Bioconductor package of R (v3.5.0) (26). The standardized mean difference (SMD) method was used to assess the continuous variable, FKBP10 expression. Data from 19 gene microarrays were combined with a random effects model when heterogeneity (I2)>50%. The results were presented as forest plots. Sensitivity analyses and publication bias were used to evaluate the combined quality. Continuous variables of FKBP10 expression were converted to true positive, false positive, false negative and true negative counts. Summary receiver operating characteristic (SROC) of 19 GEO microarrays were used to comprehensively investigate the diagnostic value of FKBP10. All analyses were conducted using STATA 12.0 software (StataCorp). The associations between FKBP10 expression and certain clinicopathological parameters were analyzed using a Student's t-test. Tumor and paracancerous samples from the same patient were analyzed using a paired t-test, while an unpaired t-test was used to analyze unpaired samples. Genetic alterations of FKBP10 were determined by cBioPortal (https://www.cbioportal.org/). The DNA methylation information of FKBP10 was obtained from the MethHC database (http://methhc.mbc.nctu.edu.tw/) (27).

Prognosis analysis of FKBP10 in GC

We aimed to investigate the prognostic potential of FKBP10 in GC in the TCGA and GEO databases independently. Overall survival (OS) and disease-free survival (DFS) curves were drawn based on data from TCGA and GEO (28) via GEPIA (http://gepia.cancer-pku.cn/) and Kaplan-Meier Plotter (http://kmplot.com/analysis/) (29). Univariate and multivariate cox analyses were conducted with adjustments to age, sex, tumor stage, histological grade and the clinical features of GC.

Detection of FKBP10 protein expression by immunohistochemistry

The immunohistochemistry results of FKBP10 from the Human Protein Atlas (HPA) were investigated, which contained protein expression profiles as determined by immunohistochemistry (https://www.proteinatlas.org/) (30). In addition, immunohistochemistry data were verified using GC tissue and paired adjacent normal mucosa tissue samples. In total, 40 cases of GC tissue and adjacent normal tissue samples (20 male and 20 female, age range 25–79 years; average age 56.8-yars old) were collected from patients with GC at The First Affiliated Hospital of Guangxi Medical University (Nanning, China), between January 2018 and August 2018. The present study was approved by the Ethics Committee of The First Affiliated Hospital of Guangxi Medical University and all patients provided written informed consent. Antigen retrieval was conducted by boiling tissue sections in sodium citrate buffer (pH 6.0) at 100–120°C for 5 min; endogenous peroxidase activity was blocked with 3% hydrogen peroxide at room temperature for 10 min; sections were then incubated with a rabbit anti-FKBP10 polyclonal antibody (bs-13175R; 1:700; BIOSS) overnight at 4°C, followed by a conjugated secondary antibody (cat. no. D-3004-15, Shanghai Long Island Biotec, Co., Ltd.) at room temperature for 30 min; followed by 3′,3′-diaminobenzidene staining at room temperature for 5 min. The average score was calculated by randomly selecting five fields under a light microscope (magnification ×200). The immunoreaction score (IRS) was calculated according to the intensity of staining and the percentage of positive cells. Intensity was scored as follows: 0, negative; 1, weak; 2, moderate and 3, strong. The percentage of positive cells was scored as follows: <5%, 0; 6–25%, 1; 26–50%, 2; 51–75%, 3; >76%, 4.

FKBP10 biological function analysis

To further investigate the biological function of FKBP10 in GC, we analyzed the possible interactions of FKBP10 using a protein-protein interaction (PPI) network. An interaction score of 0.4 was set as a cut-off value. In addition to PPI analysis, we identified the genes associated with FKBP10 expression that may also be involved in the regulation of GC development. FKBP10 co-expression networks were assessed using the GEPIA and Coexpedia online tools (http://www.coexpedia.org/) (31). Pearson correlation analysis of FKBP10 was conducted using GEPIA. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID 6.8 (https://david.ncifcrf.gov/home.jsp).

Results

FKBP10 mRNA expression levels based on TCGA and GEO databases

A flow chart of our study design was presented in Fig. 1. FKBP10 is relatively expressed at high levels in a variety of tumors, except gynecological malignancies, including cervical squamous cell carcinoma, uterine corpus endometrial carcinoma and ovarian cancer (Fig. 2A). Additionally, FKBP10 was significantly overexpressed in GC tissues {n=408, log2[transcripts per million (TPM) + 1]=5.06} compared with in adjacent gastric tissues in the TCGA database [n=36, log2(TPM + 1)=3.53; P<0.001] (Fig. 2B). Expression levels in certain GC cell lines were consistent with those in GC tissues, each exhibiting medium expression levels (Fig. 2C). Among 19 GEO microarray analyses, FKBP10 expression levels in GC tissues were significantly increased than in adjacent tissues (GSE29272, GSE54129, GSE26899, GSE27342, GSE13861, GSE63089, GSE13911, GSE65801, GSE13195, GSE33335, GSE89148, GSE19826, GSE79973, GSE103236, GSE51725, GSE56807 and GSE2701) (Fig. 3). After batch effects removal of platform GPL96, GPL570 and GPL5175, FKBP10 also exhibited significantly increased expression in the combined GC samples compared with in normal samples (Fig. 3). Furthermore, a comprehensive meta-analysis indicated that FKBP10 expression in GC tissues was upregulated than in adjacent tissues [SMD=1.31, 95% confidence interval (CI): 1.02–1.6; P<0.001] (Fig. 4A). Additionally, forest plots of removal batch effects showed a consistent expression trend in GC tissues (SMD=1.09, 95% CI: 0.81–1.37, P<0.001) (Fig. 4B). No significant publication bias was determined in either funnel plot (P=0.125 and P=0.124) (data not shown). The association between the differential expression of FKBP10 and the clinicopathological features of patients with GC was investigated. As for cancer status, FKBP10 expression in patients with GC was significantly higher than in tumor-free patients, but there were no significant differences between FKBP10 expression and stage, grade, T stage, N stage and M stage (Table II). FKBP10 expression was significantly associated with the clinicopathological factor of person neoplasm cancer status. This indicated that tumor status could be closely related to FKBP10 expression; however, FKBP10 was not significantly linked to other clinical parameters. This may be due an insufficient sample size. Histological types were classified as gastrointestinal adenocarcinoma (tubular, papillary, not otherwise specified and mucinous type) and stomach adenocarcinoma (not otherwise specified, diffuse and signet ring type). Unfortunately, FKBP10 expression did not significantly differ between gastric and gastrointestinal adenocarcinoma. In addition, there was no significant relationship between FKBP10 expression and differentiated adenocarcinoma. The receiver operating characteristic (ROC) curve for FKBP10 based on GEO was presented in Fig. 5. The area under the curve was 0.774–1 (P=0.001). The ROC curve of FKBP10 was 0.773 in TCGA (P<0.001; Fig. 5). The combined microarray data had a sensitivity of 0.77 (0.64–0.86), a specificity of 0.89 (0.83–0.93), and an area under the combined SROC curve of 0.91 (0.89–0.94) (Fig. 6). No significant publication bias was observed (P=0.04). The frequency of FKBP10 alterations in TCGA was 9% (35/393), with 24 amplifications, 6 missense mutations and 5 truncating mutations, with no alterations in the remaining sections (Fig. 7A). Subsequently, DNA methylation analysis revealed the methylation level across FKBP10 gene regions [promoter, enhancer, TSS1500, TSS200, 5′untranslated region (UTR), first exon, gene body and 3′UTR), as well as CpG islands/CPG island regions, shelves and shores (Fig. 7B).

Table II.

Association between gastric cancer and FKBP10 expression and clinicopathological features in The Cancer Genome Atlas.

Table II.

Association between gastric cancer and FKBP10 expression and clinicopathological features in The Cancer Genome Atlas.

Clinicopathological parametersCases (n)FKBP10 expression levelsTP-value
Age
  <60 years12210.6121.0890.277
  ≥60 years28810.439
Sex
  Male26810.5531.2160.225
  Female14710.369
Stage
  I+II18110.385−1.0430.298
  III+IV21110.543
Grade
  G1+G216010.6261.5190.129
  G325510.400
T stage
  T1+T211010.4990.1770.860
  T3+T429610.469
N stage
  N112310.251−1.8620.063
  N2+N327310.550
M stage
  M036710.460−1.1130.266
  M12710.786
Person neoplasm cancer status
  Tumor free23710.365−2.1070.036a
  With tumor13510.704
Recurrence
  No31310.416−1.7280.085
  Yes10210.706

a P<0.05. FKBP10, FK506 binding protein 10.

Prognostic value of FKBP10

FKBP10 has potential for predicting the prognosis of patients with GC. Patients with high FKBP10 expression had a significantly shorter OS time relative to patients with low expression FKBP10 (hazard ratio (HR)=1.5; P=0.014) (Fig. 8A). In addition, patients with high expression of FKBP10 had shorter durations of DFS than those with low expression (HR=1.6, P=0.021; Fig. 8B). We also verified in the GEO database that patients with high expression of FKBP10 had significantly shorter OS and DFS times than patients with low expression (HR=1.36, P<0.001; HR=1.27, P=0.017) (Fig. 8C and D). Using univariate cox analysis, we found that FKBP10 expression levels, age, tumor, node and metastasis (TNM) stage, grade, T stage and N stage were closely associated with prognosis. Subsequently, multivariate analysis indicated that FKBP10 expression, age and TNM staging could be independent prognostic factors for patients with GC (Table III).

Table III.

Cox regression model analysis of overall survival in patients with gastric cancer.

Table III.

Cox regression model analysis of overall survival in patients with gastric cancer.

Univariate analysisMultivariate analysis


CovariatesHR95% confidence intervalP-valueHR95% CIP-value
FKBP10 expression level1.1921.076–1.3190.001a1.1591.042–1.2890.006a
Age (<60 vs. ≥60 years)1.4821.028–2.1360.0351.6271.117–2.3700.011
Sex (male vs. female)0.7720.546–1.0910.143
T stage (T1-2 vs. T3-4)1.7991.195–2.7080.005a
N stage (N0 vs. N1-3)2.0961.389–3.164 <0.001a2.0591.357–3.1220.001a
M stage (M0 vs. M1)1.1370.614–2.1030.683
Histological grade (G1-2 vs. G3)1.3980.997–1.9610.052

a P<0.05. FKBP10, FK506 binding protein 10.

FKBP10 protein expression in GC

We downloaded FKBP10 protein expression data pertaining to GC from the HPA. We reported that 4/10 GC tissue samples exhibited positive staining with HPA057021 antibody (Fig. 9A). Compared with cancer tissues, upregulated FKBP10 protein expression was not detected in normal tissues (Fig. 9B). In addition, we validated 40 pairs of GC tissues and corresponding adjacent tissues by immunohistochemistry and calculated the IRS scores. The expression of FKBP10 in GC tissues (IRS=5.6) was significantly increased than in adjacent tissues (IRS=0.002, P<0.001; Fig. 9C and D).

Biological function analysis

To explore the biological function of FKBP10, we identified and analyzed the proteins that interact with FKBP10 via PPI analysis. We found that dystonin, leucine- and proline-enriched proteoglycan 1 (also known as P3H1), keratin 14 and transmembrane protein 38B could interact with FKBP10 (genes combined score >0.7) (Fig. 10). We also searched for genes closely related to FKBP10 expression; a FKBP10 co-expression network of TCGA and GEO data was created and analyzed by GEPIA and Coexpedia, respectively (Fig. 11). Additionally, prolyl 3-hydroxylase family member 4 (P3H4, also known as LEPREL4) was identified as the most closely related gene in from TCGA and GEO network analyses (R=0.89, P<0.001; Fig. 12). We also ran GO and KEGG pathway analyses of co-expressed genes. The results of KEGG enrichment showed that the most significantly enriched pathways were regulating the ‘pluripotency of stem cells’, ‘cell adhesion molecules’, ‘vasopressin-regulated water reabsorption’, ‘ras signaling’, ‘lysine degradation’, ‘insulin signaling’, ‘glutathione metabolism’, ‘glucagon signaling’ and ‘estrogen signaling pathway’ (Fig. 13).

Discussion

To the best of our knowledge, the present study is the first to comprehensively investigate the characterization of FKBP10 in GC. We initially found that FKBP10 is highly expressed in GC using the GEO and TCGA databases. Subsequently, we verified that the protein expression levels of FKBP10 were consistent with the data of GEO and TCGA by immunohistochemistry. In addition, the expression of FKBP10 was determined to be closely associated with clinical prognosis. It was found in the TCGA and GEO datasets that patients with GC and high expression of FKBP10 had lower DFS and OS times than those with low expression. In addition, multivariate COX analysis demonstrated that FKBP10 was an independent prognostic factor for GC. Collectively, the results of the present study suggests that FKBP10 may be a key target gene involved in the development of GC.

As an endoplasmic reticulum localization protein, FKBP65 binds to tropoelastin throughout the secretory process (32). Investigations into FKBP10 have primarily focused on pulmonary fibrosis and osteochondrosis; FKBP10 mutations has been linked to the onset of many diseases (33,34). As a connective tissue disease, Bruck syndrome is mainly characterized by the loss of endopeptide lysine hydroxylation at the molecular level, leading to the reduction of collagen pyrimidine cross-linking (35). The literature indicates that FKBP65 crosslinks with pyridine by mediating the dimerization of LH2 (3537). FKBP10 expression was determined to be upregulated in idiopathic pulmonary fibrosis and bleomycin-induced lung fibrosis (12). Importantly, the loss of FKBP10 expression significantly suppressed collagen secretion by primary human lung fibroblasts (12). Downregulated expression of FKBP10 leads to decreased collagen type I a 1chain mRNA levels, resulting in liver fibrosis and collagen accumulation (38).

In recent years, the relationship between FKBP10 and cancer has been investigated. The main function of FKBP10 is to control folding, trafficking and secretion of protein during the production of extracellular matrix proteins (32). FKBP10 is highly expressed in melanoma and colorectal cancer (22,39). Downregulated FKBP10 can suppress tumor growth in KRAS-driven lung tumors (18). Decreasing the expression of FKBP10 can inhibit the proliferation and migration of renal cancer cells, affecting the cell cycle (21). The transcription factor ETS variant 1 targets FKBP10 to regulate the invasion and migration of prostate cancer cells (19,40). However, in ovarian cancer (16,17), the expression profile of FKBP10 opposed that to other tumors; the mechanism of action by which FKBP10 may be involved differs, yet further investigation is required. In addition, the expression and function of FKBP10 has not yet been reported in GC.

According to GO analysis, FKBP10 significantly correlated with protein and collagen production. KEGG pathway enrichment analysis showed that FKBP10 may be related to cell migration, particularly via cell adhesion molecules and ECM-receptor interaction pathways. These results are similar to those of Romano et al (32). FKBP10 protein localizes to the endoplasmic reticulum (ER) and acts as a molecular chaperone (18). We reported that P3H4 was the most commonly expressed gene with FKBP10. The data indicated that P3H4 acts as part of an ER complex with prolyl 3-hydroxylase 3, which affects collagen lysine hydroxylation (41). This suggests that FKBP10 may interact with proline 3-hydroxylase to affect collagen synthesis. Although our study found that the expression of FKBP10 could be related to the prognosis of patients with GC, how FKBP10 regulates the progression of GC and how this can be applied for the targeted treatment of GC requires further investigation.

Of note, there were limitations to the present study. First, survival analysis was conducted using GEPIA and Kaplan Meier Plotter tools. The survival time of DFS should be shorter than OS. However, the result of our analysis showed the reverse. Among the TCGA and GEO data, some patients are still being followed up, so the data are incomplete and still required further sample verification. We aim to verify the relationship between FKBP10 and patient prognosis using our own samples in the future. At the protein level, we only verified this relationship using immunohistochemistry; however, further validation using PCR and western blotting will be conducted in the future. In addition, the biological role of FKBP10 in gastric cancer cells should be investigated. Regarding the biological mechanism of FKBP10 in cancer, we have only proposed some options, yet the oncogenic function of FKBP10 should be further confirmed by in vitro and in vivo experiments.

In summary, the present study reported that FKBP10 is significantly elevated in GC; thus; FKBP10 could be considered as a potential novel therapeutic target for the treatment of this disease.

Acknowledgements

Not applicable.

Funding

The present study was supported by the Key Research and Development Program of Science and Technology Department of Guangxi (grant no. 2017AA45153), the Scientific Research and Technology-development Program of Guangxi (grant no. 1598011-4) and Innovation Project of Guangxi Graduate Education (grant no. YCBZ2019043).

Availability of data and materials

The datasets used during the present study are available from the corresponding author upon reasonable request.

Authors' contributions

JQC and GC designed and directed the project. LL and KZ processed and analyzed data; LL wrote and revised the manuscript. XGQ and JHZ performed the immunohistochemistry experiment. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

The present study was approved by the Ethics Committee of The First Affiliated Hospital of Guangxi Medical University and all patients provided written informed consent.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Correa P: Gastric cancer: Overview. Gastroenterol Clin North Am. 42:211–217. 2013. View Article : Google Scholar : PubMed/NCBI

2 

Tsukanov VV, Butorin NN, Maady AS, Shtygasheva OV, Amelchugova OS, Tonkikh JL, Fassan M and Rugge M: Helicobacter pylori infection, intestinal metaplasia, and gastric cancer risk in Eastern Siberia. Helicobacter. 16:107–112. 2011. View Article : Google Scholar : PubMed/NCBI

3 

Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI

4 

Song Z, Wu Y, Yang J, Yang D and Fang X: Progress in the treatment of advanced gastric cancer. Tumour Biol. 39:10104283177146262017. View Article : Google Scholar : PubMed/NCBI

5 

Sitarz R, Skierucha M, Mielko J, Offerhaus GJA, Maciejewski R and Polkowski WP: Gastric cancer: Epidemiology, prevention, classification, and treatment. Cancer Manag Res. 10:239–248. 2018. View Article : Google Scholar : PubMed/NCBI

6 

Japanese Gastric Cancer Association: Japanese gastric cancer treatment guidelines 2014 (ver. 4). Gastric Cancer. 20:1–19. 2017. View Article : Google Scholar

7 

Chon SH, Berlth F, Plum PS, Herbold T, Alakus H, Kleinert R, Moenig SP, Bruns CJ, Hoelscher AH and Meyer HJ: Gastric cancer treatment in the world: Germany. Transl Gastroenterol Hepatol. 2:532017. View Article : Google Scholar : PubMed/NCBI

8 

Kang CB, Hong Y, Dhe-Paganon S and Yoon HS: FKBP family proteins: Immunophilins with versatile biological functions. Neurosignals. 16:318–325. 2008. View Article : Google Scholar : PubMed/NCBI

9 

Coss MC, Winterstein D, Sowder RC II and Simek SL: Molecular cloning, DNA sequence analysis, and biochemical characterization of a novel 65-kDa FK506-binding protein (FKBP65). J Biol Chem. 270:29336–29341. 1995. View Article : Google Scholar : PubMed/NCBI

10 

Patterson CE, Schaub T, Coleman EJ and Davis EC: Developmental regulation of FKBP65. An ER-localized extracellular matrix binding-protein. Mol Biol Cell. 11:3925–3935. 2000. View Article : Google Scholar : PubMed/NCBI

11 

Ishikawa Y, Vranka J, Wirz J, Nagata K and Bachinger HP: The rough endoplasmic reticulum-resident FK506-binding protein FKBP65 is a molecular chaperone that interacts with collagens. J Biol Chem. 283:31584–31590. 2008. View Article : Google Scholar : PubMed/NCBI

12 

Staab-Weijnitz CA, Fernandez IE, Knüppel L, Maul J, Heinzelmann K, Juan-Guardela BM, Hennen E, Preissler G, Winter H, Neurohr C, et al: FK506-binding protein 10, a potential novel drug target for idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 192:455–467. 2015. View Article : Google Scholar : PubMed/NCBI

13 

Knüppel L, Heinzelmann K, Lindner M, Hatz R, Behr J, Eickelberg O and Staab-Weijnitz CA: FK506-binding protein 10 (FKBP10) regulates lung fibroblast migration via collagen VI synthesis. Respir Res. 19:672018. View Article : Google Scholar : PubMed/NCBI

14 

Solassol J, Mange A and Maudelonde T: FKBP family proteins as promising new biomarkers for cancer. Curr Opin Pharmacol. 11:320–325. 2011. View Article : Google Scholar : PubMed/NCBI

15 

Yao YL, Liang YC, Huang HH and Yang WM: FKBPs in chromatin modification and cancer. Curr Opin Pharmacol. 11:301–307. 2011. View Article : Google Scholar : PubMed/NCBI

16 

Quinn MC, Wojnarowicz PM, Pickett A, Provencher DM, Mes-Masson AM, Davis EC and Tonin PN: FKBP10/FKBP65 expression in high-grade ovarian serous carcinoma and its association with patient outcome. Int J Oncol. 42:912–920. 2013. View Article : Google Scholar : PubMed/NCBI

17 

Henriksen R, Sørensen FB, Ørntoft TF and Birkenkamp-Demtroder K: Expression of FK506 binding protein 65 (FKBP65) is decreased in epithelial ovarian cancer cells compared to benign tumor cells and to ovarian epithelium. Tumour Biol. 32:671–676. 2011. View Article : Google Scholar : PubMed/NCBI

18 

Ramadori G, Konstantinidou G, Venkateswaran N, Biscotti T, Morlock L, Galié M, Williams NS, Luchetti M, Santinelli A, Scaglioni PP and Coppari R: Diet-induced unresolved ER stress hinders KRAS-driven lung tumorigenesis. Cell Metab. 21:117–125. 2015. View Article : Google Scholar : PubMed/NCBI

19 

Paulo P, Ribeiro FR, Santos J, Mesquita D, Almeida M, Barros-Silva JD, Itkonen H, Henrique R, Jerónimo C, Sveen A, et al: Molecular subtyping of primary prostate cancer reveals specific and shared target genes of different ETS rearrangements. Neoplasia. 14:600–611. 2012. View Article : Google Scholar : PubMed/NCBI

20 

Sun Z, Dong J, Zhang S, Hu Z, Cheng K, Li K, Xu B, Ye M, Nie Y, Fan D and Zou H: Identification of chemoresistance-related cell-surface glycoproteins in leukemia cells and functional validation of candidate glycoproteins. J Proteome Res. 13:1593–1601. 2014. View Article : Google Scholar : PubMed/NCBI

21 

Ge Y, Xu A, Zhang M, Xiong H, Fang L, Zhang X, Liu C and Wu S: FK506 binding protein 10 is overexpressed and promotes renal cell carcinoma. Urol Int. 98:169–176. 2017. View Article : Google Scholar : PubMed/NCBI

22 

Olesen SH, Christensen LL, Sørensen FB, Cabezón T, Laurberg S, Orntoft TF and Birkenkamp-Demtröder K: Human FK506 binding protein 65 is associated with colorectal cancer. Mol Cell Proteomics. 4:534–544. 2005. View Article : Google Scholar : PubMed/NCBI

23 

Wu JG, Wang JJ, Jiang X, Lan JP, He XJ, Wang HJ, Ma YY, Xia YJ, Ru GQ, Ma J, et al: MiR-125b promotes cell migration and invasion by targeting PPP1CA-Rb signal pathways in gastric cancer, resulting in a poor prognosis. Gastric Cancer. 18:729–739. 2015. View Article : Google Scholar : PubMed/NCBI

24 

Imaoka H, Toiyama Y, Okigami M, Yasuda H, Saigusa S, Ohi M, Tanaka K, Inoue Y, Mohri Y and Kusunoki M: Circulating microRNA-203 predicts metastases, early recurrence, and poor prognosis in human gastric cancer. Gastric Cancer. 19:744–753. 2016. View Article : Google Scholar : PubMed/NCBI

25 

Liang M, Shi B, Liu J, He L, Yi G, Zhou L, Yu G and Zhou X: Downregulation of miR203 induces overexpression of PIK3CA and predicts poor prognosis of gastric cancer patients. Drug Des Devel Ther. 9:3607–3616. 2015.PubMed/NCBI

26 

Leek JT, Johnson WE, Parker HS, Fertig EJ, Jaffe AE, Storey JD, Zhang Y and Torres LC: sva: Surrogate variable analysis. R package version 3.32.1. 2019.

27 

Huang WY, Hsu SD, Huang HY, Sun YM, Chou CH, Weng SL and Huang HD: MethHC: A database of DNA methylation and gene expression in human cancer. Nucleic Acids Res. 43:D856–D861. 2015. View Article : Google Scholar : PubMed/NCBI

28 

Tang Z, Li C, Kang B, Gao G, Li C and Zhang Z: GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 45:W98–W102. 2017. View Article : Google Scholar : PubMed/NCBI

29 

Szász AM, Lánczky A, Nagy Á, Förster S, Hark K, Green JE, Boussioutas A, Busuttil R, Szabó A and Győrffy B: Cross-validation of survival associated biomarkers in gastric cancer using transcriptomic data of 1,065 patients. Oncotarget. 7:49322–49333. 2016. View Article : Google Scholar : PubMed/NCBI

30 

Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, et al: Proteomics. Tissue-based map of the human proteome. Science. 347:12604192015. View Article : Google Scholar : PubMed/NCBI

31 

Yang S, Kim CY, Hwang S, Kim E, Kim H, Shim H and Lee I: COEXPEDIA: Exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH). Nucleic Acids Res. 45:D389–D396. 2017. View Article : Google Scholar : PubMed/NCBI

32 

Romano S, D'Angelillo A and Romano MF: Pleiotropic roles in cancer biology for multifaceted proteins FKBPs. Biochim Biophys Acta. 1850:2061–2068. 2015. View Article : Google Scholar : PubMed/NCBI

33 

Christiansen HE, Schwarze U, Pyott SM, AlSwaid A, Al Balwi M, Alrasheed S, Pepin MG, Weis MA, Eyre DR and Byers PH: Homozygosity for a missense mutation in SERPINH1, which encodes the collagen chaperone protein HSP47, results in severe recessive osteogenesis imperfecta. Am J Hum Genet. 86:389–398. 2010. View Article : Google Scholar : PubMed/NCBI

34 

Essawi O, Symoens S, Fannana M, Darwish M, Farraj M, Willaert A, Essawi T, Callewaert B, De Paepe A, Malfait F and Coucke PJ: Genetic analysis of osteogenesis imperfecta in the Palestinian population: Molecular screening of 49 affected families. Mol Genet Genomic Med. 6:15–26. 2018. View Article : Google Scholar : PubMed/NCBI

35 

Chen Y, Terajima M, Banerjee P, Guo H, Liu X, Yu J, Yamauchi M and Kurie JM: FKBP65-dependent peptidyl-prolyl isomerase activity potentiates the lysyl hydroxylase 2-driven collagen cross-link switch. Sci Rep. 7:460212017. View Article : Google Scholar : PubMed/NCBI

36 

Gjaltema RA, van der Stoel MM, Boersema M and Bank RA: Disentangling mechanisms involved in collagen pyridinoline cross-linking: The immunophilin FKBP65 is critical for dimerization of lysyl hydroxylase 2. Proc Natl Acad Sci USA. 113:7142–7147. 2016. View Article : Google Scholar : PubMed/NCBI

37 

Duran I, Martin JH, Weis MA, Krejci P, Konik P, Li B, Alanay Y, Lietman C, Lee B, Eyre D, et al: A chaperone complex formed by HSP47, FKBP65, and BiP modulates telopeptide Lysyl hydroxylation of type I procollagen. J Bone Miner Res. 32:1309–1319. 2017. View Article : Google Scholar : PubMed/NCBI

38 

Vollmann EH, Cao L, Amatucci A, Reynolds T, Hamann S, Dalkilic-Liddle I, Cameron TO, Hossbach M, Kauffman KJ, Mir FF, et al: Identification of novel fibrosis modifiers by in vivo siRNA silencing. Mol Ther Nucleic Acids. 7:314–323. 2017. View Article : Google Scholar : PubMed/NCBI

39 

Hagedorn M, Siegfried G, Hooks KB and Khatib AM: Integration of zebrafish fin regeneration genes with expression data of human tumors in silico uncovers potential novel melanoma markers. Oncotarget. 7:71567–71579. 2016. View Article : Google Scholar : PubMed/NCBI

40 

Rahim S, Minas T, Hong SH, Justvig S, Celik H, Kont YS, Han J, Kallarakal AT, Kong Y, Rudek MA, et al: A small molecule inhibitor of ETV1, YK-4-279, prevents prostate cancer growth and metastasis in a mouse xenograft model. PLoS One. 9:e1142602014. View Article : Google Scholar : PubMed/NCBI

41 

Heard ME, Besio R, Weis M, Rai J, Hudson DM, Dimori M, Zimmerman SM, Kamykowski JA, Hogue WR, Swain FL, et al: Sc65-Null mice provide evidence for a novel endoplasmic reticulum complex regulating collagen Lysyl hydroxylation. PLoS Genet. 12:e10060022016. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

August 2019
Volume 42 Issue 2

Print ISSN: 1021-335X
Online ISSN:1791-2431

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Liang, L., Zhao, K., Zhu, J., Chen, G., Qin, X., & Chen, J. (2019). Comprehensive evaluation of FKBP10 expression and its prognostic potential in gastric cancer. Oncology Reports, 42, 615-628. https://doi.org/10.3892/or.2019.7195
MLA
Liang, L., Zhao, K., Zhu, J., Chen, G., Qin, X., Chen, J."Comprehensive evaluation of FKBP10 expression and its prognostic potential in gastric cancer". Oncology Reports 42.2 (2019): 615-628.
Chicago
Liang, L., Zhao, K., Zhu, J., Chen, G., Qin, X., Chen, J."Comprehensive evaluation of FKBP10 expression and its prognostic potential in gastric cancer". Oncology Reports 42, no. 2 (2019): 615-628. https://doi.org/10.3892/or.2019.7195