Analysis of the gene expression profile in response to human epididymis protein 4 in epithelial ovarian cancer cells
- Authors:
- Published online on: July 11, 2016 https://doi.org/10.3892/or.2016.4926
- Pages: 1592-1604
Abstract
Introduction
Ovarian cancer is the most lethal gynecological malignancy and accounts for 25–30% of all malignant tumors in the female reproductive tract. An estimated 22,280 new cases of ovarian cancer and 14,240 related deaths were reported in 2016 in the US (1). Due to its innocuous symptoms, most ovarian cancer patients are diagnosed at the late stage. Despite the development of new antitumor drugs and the improvement of surgical treatment, the majority of patients with advanced disease (stages III–IV) eventually relapse and the 5-year survival rate of late stage patients is only 30% (2). Therefore, early diagnosis of ovarian cancer is crucial to reduce mortality and improve the prognosis for eventual survival. Human epididymis protein 4 (HE4), also known as whey-acidic-protein (WAP) four-disulfide core domain protein 2 (WFDC2), was first found overexpressed in ovarian cancer tissue in 1999 (3). Although it was originally recognized as a small secreted protein that plays a role in sperm maturation in males (4), numerous studies have found that HE4 is a valuable serum biomarker possessing higher sensitivity and specificity than CA125 in the confirmative early diagnosis for epithelial ovarian cancer (EOC) (5,6), and the combined use of HE4 with pelvic ultrasound achieved the best sensitivity for detecting ovarian cancers among the different algorithms tested (7). It seems to be an independent predictive factor for ideal tumor cytoreductive surgery and maintains its prognostic role even after recurrence (8). Moreover, recent investigations have shown that serum HE4 could predict chemotherapy response during first-line chemotherapy (9), and high HE4 levels are correlated with chemoresistance and decreased survival rates in EOC patients (10,11). HE4 shows its potential application as a biomarker for early detection and discrimination of endometrial (12,13) and non-small cell lung cancer (14), and pancreatic adenocarcinoma (15). A high serum HE4 level may be a useful biomarker for the poor prognosis in non-small cell lung cancer patients (16,17).
Over the past decade, much attention has been given to explore the clinical application of HE4 as a biomarker. In recent years, several studies have reported that HE4 acts as an oncogene implicated in various cancer behaviors, such as cell adhesion (18), proliferation, migration, metastasis (19–21) and chemoresistance (19,22). The molecular mechanisms may be attributed to the activation of epidermal growth factor (EGF) (18), vascular endothelial growth factor (VEGF) (19), HIF1α (19) or the interaction with Annexin A2 (23), and Lewis y glycosylation (24). Nevertheless, investigation of the role of HE4 in the malignant biological behaviors of ovarian cancer is limited.
Therefore, in the present study, we sought to investigate alteration of the gene expression profile in response to HE4 in ovarian cancer cells. The results obtained from this research may lead to a better understanding of the molecular mechanisms associated with HE4 in ovarian cancer and to facilitate the early diagnosis and therapeutic treatment of ovarian cancer.
Materials and methods
Cell culture, construction of expression vectors and HE4 gene transfection
Human EOC ES-2 cells were purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA) and maintained in RPMI-1640 medium with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin, at 37°C in a humidified atmosphere with 5% CO2. An HE4 expression construct was generated by subcloning PCR-amplified full-length human HE4 cDNA into the pEGFP-N1 or pCMV6 plasmid. The following primers were used: P1, 5′-TCC GCT CGA GAT GCC TGC TTG TCG CCT AG-3′ and P2, 5′-ATG GGG TAC CGT GAA ATT GGG AGT GAC ACA GG-3′. Two shRNA expression vectors for human HE4 were constructed using the vector pSilencer. The mRNA target sequences chosen for designing HE4-shRNA were: GTC CTG TGT CAC TCC CAA T for HE4-shRNA1 and GAT GAA ATG CTG CCG CAA T for HE4-shRNA2. Transfection was carried out using liposomes with a vector transfection kit according to the instructions. Regarding the stable cell lines, HE4-overexpressing (HE4-H) (O), HE4-shRNA low-expressing (HE4-L) (S) and their respective empty-plasmid transfected cell lines [HE4-H-Mock (OV) and HE4-L-Mock (SV)] were selected for 14 days with G418 (800 μg/ml) (Invitrogen, Carlsbad, CA, USA). All cell lines were labeled and are listed in Table I.
Transfection identification by quantitative real-time PCR and western blotting
Quantitative real-time polymerase chain reaction (RT-PCR)
Total RNA was extracted with TRIzol reagent and reverse-transcribed to cDNA using SuperScript III (both from Invitrogen). Quantitative real-time PCR was performed on Roche LightCycler 480 (Roche Diagnostics, Mannheim, Germany) sequence detection system. The primers for HE4 were: 5′-AGT GTC CTG GCC AGA TGA AAT G-3′ for forward and 5′-CAG GTG GGC TGG AAC CAG AT-3′ for reverse. GAPDH was used to normalize the quantity of complementary DNA. PCR reactions of each sample were carried out in triplicate. The cycle steps were 95°C for 5 min, 40 cycles of 95°C for 15 sec, 60°C for 1 min and 72°C for 20 sec.
Western blotting
Western blotting was performed as previously described (23). The HE4 antibody (rabbit polyclonal; 1:40; Abcam, Cambridge, MA, USA) was used and the negative control contained only HE4 antibody without protein. The protein bands were visualized by ImageJ 1.31v and normalized relative to the GAPDH protein expression level.
Total RNA extraction and quantity control
Two pairs of cells were prepared for gene chip hybridization analysis: HE4-overexpressing cells vs. HE4-overexpressing vector cells, and HE4-shRNA cells vs. HE4-shRNA vector cells. Total RNA was extracted from all cell lines using RNeasy Mini kit and further purified with RNeasy MinElute Cleanup columns (both from Qiagen, Valencia, CA, USA). RNA quantity and purity were assessed using NanoDrop ND-1000. Pass criteria for absorbance ratios were established as A260/A280 ≥1.8 and A260/A230 ≥1. RNA integrity number (RIN) values were ascertained using Agilent RNA 6000 Nano assay to determine RNA integrity. Pass criterion for RIN value was established at ≥6. The RNA samples in each group are labeled in Table I. The total RNA of each cell line was allocated to 3 chips for further hybridization and analysis to decrease the experimental error.
Gene chip hybridization, data collection and enrichment analysis
Purified RNA samples were submitted to Human Whole Genome OneArray® (HOA6.1) with Phalanx hybridization buffer using Phalanx Hybridization System for microarray analysis. This array contains 30,275 DNA oligonucleotide probes, and each probe is a 60-mer designed in the sense direction. Among the probes, 29,187 probes correspond to the annotated genes in RefSeq v38 and Ensembl v56 database. In addition, 1,088 control probes are also included. After 16 h of hybridization at 50°C, non-specific binding targets were washed away by 3 different washing steps (wash I, 42°C for 5 min; wash II, 42°C for 5 min, 25°C for 5 min; and wash III, rinse 20 times), and the slides were dried by centrifugation and scanned by Axon 4000B scanner (Molecular Devices, Sunnyvale, CA, USA). The intensities of each probe were obtained by GenePix 4.1 software (Molecular Devices). In the present study, 12 chips were used to compare the differentially expressed genes (DEGs). Scatter plots were conducted to show the repeatability of the expression signal between technical repeats. Histogram plots were made to show the fold-change distribution of all probes excluding control and flagged probes. Volcano plots were performed to visually show a distinguishable gene expression profile among samples. Venny-diagram was performed to identify the coordinated DEGs in response to HE4 (http://bioinfogp.cnb.csic.es/tools/venny/). Fold-changes were calculated by Rosetta Resolver 7.2 with error model adjusted by Amersham Pairwise Ration Builder for signal comparison of sample. Standard selection criteria to identify DEGs were as follows: i) log2 |fold-change| ≥1 and P<0.05; ii) log2 ratios ̔NA̓ and the differences in intensity between the two samples ≥1,000.
Validation of gene expression by RT-PCR and immunohistochemical (IHC) staining
RT-PCR
RT-PCR was performed in triplicate with primer sets and probes that were specific for 4 selected genes that were found to be significantly differentially expressed: FOXA2, SERPIND1, BDKRD1 and IL1A. The methods are shown as above.
IHC
To validate the DEGs at the protein level, IHC staining in ovarian samples was conducted. In our previous studies, we established a group of ovarian paraffin-embedded samples (23) including 50 malignant, 27 borderline, 15 benign and 15 normal ovarian tissues. In view of the further investigation, SERPIND1 was selected for IHC staining in these samples and then compared with the expression of HE4. The working dilution for SERPIND1 (#12741-1-AP; ProteinTech, Chicago, IL, USA) was 1:400. The staining of hepatic cancer samples was chosen as the positive control and omission of the primary antibody was designed as the negative control. The staining procedures were performed as previously described (23). Regarding the evaluation method, in brief, the presence of brown-colored granules on the cell membrane or in the cytoplasm was taken as a positive signal, and was classified according to color intensity as follows: not colored, light yellow, brown and tan were recorded as 0, 1, 2 and 3, respectively. A positive cell staining rate of <5, 5–25, 26–50 and 51–75%, and >75% were recorded as 0, 1, 2, 3 and 4. The final score was determined by multiplying the positive cell staining rate and the score values: 0–2 was considered negative (−), 3–4 was (+), 5–8 was (++), and 9–12 was (+++).− and + were considered as low expression; ++ and +++ as high expression. Two observers evaluated the sections to control error. Survival analysis was performed on these patients. The overall survival (OS) time was defined from the date of surgery (earliest was in August 2008) to the date of death or the last follow-up (September, 2015).
Enrichment analysis of DEGs
All the DEGs in each pair group were prepared to run Gene Ontology (GO) and canonical pathways analyses (Biocarta and KEGG). Gene interaction networks were visualized by Cytoscape (25). The property of the network was analyzed with the plug-in network analysis.
Statistical analysis
Statistical analyses were performed using the SPSS program (version 22 for Mac; SPSS, Inc., Chicago, IL, USA) and GraphPad Prism 6 (version 6.0 h for Mac; GraphPad Prism Software Inc., San Diego, CA, USA). Chi-square and one-way ANOVA with LSD or Bonferroni post hoc test was used for comparison between >2 groups. The correlation coefficient R of SERPIND1 with HE4 was calculated by Spearman correlation analysis. Quantitative data are presented as mean ± SD. As to the analysis of quantitative RT-PCR result, the data are expressed as mean ± SEM. Survival analysis was analyzed using Kaplan-Meier curves by log-rank test. A P-value of <0.05 was considered to indicate a statistically significant result.
Results
Identification of HE4 gene transfection
Stable transfected cell lines were established using the ES-2 cells. The gene and protein expression levels of HE4 were obviously increased after HE4 transfection and decreased after shRNA transfection, as detected by quantitative real-time PCR (Fig. 1A) and western blotting (Fig. 1B and C), whereas there was no statistical difference in regards to HE4 in the mock and untreated cells.
Gene expression analysis and clustering
The expression profiles of all the samples passed the microarray quality control (Table I). Histogram plots of fold-change distribution of all probes excluding control and flagged probes were conducted for all signals collected (Fig. 2A), and the volcano plots revealed the DEGs for each pair of gene chips (Fig. 2B). A total of 717 genes were upregulated and 898 genes were downregulated in O vs. OV, 166 genes were upregulated and 285 were downregulated in S vs. SV. The top 20 DEGs in each group are shown in Table II. Venn diagrams showed that an overlap of 16 genes was consistently upregulated and 8 genes downregulated in response to HE4 (Fig. 3).
Validation of gene expression results by RT-PCR
To validate the gene expression profile results, 4 DEGs (FOXA2, SERPIND1, BDKRD1 and IL1A) were selected for RT-PCR analysis verification (Fig. 4A). Generally, the trends for upregulation or downregulation of the DEGs by real-time PCR analysis were consistent with those of the DEG expression profiling analysis, confirming the reliability of the microarray results.
Validation of protein expression by IHC
Similar to HE4, the expression of SERPIND1 was mainly located on the membrane and in the cytoplasm (Fig. 4B). The positive expression rates of SERPIND1 in malignant, borderline, benign and normal ovarian tissues were 88, 62.96, 20 and 13.33%, respectively (Table III). Malignant groups displayed the highest positive expression and was significantly higher than the rate of the borderline (P=0.010), whereas the positive expression rate in the borderline groups was markedly higher than that in the benign groups (P=0.008). In general, the expression pattern of SERPIND1 was similar to that of HE4 (up to 74.8%). Among the 50 cases of ovarian cancer samples, a total of 37 cases simultaneously showed positive expression of both HE4 and SERPIND1 and 4 cases showed both negative expression (Table IV). Spearman correlation analysis revealed that the expression of SERPIND1 and HE4 was positively correlated (R=0.402, P=0.003). Survival analysis revealed that high expression levels of both HE4 and SERPIND1 were significantly correlated with poor prognosis (Kaplan-Meier analysis, log-rank, P=0.0145 and P=0.0009 as to OS; Fig. 4C, respectively).
GO function analysis and canonical pathway result of DEGs
GO analysis showed that all the DEGs in the different groups were predominantly involved in molecular function (MF), biological process (BP) and cellular component (CC), as shown in Table V. Canonical pathway analysis demonstrated that a total of 39 pathways were enriched in O vs. OV, and 50 pathways in S vs. SV. The top 20 pathways are shown in Table VI, such as MAPK signaling pathway, pathway in cancer and P53 signaling pathway in O vs. OV and focal adhesion, pathway in cancer and cell adhesion molecules (CAMs) in S vs. SV.
Interaction network for the DEGs
Three aforementioned pathways for the DEGs were selected to conduct interaction network analysis in 2 pairs, respectively (Fig. 5). Among the interaction networks, various interaction genes were predicted, such as MAPK3 and MAPK8 in MAPK signaling pathway, UBB and EP300 in pathways in cancer, SRC in cell adhesion molecules (CAMs) pathway, which appeared to be the connected predicted hub genes.
Discussion
The mortality rate of ovarian cancer patients ranks first among all gynecological malignant tumors, and up to 75% of diagnosed patients are already at an advanced stage. The early detection of ovarian cancer is difficult due to its indefinite symptoms in the early stage and lack of a specific marker; meanwhile, a poor understanding of the mechanisms of oncogenesis also impedes the development of new treatment modalities. In recent years, HE4 has drawn extensive attention in the study of ovarian cancer, due to its potential clinical application benefits for early detection (5), discrimination (6), better tumor cytoreductive surgery (8), chemoresistance and prognosis (9,11). Moreover, it was also reported to be a reliable marker for early diagnosis of endometrial (12), lung (14) and pancreatic cancer (15). Thus, increasing research on the clinical application of HE4 stimulated elucidation of the basic mechanisms of its function. Unfortunately, relevant research is far from enough. In the present study, by the use of human whole genome microarray detection, we investigated the gene expression profile in response to HE4 in epithelial ovarian cancer (EOC) cells. We identify a total of 717 genes that were upregulated and 898 genes that were downregulated in HE4-overexpressing cells vs. HE4-Mock and 166 genes were upregulated and 285 were downregulated in the HE4-silenced cells vs. HE4-Mock cells. Furthermore, an overlap of 16 genes was consistently upregulated and 8 genes downregulated in response to HE4. The result was validated at the mRNA and protein levels. GO and pathway enrichment analysis were applied. Multiple pathways are involved, including KEGG MAPK signaling pathway, KEGG steroid biosynthesis, and KEGG pathways in cancer. Finally, interaction network plots were constructed and interactive genes were predicted. The known functions of these genes can provide novel ideas and breakthrough points for further research.
SERPIND1, also known as heparin co-factor II (HCII or HC2), attracted our attention. HCII belongs to the serpin superfamily and it is a serum glycoprotein that acts as a thrombin inhibitor through interactions with heparin and other endogenous glycosaminoglycans (26). Over the past 3 decades, HCII has been intermittently studied as a protease inhibitor. It can inhibit thrombin in atherosclerotic lesions where thrombin can exert a proatherogenic inflammatory response (27). It has also been proposed to promote angiogenesis in response to ischaemia (28). However, to date, the biological effect of HCII on cancer occurrence and development is still largely unknown, particularly in ovarian cancer. One group reported that high HCII expression in tumor tissues was associated with increased cancer recurrence and shorter OS in non-small cell lung cancer (NSCLC) patients. HCII promoted cell motility, invasion ability and filopodium dynamics in NSCLC cells partly through the PI3K pathway (29). Recently, HCII was found to be upregulated in the serum samples of B-cell acute lymphoblastic leukemia (B-ALL) patients and it was identified as a candidate biomarker for early diagnosis of B-ALL (30). In the present study, we found that the positive expression rates of HCII in malignant, borderline, benign and normal ovarian tissues were 88, 62.96, 20 and 13.33%, respectively. The protein expression pattern of HCII was positively related to that of HE4 (Spearman coefficient ratio, R=0.402, P=0.003) and high expression of HCII was correlated with poor OS of ovarian cancer patients. HCII may act as a potential oncogene in the development of ovarian cancer. It seems to be the first study on its potential function in the tumorigenesis of ovarian cancer and sheds light on the possible application as a tumor biomarker or therapeutic target along with HE4. However, further investigations are needed to elucidate the underlying mechanism.
In our previous studies, we demonstrated that HE4 enhanced the proliferation, invasion and metastasis of ovarian cancer (20) partially via the interaction of Annexin A2 (23), and fucoglycosylation may increase this effect (24,31). A similar phenomenon was also noted in others studies (18,19,21), even in endometrial (32,33) and pancreatic cancer (33). Nevertheless, the underlying mechanisms of these phenomena warrant more discussion. One group reported that the expression of HE4 is associated with cell adhesion, migration and tumor growth via the activation of the EGFR-MAPK signaling pathway (18) in ovarian cancer cells. However, another group reported a controversial finding that HE4 may play a protective role in the progression of ovarian cancer by inhibiting cell proliferation, whereas they also speculated that this effect may be regulated by MAPK and PI3K/Akt signaling pathway. As to the influence of HE4 on the chemotherapeutic resistance in ovarian cancer, our previous study noted that the recombinant HE4 protein could repress carboplatin-induced apoptosis in ovarian cancer cells (22). Another group reported that HE4 overexpression promoted chemoresistance against cisplatin in an animal model leading to reduced survival rates (19). They further demonstrated that tumor microenvironment constituents participated in the modulation of HE4, in which HE4 could interact with EGFR, IGF1R and transcription factor HIF1α, inducing the nuclear translocation of HE4 to promote aggressive and chemoresistant disease and denote poor prognosis for ovarian cancer patients (19). Recently, a group reported that recombinant HE4 protein increased the mRNA and protein levels of cell cycle marker PCNA and cell cycle inhibitor p21 in endometrial and pancreatic cancer cell lines, indicating that HE4 function may be mediated by the p21-CDK-Rb pathway (33). In the present study, we presented the results for the possible pathways HE4 may enrich, including MAPK, steroid biosynthesis, cell cycle, p53 hypoxia pathway, focal adhesion, ECM receptor interaction, and cell adhesion molecules (CAMs). To the best of our knowledge, this is the first study to provide the most detailed, full-scale and fundamental data for the DEGs in response to HE4, thus laying the basis for further investigation on the underlying mechanisms of HE4 in ovarian cancer. More comprehensive and in-depth studies are needed, to provide more evidence for the development of novel drugs and therapeutic strategies selectively targeting HE4 in ovarian cancer.
Collectively, the present study analyzed the gene expression profile in response to HE4 in EOC cells. The identified DEGs are valuable to determine the underlying mechanism of HE4 in cancer, providing new views for the comprehensive study of ovarian cancer treatment. Prospective investigations using the identified DEGs are required to further elucidate the mechanisms of the tumorgenesis and development of ovarian cancer.
Acknowledgments
The present study was supported by the National Natural Science Foundation of China (grant nos. 81172491, 81101527, 81472437 and 81402129), the Education Department Doctor Project Fund (grant nos. 20112104110016 and 20112104120019), and the Outstanding Scientific Fund of Shengjing Hospital (grant no. 201303).
References
Siegel RL, Miller KD and Jemal A: Cancer statistics, 2016. CA Cancer J Clin. 66:7–30. 2016. View Article : Google Scholar : PubMed/NCBI | |
Pliarchopoulou K and Pectasides D: Epithelial ovarian cancer: Focus on targeted therapy. Crit Rev Oncol Hematol. 79:17–23. 2011. View Article : Google Scholar | |
Schummer M, Ng WV, Bumgarner RE, Nelson PS, Schummer B, Bednarski DW, Hassell L, Baldwin RL, Karlan BY and Hood L: Comparative hybridization of an array of 21,500 ovarian cDNAs for the discovery of genes overexpressed in ovarian carcinomas. Gene. 238:375–385. 1999. View Article : Google Scholar : PubMed/NCBI | |
Cornwall GA, von Horsten HH, Swartz D, Johnson S, Chau K and Whelly S: Extracellular quality control in the epididymis. Asian J Androl. 9:500–507. 2007. View Article : Google Scholar : PubMed/NCBI | |
Karlsen MA, Høgdall EV, Christensen IJ, Borgfeldt C, Kalapotharakos G, Zdrazilova-Dubska L, Chovanec J, Lok CA, Stiekema A, Mutz-Dehbalaie I, et al: A novel diagnostic index combining HE4, CA125 and age may improve triage of women with suspected ovarian cancer - An international multicenter study in women with an ovarian mass. Gynecol Oncol. 138:640–646. 2015. View Article : Google Scholar : PubMed/NCBI | |
Romagnolo C, Leon AE, Fabricio AS, Taborelli M, Polesel J, Del Pup L, Steffan A, Cervo S, Ravaggi A, Zanotti L, et al: HE4, CA125 and risk of ovarian malignancy algorithm (ROMA) as diagnostic tools for ovarian cancer in patients with a pelvic mass: An Italian multicenter study. Gynecol Oncol. 141:303–311. 2016. View Article : Google Scholar : PubMed/NCBI | |
Wilailak S, Chan KK, Chen CA, Nam JH, Ochiai K, Aw TC, Sabaratnam S, Hebbar S, Sickan J, Schodin BA, et al: Distinguishing benign from malignant pelvic mass utilizing an algorithm with HE4, menopausal status, and ultrasound findings. J Gynecol Oncol. 26:46–53. 2015. View Article : Google Scholar : | |
Chen WT, Gao X, Han XD, Zheng H, Guo L and Lu RQ: HE4 as a serum biomarker for ROMA prediction and prognosis of epithelial ovarian cancer. Asian Pac J Cancer Prev. 15:101–105. 2014. View Article : Google Scholar : PubMed/NCBI | |
Angioli R, Capriglione S, Aloisi A, Guzzo F, Luvero D, Miranda A, Damiani P, Montera R, Terranova C and Plotti F: Can HE4 predict platinum response during first-line chemotherapy in ovarian cancer? Tumour Biol. 35:7009–7015. 2014. View Article : Google Scholar : PubMed/NCBI | |
Vallius T, Hynninen J, Auranen A, Carpén O, Matomäki J, Oksa S, Virtanen J and Grénman S: Serum HE4 and CA125 as predictors of response and outcome during neoadjuvant chemotherapy of advanced high-grade serous ovarian cancer. Tumour Biol. 35:12389–12395. 2014. View Article : Google Scholar : PubMed/NCBI | |
Zhu LC, Gao J, Hu ZH, Schwab CL, Zhuang HY, Tan MZ, Yan LM, Liu JJ, Zhang DY and Lin B: Membranous expressions of Lewis y and CAM-DR-related markers are independent factors of chemotherapy resistance and poor prognosis in epithelial ovarian cancer. Am J Cancer Res. 5:830–843. 2015.PubMed/NCBI | |
Kemik P, Saatli B, Yıldırım N, Kemik VD, Deveci B, Terek MC, Koçtürk S, Koyuncuoğlu M and Saygılı U: Diagnostic and prognostic values of preoperative serum levels of YKL-40, HE-4 and DKK-3 in endometrial cancer. Gynecol Oncol. 140:64–69. 2016. View Article : Google Scholar | |
Li X, Gao Y, Tan M, Zhuang H, Gao J, Hu Z, Wang H, Zhu L, Liu J and Lin B: Expression of HE4 in endometrial cancer and its clinical significance. Biomed Res Int. 2015:4374682015.PubMed/NCBI | |
Jiang Y, Wang C, Lv B, Ma G and Wang L: Expression level of serum human epididymis 4 and its prognostic significance in human non-small cell lung cancer. Int J Clin Exp Med. 7:5568–5572. 2014. | |
Huang T, Jiang SW, Qin L, Senkowski C, Lyle C, Terry K, Brower S, Chen H, Glasgow W, Wei Y, et al: Expression and diagnostic value of HE4 in pancreatic adenocarcinoma. Int J Mol Sci. 16:2956–2970. 2015. View Article : Google Scholar : PubMed/NCBI | |
Lan WG, Hao YZ, Xu DH, Wang P, Zhou YL and Ma LB: Serum human epididymis protein 4 is associated with the treatment response of concurrent chemoradiotherapy and prognosis in patients with locally advanced non-small cell lung cancer. Clin Transl Oncol. 18:375–380. 2016. View Article : Google Scholar | |
Lamy PJ, Plassot C and Pujol JL: Serum HE4: An independent prognostic factor in non-small cell lung cancer. PLoS One. 10:e01288362015. View Article : Google Scholar : PubMed/NCBI | |
Lu R, Sun X, Xiao R, Zhou L, Gao X and Guo L: Human epididymis protein 4 (HE4) plays a key role in ovarian cancer cell adhesion and motility. Biochem Biophys Res Commun. 419:274–280. 2012. View Article : Google Scholar : PubMed/NCBI | |
Moore RG, Hill EK, Horan T, Yano N, Kim K, MacLaughlan S, Lambert-Messerlian G, Tseng YD, Padbury JF, Miller MC, et al: HE4 (WFDC2) gene overexpression promotes ovarian tumor growth. Sci Rep. 4:35742014. View Article : Google Scholar : PubMed/NCBI | |
Zhu L, Zhuang H, Wang H, Tan M, Schwab CL, Deng L, Gao J, Hao Y, Li X, Gao S, et al: Overexpression of HE4 (human epididymis protein 4) enhances proliferation, invasion and metastasis of ovarian cancer. Oncotarget. 7:729–744. 2016. | |
Chen Y, Mu X, Wang S, Zhao L, Wu Y, Li J and Li M: WAP four-disulfide core domain protein 2 mediates the proliferation of human ovarian cancer cells through the regulation of growth-and apoptosis-associated genes. Oncol Rep. 29:288–296. 2013. | |
Wang H, Zhu L, Gao J, Hu Z and Lin B: Promotive role of recombinant HE4 protein in proliferation and carboplatin resistance in ovarian cancer cells. Oncol Rep. 33:403–412. 2015. | |
Zhuang H, Tan M, Liu J, Hu Z, Liu D, Gao J, Zhu L and Lin B: Human epididymis protein 4 in association with Annexin II promotes invasion and metastasis of ovarian cancer cells. Mol Cancer. 13:2432014. View Article : Google Scholar : PubMed/NCBI | |
Zhuang H, Hu Z, Tan M, Zhu L, Liu J, Liu D, Yan L and Lin B: Overexpression of Lewis y antigen promotes human epididymis protein 4-mediated invasion and metastasis of ovarian cancer cells. Biochimie. 105:91–98. 2014. View Article : Google Scholar : PubMed/NCBI | |
Diaz-Montana JJ and Diaz-Diaz N: Development and use of the Cytoscape app GFD-Net for measuring semantic dissimilarity of gene networks. F1000Res. 3:1422014.PubMed/NCBI | |
Tollefsen DM: Heparin cofactor II modulates the response to vascular injury. Arterioscler Thromb Vasc Biol. 27:454–460. 2007. View Article : Google Scholar | |
Rau JC, Deans C, Hoffman MR, Thomas DB, Malcom GT, Zieske AW, Strong JP, Koch GG and Church FC: Heparin cofactor II in atherosclerotic lesions from the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) study. Exp Mol Pathol. 87:178–183. 2009. View Article : Google Scholar : PubMed/NCBI | |
Ikeda Y, Aihara K, Yoshida S, Iwase T, Tajima S, Izawa-Ishizawa Y, Kihira Y, Ishizawa K, Tomita S, Tsuchiya K, et al: Heparin cofactor II, a serine protease inhibitor, promotes angiogenesis via activation of the AMP-activated protein kinase-endothelial nitric-oxide synthase signaling pathway. J Biol Chem. 287:34256–34263. 2012. View Article : Google Scholar : PubMed/NCBI | |
Liao WY, Ho CC, Hou HH, Hsu TH, Tsai MF, Chen KY, Chen HY, Lee YC, Yu CJ, Lee CH, et al: Heparin co-factor II enhances cell motility and promotes metastasis in non-small cell lung cancer. J Pathol. 235:50–64. 2015. View Article : Google Scholar | |
Cavalcante MS, Torres-Romero JC, Lobo MD, Moreno FB, Bezerra LP, Lima DS, Matos JC, Moreira RA and Monteiro-Moreira AC: A panel of glycoproteins as candidate biomarkers for early diagnosis and treatment evaluation of B-cell acute lymphoblastic leukemia. Biomark Res. 4:12016. View Article : Google Scholar : | |
Zhuang H, Gao J, Hu Z, Liu J, Liu D and Lin B: Co-expression of Lewis y antigen with human epididymis protein 4 in ovarian epithelial carcinoma. PLoS One. 8:e689942013. View Article : Google Scholar : PubMed/NCBI | |
Li J, Chen H, Mariani A, Chen D, Klatt E, Podratz K, Drapkin R, Broaddus R, Dowdy S and Jiang SW: HE4 (WFDC2) promotes tumor growth in endometrial cancer cell lines. Int J Mol Sci. 14:6026–6043. 2013. View Article : Google Scholar : PubMed/NCBI | |
Lu Q, Chen H, Senkowski C, Wang J, Wang X, Brower S, Glasgow W, Byck D, Jiang SW and Li J: Recombinant HE4 protein promotes proliferation of pancreatic and endometrial cancer cell lines. Oncol Rep. 35:163–170. 2016. |