Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

Yuan, Fei; Zhou, You; Wang, Meng; Yang, Jing; Wu, Kai; Lu, Changhong; Kong, Xiangyin; Cai, Yu-Dong

doi:https://doi.org/10.1155/2015/462363

Computational and Mathematical Methods in Medicine

On this page

Abstract Introduction Materials and Methods Results and Discussion Conclusions Acknowledgments Supplementary Materials References Copyright Related Articles

Special Issue

Machine Learning and Network Methods for Biology and Medicine

View this Special Issue

Research Article | Open Access

Volume 2015 | Article ID 462363 | https://doi.org/10.1155/2015/462363

Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

Fei Yuan,¹You Zhou,¹Meng Wang,¹Jing Yang,¹Kai Wu,¹Changhong Lu,²Xiangyin Kong,¹and Yu-Dong Cai³

Academic Editor: Lin Lu

Received24 Jan 2015

Accepted24 Feb 2015

Published04 Oct 2015

Abstract

Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer.

1. Introduction

The prostate is a gland in the male reproductive system that surrounds the prostatic urethra and affects urinary function. Its secretion is a component of semen. Prostate cancer is a form of adenocarcinoma. Most prostate cancers grow slowly, while some grow relatively rapidly [1, 2]. In the early stage, some prostate cancer patients present no symptoms, while others display symptoms similar to benign prostatic hyperplasia. Advanced prostate cancer can spread to other parts of the body, including the bones and lymph nodes [3]. Prostate cancer can also affect sexual function, such as erection and ejaculation. It is the world’s second most common cancer [1]. More than 80% of men will be diagnosed with prostate cancer by the age of 80 [4], but, due to its slow growth, most patients do not die from this disease.

Biopsy is necessary to confirm the diagnosis of prostate cancer. Ultrasound (US) and magnetic resonance imaging (MRI) can help determine whether the cancer has metastasized [2]. Prostate specific antigen (PSA) screening is widely used in the USA to diagnose prostate cancer at an earlier age and cancer stage [5]. Noninvasive detection methods are being developed, including detecting EN2 and PCA3 mRNA in the urine [6, 7]. BCL-2, Ki-67, and ERK5 may also be useful as markers [8–10]. Treatment options for prostate cancer include surgery, radiation therapy, hormone therapy, and chemotherapy [2].

Prostate cancer risk is associated with age, family disease history, and race. It is not monogenic; many genes are involved. For example, mutations in BRCA1 and BRCA2 have been implicated in prostate cancer, while they are also risk factors for ovarian cancer and breast cancer [11]. p53 mutations are more frequently observed after prostate cancer metastasis. Additionally, one copy of the tumor suppressor gene PTEN is lost in up to 70% of prostate cancer patients [12]. Genome-wide association studies have identified several SNPs that affect prostate cancer risk [13–15]. The transcription factor RUNX2 can prevent prostate cancer cell apoptosis [16], and inhibition of X-linked inhibitor of apoptosis (XIAP) is being studied as a strategy to enhance apoptosis and prevent cancer cell proliferation [17]. Sexually transmissible infections (STI), such as HPV-16, HPV-18, and HSV-2, are significantly linked with prostate cancer [18–20].

Several chemicals have also been studied in prostate cancer. Zinc can change prostate cell metabolism to produce citrate, an important component of semen. This process requires a large amount of energy and prostate cancer cells that are devoid of zinc reserve energy for growth [21]. The prostate glands require androgens to work properly. Hormone therapies, including castration treatment (reduction of androgen/testosterone/DHT), are commonly used, but they are only effective in a subset of patients. Androgen receptor inhibition is effective in mouse studies [22]. More treatments are being tested to improve the survival of castration-resistant prostate cancer patients.

As discussed above, prostate cancer is a very complicated disease, and we have yet to identify all risk factors. Additional genes and chemicals remain to be discovered. While it is time consuming and expensive to identify genes or chemicals related to prostate cancer using traditional approaches, the development of computer science can overcome these obstacles by building effective computational methods. Here, we proposed an alternative computational method to identify new candidate genes and chemicals related to prostate cancer. To simultaneously investigate genes and chemicals, a hybrid network was constructed using chemical-chemical interactions and chemical-protein interactions from STITCH (search tool for interactions of chemicals) [23] and protein-protein interactions from STRING (search tool for the retrieval of interacting genes/proteins) [24]. By applying a shortest path approach in the hybrid network, we extracted genes and chemicals related to prostate cancer. To validate our model, several of the identified genes and chemicals were investigated in related prostate cancer literature.

2. Materials and Methods

2.1. Genes Related to Prostate Cancer

We collected genes related to prostate cancer using the following approaches: (I) 143 reviewed genes were chosen from UniProt (http://www.uniprot.org/, UniProt Release 2014_4) [25] using the search terms, “human,” “prostatic cancer,” and “reviewed”; (II) 86 genes were chosen from the TSGene Database (Tumor Suppressor Gene Database, http://bioinfo.mc.vanderbilt.edu/TSGene/cancer_type.cgi [26]) after the Entrez IDs were converted into their official symbols; and (III) 96 genes were retrieved from the NCI (National Cancer Institute, https://gforge.nci.nih.gov, released 2009.6) database [27]. After integrating the aforementioned 325 genes, we obtained 309 genes related to prostate cancer (Supplementary Material I; see Supplementary Material available online at http://dx.doi.org/10.1155/2015/462363).

2.2. Chemicals Related to Prostate Cancer

Chemicals related to prostate cancer were collected from the CTD (Comparative Toxicogenomics Database) (http://ctdbase.org/detail.go?type=disease&acc=MESH:D011471&view=chem, July 2014) [28]. These chemicals were manually assessed in the literature. Here, 177 chemicals with direct evidence of association with prostate cancer, such as “marker,” “mechanism,” or “therapeutic,” were considered. Among these 177 chemicals, 106 were present in the hybrid network described below (see Section 2.3). Thus, we employed these 106 chemicals in this study (Supplementary Material I).

2.3. Hybrid Network

The hybrid network was constructed according to information based on chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. In brief, the chemical-chemical interactions and chemical-protein interactions were retrieved from STITCH (version 4.0, http://stitch.embl.de/) [23], and the protein-protein interactions were downloaded from STRING (version 9.1, http://www.string-db.org/) [24]. The obtained interactions include both known and predicted interactions. Thus, they can widely measure the associations between chemicals and proteins, and they have been widely used to investigate many chemical-related and protein-related problems [29–40]. In addition, to measure the strength of these interactions, each interaction was assigned a score in STITCH and STRING. The score of the chemical-chemical interaction between chemicals and was denoted by (, ), the score of the chemical-protein interaction between chemical and protein by (, ), and the score of the protein-protein interaction between proteins and by (, ). Due to the large number of chemicals, we only considered chemicals with KEGG (Kyoto Encyclopedia of Genes and Genomes) records [41] to reduce search space (i.e., chemicals occurring in the retrieved chemical-protein interactions and chemical-chemical interactions must be in KEGG).

The hybrid network used proteins and chemicals from the three types of interactions as nodes. Each edge represented one of the three types of interactions, and they were assigned a weight to indicate the strength of the interaction using the following equations:Finally, we obtained a hybrid network consisting of 35,842 nodes, where 15,072 nodes represented chemicals and 20,770 nodes represented proteins. The size of the network, that is, the number of edges in the network, was 3,046,625, where 398,701 edges represented chemical-chemical interactions, 222,610 edges represented chemical-protein interactions, and 2,425,314 edges represented protein-protein interactions.

2.4. A Shortest Path Approach Used to Identify New Candidate Genes and Chemicals

Chemicals or proteins that comprise an interaction always have similar functions [31, 36, 42]. One chemical/protein and one chemical/protein that interact with a high score (low weight of the corresponding edge in the hybrid network) are more likely to share similar functions than those with a low score. Therefore, we can infer that chemicals/proteins occurring in a shortest path connecting the chemicals/proteins, and , are likely to share functions with and . Thus, we searched all the shortest paths connecting any pair of chemicals and proteins related to prostate cancer, and the corresponding chemicals and proteins occurring in these paths were considered candidate chemicals and genes. Simultaneously, the number of paths containing a certain candidate chemical or gene was termed “betweenness.”

Some of the candidate chemicals and genes may be false positives, and some chemicals or proteins may have universal associations with other chemicals or proteins, so they are observed in the shortest paths connecting any pair of randomly selected chemicals or proteins. To control for these false positives, we randomly produced 1,000 chemical and protein sets, and each set had the same numbers of chemicals and proteins as the set consisting of chemicals and genes related to prostate cancer. For each set, we searched for the shortest paths connecting any pair of chemicals or proteins and counted the betweenness of the candidate chemicals and proteins based on these paths. Then, we counted the number of randomly produced sets in which the betweenness was larger than the set consisting of chemicals and genes related to prostate cancer for each candidate chemical or gene; the value was defined as the aforementioned number divided by 1,000. Thus, a low value for a certain candidate chemical or gene indicates strong linkage with prostate cancer.

3. Results and Discussion

3.1. Candidate Genes and Chemicals

As mentioned in Sections 2.1 and 2.2, we employed 309 genes and 106 chemicals related to prostate cancer. We searched all shortest paths connecting any of these genes. Based on the obtained paths, we extracted 595 candidate genes and 102 candidate chemicals and calculated their betweenness (Supplementary Material II). According to the method in Section 2.4, the values of these candidate genes and chemicals were computed to control for false positives, which are also listed in Supplementary Material II. Then, we set the value threshold as 0.05 to select for significant candidate genes and chemicals (i.e., candidate genes and chemicals with values less than 0.05 were selected). Ultimately, 187 genes and 11 chemicals were selected (Supplementary Material III).

3.2. Analysis of Enriched KEGG Pathways of Significant Candidate Genes

As mentioned in Section 3.1, we obtained 187 significant candidate genes that were potentially related to prostate cancer pathogenesis. To analyze the relationship between these genes and prostate cancer, we employed a functional annotation tool, DAVID (Database for Annotation, Visualization and Integrated Discovery) [43], to understand their biological significance. The results of DAVID included the enrichment of the 187 significant candidate genes in KEGG pathways and GO terms (Supplementary Material IV and V, resp.).

In total, the 187 significant candidate genes shared 40 KEGG pathways. After sorting the 40 KEGG pathways according to their FDR (false discovery rate) adjusted value (last column in Supplementary Material IV), we found that the top six pathways were highly associated with prostate cancer. Figure 1 shows these pathways, the number of genes among the 187 significant candidate genes that shared each pathway and the proportion of these genes among all genes sharing the pathway. Table 1 lists the FDR of these pathways.

The most enriched pathway was hsa05200: pathways in cancer, with 30 significant candidate genes sharing this pathway (see Figure 1) and an FDR of (see Table 1, row 2). The fourth most enriched pathway was hsa05214: glioma, with 10 significant candidate genes sharing this pathway (see Figure 1) and an FDR of (see Table 1, row 5). These results indicate that prostate cancer and other types of cancer share a common mechanism.

The second most enriched pathway was hsa04010: MAPK signaling pathway, with 27 significant candidate genes (see Figure 1) and an FDR of (see Table 1, row 3). Mitogen-activated protein kinase (MAPK) pathways are evolutionarily conserved and link extracellular signals to fundamental cellular processes. Mutations in these pathways can affect Ras and B-Raf and play a critical role in cancer development [44].

The third most enriched pathway was hsa05215: prostate cancer, with 12 significant candidate genes (see Figure 1) and an FDR of (see Table 1, row 4). This result shows that some of the candidate genes have already been grouped into the pathway which was drawn based on the previous knowledge of molecular interaction and reaction networks in prostate cancer.

The fifth most enriched pathway was hsa04722: neurotrophin signaling pathway, with 13 significant candidate genes (see Figure 1) and an FDR of (see Table 1, row 6). Neurotrophins play a role in the survival of malignant prostate cells [45]. Neurotrophins include nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin 3 (NT-3), and neurotrophin 4/5 (NT4/5), and they bind with trk receptors. The survival of malignant prostate cells requires ectopic expression of trk B and trk C and continued expression of trk A. Trk inhibition has been suggested to be a drug therapeutic target [46].

The sixth most enriched pathway was hsa04310: Wnt signaling pathway, with 14 significant candidate genes (see Figure 1) and an FDR of (see Table 1, row 7). The Wnt signaling pathway is involved in carcinogenesis and embryonic development. It acts as a common element in the regulation of stem cell renewal and the maintenance of many cellular systems. Disruption of this pathway is associated with cancer [47]. Mutations in components of this pathway, including APC, Axin, Axin2/conduction, and β-catenin, are found in a variety of cancers [48]. The Wnt signaling pathway plays a critical role in prostate cancer, as its key component, β-catenin, works as an androgen receptor (AR) cofactor. β-Catenin can significantly enhance androgen-stimulated transcriptional activation by the AR [49]. Abnormal expression of Wnt ligands and receptors may also contribute to the pathogenesis of prostate cancer [50].

3.3. Analysis of Enriched GO Terms of Significant Candidate Genes

In total, the 187 significant candidate genes enriched 576 GO terms (Supplementary Material V), and we investigated the top ten GO terms sorted by FDR. Figure 2 shows these GO terms, the number of genes among the 187 significant candidate genes that shared each GO term and the proportion of these genes among all genes sharing the GO term. Table 2 lists the FDR of these GO terms.

All of these ten GO terms were biological process (BP) GO terms, and four were associated with the regulation of cell proliferation and death: GO:0042127, regulation of cell proliferation (39 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2); GO:0042981, regulation of apoptosis (35 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2); GO:0043067, regulation of programmed cell death (35 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2); and GO:0010941, regulation of cell death (35 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2). Cell proliferation and apoptosis are both important biological processes that may lead to cancer if altered by gene mutation and other risk factors. An increasing number of studies have demonstrated that important genes and miRNAs that participate in these processes could be therapeutic targets. For instance, miR-145 functions as a tumor suppressor. By targeting FSCN1, miR-145 suppresses cell proliferation in prostate cancer, and it represents an important therapeutic target [51].

Three GO terms were associated with cell responses to stimulus: GO:0010033, response to organic substance (34 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2); GO:0009719, response to endogenous stimulus 24 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2); and GO:0009725, response to hormone stimulus (22 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2). Sex hormones play an important role in the growth and development of the prostate [52]. Testosterone is implicated in the pathogenesis of prostate cancer [53]. Hormone therapy is currently used in the clinical treatment of prostate cancer, but it is only effective in a subset of patients. A recent study found no association between prediagnostic circulating sex hormones and lethal prostate cancer or total mortality [54]. This topic remains debatable, and further prospective studies are needed. Small chemicals that can stimulate prostate cells also warrant further attention.

Two GO terms were associated with cell motility: GO:0016477, cell migration (20 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2), and GO:0048870, cell motility (20 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2). Metastatic prostate cancer often spreads to bone, but the lung and liver are also common sites. More symptoms may occur depending on the site of cancer spread.

The last term was GO:0007242: intracellular signaling cascade (43 significant candidate genes sharing this GO term, refer to Figure 2) (“FDR” = , refer to Table 2). A recent report demonstrated that activation of Stat3 signaling was essential for prostate cancer progression, and inhibition of this pathway may be a therapeutic strategy [55]. Downregulation of Notch-1 and Jagged-1 could inhibit prostate cancer cell growth, migration and invasion, and induce apoptosis via inactivation of the Akt, mTOR, and NF-κB signaling pathways [56].

3.4. Analysis of Significant Candidate Genes

In our study, 187 significant candidate genes were obtained (Supplementary Material III), where 42 genes were with value 0. Among these 42 genes, 21 genes were found to be reported as prostate cancer related genes in some previous studies, which implies our method is quite effective. Please see Table 3 for the detailed information of these 21 genes. For the rest 21 significant candidate genes with value 0, four of them (listed in rows 2–5 of Table 4) were deemed to be related to prostate cancer based on their current validated functions. They were discussed as below.

PLCG1. PLCG1 (phospholipase C, gamma 1) encodes the enzyme required to catalyze the formation of inositol IP3 (1,4,5-trisphosphate) and DAG (diacylglycerol) from phosphatidylinositol 4,5-bisphosphate. In this process, IP3 uses Ca²⁺ as a cofactor for nuclear translocation and the subsequent activation of downstream targets [57]. In our study, PLCG1 was highly related to prostate cancer, as demonstrated by its high betweenness (2,110; see row 2 of Table 4) and low value (0; see row 2 of Table 4). Frequent mutations occur in the catalytic domain of PLCG1, which induce the activation of downstream signaling pathway and PLCG1 was sensitive to specific inhibition of CaN in CTCL (cutaneous T-cell lymphoma) [58]. Many receptors, such as EGF (epidermal growth factor) and PDGF (platelet-derived growth factor), are affected by PLCG1 [59, 60]. In addition, PLCG1 plays a key role in chemotaxis triggered by growth factor receptors, and it is involved in integrin-dependent cell motility in diverse types of cancer [61]. Research regarding the function of PLCG1 in prostate cancer is rare; we remind that PLCG1 is a diagnostic marker and a drug target in prostate cancer.

BIRC2. BIRC2 (baculoviral IAP repeat containing 2), also known as API1 or cIAP1 (cellular inhibitors of apoptosis), belongs to a protein family that binds TRAF1/2 (tumor necrosis factor receptor-associated factors) to inhibit apoptosis. In our study, BIRC2 was closely associated with human prostate cancer, and its betweenness and value were 1,583 and 0, respectively (see row 3 of Table 4). ARC (caspase recruitment) regulates BIRC2, and BIRC2 expression is inverse to ARC in AML (acute myeloid leukemia) [62, 63]. In addition, in metastatic human colon and breast cancer cells, BIRC2 is the molecular target of ceramide, and the Smac mimetic, BV6, targets BIRC2 to induce apoptosis via the TNFα signaling pathway [64, 65]. However, the detailed mechanism of BIRC2 action remains unknown. We speculate that BIRC2 is a key apoptosis-associated factor in prostate cancer that warrants further experimentation.

AMELY. In prostate cancer, many driver genes are gender-related. In our study, a gender-related locus gene, AMELY (amelogenin Y-linked) (betweenness: 363, value: 0; see row 4 of Table 4), was related to prostate cancer. AMELY, which belongs to the amelogenin family of extracellular matrix proteins, is a single copy gene locus on the Y chromosome (Yp11.2) [66, 67]. AMELY and its homolog, AMELX, are often used for gender identification [68]. Deletions of AMELY occur frequently in certain ethnic populations [69–71]. Research regarding AMELY function is rare, especially in human prostate cancer, but we believe that it may be a potential gender-related gene and a biomarker in human prostate cancer. In the future, more experiments and clinical samples are still needed to validate the importance of this gene in prostate cancer.

ELL. ELL, the eleven-nineteen lysine-rich leukemia gene, encodes an RNA polymerase II transcription elongation factor that suppresses transient pausing by RNA polymerase II and functions in the process of transcription [72–74]. ELL was significantly associated with prostate cancer, as demonstrated by its high betweenness (363, see row 5 of Table 4) and low value (0, see row 5 of Table 4). ELL was initially identified as a partner gene fused to MLL in the t(11;19) (q23; p13.1) translocation in AML (acute myeloid leukemia) [75]. U19/Eaf2 is an androgen-response gene that forms nuclear speckles by binding to ELL in vivo. U19/Eaf2 is downregulated in human prostate cancer, and its overexpression induces prostate cancer cell apoptosis [76]. Direct evidence regarding the function of ELL in human prostate cancer is rare, but our data and previous studies suggest that ELL is an inducer of apoptosis and a putative target in human prostate cancer.

Besides, significant candidate genes that were not discussed here still may be related to prostate cancer. We listed them in Supplementary Material III and hope that they will be the useful information for further study on prostate cancer.

3.5. Analysis of Significant Candidate Chemicals

We also obtained 11 significant candidate chemicals involved in prostate cancer (Supplementary Material III). This section discusses the relationships between several candidate chemicals and prostate cancer. Information pertaining to the discussed chemicals is listed in rows 6–10 of Table 4.

Caffeine. The betweenness and value of caffeine (PubChem ID: CID000002519) were 371 and 0.028, respectively (row 6 of Table 4). Caffeine is a bitter, white crystalline xanthine alkaloid that can be extracted from coffee, tea, and other sources. A complex relationship has been reported between caffeine and cancer. For example, Sarkaria et al. suggested that caffeine could cause checkpoint defects, and, as a result, it might be useful for cancer therapy [77]. This statement could be regarded as an evidence to support our result. However, Wilson et al. observed a strong inverse association between coffee consumption and the risk of lethal prostate cancer, but this association appeared to be related to noncaffeine components of coffee [78]. Michels et al. did not find a strong association between caffeine and colon or rectal cancer [79]. Thus, further studies are needed to determine whether caffeine is associated with prostate cancer.

Trifluoperazine. The betweenness and value of trifluoperazine (PubChem ID: CID000005566) were 363 and 0.001, respectively (row 7 of Table 4). Trifluoperazine is a typical antipsychotic medicine of the phenothiazine chemical class. Calmodulin (CaM) is critical for the proliferation and viability of cells, including cancer cells. Trifluoperazine inhibits CaM [80]. The antitumor properties of trifluoperazine have been reported in murine T-cell lymphomas, metastatic breast cancer, and prostatic cancer [81–84]. These reports support the robustness of our analysis.

Mibefradil. The betweenness and -value of mibefradil (PubChem ID: CID000060662) were 363 and 0.013, respectively (row 8 of Table 4). Mibefradil is a blocker of the L/T-type calcium channel [85], which plays an essential role in regulating cell growth and proliferation [86]. Dysregulation of this channel may lead to tumor progression [87]. Blocking the T-type Ca²⁺ channel with mibefradil inhibits tumor cell proliferation and migration in multiple types of tumors, including human astrocytoma, neuroblastoma, glioblastoma, and breast cancer cells [85, 87–89]. Our results suggest that mibefradil represents a new candidate chemical for prostate cancer.

Icilin. The betweenness and value of icilin (PubChem ID: CID000161930) were 363 and 0, respectively (row 9 of Table 4). Icilin is an artificial superagonist of the transient receptor potential M8 (TRPM8) ion channel. Cold and cooling agents activate TRPM8, inducing a cooling sensation. TRPM8 is a tumor marker for diagnosis and a target for cancer therapy. TRPM8 expression increases in the early stages of prostate cancer, and it is involved in prostate cell apoptosis [90]. Direct activation of TRPM8 by icilin inhibits prostate cancer by reducing cancer cell motility [91]. Taken together with previous studies, our results suggest that icilin is closely related to prostate cancer, and it may be a promising drug.

Allicin. The betweenness and value of allicin (PubChem ID: CID000065036) were 2 and 0.024, respectively (row 10 of Table 4). Allicin is a garlic extract with antibacterial properties. The antitumor ability of allicin can be traced back to the early 1960s [92]. Currently, many studies have reported that garlic and its extracts can prevent cancer, such as skin cancer [93], hepatocarcinoma [94], and so forth [95, 96]. Garlic may work by enhancing repair DNA synthesis (RDS), depressing nitrosamine formation and reducing carcinogen bioactivation [97, 98]. The correlation between allicin and prostate cancer may provide novel insight for future research.

4. Conclusions

This work provided an alternative computational method to investigate prostate cancer. Several candidate genes and chemicals were extracted using this method, and analysis of the literature confirmed that they are related to prostate cancer. We hope that the results of this study will lead to the validation of these genes and chemicals.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors’ Contribution

Fei Yuan and You Zhou contributed equally to this work.

Acknowledgments

This work was supported by the National Basic Research Program of China (2011CB510101, 2011CB510102), the National Natural Science Foundation of China (31371335, 91230201), and the Innovation Program of Shanghai Municipal Education Commission (12ZZ087).

Supplementary Materials

The Supplementary Material contains five files. In detail, Supplementary Material I lists genes and chemicals related to prostate cancer; Supplementary Material II lists candidate genes and chemicals, their betweenness and p-values; Supplementary Material III lists significant candidate genes and chemicals, their betweenness and p-values; Supplementary Material IV lists KEGG enrichment results of 187 significant candidate genes; Supplementary Material V lists GO enrichment results of 187 significant candidate genes.

Supplementary Material

References

B. W. Stewart and C. P. Wild, World Cancer Report 2014, International Agency for Research on Cancer, Lyon, France, WHO Press, Geneva, Switzerland, 2014.
National Cancer Institute, Prostate Cancer Treatment (PDQ), National Cancer Institute, 2014.
R. W. Ruddon, Cancer Biology, Oxford University Press, Oxford, UK, 4th edition, 2007.
D. G. Bostwick and L. Cheng, Urologic Surgical Pathology, 2nd edition, 2007.
J. M. Fitzpatrick, “Management of localized prostate cancer in senior adults: the crucial role of comorbidity,” BJU International, Supplement, vol. 101, supplement 2, pp. 16–22, 2008.
View at: Publisher Site | Google Scholar
R. Morgan, A. Boxall, A. Bhatt et al., “Engrailed-2 (EN2): a tumor specific urinary biomarker for the early diagnosis of prostate cancer,” Clinical Cancer Research, vol. 17, no. 5, pp. 1090–1098, 2011.
View at: Publisher Site | Google Scholar
A. Bourdoumis, A. G. Papatsoris, M. Chrisofos, E. Efstathiou, A. Skolarikos, and C. Deliveliotis, “The novel prostate cancer antigen 3 (PCA3) biomarker,” International Brazilian Journal of Urology, vol. 36, no. 6, pp. 665–669, 2010.
View at: Publisher Site | Google Scholar
S. D. Catz and J. L. Johnson, “BCL-2 in prostate cancer: a minireview,” Apoptosis, vol. 8, no. 1, pp. 29–37, 2003.
View at: Publisher Site | Google Scholar
S. Chakravarthi, P. Thanikachalam, H. S. Nagaraja, D. L. Wee Yang, and N. I. Bukhari, “Assessment of proliferative index and its association with Ki-67 antigen molecule expression in nodular hyperplasia of prostate,” Indian Journal of Science & Technology, vol. 2, no. 8, pp. 1–4, 2009.
View at: Google Scholar
A. K. Ramsay, S. R. C. McCracken, M. Soofi et al., “ERK5 signalling in prostate cancer promotes an invasive phenotype,” British Journal of Cancer, vol. 104, no. 4, pp. 664–672, 2011.
View at: Publisher Site | Google Scholar
J. P. Struewing, P. Hartge, S. Wacholder et al., “The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews,” The New England Journal of Medicine, vol. 336, no. 20, pp. 1401–1408, 1997.
View at: Publisher Site | Google Scholar
Scientists Discover Anti-Cancer Mechanism That Arrests Early Prostate Cancer, 2005, http://www.sciencedaily.com/releases/2005/08/050804074959.htm.
R. A. Eeles, Z. Kote-Jarai, G. G. Giles et al., “Multiple newly identified loci associated with prostate cancer susceptibility,” Nature Genetics, vol. 40, no. 3, pp. 316–321, 2008.
View at: Google Scholar
G. Thomas, K. B. Jacobs, M. Yeager et al., “Multiple loci identified in a genome-wide association study of prostate cancer,” Nature Genetics, vol. 40, no. 3, pp. 310–315, 2008.
View at: Publisher Site | Google Scholar
H. C. Whitaker, Z. Kote-Jarai, H. Ross-Adams et al., “The rs10993994 risk allele for prostate cancer results in clinically relevant changes in microseminoprotein-beta expression in tissue and urine,” PLoS ONE, vol. 5, no. 10, Article ID e13363, 2010.
View at: Publisher Site | Google Scholar
I. Leav, J. Plescia, H. L. Goel et al., “Cytoprotective mitochondrial chaperone TRAP-1 as a novel molecular target in localized and metastatic prostate cancer,” The American Journal of Pathology, vol. 176, no. 1, pp. 393–401, 2010.
View at: Publisher Site | Google Scholar
S.-I. Watanabe, Y. Miyata, S. Kanda et al., “Expression of X-linked inhibitor of apoptosis protein in human prostate cancer specimens with and without neo-adjuvant hormonal therapy,” Journal of Cancer Research and Clinical Oncology, vol. 136, no. 5, pp. 787–793, 2010.
View at: Publisher Site | Google Scholar
A. V. Sarma, J. C. McLaughlin, L. P. Wallner et al., “Sexual behavior, sexually transmitted diseases and prostatitis: the risk of prostate cancer in black men,” The Journal of Urology, vol. 176, no. 3, pp. 1108–1113, 2006.
View at: Publisher Site | Google Scholar
M. Hisada, C. S. Rabkin, H. D. Strickler, W. E. Wright, R. E. Christianson, and B. J. van den Berg, “Human papillomavirus antibody and risk of prostate cancer,” Journal of the American Medical Association, vol. 283, no. 3, pp. 340–341, 2000.
View at: Publisher Site | Google Scholar
L. K. Dennis, J. A. Coughlin, B. C. McKinnon et al., “Sexually transmitted infections and prostate cancer among men in the U.S. military,” Cancer Epidemiology Biomarkers and Prevention, vol. 18, no. 10, pp. 2665–2671, 2009.
View at: Publisher Site | Google Scholar
L. C. Costello and R. B. Franklin, “The clinical relevance of the metabolism of prostate cancer; zinc and tumor suppression: connecting the dots,” Molecular Cancer, vol. 5, article 17, 2006.
View at: Publisher Site | Google Scholar
N. V. Narizhneva, N. D. Tararova, P. Ryabokon et al., “Small molecule screening reveals a transcription-independent pro-survival function of androgen receptor in castration-resistant prostate cancer,” Cell Cycle, vol. 8, no. 24, pp. 4155–4167, 2009.
View at: Publisher Site | Google Scholar
M. Kuhn, C. von Mering, M. Campillos, L. J. Jensen, and P. Bork, “STITCH: interaction networks of chemicals and proteins,” Nucleic Acids Research, vol. 36, no. 1, pp. D684–D688, 2008.
View at: Publisher Site | Google Scholar
L. J. Jensen, M. Kuhn, M. Stark et al., “STRING 8—a global view on proteins and their functional interactions in 630 organisms,” Nucleic Acids Research, vol. 37, supplement 1, pp. D412–D416, 2009.
View at: Publisher Site | Google Scholar
U. Consortium, “Update on activities at the Universal Protein Resource (UniProt) in 2013,” Nucleic Acids Research, vol. 41, no. D1, pp. D43–D47, 2013.
View at: Publisher Site | Google Scholar
M. Zhao, J. Sun, and Z. Zhao, “TSGene: a web resource for tumor suppressor genes,” Nucleic Acids Research, vol. 41, no. 1, pp. D970–D976, 2013.
View at: Publisher Site | Google Scholar
S. McNeil, A. Budhu, N. Grantees et al., Imaging, National Cancer Institute, 2013.
A. P. Davis, C. G. Murphy, R. Johnson et al., “The comparative toxicogenomics database: update 2013,” Nucleic Acids Research, vol. 41, no. 1, pp. D1104–D1114, 2013.
View at: Publisher Site | Google Scholar
L. Chen, J. Lu, T. Huang et al., “Finding candidate drugs for hepatitis C based on chemical-chemical and chemical-protein interactions,” PLoS ONE, vol. 9, no. 9, Article ID e107767, 2014.
View at: Publisher Site | Google Scholar
L.-L. Hu, C. Chen, T. Huang, Y.-D. Cai, and K.-C. Chou, “Predicting biological functions of compounds based on chemical-chemical interactions,” PLoS ONE, vol. 6, no. 12, Article ID e29491, 2011.
View at: Publisher Site | Google Scholar
L. L. Hu, T. Huang, X. Shi, W.-C. Lu, Y.-D. Cai, and K.-C. Chou, “Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties,” PLoS ONE, vol. 6, no. 1, Article ID e14556, 2011.
View at: Publisher Site | Google Scholar
L. Zhang, “Sequence-based prediction of protein-protein interactions using random tree and genetic algorithm,” in Intelligent Computing Technology, vol. 7389 of Lecture Notes in Computer Science, pp. 334–341, 2012.
View at: Publisher Site | Google Scholar
L. Chen, J. Lu, N. Zhang, T. Huang, and Y.-D. Cai, “A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes,” Molecular BioSystems, vol. 10, no. 4, pp. 868–877, 2014.
View at: Publisher Site | Google Scholar
E.-H. Yap, T. Rosche, S. Almo, and A. Fiser, “Functional clustering of immunoglobulin superfamily proteins with protein-protein interaction information calibrated hidden markov model sequence profiles,” Journal of Molecular Biology, vol. 426, no. 4, pp. 945–961, 2014.
View at: Publisher Site | Google Scholar
B.-Q. Li, T. Huang, L. Liu, Y.-D. Cai, and K.-C. Chou, “Identification of colorectal cancer related genes with mrmr and shortest path in protein-protein interaction network,” PLoS ONE, vol. 7, no. 4, Article ID e33393, 2012.
View at: Publisher Site | Google Scholar
L. Chen, W.-M. Zeng, Y.-D. Cai, K.-Y. Feng, and K.-C. Chou, “Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities,” PLoS ONE, vol. 7, no. 4, Article ID e35254, 2012.
View at: Publisher Site | Google Scholar
C.-W. Tung, “Acquiring decision rules for predicting ames-negative hepatocarcinogens using chemical-chemical interactions,” in Pattern Recognition in Bioinformatics, vol. 8626 of Lecture Notes in Computer Science, pp. 1–9, Springer International Publishing, 2014.
View at: Publisher Site | Google Scholar
E. Klipp, R. C. Wade, and U. Kummer, “Biochemical network-based drug-target prediction,” Current Opinion in Biotechnology, vol. 21, no. 4, pp. 511–516, 2010.
View at: Publisher Site | Google Scholar
M. Re and G. Valentini, “Network-based drug ranking and repositioning with respect to DrugBank therapeutic categories,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 6, pp. 1359–1371, 2013.
View at: Publisher Site | Google Scholar
C.-W. Tung and J.-L. Jheng, “Interpretable prediction of non-genotoxic hepatocarcinogenic chemicals,” Neurocomputing, vol. 145, pp. 68–74, 2014.
View at: Publisher Site | Google Scholar
M. Kanehisa and S. Goto, “KEGG: Kyoto encyclopedia of genes and genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27–30, 2000.
View at: Publisher Site | Google Scholar
Y.-F. Gao, L. Chen, Y.-D. Cai, K.-Y. Feng, T. Huang, and Y. Jiang, “Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins,” PLoS ONE, vol. 7, no. 9, Article ID e45944, 2012.
View at: Publisher Site | Google Scholar
D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists,” Nucleic Acids Research, vol. 37, no. 1, pp. 1–13, 2009.
View at: Publisher Site | Google Scholar
A. S. Dhillon, S. Hagan, O. Rath, and W. Kolch, “MAP kinase signalling pathways in cancer,” Oncogene, vol. 26, no. 22, pp. 3279–3290, 2007.
View at: Publisher Site | Google Scholar
C. A. Dionne, A. M. Camoratto, J. P. Jani et al., “Cell cycle-independent death of prostate adenocarcinoma is induced by the trk tyrosine kinase inhibitor CEP-751 (KT6587),” Clinical Cancer Research, vol. 4, no. 8, pp. 1887–1898, 1998.
View at: Google Scholar
A. T. Weeraratna, J. T. Arnold, D. J. George, A. DeMarzo, and J. T. Isaacs, “Rational basis for Trk inhibition therapy for prostate cancer,” Prostate, vol. 45, no. 2, pp. 140–148, 2000.
View at: Google Scholar
T. Reya and H. Clevers, “Wnt signalling in stem cells and cancer,” Nature, vol. 434, no. 7035, pp. 843–850, 2005.
View at: Publisher Site | Google Scholar
B. Lustig and J. Behrens, “The Wnt signaling pathway and its role in tumor development,” Journal of Cancer Research and Clinical Oncology, vol. 129, no. 4, pp. 199–221, 2003.
View at: Google Scholar
C. I. Truica, S. Byers, and E. P. Gelmann, “Beta-catenin affects androgen receptor transcriptional activity and ligand specificity,” Cancer Research, vol. 60, no. 17, pp. 4709–4713, 2000.
View at: Google Scholar
M. Verras and Z. Sun, “Roles and regulation of Wnt signaling and β-catenin in prostate cancer,” Cancer Letters, vol. 237, no. 1, pp. 22–32, 2006.
View at: Publisher Site | Google Scholar
M. Fuse, N. Nohata, S. Kojima et al., “Restoration of miR-145 expression suppresses cell proliferation, migration and invasion in prostate cancer by targeting FSCN1,” International Journal of Oncology, vol. 38, no. 4, pp. 1093–1101, 2011.
View at: Publisher Site | Google Scholar
T. Imamoto, H. Suzuki, M. Yano et al., “The role of testosterone in the pathogenesis of prostate cancer,” International Journal of Urology, vol. 15, no. 6, pp. 472–480, 2008.
View at: Publisher Site | Google Scholar
A. W. Roddam, N. E. Allen, P. Appleby, and T. J. Key, “Endogenous sex hormones and prostate cancer: a collaborative analysis of 18 prospective studies,” Journal of the National Cancer Institute, vol. 100, no. 3, pp. 170–183, 2008.
View at: Publisher Site | Google Scholar
B. Gershman, I. M. Shui, M. Stampfer et al., “Prediagnostic circulating sex hormones are not associated with mortality for men with prostate cancer,” European Urology, vol. 65, no. 4, pp. 683–689, 2014.
View at: Publisher Site | Google Scholar
Z. Ni, W. Lou, E. S. Leman, and A. C. Gao, “Inhibition of constitutively activated Stat3 signaling pathway suppresses growth of prostate cancer cells,” Cancer Research, vol. 60, no. 5, pp. 1225–1228, 2000.
View at: Google Scholar
Z. Wang, Y. Li, S. Banerjee et al., “Down-regulation of Notch-1 and Jagged-1 inhibits prostate cancer cell growth, migration and invasion, and induces apoptosis via inactivation of Akt, mTOR, and NF-κB signaling pathways,” Journal of Cellular Biochemistry, vol. 109, no. 4, pp. 726–736, 2010.
View at: Publisher Site | Google Scholar
F. Macian, “NFAT proteins: key regulators of T-cell development and function,” Nature Reviews Immunology, vol. 5, no. 6, pp. 472–484, 2005.
View at: Publisher Site | Google Scholar
J. P. Vaque, G. Gómez-López, V. Monsálvez et al., “PLCG1 mutations in cutaneous T-cell lymphomas,” Blood, vol. 123, no. 13, pp. 2034–2043, 2034.
View at: Publisher Site | Google Scholar
J. Kassis, D. A. Lauffenburger, T. Turner, and A. Wells, “Tumor invasion as dysregulated cell motility,” Seminars in Cancer Biology, vol. 11, no. 2, pp. 105–117, 2001.
View at: Publisher Site | Google Scholar
A. Wells, “Tumor invasion: role of growth factor-induced cell motility,” Advances in Cancer Research, vol. 78, pp. 31–101, 1999.
View at: Publisher Site | Google Scholar
N. P. Jones, J. Peak, S. Brader, S. A. Eccles, and M. Katan, “PLCγ1 is essential for early events in integrin signalling required for cell motility,” Journal of Cell Science, vol. 118, no. 12, pp. 2695–2706, 2005.
View at: Publisher Site | Google Scholar
P. Y. Mak, D. H. Mak, V. Ruvolo et al., “Apoptosis repressor with caspase recruitment domain modulates second mitochondrial-derived activator of caspases mimetic-induced cell death through BIRC2/MAP3K14 signalling in acute myeloid leukaemia,” British Journal of Haematology, vol. 167, no. 3, pp. 376–384, 2014.
View at: Publisher Site | Google Scholar
B. Z. Carter, P. Y. Mak, D. H. Mak et al., “Synergistic targeting of AML stem/progenitor cells with IAP antagonist birinapant and demethylating agents,” Journal of the National Cancer Institute, vol. 106, no. 2, Article ID djt440, 2014.
View at: Publisher Site | Google Scholar
E. Varfolomeev, J. W. Blankenship, S. M. Wayson et al., “IAP antagonists induce autoubiquitination of c-IAPs, NF-kappaB activation, and TNFalpha-dependent apoptosis,” Cell, vol. 131, no. 4, pp. 669–681, 2007.
View at: Publisher Site | Google Scholar
A. V. Paschall, M. A. Zimmerman, C. M. Torres et al., “Ceramide targets xIAP and cIAP1 to sensitize metastatic colon and breast cancer cells to apoptosis induction to suppress tumor progression,” BMC Cancer, vol. 14, no. 1, article 24, 2014.
View at: Publisher Site | Google Scholar
E. C. Lau, T. K. Mohandas, L. J. Shapiro, H. C. Slavkin, and M. L. Snead, “Human and mouse amelogenin gene loci are on the sex chromosomes,” Genomics, vol. 4, no. 2, pp. 162–168, 1989.
View at: Publisher Site | Google Scholar
E. C. Salido, P. H. Yen, K. Koprivnikar, L.-C. Yu, and L. J. Shapiro, “The human enamel protein gene amelogenin is expressed from both the X and the Y chromosomes,” The American Journal of Human Genetics, vol. 50, no. 2, pp. 303–316, 1992.
View at: Google Scholar
K. M. Sullivan, A. Mannucci, C. P. Kimpton, and P. Gill, “A rapid and quantitative DNA sex test: fluorescence-based PCR analysis of X-Y homologous gene amelogenin,” BioTechniques, vol. 15, no. 4, pp. 636–641, 1993.
View at: Google Scholar
Y. M. Chang, R. Perumal, P. Y. Keat, R. Y. Y. Yong, D. L. C. Kuehn, and L. Burgoyne, “A distinct Y-STR haplotype for Amelogenin negative males characterized by a large Y_p11.2 (DYS458-MSY1-AMEL-Y) deletion,” Forensic Science International, vol. 166, no. 2-3, pp. 115–120, 2007.
View at: Publisher Site | Google Scholar
W. Lattanzi, M. C. Di Giacomo, G. M. Lenato et al., “A large interstitial deletion encompassing the amelogenin gene on the short arm of the Y chromosome,” Human Genetics, vol. 116, no. 5, pp. 395–401, 2005.
View at: Publisher Site | Google Scholar
Y. M. Chang, L. A. Burgoyne, and K. Both, “Higher failures of amelogenin sex test in an Indian population group,” Journal of Forensic Sciences, vol. 48, no. 6, pp. 1309–1313, 2003.
View at: Google Scholar
B. J. Elmendorf, A. Shilatifard, Q. Yan, J. W. Conaway, and R. C. Conaway, “Transcription factors TFIIF, ELL, and Elongin negatively regulate SII-induced nascent transcript cleavage by non-arrested RNA polymerase II elongation intermediates,” The Journal of Biological Chemistry, vol. 276, no. 25, pp. 23109–23114, 2001.
View at: Publisher Site | Google Scholar
J. C. Eissenberg, J. Ma, M. A. Gerber, A. Christensen, J. A. Kennison, and A. Shilatifard, “dELL is an essential RNA polymerase II elongation factor with a general role in development,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 15, pp. 9894–9899, 2002.
View at: Publisher Site | Google Scholar
M. Gerber, J. Ma, K. Dean, J. C. Eissenberg, and A. Shilatifard, “Drosophila ELL is associated with actively elongating RNA polymerase II on transcriptionally active sites in vivo,” The EMBO Journal, vol. 20, no. 21, pp. 6104–6114, 2001.
View at: Publisher Site | Google Scholar
M. J. Thirman, D. A. Levitan, H. Kobayashi, M. C. Simon, and J. D. Rowley, “Cloning of ELL, a gene that fuses to MLL in a t(11;19)(q23;p13.1) in acute myeloid leukemia,” Proceedings of the National Academy of Sciences of the United States of America, vol. 91, no. 25, pp. 12110–12114, 1994.
View at: Publisher Site | Google Scholar
W. Xiao, F. Jiang, and Z. Wang, “ELL binding regulates U19/Eaf2 intracellular localization, stability, and transactivation,” Prostate, vol. 66, no. 1, pp. 1–12, 2006.
View at: Publisher Site | Google Scholar
J. N. Sarkaria, E. C. Busby, R. S. Tibbetts et al., “Inhibition of ATM and ATR kinase activities by the radiosensitizing agent, caffeine,” Cancer Research, vol. 59, no. 17, pp. 4375–4382, 1999.
View at: Google Scholar
K. M. Wilson, J. L. Kasperzyk, J. R. Rider et al., “Coffee consumption and prostate cancer risk and progression in the health professionals follow-up study,” Journal of the National Cancer Institute, vol. 103, no. 11, pp. 876–884, 2011.
View at: Publisher Site | Google Scholar
K. B. Michels, W. C. Willett, C. S. Fuchs, and E. Giovannucci, “Coffee, tea, and caffeine consumption and incidence of colon and rectal cancer,” Journal of the National Cancer Institute, vol. 97, no. 4, pp. 282–292, 2005.
View at: Publisher Site | Google Scholar
D. Bar-Sagi and J. Prives, “Trifluoperazine, a calmodulin antagonist, inhibits muscle cell fusion,” The Journal of Cell Biology, vol. 97, no. 5, part 1, pp. 1375–1380, 1983.
View at: Publisher Site | Google Scholar
S. Naftalovich, E. Yefenof, and Y. Eilam, “Antitumor effects of ketoconazole and trifluoperazine in murine T-cell lymphomas,” Cancer Chemotherapy and Pharmacology, vol. 28, no. 5, pp. 384–390, 1991.
View at: Publisher Site | Google Scholar
E. Cifuentes, J. M. Mataraza, B. A. Yoshida et al., “Physical and functional interaction of androgen receptor with calmodulin in prostate cancer cells,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 2, pp. 464–469, 2004.
View at: Publisher Site | Google Scholar
G. T. Budd, R. M. Bukowski, A. Lichtin, L. Bauer, P. Van Kirk, and R. Ganapathi, “Phase II trial of doxorubicin and trifluoperazine in metastatic breast cancer,” Investigational New Drugs, vol. 11, no. 1, pp. 75–79, 1993.
View at: Publisher Site | Google Scholar
A. Sivanandam, S. Murthy, K. Chinnakannu et al., “Calmodulin protects androgen receptor from calpain-mediated breakdown in prostate cancer cells,” Journal of Cellular Physiology, vol. 226, no. 7, pp. 1889–1896, 2011.
View at: Publisher Site | Google Scholar
K. Hayashi, S. Wakino, Y. Ozawa et al., “Role of protein kinase C in Ca channel blocker-induced renal arteriolar dilation in spontaneously hypertensive rats—studies in the isolated perfused hydronephrotic kidney,” Keio Journal of Medicine, vol. 54, no. 2, pp. 102–108, 2005.
View at: Publisher Site | Google Scholar
B. Ciapa, D. Pesando, M. Wilding, and M. Whitaker, “Cell-cycle calcium transients driven by cyclic changes in inositol trisphosphate levels,” Nature, vol. 368, no. 6474, pp. 875–878, 1994.
View at: Publisher Site | Google Scholar
J. Pottle, C. Sun, L. Gray, and M. Li, “Exploiting MCF-7 cells’ calcium dependence with interlaced therapy,” Journal of Cancer Therapy, vol. 4, no. 7, pp. 32–40, 2013.
View at: Google Scholar
A. Panner, L. L. Cribbs, G. M. Zainelli, T. C. Origitano, S. Singh, and R. D. Wurster, “Variation of T-type calcium channel protein expression affects cell division of cultured tumor cells,” Cell Calcium, vol. 37, no. 2, pp. 105–119, 2005.
View at: Publisher Site | Google Scholar
Y. Zhang, J. Zhang, D. Jiang et al., “Inhibition of T-type Ca²⁺ channels by endostatin attenuates human glioblastoma cell proliferation and migration,” British Journal of Pharmacology, vol. 166, no. 4, pp. 1247–1260, 2012.
View at: Publisher Site | Google Scholar
B. Beck, G. Bidaux, A. Bavencoffe et al., “Prospects for prostate cancer imaging and therapy using high-affinity TRPM8 activators,” Cell Calcium, vol. 41, no. 3, pp. 285–294, 2007.
View at: Publisher Site | Google Scholar
D. Gkika and N. Prevarskaya, “TRP channels in prostate cancer: the good, the bad and the ugly?” Asian Journal of Andrology, vol. 13, no. 5, pp. 673–676, 2011.
View at: Publisher Site | Google Scholar
J. A. Dipaolo and C. Carruthers, “The effect of allicin from garlic on tumor growth,” Cancer Research, vol. 20, pp. 431–434, 1960.
View at: Google Scholar
H.-C. Wang, J. Pao, S.-Y. Lin, and L.-Y. Sheen, “Molecular mechanisms of garlic-derived allyl sulfides in the inhibition of skin cancer progression,” Annals of the New York Academy of Sciences, vol. 1271, no. 1, pp. 44–52, 2012.
View at: Publisher Site | Google Scholar
C.-L. Zhang, T. Zeng, X.-L. Zhao, L.-H. Yu, Z.-P. Zhu, and K.-Q. Xie, “Protective effects of garlic oil on hepatocarcinoma induced by N-nitrosodiethylamine in rats,” International Journal of Biological Sciences, vol. 8, no. 3, pp. 363–374, 2012.
View at: Publisher Site | Google Scholar
J.-Y. Hu, Y.-W. Hu, J. J. Zhou, M.-W. Zhang, D. Li, and S. Zheng, “Consumption of garlic and risk of colorectal cancer: an updated meta-analysis of prospective studies,” World Journal of Gastroenterology, vol. 20, no. 41, pp. 15413–15422, 2014.
View at: Publisher Site | Google Scholar
A. Y. Nasr and H. A. Saleh, “Aged garlic extract protects against oxidative stress and renal changes in cisplatin-treated adult male rats,” Cancer Cell International, vol. 14, article 92, 2014.
View at: Publisher Site | Google Scholar
G. N. Lvova and G. D. Zasukhina, “Modification of repair DNA synthesis in mutagen-treated human fibroblasts during adaptive response and the antimutagenic effect of garlic extract,” Genetika, vol. 38, no. 3, pp. 306–309, 2002.
View at: Google Scholar
J. A. Milner, “Mechanisms by which garlic and allyl sulfur compounds suppress carcinogen bioactivation. Garlic and carcinogenesis,” Advances in Experimental Medicine and Biology, vol. 492, pp. 69–81, 2001.
View at: Publisher Site | Google Scholar
H. E. Mäki, K. K. Waltering, M. J. Wallén et al., “Screening of genetic and expression alterations of SRC1 gene in prostate cancer,” Prostate, vol. 66, no. 13, pp. 1391–1398, 2006.
View at: Publisher Site | Google Scholar
J. Bouchal, F. R. Santer, P. P. S. Höschele, E. Tomastikova, H. Neuwirt, and Z. Culig, “Transcriptional coactivators p300 and CBP stimulate estrogen receptor-beta signaling and regulate cellular events in prostate cancer,” Prostate, vol. 71, no. 4, pp. 431–437, 2011.
View at: Publisher Site | Google Scholar
M. Abdul and N. Hoosein, “Changes in beta-2 microglobulin expression in prostate cancer,” in Urologic Oncology: Seminars and Original Investigations, Elsevier, New York, NY, USA, 2000.
View at: Google Scholar
M. Ruscica, P. Magni, L. Steffani et al., “Characterization and sub-cellular localization of SS1R, SS2R, and SS5R in human late-stage prostate cancer cells: effect of mono- and bi-specific somatostatin analogs on cell growth,” Molecular and Cellular Endocrinology, vol. 382, no. 2, pp. 860–870, 2014.
View at: Publisher Site | Google Scholar
W. Chen, G.-M. Wang, J.-M. Guo, L.-A. Sun, and H. Wang, “NGF/γ-IFN inhibits androgen-independent prostate cancer and reverses androgen receptor function through downregulation of FGFR2 and decrease in cancer stem cells,” Stem Cells and Development, vol. 21, no. 18, pp. 3372–3380, 2012.
View at: Publisher Site | Google Scholar
Y.-W. Kwon, I. H. Chang, K. D. Kim et al., “Significance of S100A2 and S100A4 expression in the progression of prostate adenocarcinoma,” The Korean Journal of Urology, vol. 51, no. 7, pp. 456–462, 2010.
View at: Publisher Site | Google Scholar
L. Sviridonov, M. Dobkin-Bekman, B. Shterntal et al., “Differential signaling of the GnRH receptor in pituitary gonadotrope cell lines and prostate cancer cell lines,” Molecular and Cellular Endocrinology, vol. 369, no. 1-2, pp. 107–118, 2013.
View at: Publisher Site | Google Scholar
Q. Hong, E. Kuo, L. Schultz, R. J. Boackle, and N.-S. Chang, “Conformationally altered hyaluronan restricts complement classical pathway activation by binding to C1q, C1r, C1s, C2, C5 and C9, and suppresses WOX1 expression in prostate DU145 cells,” International Journal of Molecular Medicine, vol. 19, no. 1, pp. 173–179, 2007.
View at: Google Scholar
S. di Meo, I. Airoldi, C. Sorrentino, A. Zorzoli, S. Esposito, and E. di Carlo, “Interleukin-30 expression in prostate cancer and its draining lymph nodes correlates with advanced grade and stage,” Clinical Cancer Research, vol. 20, no. 3, pp. 585–594, 2014.
View at: Publisher Site | Google Scholar
J. Feng, J. Sun, S.-T. Kim et al., “A genome-wide survey over the ChIP-on-chip identified androgen receptor-binding genomic regions identifies a novel prostate cancer susceptibility locus at 12q13.13,” Cancer Epidemiology Biomarkers & Prevention, vol. 20, no. 11, pp. 2396–2403, 2011.
View at: Publisher Site | Google Scholar
S. Xiong, Q. Wang, S. V. Liu et al., “Effects of luteinizing hormone receptor signaling in prostate cancer cells,” The Prostate, vol. 75, no. 2, pp. 141–150, 2015.
View at: Publisher Site | Google Scholar
G. Botchkina and I. Ojima, Prostate and Colon Cancer Stem Cells as a Target for Anti-Cancer Drug Development, INTECH, 2011.
A. J. Najy, J. J. Won, L. S. Movilla, and H.-R. C. Kim, “Differential tumorigenic potential and matriptase activation between PDGF B versus PDGF D in prostate cancer,” Molecular Cancer Research, vol. 10, no. 8, pp. 1087–1097, 2012.
View at: Publisher Site | Google Scholar
S. Zha, S. Ferdinandusse, J. L. Hicks et al., “Peroxisomal branched chain fatty acid beta-oxidation pathway is upregulated in prostate cancer,” Prostate, vol. 63, no. 4, pp. 316–323, 2005.
View at: Publisher Site | Google Scholar
O. Kankavi, M. Baykara, M. I. Eren Karanis, C. I. Bassorgun, H. Ergin, and M. A. Ciftcioglu, “Evidence of surfactant protein A and D expression decrement and their localizations in human prostate adenocarcinomas,” Renal Failure, vol. 36, no. 2, pp. 258–265, 2014.
View at: Publisher Site | Google Scholar
W. L. Jamieson, S. Shimizu, J. A. D'Ambrosio, O. Meucci, and A. Fatatis, “CX3CR1 is expressed by prostate epithelial cells and androgens regulate the levels of CX3CL1/fractalkine in the bone marrow: potential role in prostate cancer bone tropism,” Cancer Research, vol. 68, no. 6, pp. 1715–1722, 2008.
View at: Publisher Site | Google Scholar
A. P. Singh, S. Bafna, K. Chaudhary et al., “Genome-wide expression profiling reveals transcriptomic variation and perturbed gene networks in androgen-dependent and androgen-independent prostate cancer cells,” Cancer Letters, vol. 259, no. 1, pp. 28–38, 2008.
View at: Publisher Site | Google Scholar
W. Luo, A. R. Karpf, K. K. Deeb et al., “Epigenetic regulation of vitamin D 24-hydroxylase/CYP24A1 in human prostate cancer,” Cancer Research, vol. 70, no. 14, pp. 5953–5962, 2010.
View at: Publisher Site | Google Scholar
B. Ning, C. Wang, F. Morel et al., “Human glutathione S-transferase A2 polymorphisms: variant expression, distribution in prostate cancer cases/controls and a novel form,” Pharmacogenetics, vol. 14, no. 1, pp. 35–44, 2004.
View at: Publisher Site | Google Scholar
O. R. Saramäki, K. P. Porkka, R. L. Vessella, and T. Visakorpi, “Genetic aberrations in prostate cancer by microarray analysis,” International Journal of Cancer, vol. 119, no. 6, pp. 1322–1329, 2006.
View at: Publisher Site | Google Scholar
K. Knerr, K. Ackermann, T. Neidhart, and W. Pyerin, “Bone metastasis: osteoblasts affect growth and adhesion regulons in prostate tumor cells and provoke osteomimicry,” International Journal of Cancer, vol. 111, no. 1, pp. 152–159, 2004.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2015 Fei Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

1753

Downloads

1237

Citations