Gene Network Analysis and Key Genes Associated with Biological Pathways Involved in Docetaxel-Resistant Prostate Cancer Cells In Silico

Prostate cancer is the most common cancer diagnosed among men worldwide and the second most common cause of cancer death [1]. However, prostate cancer can be classified to castration-resistant and hormone- dependent prostate cancer. According to previous reports related to prostate cancer, apoptosis plays an important role in the chemotherapy response, suggesting a relationship between apoptosis induced by drug and treatment efficiency [2]. The resistance of drug is a critical issue for cancer treatment but its underlying mechanisms are unclear.

Docetaxel is an anti-microtubule drug with antitumor activity in different cancers, such as prostate cancer, which acts by binding to tubulin and disrupting the balance between disassembly during mitosis and microtubule assembly [3]. It was previously reported which docetaxel downregulates some genes related to proliferation of cell, formation of mitotic spindle, transcription factors and oncogenesis and upregulates some genes associated with apoptosis induction and cell cycle arrest in prostate cancer cells [4]. The mechanism of resistant prostate cancer to docetaxel is related to different factors, such as expression of androgen receptor splice variant, β-tubulin expression alternations, multidrug resistance caused by unnatural expression of the ATP binding cassette (ABC) transporter family and unnatural expression of NF-κb/interleukin (IL) 6 and PI3K/AKT/mTOR signaling pathways [5].

CDH1 gene has identified in PC-3 and DU145 cells resistant to docetaxel and confirmed in prostate tumors from patients resistant to docetaxel [6]. According to the results of Jiang et al [7], mutation of the CDH1 gene is related to invasion and metastasis in various types of cancer, because it alters the transcriptional activity of epithelial cells. Previous research has shown that enhanced expression of the ABCB1 gene causes resistance to docetaxel and that ABCB1 expression is associated with prostate tumors [8].

Calcium-binding protein S100 A4 (S100A4) plays an important role in the cancer metastasis progression and has been reported to mediate migration, invasion and prostate cancer apoptosis and also plays a role in the extracellular and intracellular regions of tumors [9]. TSPAN1 is a novel member of the tetraspanin family and its role in the progression of tumor has been reported and it can enhance the proliferation and invasion of tumor cell in the laboratory and its expression is increased in human tumor cells [10].

Results from studies that combine different results in the form of meta-analysis are more reliable and provide a better answer than single results. In cancer-related studies, results from meta-analysis provide a better understanding of molecular mechanisms. Microarrays technology has allowed us to simultaneously examine the expression of more genes and identify the molecular mechanism involved in the resistance of prostate cancer cells to docetaxel. Therefore, the results from the integration of various studies could lead to the identification of important genes that are associated with the molecular mechanisms of prostate cancer cell resistance to docetaxel. Generally, this study provides information about regulatory genes, transcription factors (TFs) and gene ontology (GO) related to docetaxel resistance in prostate cancer.

To investigate the expression of microarray genes associated with docetaxel-resistant prostate cancer cells were extracted through the Gene Expression Omnibus (GEO). The samples tested in this study after quality control are listed in the Table 1. The general methodology, from raw data extraction to identification of key genes and related work in this research, is presented in Figure 1.

Table 1: Docetaxel-Resistant Prostate Cancer Cell Samples Studied from a Meta-Analysis of Microarray Data

Figure 1: Meta-Analysis Scheme of Microarray Data

Meta-Analysis and Differentially Expressed Genes (DEGs)

Microarray expression data were pre-analyzed using the R language package. Data control was done using boxplot and Heatmap (S2). The background correction and quantile normalization were performed on the unnormalized data. The Limma software package was used to perform various analyses on the selected data and finally a linear model was drawn. An empirical model of simple Bayesian was applied for correct standard errors [11]. For unadjusted pooled p-values, the log-sum Fisher's exact approach was used and the ‘fdr’ method was used to apply the p.adjust function in meta-RNASeq to adjust the combined p-values for the microarray expression data (S3). The genes with p-values ≤0.01 and |log2FoldChange|>1 were used as DEGs. The method and analyses used on each GSEs (for example GSE33455) to extract DEGs are shown in Supplementary file (S1).

GO and Biological Pathways

Enrichment analysis for GO was performed for genes with increased (up genes) and decreased (down genes) expression metaDEGs using g.profiler, (https://biit.cs.ut.ee/gprofiler/ gost). Shared GO terms for up and downregulated genes (metaDEGs) were identified through the web tool Venny v2.1. Biological pathways related to metaDEGs for up and downgenes were identified through Kyoto Genes and Genomes Encyclopedia (KEGG) (https://www.kegg.jp/ kegg/pathway.html).

Investigation of Motifs and TFs Related to Promoters

For the analysis TFs and motifs, the enrichment score threshold of 3.5 and a minimum similarity between orthologous genes of 0.05 were chosen. For this analysis, the obtained results related to DEGs of up and downregulated genes were analyzed using iRegulon through Cytoscape 3.6.1 software.

Identifying Hub Genes Through PPI

To analyze the Protein-Protein Interaction (PPI) of up and down regulated genes, the STRING (version db 12) was used and the PPI gene network was drawn using the CytoHubba plugin in Cytoscape 3.6.1. Gene network was drawn using different methods including EPC, Bottle Neck, Closeness, Degree, DMNC, EcCentricity, MNC, Betweenness, Stress, ClusteringCoefficient, Radiality and MCC for the identified up- and down-regulated genes and high-ranking genes (high degree) were selected as central genes. The CytoHubba plugin identified more relevant genes as central genes in the biological network using various methods [12].

Validation of DEGs for Up and Downregulated Genes Through Reported Studies

Based on the genes obtained from this study related to DEGs, the gene expression of eight genes (four upregulated and four downregulated genes) was compared with reported qRT-PCR studies. For final confirmation, it is suggested that the expression of these genes be experimentally confirmed and tested in the laboratory so that these genes can be introduced as key and effective genes associated with docetaxel-resistant prostate cancer cells.

Identify of DEGs

The present study showed 373 upregulated and 558 downregulated genes associated with prostate cancer cells resistant to docetaxel (p-value <0.05 and |log2FoldChange|> 1). Moreover, top down and upregulated meta-DEGs (|log2FoldChange|>1.5 and meta-pvalue <0.05) related to docetaxel-resistant prostate cancer cells are represented in Table 2. Accordingly, the upregulated genes involved in docetaxel-resistant prostate cancer cell lines mainly included IGFBP3, ABCB1, GSPT2, ROBO1, FSTL1, NID2, S100A4, CDH1 and ADAMTS1 and downregulated genes mainly related to CD24, ZNF587B, PARP2, MIR4271, OCLN, TSPAN1, ADGRG2, MPZL2 and POP7.

Table 2: Key Genes with High log2FC Related to Prostate Cancer Cells Resistant to Docetaxel

GO and biological pathways

To identify meaningful (p<0.05 and |log2FoldChange|>1) GO terms for biological processes related to resistant prostate cancer cells to docetaxel, 145 and 187 terms were enriched in down and upregulated genes, respectively (Figure 2). Shared GO terms in down and upregulated genes are mainly related to the regulation of biological processes, cell death, stimulus response and apoptotic process (Table 3). According to the top 15 GO, GO terms in the upregulated genes mostly associated with the regulation of cell differentiation, signal transduction, cell death and apoptotic process; and in the downregulated genes mainly belonged to response to chemicals, defense and immune system process, cell death and proliferation (Table 4).

Table 3: Common GO Enrichment in Down and Up DEGs Involved in the Resistant Prostate Cancer Cells to Docetaxel

Table 4: Top 15 GO Annotation’s Biological Process Involved in Docetaxel-Resistant Prostate Cancer Cells

Figure 2: Common GO Term of Up and Downregulated Genes

According to our results, the down and up meta-DEGs by KEGG pathways mainly related to cancer pathways (up genes such as HMOX1, GSTP1 and CEBPA and down gene such as CSF2RA), PI3K-Akt signaling pathway (up genes such as PTK2 and ITGAV and BRCA1as down gene), cytokine-cytokine receptor interaction (up genes such as IL13RA2 and TNFSF4 and down gene such as IL15RA, CSF2RA and LTB), MAPK signaling pathway (up genes such as FOS and JUND and down gene such as FAS), cancer transcriptional misregulation (up genes such as ZEB1 and down gene such as PLAU and IL1R2), MicroRNAs in cancer (EZH2, GLS2 and KIF23 as down genes), apoptosis (up genes such as TUBA1A and LMNB1 and PARP2 as down genes), p53 signaling pathway (up genes such as TP53I3 and IGFBP3 and FAS and BID as down genes), prostate cancer (ZEB1 as up gene and PLAU and IL1R2 as down genes) and DNA replication (PRIM1 and RFC2/5 as down genes).

Identification of TFs

The motifs and TFs of down and up meta-DEGs in resistant prostate cancer cells to docetaxel are shown in S Table 1. The dominant TFs associated with upregulated genes included SRF, TBPs, POLR2A, TAF1 and RREB as well as downregulated genes included IRFs, ZEB1, PRDM1, SPI1, JUNs, FOSs and NFKBs. Moreover, common TFs in up and down meta-DEGs were identified as STATs, E2F1, MYB, FOXs, ZNFs and BCL6.

PPI Gene Network and Hub Genes

PPI gene network for key up and down genes (30 genes) was drawn separately using CytoHubba plugin in Cytoscape 3.6.1 based on level degrees and STRING was also used for the interaction between proteins (Figure 3). The string results for up and down genes are given in the supplementary file (S4). Based on the findings, COL4A1 (collagen type IV alpha 1 chain), LOX (lysyl oxidase), SMAD2 (SMAD family member 2), TIMP1 (TIMP metallopeptidase inhibitor 1) and FOS (Fos proto-oncogene) were identified as key upregulated hub genes. Generally, the hub genes of upregulated mostly associated with collagen types and metastasis-related factors. Next, EXO1 (exonuclease 1), RRM2 (ribonucleotide reductase regulatory subunit M2), CDC45 (cell division cycle 45), KIAA0101 (PCNA clamp associated factor), MCM10 (minichromosome maintenance 10 replication initiation factor) and HMMR (hyaluronan mediated motility receptor) were reported as key downregulated hub genes. Moreover, the hub genes of downregulated mainly related to molecular processes and cancer-related factors (Table 5).

Table 5: Key Hub Genes of Up and Downregulated Genes in The Resistant Prostate Cancer Cells to Docetaxel by MCC Method

Figure 3: PPI Gene Network of (a) Key Hub Genes of Upregulated and (b) Key Hub Genes of Downregulated in Docetaxel-Resistant Prostate Cancer Cells via MCC Method

Based on the results obtained from various methods (twelve methodes) used to identify hub genes through Cytohubba plugin in Cytoscape 3.6.1, LOX (lysyl oxidase), SMAD2 (SMAD family member 2), SERPINE1 (serpin family E member 1) and FOS (Fos proto-oncogene, AP-1 transcription factor subunit) in eleven methods; PTK2 (protein tyrosine kinase 2) in eight methods and TIMP1(TIMP metallopeptidase inhibitor 1) in five methods were identified as key hub genes related to upregulated genes (Figure 4).

Figure 4: PPI Gene Network of Key Hub Genes (10 Genes) of Upregulated in Resistant Prostate Cancer Cells to Docetaxel Through Twelve Methods

Moreover, BRCA1 (BRCA1 DNA repair associated) in ten methods; EGFR (epidermal growth factor receptor) in eight methods; IL6 (interleukin 6) in seven methods; CXCL8 (C-X-C motif chemokine ligand 8) in sex methods; EPRS (glutamyl-prolyl-tRNA synthetase 1), MKI67 (marker of proliferation Ki-67) and EXO1 (exonuclease 1) in five methods were identified as key hub genes related to downregulated genes (Figure 5). The results obtained from the analysis of twelve methods to identify 10 key hub genes are shown in the Table 6.

Table 6: Key Hub Genes (10 Genes) of Up and Downregulated Genes by Twelve Topological Algorithms of PPI Gene Network Analysis Through Cytohubba

Figure 5: PPI Gene Network of Key Hub Genes (10 Genes) of Downregulated in Resistant Prostate Cancer Cells to Docetaxel Through Twelve Methods

To prove the results obtained from this research in the first stage; our results were compared with the results of research reported by other researchers based on gene expression data (fold-change expression) from molecular studies, including qRT-PCR results. The upregulated genes of this study including (CLU and DDIT3 [13], GSPT2 and CYBRD1 [14]) and the downregulated genes including (MPZL2 [15], FOXM1 [16], SCEL and TXNIP [14]) match and agree with the results of reported qRT-PCR studies (Figure 6). To confirm the results of this research, scientific confirmation in the laboratory is needed experimentally.

Figure 6: Comparison of the Present Results with the qRT-PCR Results of Reported Studies, (a) Up Regulated Genes and (b) Down Regulated Genes

Microarray technologies have widely been used to simultaneously examine gene on large scale, providing an efficient method for examine the expression of thousands of genes. So, the integration and analysis of data related to microarray data supply precious information for the investigation of resistance prostate cancer cells to docetaxel [17]. Based on our results, 373 upregulated genes and 558 downregulated genes were identified. Accordingly, top-upregulated genes associated with docetaxel-resistant prostate cancer cells were mostly included IGFBP3, ABCB1, GSPT2, ROBO1, FSTL1, NID2, S100A4, CDH1 and ADAMTS1 (Table 2). Insulin-like growth factor binding protein 3 (IGFBP3) inhibits adhesion of cell, endometrial cancer invasion and prostate cancer metastasis [18]. According to previous reports, ROBO1is expressed as a member of the immunoglobulin protein ROBO and its expression has been observed in primary tumors [19]. In cancer, FSTL1 may affect cancer cells by directly interacting with cells expressing TGFβ/BMP family receptors and DIP2A protein and indirectly by affecting the immune system [20]. Next, in this study top-downregulated genes mainly related to CD24, ZNF587B, PARP2, MIR4271, OCLN, TSPAN1, ADGRG2, MPZL2 and POP7 (Table 2). CD24 encodes a glycosyl phosphatidyl inositol and is most abundantly expressed in hematopoietic cells as well as has demonstrated its’ central role in tumor formation, progression and metastasis in prostate cancer [21]. ZNF587B is known as a member of the Krüppel-type zinc-finger proteins (KRAB-ZFPs) and has been shown to be associated with the regulating the proliferation of cell, cancer and apoptosis [22]. Accordingly, NF587B is a potential novel tumor suppressor for prostate cancer and may be a treatment target for this cancer. Occludin (OCLN) is a key tight junction protein which regulates remodeling of cytoskeletal and suppresses activation of the AKT/PI3K signaling pathway and proliferation of cell, thereby enhancing apoptosis of tumor in prostate cancer [23].

In the biological processes, GO enrichment in the upregulated genes mostly associated with the regulation of cell differentiation, signal transduction, cell death and process of apoptotic; and in the downregulated genes mainly belonged to response to chemicals, defense and immune system process, cell death and proliferation (Table 4). Moreover, meta-DEGs by KEGG pathways mostly associated with pathways in cancer, PI3K-Akt signaling pathway, interaction of cytokine-cytokine receptor, MAPK signaling pathway, transcriptional misregulation in cancer, MicroRNAs in cancer, apoptosis, p53 signaling pathway, prostate cancer and DNA replication. According to the KEGG analysis in prostate cancer, the DEGs were mostly related to cancer pathways, metabolic pathways, signaling pathway of PI3K‑Akt, Jak‑STAT signaling pathway, cancer proteoglycans and signaling pathway of NF‑κb [24].

In this study, the TFs associated with upregulated genes mostly identified as SRF, TBPs, POLR2A, TAF1 and RREB and downregulated genes were detected as IRFs, ZEB1, PRDM1, SPI1, JUNs, FOSs and NFKBs as well as common TFs in down and up genes identified as STATs, E2F1, MYB, FOXs, ZNFs, BCL6 (S Table 1). Activation of STAT3 has been identified in most of prostate cancer cell lines and has been shown to increase cell survival, tumor growth and apoptosis resistant [25]. Serum response factor (SRF) is a member of the TFs MADS-box family and is one of the important-known DNA-binding proteins in the human proteome. In addition, SRF plays a role in promoting cell proliferation, resistance to cell death and induction of metastasis and invasion [26].

Based on the present study, COL4A1, LOX, SMAD2, TIMP1 and FOS were identified as key hub genes of upregulated (Table 5). COL4A1 encodes the alpha1 chain of the Col IV gene, that is a dominant structural component of the microenvironment of tumor [27]. COL4A1 enhances invasion of tumor by inducing of tumor budding in various cancer cells and also involved in the migration and proliferation cancers such as to prostate cancer [28]. LOX is involved in the cross-linking of collagen and elastin as key enzymatic step and it is a copper-dependent amine oxidase [29]. The role of LOX in prostate cancer is to either enhance or suppress tumorigenesis, cell type, location and status of transformation [30]. The Small Mothers Against Decapentaplegic (SMAD) is an intracellular signal transduction protein related to the prognosis and growth of various types of tumors. SMADs have been shown to play a role in proliferation of cell, apoptosis, migration and regulation of cancer cells immune [31]. Based on our study, the hub genes of the upregulated DEGs mostly associated with collagen types and metastasis -related factors.

Additionally, EXO1, RRM2, CDC45, KIAA0101, MCM10 and HMMR were detected as key hub genes of downregulated genes (Table 5). Increased expression of EXO1 has been reported to be related to invasion of tumor and metastasis. EXO1 overexpression is associated with poor survival in prostate cancer patients and increases progression of tumor and metastasis [32]. RRM2 may lead to genome instability and enhanced mutation, thereby affecting the progression of tumor [7] and overexpression of RRM2 plays an important role in the proliferation of cell, metastasis and dependence of drug in prostate cancer [33]. CDC45 plays an important role in the replication of the CDC45‑MCM‑GINS helicase holoenzyme and activates MCM2‑7, which leading to replication of DNA [34] and also, it inhibits replication of DNA and suppresses proliferation of cell in human cells [35]. Based on our study, the hub genes of downregulated genes mostly associated with DNA molecular processes and cancer-related factors.

To validate our research, this study was compared with the reported qRT-PCR studies through the log2 fold change score of some upregulated genes including CLU and DDIT3 ([13], GSPT2 and CYBRD1 [14]) and downregulated genes including MPZL2 [15], FOXM1 [16], SCEL and TXNIP [14] (Figure 6).

Cancer is initially caused by oncogenic changes in oncology, as well as by genes that are effective in tumor suppression [36]. Based on the reported results, no comprehensive report has been presented so far on the integration of microarray data from docetaxel-resistant prostate cancer cells for transcriptomic analyses. Accordingly, it was considered necessary to integrate and analyze data from different microarray experiments so that comprehensive results can be reported on the genes involved in docetaxel-resistant prostate cancer cells. According to this results, key upregulated consensus hub genes including LOX, SERPINE1, FOS and PTK2 and downregulated consensus hub genes including BRCA1, EGFR, IL6 and CXCL8 were identified. Based on KEGG pathways analysis, dominant biological pathways including cancer pathways, PI3K-Akt signaling pathway, cytokine-cytokine receptor interaction, MAPK signaling pathway, apoptosis, p53 signaling pathway were identified. The current confirmation is not sufficient for this research and for strong and robust validation in the future, molecular tests, including qRT-PCR are needed experimentally in the laboratory.

In conclusion, this study showed several hub genes associated with docetaxel resistance in prostate cancer cells by advanced bioinformatics analysis in Silico to understand the molecular mechanisms and can also suggested them as effective genes for treatment of docetaxel-resistance prostate cancer cells. To finally confirm this this idea, the key genes obtained need to be experimentally validated in the laboratory.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

1. Kamangar, F. et al. “Patterns of Cancer Incidence, Mortality and Prevalence across Five Continents: Defining Priorities to Reduce Cancer Disparities in Different Geographic Regions of the World.” Journal of Clinical Oncology, vol. 24, no. 14, 2006, pp. 2137-2150. https://doi.org/10.1200/JCO.2005.05. 2308.

2. Sun, X. et al. “Systems Modelling of Anti-Apoptotic Pathways in Prostate Cancer: Psychological Stress Triggers a Synergism Pattern Switch in Drug Combination Therapy.” PLoS Computational Biology, vol. 9, no. 12, 2013. https:// doi.org/10.1371/journal.pcbi.1003358.

3. Coffman, J.A. “Cell Cycle Development.” Developmental Cell, vol. 6, no. 3, 2004, pp. 321-327. https://doi.org/10.1016/ S1534-5807(04)00067-X.

4. Li, Y. et al. “Regulation of Microtubule, Apoptosis and Cell Cycle-Related Genes by Taxotere in Prostate Cancer Cells Analyzed by Microarray.” Neoplasia, vol. 6, no. 2, 2004, pp. 158-167. https://doi.org/10.1593/neo.03391.

5. Chen, H. et al. “INPP4B Reverses Docetaxel Resistance and Epithelial-to-Mesenchymal Transition via the PI3K/Akt Signaling Pathway in Prostate Cancer.” Biochemical and Biophysical Research Communications, vol. 477, no. 3, 2016, pp. 467-472. https://doi.org/10.1016/j.bbrc.2016.06. 073.

6. Marin-Aguilera, M. et al. “Identification of Docetaxel Resistance Genes in Castration-Resistant Prostate Cancer.” Molecular Cancer Therapeutics, vol. 11, no. 2, 2012, pp. 329-339. https://doi.org/10.1158/1535-7163.MCT-11-0289.

7. Jiang, L. et al. “Epigenetic Loss of CDH1 Correlates with Multidrug Resistance in Human Hepatocellular Carcinoma Cells.” Biochemical and Biophysical Research Communications, vol. 422, no. 4, 2012, pp. 739-744. https:// doi.org/10.1016/j.bbrc.2012.05.072.

8. Sanchez, C. et al. “Chemotherapy Sensitivity Recovery of Prostate Cancer Cells by Functional Inhibition and Knock Down of Multidrug Resistance Proteins.” The Prostate, vol. 71, no. 11, 2011, pp. 1810-1817. https://doi.org/10.1002/ pros.21398.

9. Siddique, H.R. et al. “The S100A4 Oncoprotein Promotes Prostate Tumorigenesis in a Transgenic Mouse Model: Regulating NFκB through the RAGE Receptor.” Genes & Cancer, vol. 4, nos. 5-6, 2013, pp. 224-234. https://doi.org/ 10.1177/1947601913492420.

10. Hölters, S. et al. “Tetraspanin 1 Promotes Invasiveness of Cervical Cancer Cells.” International Journal of Oncology, vol. 43, no. 2, 2013, pp. 503-512. https://doi.org/10.3892/ijo. 2013.1980.

11. de Abreu Neto, J.B. and M. Frei. “Microarray Meta-Analysis Focused on the Response of Genes Involved in Redox Homeostasis to Diverse Abiotic Stresses in Rice.” Frontiers in Plant Science, vol. 6, 2016. https://doi.org/10.3389/fpls. 2015.01260.

12. Chin, C.H. et al. “CytoHubba: Identifying Hub Objects and Sub-Networks from Complex Interactome.” BMC Systems Biology, vol. 8, suppl. 4, 2014. https://doi.org/10.1186/1752-0509-8-S4-S11.

13. O’Neill, A.J. et al. “Characterisation and Manipulation of Docetaxel Resistant Prostate Cancer Cell Lines.” Molecular Cancer, vol. 10, no. 1, 2011. https://doi.org/10.1186/1476-4598-10-126.

14. Marín-Aguilera, M. et al. “Identification of Docetaxel Resistance Genes in Castration-Resistant Prostate Cancer.” Molecular Cancer Therapeutics, vol. 11, no. 2, 2012, pp. 329-339. https://doi.org/10.1158/1535-7163.MCT-11-0289.

15. Li, S. et al. “Transcriptome Analysis of Differentially Expressed Genes and Pathways Associated with Mitoxantrone Treatment Prostate Cancer.” Journal of Cellular and Molecular Medicine, vol. 23, no. 3, 2018, pp. 1987-2000. https://doi.org/10.1111/jcmm.14100.

16. Li, Y. et al. “Gene Expression Profiling Revealed Novel Molecular Targets of Docetaxel and Estramustine Combination Treatment in Prostate Cancer Cells.” Molecular Cancer Therapeutics, vol. 4, no. 3, 2005, pp. 389-398. https:// doi.org/10.1158/1535-7163.MCT-04-0244.

17. Sotiriou, C. and M.J. Piccart. “Taking Gene-Expression Profiling to the Clinic: When Will Molecular Signatures Become Relevant to Patient Care?” Nature Reviews Cancer, vol. 7, no. 7, 2007, pp. 545-553. https://doi.org/10.1038/nrc 2173.

18. Lee, H.J. et al. “Insulin-Like Growth Factor Binding Protein-3 Inhibits Cell Adhesion via Suppression of Integrin Beta4 Expression.” Oncotarget, vol. 6, no. 17, 2015, pp. 15150-15163. https://doi.org/10.18632/oncotarget.3825.

19. Khusial, P.R. et al. “Src Activates Abl to Augment Robo1 Expression in Order to Promote Tumor Cell Migration.” Oncotarget, vol. 1, no. 3, 2010, pp. 198-209. https://doi.org/ 10.18632/oncotarget.126.

20. Bae, K. et al. “Mitotic Cell Death Caused by Follistatin-Like 1 Inhibition Is Associated with Up-Regulated Bim by Inactivated Erk1/2 in Human Lung Cancer Cells.” Oncotarget, vol. 7, no. 14, 2016, pp. 18076-18084. https:// doi.org/10.18632/oncotarget.6729.

21. Smith, S.C. et al. “The Metastasis-Associated Gene CD24 Is Regulated by Ral GTPase and Is a Mediator of Cell Proliferation and Survival in Human Cancer.” Cancer Research, vol. 66, no. 4, 2006, pp. 1917-1922. https://doi.org/ 10.1158/0008-5472.CAN-05-3855.

22. Wang, J. et al. “KRAB-Containing Zinc Finger Protein ZNF496 Inhibits Breast Cancer Cell Proliferation by Selectively Repressing ERalpha Activity.” Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, vol. 1861, no. 9, 2018, pp. 841-853. https://doi.org/10.1016/j. bbagrm.2018.07.003.

23. Wang, M. et al. “Downregulation of Occludin Affects the Proliferation, Apoptosis and Metastatic Properties of Human Lung Carcinoma.” Oncology Reports, vol. 40, no. 1, 2018, pp. 454-462. https://doi.org/10.3892/or.2018.6408.

24. Deng, L. et al. “Identification and Characterization of Biomarkers and Their Functions for Docetaxel-Resistant Prostate Cancer Cells.” Oncology Letters, vol. 18, no. 3, 2019, pp. 3236-3248. https://doi.org/10.3892/ol.2019.10623.

25. Yu, H. et al. “Revisiting STAT3 Signalling in Cancer: New and Unexpected Biological Functions.” Nature Reviews Cancer, vol. 14, no. 11, 2014, pp. 736-746. https://doi.org/10. 1038/nrc3818.

26. Azam, H. et al. “Emerging Role for the Serum Response Factor (SRF) as a Potential Therapeutic Target in Cancer.” Expert Opinion on Therapeutic Targets, vol. 26, no. 2, 2022, pp. 155-169. https://doi.org/10.1080/14728222.2022.2032 652.

27. Kalluri, R. “Basement Membranes: Structure, Assembly and Role in Tumour Angiogenesis.” Nature Reviews Cancer, vol. 3, no. 6, 2003, pp. 422-433. https://doi.org/10.1038/nrc1094.

28. Jin, R. et al. “The Highly Expressed COL4A1 Gene Contributes to the Proliferation and Migration of the Invasive Ductal Carcinomas.” Oncotarget, vol. 8, no. 35, 2017. https:// doi.org/10.18632/oncotarget.17345.

29. Johnston, K.A. and K.M. Lopez. “Lysyl Oxidase in Cancer Inhibition and Metastasis.” Cancer Letters, vol. 417, 2008, pp. 174-181. https://doi.org/10.1016/j.canlet.2018.01.006.

30. Nilsson, M. et al. “Inhibition of Lysyl Oxidase and Lysyl Oxidase-Like Enzymes Has Tumour-Promoting and Tumour Suppressing Roles in Experimental Prostate Cancer.” Scientific Reports, vol. 6, no. 1, 2016. https://doi.org/10. 1038/srep19608.

31. Guan, R. et al. “Chitinase-Like Protein YKL-40 Regulates Human Bronchial Epithelial Cells Proliferation, Apoptosis and Migration through TGF-β1/Smads Pathway.” Human & Experimental Toxicology, vol. 39, no. 4, 2020, pp. 451-463. https://doi.org/10.1177/0960327119891218.

32. Luo, F. et al. “Exonuclease 1 Expression Is Associated with Clinical Progression, Metastasis and Survival Prognosis of Prostate Cancer.” Journal of Cellular Biochemistry, vol. 120, no. 7, 2019, pp. 11383-11389. https://doi.org/10.1002/jcb. 28415.

33. Yang, B. et al. “The High Expression of RRM2 Can Predict the Malignant Transformation of Endometriosis.” Advances in Therapy, vol. 38, no. 10, 2021, pp. 5178-5190. https://doi. org/10.1007/s12325-021-01888-3.

34. Tanaka, S. and H. Araki. “Helicase Activation and Establishment of Replication Forks at Chromosomal Origins of Replication.” Cold Spring Harbor Perspectives in Biology, vol. 5, no. 12, 2013. https://doi.org/10.1101/cshperspect. a010371.

35. Feng, D. et al. “Inhibiting the Expression of DNA Replication-Initiation Proteins Induce Apoptosis in Human Cancer Cells.” Cancer Research, vol. 63, no. 21, 2003, pp. 7356-7364.

36. Kirkwood, J.M. et al. “Immunotherapy of Cancer in 2012.” CA: A Cancer Journal for Clinicians, vol. 62, no. 5, 2012, pp. 309-335. https://doi.org/10.3322/caac.20132.