Bioinformatics analysis combined with experimental validation reveals the biological role of the ILK gene in prostate cancer

doi:10.21203/rs.3.rs-4934395/v1

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Research Article

Bioinformatics analysis combined with experimental validation reveals the biological role of the ILK gene in prostate cancer

https://doi.org/10.21203/rs.3.rs-4934395/v1

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Background

Prostate cancer (PCa) is a prevalent urological malignancy. The integrin-linked kinase (ILK) gene has been identified as an oncogenic driver in hormonal cancers, including PCa.

Methods

To identify key genes in PCa, we utilized differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The ILK gene was silenced using short interfering RNA (siRNA), and subsequent experiments focusing on cellular functionality were conducted to evaluate its impact on cell proliferation, apoptosis, and cell cycle. We examined the expression of autophagy-related and cell cycle-related proteins, including MAP1LC3A, BECN1, C-MYC, TP53, and MDM2. Moreover, we conducted Mfuzz expression pattern clustering analysis, gene set enrichment analysis (GSEA), immune function analysis, transcription factor (TF) analysis, and drug prediction.

Results

544 significant genes were identified by WGCNA. The protein-protein interaction (PPI) network analysis revealed that MYC was the central regulatory gene, with the intersected genes mainly involved in regulating cell adhesion and drug metabolism in prostate cancer (PCa). Experimental results showed LNCaP cell proliferation was significantly inhibited in the knockdown groups (P < 0.001). Moreover, ILK silencing increased apoptosis in LNCaP cells compared to normal cells and empty vectors, and transfected LNCaP cells were arrested in the S phase of the cell cycle. Notably, C-MYC expression decreased following ILK silencing. Subsequently, we further identified ILK-related regulatory biomarkers.

Conclusions

The ILK is an oncogene mainly through influencing the C-MYC in PCa. Inhibition of ILK expression would be a promising method for treating the development and progression of PCa.

prostate cancer

LNCaP

integrin-linked kinase

Mfuzz

biomarkers

Prostate cancer (PCa) is a common urological malignancy. Prostate cancer has the highest incidence rate among male cancers in 112 countries globally. It accounts for about 1.4 million new cases annually, leading to around 375,000 deaths each year. PCa is the second most common cancer among males, with a mortality rate ranking fifth [1]. In China, the incidence and mortality rates of PCa also increased over the years [2]. However, the exact pathogenesis of PCa is unclear. It suggested that complex factors are involved in the development and progression of PCa, such as ethnicity, diet, environmental and genetic factors [3]. Many hereditary genes including the human oxoguanine glycosylase 1 (hOGG1), apurinic/apyrimidinic endonuclease (APEX1), epidermal growth factor receptor (EGFR), platelet-derived growth factor (PDGF), vascular endothelial growth factor (VEGF), etc., were identified to be associated with PCa risk[4–8]. Among them, integrin-linked kinase (ILK) has also been identified [9].

ILK is a serine/threonine protein kinase containing four ankyrin-like repeats and a putative phosphoinositide phospholipid-binding motif, which plays a key role in signal transduction [10]. Previous studies have identified that ILK is significantly associated with the development, progression, and metastasis of many hormone-related cancers[11, 12]. The main mechanisms might be involved in the phosphorylation of a variety of intracellular substrates, including protein kinase B (PKB/Akt), glycogen synthase kinase-3 (GSK-3), and myosin light chain. Overexpression of ILK makes breast cancer patients resistant to nonreceptor tyrosine kinase drugs and is associated with poor prognosis [13]. Additionally, PCa, as a hormone-related cancer, has also been identified to be associated with the ILK gene recently. Maria et al [14] highlight the integration of β1-integrin by an integrin-linked kinase (ILK) within this signaling complex, orchestrating the modulation of both invadopodia activity and invasion in prostate (PC-3) cancer cells. Meanwhile, ILK regulates the invasive properties of prostate cancer by influencing MAPK/ERK and MLC2 signaling pathways [15]. In addition, autophagy is a cellular degradation pathway that maintains homeostasis and can influence cancer progression. In prostate cancer, autophagy plays a dual role, acting as both a tumor suppressor and a survival mechanism for cancer cells under stress conditions [16]. The role of BECN1 and MAP1LC3A in this process is crucial, as it is directly involved in the formation of autophagosomes, a key step in autophagy [17, 18]. In prostate cancer, TP53 mutations are associated with aggressive disease, resistance to therapy, and poor clinical outcomes [19]. Meanwhile, MDM2, an E3 ubiquitin ligase, is a key regulator of the TP53 tumor suppressor. Overexpression of MDM2 in prostate cancer can lead to decreased TP53 activity [20]. Therefore, these genes were included in our study. Three common cell lines were applied in studying the PCa, including the LNCaP, PC3, and DU145, in which PC3 and DU145 are mainly represented as the androgen-independent PCa. Only the LNCaP cell line is an androgen-dependence cell. To some extent, the LNCaP cell line was closer to clinical PCa. So, on the basis of LNCaP, our study was conducted to discuss the effect of the ILK gene on the growth and metastasis of PCa. Finally, we further clarified the clinical significance and regulatory mechanism of the ILK gene using bioinformatics methods.

Data acquisition

RNA-seq count data for 501 PCa tissues acquired from TCGA was normalized using the varianceStabilizingTransformation (VST) function from DESeq2 [21], while the microarray data of GSE70770 (219 PCa tissues and 74 normal tissues) from GEO was analyzed using the limma package [22].

Identification of specific differentially expressed genes in PCa

The “limma” package in R (4.3.1) software was used to screen DEGs between the tumor group and the normal group with a P-value < 0.05 and |LogFC| > 0.75 [22].

Weighted gene co-expression network analysis (WGCNA)

Using the WGCNA package in R (4.3.1), the genes with the top 25% of variance were selected for the construction of the gene co-expression network. Next, Pearson correlation matrix analysis was performed on the genes, the β value was determined, a scale-free network was constructed, and the adjacencies were transformed into a topological overlap matrix. At least 30 genes with high correlation were divided into different modules by hierarchical clustering. The differences in the eigengenes of the modules were calculated, and the modules with high correlation were combined. Finally, gene significance (GS) and gene module membership (MM) were calculated to identify clinical features and gene correlation, and important modules and genes were screened. Finally, the intersection between DEGs and WGCNA-derived blue module genes was used to obtain hub gene targets.

Protein-protein interaction (PPI) and functional enrichment analysis

The STRING (https://cn.string-db.org/) database was used to construct an interaction network for potential targets, with an interaction score threshold set at moderate confidence (0.4), and Cytoscape software was used for result visualization. Finally, genes with high connectivity included in the network were considered as hub genes in the occurrence and development process of prostate cancer, and they were selected for subsequent research. Functional analysis (Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG)) was performed by the “clusterProfiler” package using a P-value < 0.05 as a filtering criterion. GSEA functional analysis was carried out using “GSEABase” packages.

Cell lines, siRNA transfection, and quantitative RT-PCR

The LNCaP prostate cancer cell lines were cultured in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal calf serum (FCS), 50 kU/L penicillin, and 50 mg/L streptomycin (Gibco, Thermo Fisher Scientific), maintained at 37°C in a 5% CO2 atmosphere. Given the potential of high ILK gene expression to promote PCa progression, we conducted siRNA-mediated knockdown assays, transfecting ILK-targeted siRNAs into LNCaP cells seeded in 96-well plates. The Lenti-shRNA vector system (pGCSIL-GFP), including ILK siRNAs (KD1: GCCGTAGTGTAATGATTGA; KD2: TTGGTGCCTTCATGTAGTA; KD3: AGAAGCCTGAAGACACAAA) and a Negative Control, was constructed, packaged, and purified by GeneChem (Shanghai, China), following the manufacturer's protocol. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR, SYBR Master Mixture, DRR041B, TAKARA) was used to evaluate the knockdown effect of the siRNAs on ILK. The primer sequences used for ILK are F 5’-AATGTCCTGGTCGCATGTAT-3’ and R 5’-CATGTCAAATTTGGGTCGCT-3’. The primer sequences of GAPDH were as follows: F: 5’- TGACTTCAACAGCGACACCCA − 3’ and R༚5’- CACCCTGTTGCTGTAGCCAAA − 3’.

Western blot

To detect the protein expression of ILK in three groups (CON: without transfection; NC: empty vector; KD: ILK silencing), cells were collected with a gum rubber-scraping device. RIPA Lysis Buffer (P0013B, Beyotime Institute of Biotechnology) was used to lyse cells and the acquired protein was quantified with BCA Protein Assay Kit (P0010S, Beyotime Institute of Biotechnology) according to the manufacturer’s protocol. Then, 20ug proteins were separated by SDS-PAGE, transferred to nitrocellulose membranes, and incubated with antibodies against anti-ILK (dilution: 1:2000, Abcam). As the reference, GAPDH was applied. To understand the possible mechanism of the ILK in suppressing the development of PCa, the expressions of autophagy-related proteins and proliferation and apoptosis-related proteins were also detected, including MAP1LC3A (dilution: 1:500, Santa Cruz), BECN1 (dilution: 1:500, CST), C-MYC (dilution: 1:500, Abcam), TP53 (dilution: 1:500, CST), and MDM2 (dilution: 1:500, Abcam).

Cell proliferation assay

3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide assay (JT343, Genview) was used to evaluate cell viability and proliferation. Transfected LNCaP cells were seeded in 96-well plates. Before the termination of the culture, 20ul MTT (5mg/ml) was added to each well, incubating for 4 h at 37°C. Then we removed the medium and added the dimethyl sulfoxide (DMSO, 100 µL) to dissolve the precipitates. The OD was detected at 490 nm with Infinite 200 PRO (Tecan, Thermo Fisher Scientific). The measures were performed at 1 day, 2 d, 3d, 4d, and 5 d. 5 wells of LNCaP cells per time point for three groups (CON, NC, and KD) were compared independently. Referring to the OD on the first day, the OD490/fold for the following days was calculated. Student’s T-test was applied to assess the significance. All the statistics were two-tailed.

Cell apoptosis

After cell culture for 3 days, transfected LNCaP and normal cells were passaged. On the fifth day, cell apoptosis status was detected with the Annexin V-APC/PI Apoptosis Analysis Kit (88-8007-72, eBioscience). According to the instructions of this kit, cells were collected. Then, 10 µL Annexin V-APC and appropriate 1×binding buffer were added to these cells. For each group (CON, NC, and KD), three independent assays were performed at the same time with Guava easyCyte HT flow cytometry (Millipore).

Cell cycle

The same to cell apoptosis, LNCaP cells were passaged after 3 days of culture. The cell cycle was detected with propidium iodide-fluorescence associated cell sorting (PI-FACS) on the fifth day. Cells were digested and collected, with three repeats for every group. After washing and fixed with D-Hanks and 75% alcohol respectively, cells were dyed with the mix of 40x PI (2mg/ml, P4170, Sigma), 100x RNase (10mg/ml, EN0531, Fermentas) and 1x D-Hanks (25:10:1000). The results were obtained from Guava easyCyte HT flow cytometry (Millipore).

Mfuzz clustering analysis based on ILK expression

The 'Mfuzz' algorithm clustered Mfuzz expression patterns based on the expression levels of ILK, and ssGSEA scores were calculated for the high and low ILK expression groups. We compared the expression characteristics between these two groups, then calculated the correlation between the clustering module and ILK, and ultimately obtained the gene module most strongly associated with ILK.

Then, we intersected the genes of the ILK-related module with previously obtained potential PCa biomarkers (DEGs∩WGCNA) to acquire core genes interacting with ILK.

Clinical diagnostic value of ILK-related hub genes

A ROC curve was plotted by the “pROC” package, and the area under the ROC curve (AUC) was used to determine the discrimination ability of the hub genes for both tissue types. AUC > 0.7 was considered to have higher accuracy, suggesting potential diagnostic value.

Tumor immune microenvironment

CIBERSORT, used for calculating the composition percentage of 22 distinct immune cells in the human tissues, was used to explore immune cell infiltration in tumor tissues and normal tissues, as well as the high-ILK group and the low-ILK group.

Identification of regulatory patterns and potential drug of ILK

To elucidate the potential mechanisms of ILK in PCa onset and progression, we used the NetworkAnalyst (https://www.networkanalyst.ca/) platform for predicting ILK-related transcription factors (TFs), as well as the prediction of miRNAs involved in ILK regulation. In addition, we utilized the Drug Signatures Database (DSigDB) on the Enrichr (https://maayanlab.cloud/Enrichr/) website for potential prediction of ILK-related drugs, and employed the PubChem (https://pubchem.ncbi.nlm.nih.gov/) database to screen and visualize the corresponding drug structures.

Differentially expressed genes and WGCNA in PCa

The microarray expression matrix was first standardized before conducting differential gene expression analysis (Figs. 1A, B). Based on the given filtering criteria, a total of 558 differentially expressed genes were obtained, comprising 156 upregulated genes and 388 downregulated genes (Fig. 1C). The top 30 DEGs were shown in a heat map (Fig. 1D). In WGCNA analysis, a total of 8674 genes in the top 25% of variance were included in the gene co-expression network analysis. The fitting index of the defined scale-free network topology was 0.9, and the adjacency matrix at the best soft threshold of β = 5 was obtained (Fig. 2A). The module filtering criteria was set to obtain 7 modules under the conditions where the module genes were greater than 50 and the eigengenes were greater than 0.75. After correlation analysis between the modules, 6 modules were obtained (Figs. 2B, C). Of these, the blue module (MEblue) had the highest correlation (0.73) with the clinical trait (tumor or normal tissues), and it was then used for hub gene screening (Fig. 2B). Finally, the intersection of the blue module genes and DEGs resulted in 544 genes associated with PCa (Fig. 2D). Through PPI analysis, MYC was discovered as the core hub gene regulating PCa (Fig. 2E).

GO, DO, and KEGG enrichment analyses

Firstly, we explored the biological functions of the intersecting genes in PCa. GO showed that intersecting genes are primarily involved in cell-cell junction organization (BP), focal adhesion (CC), and actin-binding (MF) (Fig. 3A). The outcome of KEGG suggests that these genes regulated multiple pathways such as Focal adhesion, Glutathione metabolism, Leukocyte transendothelial migration, and Drug metabolism-cytochrome P450 (Fig. 3B, D). DO analysis suggests a close association of these genes with renal cell carcinoma, bladder cancer, ovarian cancer, and colorectal cancer (Fig. 3C).

siRNA transfection and quantitative RT-PCR

To silence the expression of the ILK gene, we designed the siRNA first. Compared to NC (normal LNCaP cells with the negative control of virus infection), the know-down reduction efficiency is 70.7%, assessed by qRT-PCR with three repeats (Fig. 4A). Then, this eligible siRNA against ILK was transfected into the LNCaP cells. After 3 days, results suggested that the transfection was successful (Fig. 4B).

Cell proliferation assay

Given the effect of ILK in LNCaP cells, cell viability and proliferation were evaluated with the MTT assay. The detections were performed after 1d, 2d, 3d, 4d, and 5 days of culture. At every time point, 5 repeats were conducted for three groups (CON, NC, and KD). As shown in Fig. 4C, the ILK protein expression of transfected LNCaP cells significantly decreased, compared to CON and NC. Meanwhile, from the first day, LNCaP cell proliferation was inhibited significantly in KD groups (P < 0.001) (Fig. 4D). Additionally, we also detected the LNCaP cell apoptosis. Consistent with previous results, we also discovered that ILK silencing could increase the apoptosis of LNCaP cells compared to normal cells and empty vectors (Fig. 4E). These results suggested that the ILK gene could promote the development and progression of PCa.

The effect of ILK on cell cycle

On the fifth day, the cell cycle was also detected for transfected LNCaP cells. Three repeats were performed for every group. PI-FACS suggested that the number of cells decreased in the G1 and G2/M phases (P < 0.05), and increased in the S phase (P < 0.05) (Fig. 5A). This result indicated that known-down ILK gene mainly arrested the cell in the S phase. Combining with the MTT, we proposed that the low expression of ILK would influence the DNA replication of tumor cells, resulting in cell viability and proliferation.

Identification of core genes regulated by ILK

To further understand the potential mechanism for the ILK gene in influencing the development of PCa, the expressions of MAP1LC3A, BECN1, C-MYC, TP53, and MDM2 on protein level were detected (Figs. 5C-F). The results suggested that C-MYC significantly decreased after inhibiting the ILK expression (P = 0.014) (Fig. 5F).

Mfuzz

The Mfuzz expression pattern was clustered based on the expression level of ILK, resulting in 50 clustering outcomes (Fig. 6A). These were integrated with the ssGSEA score of the clustering module and expression characteristics for classifying the high- and low-expression ILK groups. Next, we conducted a correlation analysis between the Mfuzz clustering modules and ILK expression levels (Fig. 6B), revealing significant differences in the ssGSEA scores of cluster38 between the high- and low-expression ILK groups (Fig. 6C). Additionally, cluster38 showed the highest correlation with ILK expression (Fig. 6D).

Screening of ILK-related hub gene and their diagnostic capability

Subsequently, we performed a PPI analysis on the intersection of genes between previously obtained disease-related genes and cluster38 (Figs. 7A-C). After visualization via Cytoscape, we identified 8 genes associated with ILK expression. The diagnostic value analysis revealed that all hub genes had ROC values greater than 0.8 for distinguishing between tumor and normal tissues in PCa, indicating excellent diagnostic efficiency (Fig. 7D).

Functional enrichment analysis of 152 ILK-related genes

The GO functional enrichment showed that these genes were enriched in cell-substrate adhesion, extracellular matrix organization, and extracellular structure organization. Cellular component analysis showed that focal adhesion and cell-substrate junction were mainly enriched. Molecular function analysis highlighted they are primarily associated with extracellular matrix structural constituent, actin binding, and sulfur compound binding (Fig. 8A). The KEGG pathway analysis unveiled their primary involvement in the Focal adhesion, cAMP signaling pathway, and Wnt signaling pathway (Figs. 8B, C).

GSEA analysis of ILK expression in TCGA

For further investigating the biological role of ILK in prostate cancer, we conducted a GSEA analysis utilizing TCGA data. This analysis revealed that genes in the high ILK expression group primarily participate in regulating multiple pathways related to both tumorigenesis and the natural immune, including PATHWAYS_IN_CANCER, the TGF_BETA_SIGNALING_PATHWAY, the T_CELL_RECEPTOR_SIGNALING_PATHWAY, CELL_ADHESION_MOLECULES_CAMS, and the B_CELL_RECEPTOR_SIGNALING_PATHWAY (Fig. 8D).

Immune microenvironment analysis

As the GSEA results suggested an association between ILK and the tumor immune microenvironment, we attempted to calculate the infiltration scores of 22 immune cell types for each sample in GSE70770 (Fig. 9A) and calculated the cell matrix scores for normal and tumor prostate tissues and found a higher presence of the cellular matrix component in normal tissues (Fig. 9B). The CIBERSORT algorithm is used to assess the distribution changes of infiltrating immune cells under various conditions. As indicated by the outcomes, tumor tissues had a positive link to the infiltration of CD8 T cells, regulatory T cells (Tregs), and Macrophages M0/M1, M2 (Fig. 9C), while there was no difference in immune cell infiltration between the high- and low-ILK expression PCa groups (Fig. 9D).

Prediction of ILK-related TF, miRNA, and therapeutic drugs

To predict ILK-related TFs, a regulatory TF network was created (Fig. 10A). It was observed that KLF1/8/13/16, SMAD4/5, and SMARCA4 may regulate ILK. Subsequently, a miRNA biomarker regulatory network was created to find miRNAs that play an important regulatory role (Fig. 10B). Drugs with a correlated effect were predicted based on the ILK biomarker in tumor tissues. We considered the top 6 drugs in the predicted results based on P-values as potential drugs interacting with ILK biomarkers, including ambroxol PC3, amikacin, and puromycin (Fig. 10C).

ILK is an oncogene, which is identified to be associated with the development and progression of many cancers, including breast, ovary, and prostate cancer. In our study, we conducted a comprehensive analysis of gene expression in prostate cancer (PCa), identifying 558 differentially expressed genes, including 156 upregulated and 388 downregulated genes. Weighted gene co-expression network analysis (WGCNA) further revealed a significant module (MEblue) highly correlated with PCa, leading to the identification of 544 genes associated with the disease. Among these, MYC emerged as a central hub gene. Functional enrichment analyses indicated the involvement of these genes in crucial biological processes and pathways, such as cell-cell junction organization and focal adhesion. We also demonstrated the significant role of ILK in PCa development and progression, where its silencing reduced cell proliferation and viability, altered the cell cycle, and increased apoptosis. LNCaP cell line as androgen-dependent cells was used in the experimental validation process of this study. Our results confirmed that the ILK could promote the viability and proliferation of LNCaP cells. The ILK silencing would mainly arrest the cells into the S phase. Additionally, our analysis highlighted the potential of ILK-related genes as diagnostic markers and therapeutic targets, with a focus on pathways involved in tumorigenesis and immune responses, as well as the prediction of ILK-related transcription factors, miRNAs, and therapeutic drugs.

ILK gene located on the 11p15.4, from 6624938bp to 6632105bp. Its encoded protein is 59 kDa, containing four ankyrin-like repeats. In 1996, Hannigan GE et al.[8] discovered that ILK could regulate integrin-mediated signal transduction, as a receptor-proximal protein kinase. Further studies identified that the ILK might be significantly associated with cancer development. Overexpression of ILK can suppress cellular senescence and is implicated in the mechanisms of genomic instability in human cancer [23, 24]. Especially for hormonal cancers, the expression of ILK was said to be dysregulated[12]. In 2009, a study identified that compared to controls without ovarian cancer, patients contain higher cell-free immunoreactive ILK1[25]. Silencing of the ILK gene could result in apoptosis in ovarian carcinoma [26]. Along with the increasing expression of ILK, the metastatic behavior of ovarian cancer cells was more obvious [27]. As for breast cancer, the same functions were also identified. In 2013, Yang HJ et al.[28] suggested that overexpression of ILK1 in breast cancer was associated with poor prognosis. A previous study proposed IL-6-NF-κB signaling loop could promote aggressive phenotypes in breast cancer. In 2016, Hsu EC et al.[29] found that ILK might play a key role in the IL-6-NF-κB signaling loop. In males, PCa is a common hormonal cancer, which brings about enormous damage to health. Similar to other cancers, the ILK gene also promotes the development and progression of PCa. Overexpressed ILK in PCa cells was said to suppress anoikis, promote anchorage-independence, and induce tumorigenesis [12]. When inhibited the ILK1, the PCa cell cycle is arrested and induces apoptosis [30]. The mRNA expression of ILK was also upregulated in PCa, compared to normal cells [31]. Yuan Y et al [32] conducted a study about the effect of ILK in PCa DU145 cells, in which ILK was identified to be important in oncogenesis and tumor progression. Recent studies have highlighted the significant role of Integrin-Linked Kinase (ILK) in the development and progression of prostate cancer (PCa). Yuan et al. [32] found that silencing ILK using short hairpin RNA (shRNA) significantly impairs cell growth, and motility, and delays tumor proliferation in prostate cancer cells. Additionally, Qi et al. [33] noted that ILK expression plays a crucial role in the development of primary prostate cancer, and its detection may be useful for judging tumor development and prognosis. This aligns with our findings, where we observed that silencing ILK significantly inhibited the proliferation of LNCaP cells and increased apoptosis. Furthermore, the study by Persad et al.[34] demonstrates that inhibition of ILK suppresses the activation of protein kinase B/Akt and induces cell cycle arrest and apoptosis in PTEN-mutant prostate cancer cells. This further supports our observation that ILK plays a role in regulating the cell cycle and apoptosis [35]. Additionally, Yoganathan et al.[36] emphasized the role of ILK in promoting cell survival, migration, and invasion, making it a promising target for prostate cancer therapy. In our study, we investigated the function of ILK in the LNCaP cell line. Consistent with previous studies, we also confirmed that ILK silencing could inhibit cell viability and proliferation, and promote apoptosis. The cells were arrested in the S phase of the cell cycle. Combined with previous studies, we had reasons to believe that the ILK is an oncogene. Inhibition of ILK expression would be a promising method for treating the development and progression of PCa.

As WGCNA and differential expression analysis showed that the MYC gene is the core regulatory gene in PCa, we have paid particular attention to the impact of Integrin-Linked Kinase (ILK) on the expression of C-MYC and its potential role in the development of prostate cancer. C-MYC is a critical cell cycle regulator, extensively studied and recognized for its significant role in various types of cancers. According to the research by Zajac-Kaye, C-MYC plays a role in cell proliferation, apoptosis, and tumor development by modulating the expression of key cell cycle regulators such as p27Kip1 [37]. Furthermore, the study by Chandramohan et al. indicates that C-MYC affects cell cycle progression and apoptosis by repressing FOXO3a-mediated transcription of the p27Kip1 cyclin-dependent kinase inhibitor [38]. A study found that JMJD1A enhances c-Myc activity by promoting androgen receptor recruitment to the c-Myc gene enhancer, reducing H3K9 demethylation, and inhibiting HUWE1, an E3 ubiquitin ligase that targets c-Myc for degradation. In our research, we observed a close correlation between the expression of ILK and the regulation of C-MYC. This finding aligns with previous studies, suggesting that ILK may regulate the proliferation and survival of prostate cancer cells by influencing the expression and activity of C-MYC. This mechanism may involve the modulation of the Akt signaling pathway by ILK, subsequently affecting the transcriptional activity and stability of C-MYC. Therefore, our study underscores the significant role of ILK in the development of prostate cancer, particularly through the regulation of C-MYC, a key oncogenic factor. The interaction between ILK and C-MYC presents a new target for the treatment of prostate cancer. Future research should further explore the specific mechanisms of interaction between ILK and C-MYC, and how this knowledge can be effectively utilized to develop therapeutic strategies against prostate cancer. Autophagy, a process of cellular self-digestion, plays a significant role in cancer therapy. It is induced by various stresses caused by cancer therapeutics, including nutritional starvation, DNA damage, and oxidative stress [16]. The PI3K/AKT/mTOR pathway is one of the key molecular pathways involved in autophagy regulation in prostate cancer. Autophagy is involved in the response to Androgen Deprivation Therapy (ADT), a common treatment for prostate cancer. It might contribute to the development of castration-resistant prostate cancer (CRPC) [39]. However, our research did not find that autophagy-related genes are involved in the regulation of ILK.

Focal Adhesions are integral components linking the cytoskeleton to the extracellular matrix, playing a key role in orchestrating cell movement. Our study's findings align with previous research that underscores the critical role of focal adhesion, particularly mediated by Focal Adhesion Kinase (FAK), in the progression of prostate cancer [40]. Focal adhesions are specialized structures that facilitate cell adhesion to the extracellular matrix and are pivotal in cell migration, proliferation, and survival, all of which are key processes in cancer progression[40]. Liu et al. [41] identified and validated the dormancy-mimicking effect of PF-562,271 (PF-271), a focal adhesion kinase (FAK) inhibitor. This was evidenced by decreased FAK phosphorylation and increased nuclear translocation in treated PCa cells, suggesting FAK's critical role in osteoblast-induced PCa cell dormancy. Targeting the FAK/WNK1 axis represents a promising strategy to overcome lenvatinib resistance in HCC patients [42]. Machine learning research found that the Focal Adhesions model is a potential tool for predicting the prognosis of gastric cancer patients, providing insights into patient stratification and individualized treatment planning [43]. Moreover, the targeting of focal adhesions and tight junctions in prostate cancer cells, as evidenced by the efficacy of compounds like DZ-50, has been shown to impair tumor growth and metastasis. The role of focal adhesions in orchestrating cell migration is also crucial. The regulation of these structures by various signaling pathways, such as HGF and Rap1, is essential in prostate carcinoma cells [44]. Our research findings, which identify focal adhesion as a core pathway in prostate cancer and highlight the involvement of Integrin-Linked Kinase (ILK) in this crucial pathway, are consistent with previous studies. This congruence reinforces the significance of the focal adhesion pathway, and particularly the role of ILK within it, as a central element in the molecular landscape of prostate cancer.

The study found that higher Tregs cells in prostate cancer patients are associated with poor prognosis and low survival rates [45]. While Tregs cells are crucial for maintaining self-tolerance and preventing harmful immune responses, their ability to recognize tumor-related antigens (which are mostly self-antigens) means they can inadvertently aid tumor progression. Suppressing anti-tumor responses by targeting Tregs can be an effective approach for prostate cancer immunotherapy [45]. The role of macrophages in prostate cancer has been extensively studied, they may aid in the retention of prostate tumor cells in circulation by arresting, phagocytosing, and digesting them [46, 47]. Prostate cancer-associated fibroblasts and M2-polarized macrophages synergize to increase tumor cell motility and foster cancer cell escape from the primary tumor and metastatic spread [48]. Macrophages appear to be directly involved in tumor progression and metastasis, with their absence facilitating the retention of prostate cancer cells in circulation. Enhanced glycolytic activity in prostate cancer may contribute to the formation of a pro-tumor immune microenvironment, with M2 macrophage infiltration being associated with poor prognosis[49]. Macrophages co-cultured with prostate cancer cells increase prostate cancer cell migration and invasion and induce the secretion of CCL2, a chemokine involved in tumor progression [50]. In our study, prostate cancer tissues had increased cell infiltration levels of Tregs cells, Macrophages M0/M1/M2, and CD8 T cells; however, we observed no changes in immune cell infiltration in the group with high ILK expression.

Next, we investigated the regulatory patterns of ILK including miRNA and transcription factor prediction, and further investigations are required to validate them. Moreover, antineoplastic drugs such as disodium paclitaxel and puromycin were predicted. Paclitaxel, a microtubule-stabilizing drug, inhibits signaling from the androgen receptor by preventing its nuclear accumulation [51]. It is an important chemotherapeutic agent in the treatment of prostate cancer, particularly in the advanced stages of the disease [51]. Its effectiveness is enhanced when used in combination with other drugs, and its mechanism of action involves disrupting microtubule dynamics and androgen receptor signaling [52]. Puromycin, along with blasticidin, exhibits dose- and time-dependent anticancer effects on the growth, survival, and metastasis of metastatic castration-resistant prostate cancer cells. A study on S1-Puro, a puromycin prodrug, shows selective activation by thioredoxin reductase and demonstrates TrxR-dependent cytotoxicity to cancer cells. Puromycin, along with blasticidin, exhibits dose- and time-dependent anti-cancer effects on the growth, survival, and metastasis of metastatic castration-resistant prostate cancer cells, suggesting a potential role for puromycin in treating advanced prostate cancer. Further research is needed to fully understand its mechanisms of action and potential clinical applications in prostate cancer treatment. This research has certain limitations. The study has only been validated at the level of a single cell line, and further validation is needed on tissue samples and animal experiments.

To sum up, our study demonstrates that the integrin-linked kinase (ILK) gene functions as an oncogene in prostate cancer (PCa), primarily through its influence on the C-MYC gene. Targeting ILK expression emerges as a promising therapeutic strategy for managing the development and progression of prostate cancer. However, the action mechanisms of ILK in the progression of PCa need to be further explored.

Funding

This study was supported by grants from the Guangxi Natural Science Foundation (2014GXNSFBA118201) and the Self-Raised Scientific Research Fund of the Health Commission of Guangxi Zhuang Autonomous Region (Z-A20231089), and the Key Special Project of China's Key R&D Program "Active Health and Scientific Response to Aging" (2021YFC2009300).

Competing interest

All the authors declare that they have no conflict of interest regarding the publication of the paper.

Data availability

Access to all original data collected for this study will be granted upon the reasonable request of the corresponding author.

Code availability

Access to all original data collected for this study will be granted upon the reasonable request of the corresponding author.

Author Contributions

RL and YL performed the data analysis work and aided in writing the manuscript. RL, WC and YL was responsible for conducting the fundamental cell-based experiments. XY, ZS, WC, and YL designed the study and assisted in writing the manuscript. FW and ZM edited the manuscript. All authors read and approved the final manuscript.

Acknowledgments

This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.

Ethics approval and consent to participate Not applicable.

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No competing interests reported.

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Bioinformatics analysis combined with experimental validation reveals the biological role of the ILK gene in prostate cancer

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and methods

Data acquisition

Identification of specific differentially expressed genes in PCa

Weighted gene co-expression network analysis (WGCNA)

Protein-protein interaction (PPI) and functional enrichment analysis

Cell lines, siRNA transfection, and quantitative RT-PCR

Western blot

Cell proliferation assay

Cell apoptosis

Cell cycle

Results

Differentially expressed genes and WGCNA in PCa

GO, DO, and KEGG enrichment analyses

siRNA transfection and quantitative RT-PCR

Cell proliferation assay

Mfuzz

Immune microenvironment analysis

Discussion

Declarations

References

Additional Declarations

Status:

Version 1