Next Article in Journal
Barth Syndrome Cardiomyopathy: An Update
Previous Article in Journal
Bioinformatics Analysis Highlight Differentially Expressed CCNB1 and PLK1 Genes as Potential Anti-Breast Cancer Drug Targets and Prognostic Markers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 13
Issue 4
10.3390/genes13040655
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods

by
Mohd Mabood Khan
1,2,
Mohammad Taleb Mohsen
1,3,
Md. Zubbair Malik
4,5,
Sali Abubaker Bagabir
6,
Mustfa F. Alkhanani
7,
Shafiul Haque
8,
Mohammad Serajuddin
2 and
Mausumi Bharadwaj
1,*
1
Division of Molecular Genetics & Biochemistry, National Institute of Cancer Prevention & Research (ICMR-NICPR), I-7, Sector-39, Noida 201301, India
2
Department of Zoology, University of Lucknow, Lucknow 226007, India
3
Department of Biosciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India
4
School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
5
Department of Biotechnology, Jamia Hamdard University, New Delhi 110062, India
6
Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
7
Emergency Medical Service Department, College of Applied Sciences, AlMaarefa University, Riyadh 11597, Saudi Arabia
8
Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan 45142, Saudi Arabia
*
Author to whom correspondence should be addressed.
Genes 2022, 13(4), 655; https://doi.org/10.3390/genes13040655
Submission received: 18 February 2022 / Revised: 22 March 2022 / Accepted: 28 March 2022 / Published: 8 April 2022
(This article belongs to the Section Bioinformatics)
Browse Figures
Versions Notes

Abstract

:
Prostate cancer (PCa) is the most prevalent cancer (20%) in males and is accountable for a fifth (6.8%) cancer-related deaths in males globally. Smoking, obesity, race/ethnicity, diet, age, chemicals and radiation exposure, sexually transmitted diseases, etc. are among the most common risk factors for PCa. However, the basic change at the molecular level is the manifested confirmation of PCa. Thus, this study aims to evaluate the molecular signature for PCa in comparison to benign prostatic hyperplasia (BPH). Additionally, representation of differentially expressed genes (DEGs) are conducted with the help of some bioinformatics tools like DAVID, STRING, GEPIA, Cytoscape. The gene expression profile for the four data sets GSE55945, GSE104749, GSE46602, and GSE32571 was downloaded from NCBI, Gene Expression Omnibus (GEO). For the extracted DEGs, different types of analysis including functional and pathway enrichment analysis, protein–protein interaction (PPI) network construction, survival analysis and transcription factor (TF) prediction were conducted. We obtained 633 most significant upregulated genes and 1219 downregulated genes, and a sum total of 1852 DEGs were found from all four datasets after assessment. The key genes, including EGFR, MYC, VEGFA, and PTEN, are targeted by TF such as AR, Sp1, TP53, NF-KB1, STAT3, RELA. Moreover, miR-21-5p also found significantly associated with all the four key genes. Further, The Cancer Genome Atlas data (TCGA) independent database was used for validation of key genes EGFR, MYC, VEGFA, PTEN expression in prostate adenocarcinoma. All four key genes were found to be significantly correlated with overall survival in PCa. Therefore, the therapeutic target may be determined by the information of these key gene’s findings for the diagnosis, prognosis and treatment of PCa.

1. Introduction

Prostate cancer (PCa) is the most prevalent cancer (20%) in males and accountable for a fifth (6.8%) cancer-related deaths in males globally [1]. According to the World Cancer Survey Statistics (GLOBOCAN), the number of newly diagnosed cases of PCa was ~1.41 million in 2020, with ~375 thousand new deaths [2,3]. By 2040, the global PCa burden is expected to increase to 2.43 million new cases and 740 thousand new deaths due to population growth and ageing [2,3]. On the other hand, PCa incidence has been steadily increasing in India. According to India’s population-based cancer registries, PCa is the second most common cause of cancer in men living in metropolitan areas [4,5].
Smoking, obesity, race/ethnicity, diet, age, chemicals and radiation exposure, sexually transmitted diseases, etc., are among the most common risk factors for prostate cancer [6]. However, the basic change at the molecular level is the manifested confirmation of PCa. Although prostate specific antigen (PSA) level is the most frequently used screening tool for prostate cancer detection, it doesn’t stand as an absolute method to predict disease malignancy. Furthermore, the use of genetic profiling may provide additional benefits for early PCa detection [7,8,9,10]. Prostate cancer may be rectified at an early stage of cancer by surgery or radiation therapy, but patients with advanced or metastatic disease may have no curative therapeutic options [11,12]. Consequently, there is an urgent need for treatments capable of preventing or combating PCa.
Currently, gene expression data mining and bioinformatics microarray analysis are widely used to find key genes for disease severity, pathogenesis, complexity, recognition of suitable biomarkers, miRNA targets, transcription factor (TF) recognition and drug targets. The gene expression is a molecular signature used as a diagnostic and prognostic marker in cancer research. Gene expression profiling reveals several differentially expressed genes (DEGs) in patient samples, but many genes cannot be related by the prevailing methods. Protein-protein interaction (PPI) network research plays a great role in understanding the molecular function; and any malfunctioning pathway linked to the disease network may result from changes in the PPI network locality. PPI gives elaboration on protein function, molecular magnitude, gene ontology (GO), disease regulator genes, miRNA and novel drug targets.
Previous PCa meta-analysis studies have revealed mostly one type of molecular markers, like genetic molecular markers by Zhao et al., (2014) who reported Ki-67, Bcl-2, CD147, COX-2, ALDH1A1 and FVIII genes [13]. Or miRNAs as reported by Song et al., (2017) of up/down regulated miRNAs like miR-18a, miR-34a, miR-129, miR-145, etc. [14] However, limited studies have reported different types of biomarkers in the same study [15]. This study aims to evaluate the molecular-signature for PCa in comparison to benign prostatic hyperplasia (BPH). Additionally, in this study, identification and classification of DEGs were conducted with the help of some bioinformatics tools like DAVID (to know about the functional annotation of a gene), STRING (to find out PPI network functional enrichment analysis), GEPIA (to find differential gene expression analysis and profiling plotting), and Cytoscape (to make an interaction map). Furthermore, to find key genes associated with pathogenesis, the degree of network interaction as well as the module of the most related gene; are used to discover hub genes that may work as key targets for treatment.

2. Materials and Methods

2.1. Microarray Data Extraction

The National Centre for Biotechnology Information (NCBI)-Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/, accessed on 11 March 2022) [16] is a global public domain for gene expression database from which we have retrieved the four data sets (GSE55945, GSE104749, GSE46602, GSE32571) belonging to the prostate tumor (n = 112) and the BPH tissue (n = 65). Only datasets that met the following inclusion and exclusion criteria for PCa datasets were incorporated in this study. First, inclusion criteria take in (i) datasets with expression data for PCa tissue and BPH tissue samples; (ii) datasets with at least four samples’ gene expression data (both PCa tumor and BPH) and (iii) homo sapiens datasets. Table 1 summarizes all of the information regarding the datasets that were chosen. The matrix files of GSE55945, GSE104749, GSE46602, GSE32571 were obtained by GEO2R, and the raw data of the datasets were downloaded by the GEO query package of the R language.

2.2. Differentially Expressed Genes Identification

The study was carried out in accordance with the flowchart shown in Figure 1. The differentially expressed genes (DEGs) in GSE55945, GSE104749, GSE46602, and GSE32571 were obtained with GEO2R analysis of the GEO database. In brief, the GEO2R tool of the GEO database was used to extract the matrix files for GSE55945, GSE104749, GSE46602, and GSE32571 from their respective GSE databases. There were 13 prostate tumor and 8 BPH tissue samples for GSE55945, 4 prostate tumor and 4 BPH tissue samples for GSE104749, 36 prostate tumor and 14 BPH tissue samples for GSE46602, 59 prostate tumor and 39 BPH tissue samples for GSE32571. These four datasets were chosen for further GEO2R investigation. Moreover, mRNAs having an adjusted p-value < 0.05 and [log2FC] > +1, [log2FC] < −1 were chosen as significant DEGs. The DEGs were visualised in a volcano plot using the R package “ggplot2” (https://cran.r-project.org/web/packages/ggplot2/ accessed on 18 February 2022). The BRCW computing website (http://jura.wi.mit.edu/bioc/tools/compare.php, accessed on 18 March 2022) was used to choose unique DEGs that were shared by at least two gene expression profile datasets. As a result, we were able to be more precise in our DEG selection, and the possibility of biassed data compilation was reduced to a negligible level. The Venn Diagram was used to visualise the upregulated, downregulated, and unique DEGs identified from four datasets.

2.3. Gene Ontology and Pathway Enrichment Analysis

The DEGs functional interpretation was evaluated and visualized in web resource such as DAVID (DAVID; version 6.7, http://david.abcc.ncifcrf.gov, accessed on 18 February 2022) [24] as per the molecular function, biological process and cellular component of DEGs. For the metabolic pathway enrichment study, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (http://www.genome.jp/kegg, accessed on 18 February 2022) [25] tool was used. The adjusted p-value < 0.05 cutoff score was taken into consideration for obtaining significant expressed genes.

2.4. Protein-Protein Network Screening (PPI), Key Genes Identification and Module Network Construction

The online program STRING (http://www.string-db.org/, accessed on 18 February 2022) was used to extract interconnected genes to create a network of PPI [26]. To visualize PPI, the tool Cytoscape version 3.8.2 (http://www.cytoscape.org/, accessed on 18 February 2022) [27] was introduced. To identify significant genes in the subnetwork, a Cytoscape plugin Molecular Complex Detection (MCODE) was implemented with the parameters K-score (2), degree cutoff score (2), node cutoff score (0.2) and 100 maximum depths. To find the most intersected key genes and modules, the Cytohubba plugin tool (http://hub.iis.sinica.edu.tw/cytohubba/, accessed on 18 February 2022) [28] was used in Cytoscape and the PPI-MCODE modules were also merged. Multiple topological characteristics such as Maximal Clique Centrality, Density of Maximum Neighborhood, Betweenness Centrality, Closeness Centrality, Degree, Stress and Bottleneck were also added to the network for identification of key genes and modules.

2.5. Genetic Alteration and Validation of Key Genes Expression Paradigm

As per the manual, examination of genetic alteration for key genes was performed by the cBioportal for Cancer Genomics [29]. The oncoplot for key genes was created by the cBioPortal for Cancer Genomics. Further, the GEPIA (Gene Expression Profiling Interactive Analysis) online software (http://gepia.cancer-pku.cn, accessed on 18 February 2022) was used for investigation of key genes expression in PCa. The verification of key gene expression was conducted by GEPIA between 492 PCa and 152 non-cancer tissues. GEPIA has synergetic and adjustable features such as analysis of differential expression, analysis of correlation and analysis of patient survival and can provide rapid results from The Cancer Genome Atlas (TCGA) data [30]. The values greater than the transcripts median were classified as increased expressions, and the values lower than the transcripts median were classified as decreased expressions.

2.6. Survival Analysis of Key Genes

The online web UALCAN based on the TCGA database was used for the survival analysis of key genes expression in PCa [31]. The transcript per million (TPM) enrichment analysis was used for classification of PCa patients’ expression into high and medium/low expression. The Kaplan-Meier (KM) survival analysis (p < 0.05) was used for the evaluation of key genes prognostic value along with Gleason Score.

2.7. miRNA and Transcription Factor Associated Network with Key Genes

Multiple experimentally verified online miRNA network software’s are available to estimate the miRNA interaction with genes. The miRNA selection was done through Enrichr (https://maayanlab.cloud/Enrichr/, accessed on 18 February 2022) [32] and the TRRUST online database (https://www.grnpedia.org/trrust/, accessed on 18 February 2022) [33] was used to identify transcription factors. Selection of putative target miRNA and transcription factor of key genes was done on the basis of a selected online tool. Additionally, the TransmiR v2.0 database (http://www.cuilab.cn/transmir, accessed on 18 February 2022) [34] was used to see the association between transcription factors and miRNAs. Further validation of miRNA along with TF for key genes was done by the online public database miRNet (https://www.mirnet.ca/, accessed on 18 February 2022) visual interaction platform [35]. The Cytoscape was used for the formation of an integrative network of key genes, miRNAs, and transcription factors on the basis of source and target association.

3. Result

3.1. Deferentially Expressed Genes Discovery

A total number of 164,526 annotated transcripts were obtained from the included GSEs evaluated in the study, 132 upregulated and 387 downregulated DEGs were selected in the GSE55945 data files on the basis of selection criteria (adjusted p-value < 0.05 and [log2FC] > +1, [log2FC] < −1) as compared between prostate cancer and BPH patients. Subsequently, 98 upregulated and 122 downregulated DEGs were identified in the GSE104749 data files, 501 upregulated and 892 downregulated DEGs in the GSE46602 data files, 60 upregulated and 166 downregulated DEGs in the GSE32571 data files from the same criteria selection. The most significant upregulated and downregulated genes for the GSE55945, GSE46602, GSE104749, GSE32571 data set are shown in the Supplementary file S1. The Volcano plots showed upregulated and downregulated genes for all datasets by implementing upper and lower limit criteria [logFC] > +1, [logFC] < −1 (Figure 2). Pre raw value and post normalized value is indicated by all four-dataset box plots (Supplementary file S2). A comparison of the complete gene and top 100 genes expression profile of prostate cancer versus benign prostate hyperplasia (BPH) was further demonstrated in a graded manner by heatmap construction (Supplementary file S3).

3.2. DEGs Functional Annotation and KEGG Pathway Analysis

To classify unique upregulated and downregulated of all four data sets GSE55945, GSE104749, GSE46602 and GSE32571 the bioinformatics and research computing online tool (http://barc.wi.mit.edu/tools/, accessed on 18 March 2022) was used. We obtained 633 most significant unique upregulated genes and 1219 unique downregulated genes from all four datasets after assessment (Figure 3). In order to evaluate the gene-ontology functional analysis of selected DEGs for biological process (BP), cellular process (CC), molecular function (MF) and KEGG pathway significance, DAVID online software was used. The most significant biological process for upregulated genes involved in cell proliferation (FDR:0.003528782), mitotic nuclear division (FDR:0.005441812), G1/S transition of mitotic cell cycle (FDR:0.007227173) and most significant biological process for downregulated genes involved in angiogenesis (FDR:7.53 × 10−6), cell adhesion (FDR:7.53 × 10−6), response to hypoxia (FDR:0.001023707). The most significant upregulated genes were available in the portion of the spindle microtubule (FDR:0.012514875), cytoplasm (FDR:0.093997223), mitotic spindle (FDR:0.093997223), and the most significant downregulated genes were available in the area of focal adhesion (FDR:2.39 × 10−9), plasma membrane (FDR:5.86 × 10−8), caveola region (FDR:2.95 × 10−7) under the cellular component category. Molecular function enrichment assessment for most significant upregulated genes included ATP binding activity (FDR:0.149334827), structural constituent of ribosome activity (FDR:0.704002703), protein serine/threonine kinase activity (FDR:0.704002703) and for most significant downregulated genes included calcium ion binding (FDR:0.002557467), protein homodimerization activity (FDR:0.042898325), glutathione transferase activity (FDR:0.042898325). The most significant upregulated genes under KEGG pathway analysis are involved in Cell cycle signaling (FDR:0.74589722), Mucin type O-Glycan biosynthesis pathway (FDR:0.74589722), ECM-receptor interaction pathway (FDR:0.74589722) and in the case of the most significant downregulated genes under KEGG pathway analysis, they are involved in Focal adhesion (FDR:2.42 × 10−7), Dilated cardiomyopathy (FDR:4.04 × 10−5), Hypertrophic cardiomyopathy (HCM) signaling pathway (FDR:1.94 × 10−4) (Figure 4 and Figure 5, Supplementary files S4–S11).

3.3. Protein-Protein (PPI) Network and Module Analysis

In the Cytoscape platform for up regulated and downregulated genes, we found 1572 nodes, 11,279 edges and 13.8 mean node degree after topological property analysis of the PPI network (Figure 6A). After topological enrichment, the most connected upregulated genes MYC, CDK1, CCNB1 etc. and the most connected downregulated genes EGFR, VEGFA, STAT3 etc. were categorized according to their highest degree count associated with prostate cancer (Figure 6B and Table 2). The characterization of this newly synthesized PPI with MCODE score ≥ 3 and nodes ≥ 3 in the retrieval of two modules. Module A had 92 nodes, 580 edges with an MCODE score of 12.747 and module B had 45 nodes, 153 edges with an MCODE score of 6.955 (Figure 7A,B).

3.4. Hub-Bottle Neck Genes and Key Genes Identification

A Cytoscape plug-in, Cytohubba was applied to the established PPI network together with the decreasing score of the Degree algorithm to deduce the top most hub genes. The shortest path, centrality algorithm along with bottle neck score were used in Cytohubba software to discover the top most bottle neck genes. For further investigation, the top 10 hub genes (Figure 6C) and the top 10 bottle neck genes were selected (Figure 6D). After study, EGFR, MYC, VEGFA, PTEN, STAT3, CDK1, a total of six were found common in both hub and bottleneck genes, along with the most connected genes were EGFR and VEGFA. There were four unique hub genes including CCNB1, EZH2, AURKA and CD44, and four unique bottleneck genes, including PIK3R1, GART, PITGS2 and RHOC (Supplementary file S12). After taking certain topological algorithms such as betweenness centrality, closeness Centrality, degree score, stress, bottleneck score, we identified four key regulator genes including EGFR, MYC, VEGFA, PTEN. According to our findings, common TFs such AR, Sp1, TP53, NF-KB1, STAT3, and RELA are targeted by all identified four key genes. Also, miR-21-5p has a strong connection to each of the four key genes investigated in this study (Figure 6E). The genes follow more than one criterion such as (or including?) STAT3, CAT, VCL, EZH2, CD44, CAV1; unique genes C11or f96, TMEM106C, DMKN, PRELID2, UBXN10, ZNF613, CCNB1, AURKA, GART, PTGS2, RHOC, PIK3R1 were revealed after integrated analysis (Supplementary file S12).

3.5. Genetic Alteration and Validation of Four Key Genes Expression Paradigm

After statistical analysis by cBioPortal for Cancer Genomics, we observed that 1766 (26%) of the 6875 prostate cancer patients documented genetic alteration in the four key genes. Deletion and amplification were the utmost prevalent genetic variations. (Figure 8A). Further, the GEPIA tool (http://gepia.cancer-pku.cn, accessed on 18 February 2022) was used for the evaluation of key genes EGFR, MYC, VEGFA, PTEN expression in The Cancer Genome Atlas data. As per the GEO dataset findings, the key genes EGFR, VEGFA, PTEN expression level in prostate adenocarcinoma (PRAD) was significantly lower (p < 0.05) compared to non-tumor prostate tissue and the MYC expression level was significantly higher (p < 0.05) in prostate adenocarcinoma (PRAD) compared to non-tumor prostate tissue verified by the TCGA database (Figure 8B–E).

3.6. Survival Analysis of Key Genes

The TCGA based UALCAN transcriptomic cancer data was used for the survival assessment of PCa patients and gene expression analysis. The four key regulator genes expression were analyzed in the UALCAN database using the Kaplan Meier method. The classification of PCa tumor tissue is based on the Gleason Score (GS) method, according to this grading system, gleason score GS ≤ 6, 3 + 4, 4 + 3, 8, 4 + 5, 5 + 4, 10 is related to Gleason Grading Group 1, 2, 3, 4, and 5 respectively [36]. It was noted that all the four key genes were found significantly correlated with overall survival in PCa. The overall survival of the low/medium expression group was observed to be significantly lower than the high expression group for EGFR, VEGFA, PTEN genes and the overall survival of the high expression group was observed to be significantly lower than the low/medium expression group for MYC genes in PCa patients after integrated analysis with the Gleason Score system (Figure 8F–I).

3.7. miRNA and Transcription Factor Associated Network with Key Genes

The miRNAs play an important role in gene expression regulation at multiple stages after RNA synthesis. miRNA up and downregulation defects are linked with prostate cancer and they have an ability to differentiate between benign and malignant tumors [37] and the disease complexity can be more readable by miRNA changes. The four key identified genes were connected to approximately 394 miRNAs (Supplementary file S13) and 233 transcription factors (Supplementary file S14), which could be responsible for controlling key genes. Further results demonstrated EGFR-associated 38 miRNAs, MYC-associated 115 miRNAs, VEGFA-associated 121 miRNAs, PTEN-associated 120 miRNAs. Moreover, TF analysis revealed the following: EGFR-associated 32 transcription factors, MYC-associated 95 transcription factors, VEGFA-associated 83 transcription factors and PTEN-associated 23 transcription factors. In addition, the AR transcription factor associated with 13111 miRNAs possible binding sites, STAT3 transcription factor associated with 72 miRNAs and RELA transcription factor associated with 227 miRNAs in PCa (Supplementary file S15).

4. Discussion

Prostate cancer is a widely spread one across the world among males. PCa can metastasize through the circulatory system reaching distant parts of the body [38]. The disease’s pathogenicity, magnitude detection, ambiguity, predictive and clinical biomarker scarcity are the main barriers in the path of PCa care [6,39]. As a result, the integrated PCa key regulatory genes profiling may help to achieve more successful treatment of PCa patients. This research has highlighted the DEGs related to PCa out of 112 tumor samples and 65 BPH tissues, pooled from GSE 55945, GSE 104749, GSE46602, GSE32571 datasets, with the help of bioinformatic techniques. As a result, a total number of 1866 significantly DEGs were found, in which there were 638 up and 1228 downregulated genes. In order to demonstrate the relationship between up and down DEGs with PPI network formation, two modules were found to be significantly crucial in this PPI network study. Taking this into the account, the highest ranked genes were screened by Cytohubba in terms of hub and bottle neck genes. These techniques of gene ontology (GO) and KEGG pathways were used to describe the role of DEGs. The top characterized and screened genes including EGFR, MYC, VEGFA, PTEN which are involved in prostate cancer progression, cell proliferation and division, cell cycle signaling, metabolic and signaling pathway regulation, angiogenesis, focal adhesion etc. [40,41,42,43,44] Which makes them further as molecular candidates involved in treatment. Furthermore, various hub genes and bottle neck genes linked to prostate cancer were uncovered through this study. In addition to that, identification of miRNAs and screening of the transcription factors revealed molecular markers for prostate cancer progression control.
The Epidermal Growth Factor Receptor (EGFR) is involved in many biological processes, such as proliferation, vitality, migration, progression and cell signaling [40,41]. By contrast, EGFR under expression has been reported with tumor progression [42]. In the Pakistani population, Hashmi and his colleagues have shown that EGFR under expression was linked to prostatic adenocarcinoma, suggesting it as clinical biomarker in cases with higher Gleason score, high grade and perineural association with prostate carcinoma [43]. Recent evidence has shown that EGFR and the related AKT pathway are effectively associated with AR phosphorylation [44], but it has been observed in PCa that an inverse relationship has been seen in terms of EGFR and AR protein/expression [45]. The regulators for EGFR, TF and microRNAs included miRNA-145 which is boosted by TF p53 (TP53) and inhibits EGRF expression [46,47,48]. Also, miR-199a-3p which targets EGFR as well as c-Myc [49,50].
In the light of PCa progression, MYC is another significant gene. C-Myc, N-myc and L-myc are MYC subtypes that encode similar MYC protein which have the same function [51,52]. The level of expression is contrasted in various tissues, such as N-myc upregulated in solidified tumor glioma and neuroblastoma, c-Myc upregulated in solidified cancer and blood-related cancer, acting as proto-oncogene as well as transcription factor while L-myc upregulated in lung carcinoma [53]. C-Myc is a critical factor in PCa progression and related to cell expansion [54,55] which goes together with our findings. Inhibition of c-Myc could be carried by multiple miRNAs, including miR-let-7a which also has a tumor suppression function in PCa cells through downregulating AR expression [56]. Also, miRNA-34a plays a critical role as a tumor suppressor along with p53 arbitrator activity, which has been down expressed in PCa; it lacks the capability to suppress c-Myc in PCa cells [57]. Also, miR-23b can play the same role as miR-34a, but other oncogenic transcription factors like NF-KB and Sp1 can activate the alternative way in MYC-dependent miR-23b inhibition, for cell survival and growth [58] which is shown in our TF data.
The VEGF family’s key role is to facilitate angiogenesis in malignant cancer, making it an effective therapeutic candidate for tumor disease cure. Bender and co-workers speculated that primary prostate tumors experiencing VEGFA (vascular endothelial growth factor-A) low expression [59,60]. The VEGF level could be regulated by EGFR’s action via the signaling network of mitogen-activated protein kinase (MAPK) as well as phosphoinositide 3-kinase (PI3K) [61] which has been observed in our finding for EGFR. In this study, the obtained final TFs and microRNAs have a direct connection with VEGFA, like AR, TP53, SP1, miR-299-3p [62,63] which have been found and reported in our supplementary file.
PTEN (Phosphatase and Tensin Homolog) belongs to the phosphatase group that regulates the signaling pathway of PI3K as well as AKT [64]; PTEN has tumor suppressor activity and is generally found idle connected to PCa [65]. Downregulated PTEN expression in our findings goes in line with the previously reported results in Iranian PCa patients [66]. PTEN plays a reverse role in the signaling effect of PI3K/AKT and dephosphorylates PIP3 [64,67]. Also, PTEN has the capability to inhibit AR in a clinical manner through AR nuclear translocation blocking as well as depletion of AR protein, and this matches AR over expression as per our findings [68,69,70]. Regulation of PTEN was targeted by miRNA-let-7b and miR-548 and down expression of miR-let-7b (tumor suppressor) was detected in our findings, but PTEN was not correlated with miR-548 in terms of expression [62]. Another finding indicated that miR-21 and miR-181b-1 both repressed PTEN as well as CYLD, which were subsequently turned on by STAT3 in the signaling [71].
In addition, the examined PPI network was strongly correlated with the rest of the hub and bottleneck genes which are essential genes in the network pathway operations. In this study, DEGs were obtained through a comparison between PCa and BPH samples. After that, the PPI network was established and chosen for further analysis. The significance of this study lies in detecting and revealing unique key hub genes including EGFR, MYC, VEGFA, PTEN from different sources collectively and their regulation by common transcription factors. Besides, Androgen receptor (AR), Sp1, TP53, NF-KB1, STAT3, RELA and signature microRNAs such as miR-21-5p, miR-125a-5p, miR145-5p and miR-155-5p have been reported jointly in the same study for the first time in PCa tissue samples as tumor markers and clinical targets.

5. Conclusions

In comparison to BPH, our bioinformatics combined enrichment analysis revealed that key genes EGFR, MYC, VEGFA, and PTEN were identified as potent molecular biomarkers of PCa from gene expression profiling. We found that all four key genes are targeted by common transcription factors such as AR, Sp1, TP53, NF-KB1, STAT3 and RELA. Moreover, MYC as a transcription factor has a target for TP53 and shares a target with it as well. MYC shares a target with other transcription factors such as NF-KB1, STAT3 and RELA. Additionally, our analysis determined that miR-21-5p was significantly associated with all four key genes while miR-125a-5p and miR145-5p were significantly associated with EGFR, MYC, VEGFA, but not with PTEN; and miR-155-5p was significantly associated with EGFR, MYC, PTEN. Furthermore, we found that miR-21 is also connected with AR, STAT3 transcription factor and miR-155 is connected with AR. The clinical therapeutic target of PCa can be determined by the information in these findings as well as by giving clinical insight clues for the development of new novel PCa therapies. However, this study has a limitation of absent confirmatory experimental validation but provides a new door for future study.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/genes13040655/s1, Supplementary file S1: List of the most significant upregulated and downregulated genes for the GSE55945, GSE46602, GSE104749, GSE32571 dataset. Supplementary file S2: GEO dataset processing. Boxplots show the expression intensity of each sample for the dataset GSE55945, GSE46602, GSE104749, GSE32571 before (upper panel) and after (lower panel) normalization. Supplementary file S3: The heatmap of gene expression depicts the expression levels of several genes that have undergone considerable up- or down-regulation. Upregulation (red) and down-regulation (green) are indicated by the colour. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). Supplementary files S4–S7: Significant upregulated differentially expressed genes involved in GO Biological Process, Cellular Component, Molecular Function, KEGG Pathway. Supplementary files S8–S11: Significant downregulated differentially expressed genes involved in GO Biological Process, Cellular Component, Molecular Function, KEGG Pathway. Supplementary file S12: List of the key genes, hub genes, bottleneck genes, hub & bottleneck genes, unique genes, genes with more than one criterion. Supplementary file S13: List of the key genes connected to miRNAs. Supplementary file S14: List of the key genes connected to transcription factor. Supplementary file S15: List of the transcription factor connected to miRNAs.

Author Contributions

M.Z.M. & M.B. conceptualized and designed the model. M.M.K. wrote original draft. M.M.K. did Data curation, Formal analysis, Methodology, Validation. M.M.K. & M.T.M. did the investigation & resources. Critical evaluation of the manuscript M.B. & M.M.K. M.M.K., M.T.M., S.A.B., M.F.A., S.H., did review & editing. M.S. and M.B. were jointly supervised the study. All authors have read and agreed to the published version of the manuscript.

Funding

The ‘Indian Council of Medical Research (ICMR)’ has generously supported MMK financially. The National Institute of Cancer Prevention and Research (ICMR-NICPR) provided infrastructure and core funds to M.B. for this study. M.Z.M. is grateful for financing from the Indian government’s Department of Health Research, Ministry of Health and Family Welfare, as part of the Young Sci-entist scheme (Sanction File No. R.12014/01/2018-HR, FTS No. 3146887). Mustfa F Alkhanani would like to express his gratitude to AlMaarefa University, Riyadh, Saudi Arabia, for providing funding (TUMA-2021-53) to do this research.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used in the current study available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Stuopelyte, K.; Daniunaite, K.; Bakavicius, A.; Lazutka, J.R.; Jankevicius, F.; Jarmalaite, S. The utility of urine-circulating miRNAs for detection of prostate cancer. Br. J. Cancer 2016, 115, 707–715. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  3. Ferlay, J.; Colombet, M.; Soerjomataram, I.; Mathers, C.; Parkin, D.M.; Piñeros, M.; Znaor, A.; Bray, F. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int. J. Cancer 2019, 144, 1941–1953. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Jain, S.; Saxena, S.; Kumar, A. Epidemiology of prostate cancer in India. Meta Gene 2014, 2, 596–605. [Google Scholar] [CrossRef] [PubMed]
  5. Center, M.M.; Jemal, A.; Lortet-Tieulent, J.; Ward, E.; Ferlay, J.; Brawley, O.; Bray, F. International variation in prostate cancer incidence and mortality rates. Eur. Urol. 2012, 61, 1079–1092. [Google Scholar] [CrossRef]
  6. Shen, M.M.; Abate-Shen, C. Molecular genetics of prostate cancer: New prospects for old challenges. Genes Dev. 2010, 24, 1967–2000. [Google Scholar] [CrossRef] [Green Version]
  7. Kuriyama, M.; Wang, M.C.; Papsidero, L.D.; Killian, C.S.; Shimano, T.; Valenzuela, L.; Nishiura, T.; Murphy, G.P.; Chu, T.M. Quantitation of prostate-specific antigen in serum by a sensitive enzyme immunoassay. Cancer Res. 1980, 40, 4658–4662. [Google Scholar]
  8. Herget, K.A.; Patel, D.P.; Hanson, H.A.; Sweeney, C.; Lowrance, W.T. Recent decline in prostate cancer incidence in the United States, by age, stage, and Gleason score. Cancer Med. 2016, 5, 136–141. [Google Scholar] [CrossRef]
  9. Deng, J.; Tang, J.; Wang, G.; Zhu, Y.S. Long non-coding RNA as potential biomarker for prostate cancer: Is it making a difference? Int. J. Environ. Res. Public Health 2017, 14, 270. [Google Scholar] [CrossRef] [Green Version]
  10. Liu, Y.; Liu, Y.; Yuan, B.; Yin, L.; Peng, Y.; Yu, X.; Zhou, W.; Gong, Z.; Liu, J.; He, L.; et al. FOXM1 promotes the progression of prostate cancer by regulating PSA gene transcription. Oncotarget 2017, 8, 17027. [Google Scholar] [CrossRef] [Green Version]
  11. Albertsen, P. Predicting survival for men with clinically localized prostate cancer: What do we need in contemporary practice? Cancer 2008, 112, 1–3. [Google Scholar] [CrossRef] [PubMed]
  12. So, A.; Gleave, M.; Hurtado-Col, A.; Nelson, C. Mechanisms of the development of androgen independence in prostate cancer. World J. Urol. 2005, 23, 1–9. [Google Scholar] [CrossRef] [PubMed]
  13. Zhao, L.; Yu, N.; Guo, T.; Hou, Y.; Zeng, Z.; Yang, X.; Hu, P.; Tang, X.; Wang, J.; Liu, M. Tissue biomarkers for prognosis of prostate cancer: A systematic review and meta-analysis. Cancer Epidemiol. Prev. Biomark. 2014, 23, 1047–1054. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Song, C.J.; Chen, H.; Chen, L.Z.; Ru, G.M.; Guo, J.J.; Ding, Q.N. The potential of microRNAs as human prostate cancer biomarkers: A meta-analysis of related studies. J. Cell. Biochem. 2018, 119, 2763–2786. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Filella, X.; Fernández-Galan, E.; Fernández Bonifacio, R.; Foj, L. Emerging biomarkers in the diagnosis of prostate cancer. Pharm. Pers. Med. 2018, 11, 83–94. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M.; et al. NCBI GEO: Archive for functional genomics data sets—Update. Nucleic Acids Res. 2013, 41, D991–D995. [Google Scholar] [CrossRef] [Green Version]
  17. Arredouani, M.S.; Lu, B.; Bhasin, M.; Eljanne, M.; Yue, W.; Mosquera, J.M.; Bubley, G.J.; Li, V.; Rubin, M.A.; Libermann, T.A.; et al. Identification of the transcription factor single-minded homologue 2 as a potential biomarker and immunotherapy target in prostate cancer. Clin. Cancer Res. 2009, 15, 5794–5802. [Google Scholar] [CrossRef] [Green Version]
  18. Shan, M.; Xia, Q.; Yan, D.; Zhu, Y.; Zhang, X.; Zhang, G.; Guo, J.; Hou, J.; Chen, W.; Zhu, T.; et al. Molecular analyses of prostate tumors for diagnosis of malignancy on fine-needle aspiration biopsies. Oncotarget 2017, 8, 104761–104771. [Google Scholar] [CrossRef]
  19. Mortensen, M.M.; Høyer, S.; Lynnerup, A.S.; Ørntoft, T.F.; Sørensen, K.D.; Borre, M.; Dyrskjøt, L. Expression profiling of prostate cancer tissue delineates genes associated with recurrence after prostatectomy. Sci. Rep. 2015, 5, 16018. [Google Scholar] [CrossRef]
  20. Kuner, R.; Fälth, M.; Pressinotti, N.C.; Brase, J.C.; Puig, S.B.; Metzger, J.; Gade, S.; Schäfer, G.; Bartsch, G.; Steiner, E.; et al. The maternal embryonic leucine zipper kinase (MELK) is upregulated in high-grade prostate cancer. J. Mol. Med. 2013, 91, 237–248. [Google Scholar] [CrossRef]
  21. Ostano, P.; Mello-Grand, M.; Sesia, D.; Gregnanin, I.; Peraldo-Neia, C.; Guana, F.; Jachetti, E.; Farsetti, A.; Chiorino, G. Gene Expression Signature Predictive of Neuroendocrine Transformation in Prostate Adenocarcinoma. Int. J. Mol. Sci. 2020, 21, 1078. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Yang, Z.; Qu, C.B.; Zhang, Y.; Zhang, W.F.; Wang, D.D.; Gao, C.C.; Ma, L.; Chen, J.S.; Liu, K.L.; Zheng, B.; et al. Dysregulation of p53-RBM25-mediated circAMOTL1L biogenesis contributes to prostate cancer progression through the circAMOTL1L-miR-193a-5p-Pcdha pathway. Oncogene 2019, 38, 2516–2532. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Kreuz, M.; Otto, D.J.; Fuessel, S.; Blumert, C.; Bertram, C.; Bartsch, S.; Loeffler, D.; Puppel, S.H.; Rade, M.; Buschmann, T.; et al. ProstaTrend—A Multivariable Prognostic RNA Expression Score for Aggressive Prostate Cancer. Eur. Urol. 2020, 78, 452–459. [Google Scholar] [CrossRef] [PubMed]
  24. Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009, 4, 44–57. [Google Scholar] [CrossRef] [PubMed]
  25. Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef] [PubMed]
  26. Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P.; et al. STRING v10: Protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015, 43, D447–D452. [Google Scholar] [CrossRef]
  27. Saito, R.; Smoot, M.E.; Ono, K.; Ruscheinski, J.; Wang, P.L.; Lotia, S.; Pico, A.R.; Bader, G.D.; Ideker, T. A travel guide to Cytoscape plugins. Nat. Methods 2012, 9, 1069–1076. [Google Scholar] [CrossRef] [Green Version]
  28. Chin, C.H.; Chen, S.H.; Wu, H.H.; Ho, C.W.; Ko, M.T.; Lin, C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014, 8, 1–7. [Google Scholar] [CrossRef] [Green Version]
  29. Gao, J.; Aksoy, B.A.; Dogrusoz, U.; Dresdner, G.; Gross, B.; Sumer, S.O.; Sun, Y.; Jacobsen, A.; Sinha, R.; Larsson, E.; et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 2013, 6, pl1. [Google Scholar] [CrossRef] [Green Version]
  30. Tang, Z.; Li, C.; Kang, B.; Gao, G.; Li, C.; Zhang, Z. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017, 45, W98–W102. [Google Scholar] [CrossRef] [Green Version]
  31. Chandrashekar, D.S.; Bashel, B.; Balasubramanya, S.A.; Creighton, C.J.; Ponce-Rodriguez, I.; Chakravarthi, B.V.; Varambally, S. UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia 2017, 19, 649–658. [Google Scholar] [CrossRef] [PubMed]
  32. Kuleshov, M.V.; Jones, M.R.; Rouillard, A.D.; Fernandez, N.F.; Duan, Q.; Wang, Z.; Koplev, S.; Jenkins, S.L.; Jagodnik, K.M.; Lachmann, A.; et al. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016, 44, W90–W97. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Han, H.; Cho, J.W.; Lee, S.; Yun, A.; Kim, H.; Bae, D.; Yang, S.; Kim, C.Y.; Lee, M.; Kim, E.; et al. TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018, 46, D380–D386. [Google Scholar] [CrossRef] [PubMed]
  34. Tong, Z.; Cui, Q.; Wang, J.; Zhou, Y. TransmiR v2.0: An updated transcription factor-microRNA regulation database. Nucleic Acids Res. 2019, 47, D253–D258. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Chang, L.; Zhou, G.; Soufan, O.; Xia, J. miRNet 2.0: Network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 2020, 48, W244–W251. [Google Scholar] [CrossRef] [PubMed]
  36. Epstein, J.I.; Zelefsky, M.J.; Sjoberg, D.D.; Nelson, J.B.; Egevad, L.; Magi-Galluzzi, C.; Vickers, A.J.; Parwani, A.V.; Reuter, V.E.; Fine, S.W.; et al. A contemporary prostate cancer grading system: A validated alternative to the Gleason score. Eur. Urol. 2016, 69, 428–435. [Google Scholar] [CrossRef] [Green Version]
  37. Kojima, S.; Goto, Y.; Naya, Y. The roles of microRNAs in the progression of castration-resistant prostate cancer. J. Hum. Genet. 2017, 62, 25–31. [Google Scholar] [CrossRef]
  38. Lee, K.C.; Bradley, D.A.; Hussain, M.; Meyer, C.R.; Chenevert, T.L.; Jacobson, J.A.; Johnson, T.D.; Galban, C.J.; Rehemtulla, A.; Pienta, K.J.; et al. A feasibility study evaluating the functional diffusion map as a predictive imaging biomarker for detection of treatment response in a patient with metastatic prostate cancer to the bone. Neoplasia 2007, 9, 1003–1011. [Google Scholar] [CrossRef] [Green Version]
  39. Perabo, F.G.; Von Löw, E.C.; Ellinger, J.; Von Rücker, A.; Müller, S.C.; Bastian, P.J. Soy isoflavone genistein in prevention and treatment of prostate cancer. Prostate Cancer Prostatic Dis. 2008, 11, 6–12. [Google Scholar] [CrossRef] [Green Version]
  40. Guérin, O.; Fischel, J.L.; Ferrero, J.M.; Bozec, A.; Milano, G. EGFR targeting in hormone-refractory prostate cancer: Current appraisal and prospects for treatment. Pharmaceuticals 2010, 3, 2238–2247. [Google Scholar] [CrossRef] [Green Version]
  41. Di Lorenzo, G.; Tortora, G.; D’Armiento, F.P.; De Rosa, G.; Staibano, S.; Autorino, R.; D’Armiento, M.; De Laurentiis, M.; De Placido, S.; Catalano, G.; et al. Expression of epidermal growth factor receptor correlates with disease relapse and progression to androgen-independence in human prostate cancer. Clin. Cancer Res. 2002, 8, 3438–3444. [Google Scholar] [PubMed]
  42. Roepstorff, K.; Grøvdal, L.; Grandal, M.; Lerdrup, M.; van Deurs, B. Endocytic downregulation of ErbB receptors: Mechanisms and relevance in cancer. Histochem. Cell Biol. 2008, 129, 563–578. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Hashmi, S.K.; Irfan, M.; Asif, H.; Nisar, L.; Naeem, M.; Khan, E.Y.; Baloch, S.; Faridi, N. Prognostic utility of epidermal growth factor receptor (EGFR) expression in prostatic acinar adenocarcinoma. Appl. Cancer Res. 2019, 39, 1–8. [Google Scholar] [CrossRef] [Green Version]
  44. Mandel, A.; Larsson, P.; Sarwar, M.; Semenas, J.; Syed Khaja, A.S.; Persson, J.L. The interplay between AR, EGF receptor and MMP-9 signaling pathways in invasive prostate cancer. Mol. Med. 2018, 24, 1–3. [Google Scholar] [CrossRef] [PubMed]
  45. Baek, K.H.; Hong, M.E.; Jung, Y.Y.; Lee, C.H.; Lee, T.J.; Park, E.S.; Kim, M.K.; Yoo, J.H.; Lee, S.W. Correlation of AR, EGFR, and HER2 expression levels in prostate cancer: Immunohistochemical analysis and chromogenic in situ hybridization. Cancer Res. Treat. Off. J. Korean Cancer Assoc. 2012, 44, 50. [Google Scholar] [CrossRef] [PubMed]
  46. Sachdeva, M.; Zhu, S.; Wu, F.; Wu, H.; Walia, V.; Kumar, S.; Elble, R.; Watabe, K.; Mo, Y.Y. p53 represses c-Myc through induction of the tumor suppressor miR-145. Proc. Natl. Acad. Sci. USA 2009, 106, 3207–3212. [Google Scholar] [CrossRef] [Green Version]
  47. Suzuki, H.I.; Yamagata, K.; Sugimoto, K.; Iwamoto, T.; Kato, S.; Miyazono, K. Modulation of microRNA processing by p53. Nature 2009, 460, 529–533. [Google Scholar] [CrossRef]
  48. Cho, W.C.; Chow, A.S.; Au, J.S. MiR-145 inhibits cell proliferation of human lung adenocarcinoma by targeting EGFR and NUDT1. RNA Biol. 2011, 8, 125–131. [Google Scholar] [CrossRef] [Green Version]
  49. Liu, R.; Liu, C.; Zhang, D.; Liu, B.; Chen, X.; Rycaj, K.; Jeter, C.; Calhoun-Davis, T.; Li, Y.; Yang, T.; et al. miR-199a-3p targets stemness-related and mitogenic signaling pathways to suppress the expansion and tumorigenic capabilities of prostate cancer stem cells. Oncotarget 2016, 7, 56628. [Google Scholar] [CrossRef] [Green Version]
  50. He, S.; Huang, Y.; Dong, S.; Qiao, C.; Yang, G.; Zhang, S.; Wang, C.; Xu, Y.; Zheng, F.; Yan, M. MiR-199a-3p/5p participated in TGF-β and EGF induced EMT by targeting DUSP5/MAP3K11 in pterygium. J. Transl. Med. 2020, 18, 1–9. [Google Scholar] [CrossRef]
  51. Nesbit, C.E.; Grove, L.E.; Yin, X.; Prochownik, E.V. Differential apoptotic behaviors of c-myc, N-myc, and L-myc oncoproteins. Cell Growth Differ. Publ. Am. Assoc. Cancer Res. 1998, 9, 731–742. [Google Scholar]
  52. Barrett, J.; Birrer, M.J.; Kato, G.J.; Dosaka-Akita, H.; Dang, C.V. Activation domains of L-Myc and c-Myc determine their transforming potencies in rat embryo cells. Mol. Cell. Biol. 1992, 12, 3130–3137. [Google Scholar] [PubMed] [Green Version]
  53. Tansey, W.P. Mammalian MYC proteins and cancer. New J. Sci. 2014, 2014, 757534. [Google Scholar] [CrossRef] [Green Version]
  54. Koh, C.M.; Bieberich, C.J.; Dang, C.V.; Nelson, W.G.; Yegnasubramanian, S.; De Marzo, A.M. MYC and prostate cancer. Genes Cancer 2010, 1, 617–628. [Google Scholar] [CrossRef] [Green Version]
  55. Gorrini, C.; Donzelli, M.; Torriglia, A.; Supino, R.; Brison, O.; Bernardi, R.; Negri, C.; Denegri, M.; Counis, M.F.; Ranzani, G.N.; et al. Effect of apoptogenic stimuli on colon carcinoma cell lines with a different c-myc expression level. Int. J. Mol. Med. 2003, 11, 737–742. [Google Scholar] [CrossRef]
  56. Nadiminty, N.; Tummala, R.; Lou, W.; Zhu, Y.; Zhang, J.; Chen, X.; White, R.W.D.; Kung, H.J.; Evans, C.P.; Gao, A.C.; et al. MicroRNA let-7c suppresses androgen receptor expression and activity via regulation of Myc expression in prostate cancer cells. J. Biol. Chem. 2012, 287, 1527–1537. [Google Scholar] [CrossRef] [Green Version]
  57. Yamamura, S.; Saini, S.; Majid, S.; Hirata, H.; Ueno, K.; Deng, G.; Dahiya, R. MicroRNA-34a modulates c-Myc transcriptional complexes to suppress malignancy in human prostate cancer cells. PLoS ONE 2012, 7, e29722. [Google Scholar] [CrossRef] [Green Version]
  58. Fulciniti, M.; Amodio, N.; Bandi, R.L.; Cagnetta, A.; Samur, M.K.; Acharya, C.; Prabhala, R.; D’Aquila, P.; Bellizzi, D.; Passarino, G.; et al. miR-23b/SP1/c-myc forms a feed-forward loop supporting multiple myeloma cell growth. Blood Cancer J. 2016, 6, e380. [Google Scholar] [CrossRef]
  59. Bender, R.J.; Mac Gabhann, F. Dysregulation of the vascular endothelial growth factor and semaphorin ligand-receptor families in prostate cancer metastasis. BMC Syst. Biol. 2015, 9, 55. [Google Scholar] [CrossRef] [Green Version]
  60. Lu, W.; Ding, Z. Identification of key genes in prostate cancer gene expression profile by bioinformatics. Andrologia 2019, 51, e13169. [Google Scholar] [CrossRef]
  61. Song, Z.Y.; Wang, F.; Cui, S.X.; Qu, X.J. Knockdown of CXCR4 inhibits CXCL12-induced angiogenesis in HUVECs through downregulation of the MAPK/ERK and PI3K/AKT and the Wnt/β-catenin pathways. Cancer Investig. 2018, 36, 10–18. [Google Scholar] [CrossRef] [PubMed]
  62. Wang, J.Y.; Jiang, J.B.; Li, Y.; Wang, Y.L.; Dai, Y. MicroRNA-299-3p suppresses proliferation and invasion by targeting VEGFA in human colon carcinoma. Biomed. Pharmacother. 2017, 93, 1047–1054. [Google Scholar] [CrossRef] [PubMed]
  63. Östling, P.; Leivonen, S.K.; Aakula, A.; Kohonen, P.; Mäkelä, R.; Hagman, Z.; Edsjö, A.; Kangaspeska, S.; Edgren, H.; Nicorici, D.; et al. Systematic analysis of microRNAs targeting the androgen receptor in prostate cancer cells. Cancer Res. 2011, 71, 1956–1967. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Saffari, M.; Ghaderian, S.M.; Omrani, M.D.; Afsharpad, M.; Shankaie, K.; Samadaian, N. The Association of miR-let 7b and miR-548 with PTEN in Prostate Cancer. Urol. J. 2019, 16, 267–273. [Google Scholar]
  65. Morais, C.E.; Gurgel, D.C.; Teixeira, A.C.; Mattos, T.V.A.; Silva, A.V.A.D.; Tavora, F. Prevalence of ERG expression and PTEN loss in a Brazilian prostate cancer cohort. Braz. J. Med. Biol. Res. 2019, 52, e8483. [Google Scholar] [CrossRef]
  66. Nodouzi, V.; Nowroozi, M.; Hashemi, M.; Javadi, G.; Mahdian, R. Concurrent down-regulation of PTEN and NKX3. 1 expression in Iranian patients with prostate cancer. Int. Braz. J. Urol. 2015, 41, 898–905. [Google Scholar] [CrossRef] [Green Version]
  67. Song, M.S.; Salmena, L.; Pandolfi, P.P. The functions and regulation of the PTEN tumour suppressor. Nat. Rev. Mol. Cell Biol. 2012, 13, 283–296. [Google Scholar] [CrossRef]
  68. Mangangcha, I.R.; Malik, M.Z.; Kucuk, O.; Ali, S.; Singh, R.B. Kinless hubs are potential target genes in prostate cancer network. Genomics 2020, 112, 6. [Google Scholar] [CrossRef]
  69. Mangangcha, I.R.; Malik, M.; Küçük, Ö.; Ali, S.; Singh, R.K. Identification of key regulators in Prostate cancer from gene expression datasets of patients. Sci. Rep. 2019, 9, 16420. [Google Scholar] [CrossRef] [Green Version]
  70. Lin, H.K.; Hu, Y.C.; Lee, D.K.; Chang, C. Regulation of androgen receptor signaling by PTEN (phosphatase and tensin homolog deleted on chromosome 10) tumor suppressor through distinct mechanisms in prostate cancer cells. Mol. Endocrinol. 2004, 18, 2409–2423. [Google Scholar] [CrossRef]
  71. Iliopoulos, D.; Jaeger, S.A.; Hirsch, H.A.; Bulyk, M.L.; Struhl, K. STAT3 activation of miR-21 and miR-181b-1 via PTEN and CYLD are part of the epigenetic switch linking inflammation to cancer. Mol. Cell 2010, 39, 493–506. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Genes 13 00655 g001 550
Figure 1. Flowchart of methodology containing sorting of GEO datafile, selection of DEGs, GO annotation analysis, and identification of key genes. DEGs (differentially expressed genes), GEO (gene expression omnibus), GO (gene ontology).
Figure 1. Flowchart of methodology containing sorting of GEO datafile, selection of DEGs, GO annotation analysis, and identification of key genes. DEGs (differentially expressed genes), GEO (gene expression omnibus), GO (gene ontology).
Genes 13 00655 g001
Genes 13 00655 g002 550
Figure 2. (A–D) Volcano plots showing DEGs between prostate cancer and benign prostate hyperplasia patients in GSE32571, GSE46602, GSE55945 and GSE104749 datafiles. The log2FC value > 1, log2FC value < −1 and p < 0.05 are the cutoff value for significant upregulated (red color), non-significant (black color) and downregulated (cyan color) DEGs.
Figure 2. (A–D) Volcano plots showing DEGs between prostate cancer and benign prostate hyperplasia patients in GSE32571, GSE46602, GSE55945 and GSE104749 datafiles. The log2FC value > 1, log2FC value < −1 and p < 0.05 are the cutoff value for significant upregulated (red color), non-significant (black color) and downregulated (cyan color) DEGs.
Genes 13 00655 g002
Genes 13 00655 g003 550
Figure 3. A plot depicting the upregulated-downregulated & unique DEGs discovered in four datafiles GSE32571, GSE104749, GSE46602, GSE55945. Statistically significant DEGs were characterized by the cut-off criterion of log2FC value > 1, log2FC value < −1 and p < 0.05.
Figure 3. A plot depicting the upregulated-downregulated & unique DEGs discovered in four datafiles GSE32571, GSE104749, GSE46602, GSE55945. Statistically significant DEGs were characterized by the cut-off criterion of log2FC value > 1, log2FC value < −1 and p < 0.05.
Genes 13 00655 g003
Genes 13 00655 g004 550
Figure 4. Gene ontology functional classification and pathway analysis of significant differentially expressed genes from various datafiles. Bar-graph of top most significant upregulated genes representing biological process, cellular component, molecular function, KEGG pathway analysis. KEGG (Kyoto Encyclopedia of Genes and Genomes).
Figure 4. Gene ontology functional classification and pathway analysis of significant differentially expressed genes from various datafiles. Bar-graph of top most significant upregulated genes representing biological process, cellular component, molecular function, KEGG pathway analysis. KEGG (Kyoto Encyclopedia of Genes and Genomes).
Genes 13 00655 g004
Genes 13 00655 g005 550
Figure 5. Gene ontology functional classification and pathway analysis of significant differentially expressed genes from various datafiles. Bar-graph of top most significant downregulated genes representing biological process, cellular component, molecular function, KEGG pathway analysis. KEGG (Kyoto Encyclopedia of Genes and Genomes).
Figure 5. Gene ontology functional classification and pathway analysis of significant differentially expressed genes from various datafiles. Bar-graph of top most significant downregulated genes representing biological process, cellular component, molecular function, KEGG pathway analysis. KEGG (Kyoto Encyclopedia of Genes and Genomes).
Genes 13 00655 g005
Genes 13 00655 g006 550
Figure 6. (A) Most significant upregulated and downregulated genes PPI network map. (upregulated genes represented by red colour and downregulated genes represented by blue colour) (B) Top 10 most connected upregulated and downregulated genes according to their highest degree score associated with prostate cancer. (upregulated genes represented by red colour and downregulated genes represented by blue colour) (C) Top 10 hub significant genes extracted by PPI Cyto-hubba network & (D) Top 10 bottleneck significant genes excavated from PPI Cyto-hubba network. Higher score is represented by colour intensity in hub and bottleneck genes both. (upregulated genes represented by red colour and downregulated genes represented by blue colour) (E) Deep network extraction showing the key genes, TF and miRNA interaction with each other. (upregulated genes represented by red colour, downregulated genes represented by blue colour, miRNA represented by green colour & TFs represented by violet colour).
Figure 6. (A) Most significant upregulated and downregulated genes PPI network map. (upregulated genes represented by red colour and downregulated genes represented by blue colour) (B) Top 10 most connected upregulated and downregulated genes according to their highest degree score associated with prostate cancer. (upregulated genes represented by red colour and downregulated genes represented by blue colour) (C) Top 10 hub significant genes extracted by PPI Cyto-hubba network & (D) Top 10 bottleneck significant genes excavated from PPI Cyto-hubba network. Higher score is represented by colour intensity in hub and bottleneck genes both. (upregulated genes represented by red colour and downregulated genes represented by blue colour) (E) Deep network extraction showing the key genes, TF and miRNA interaction with each other. (upregulated genes represented by red colour, downregulated genes represented by blue colour, miRNA represented by green colour & TFs represented by violet colour).
Genes 13 00655 g006
Genes 13 00655 g007 550
Figure 7. Identification of modules from PPI network. (A) Most significant module-A & (B) modules-B in the MCODE-PPI network analysis. DEGs is represented by nodes and interaction between them represented by edges.
Figure 7. Identification of modules from PPI network. (A) Most significant module-A & (B) modules-B in the MCODE-PPI network analysis. DEGs is represented by nodes and interaction between them represented by edges.
Genes 13 00655 g007
Genes 13 00655 g008 550
Figure 8. Genetic alteration, validation and survival analysis of four key genes expression paradigm related to PCa using TCGA data. (A) The oncoplot of four key genes. (B–E) The validation of four key genes expression paradigm in PRAD (n = 492) tumor and non-tumor (n = 152). (F–I) Survival analysis of four key genes associated with PCa using Kaplan Meier method, * Significant p-value < 0.05.
Figure 8. Genetic alteration, validation and survival analysis of four key genes expression paradigm related to PCa using TCGA data. (A) The oncoplot of four key genes. (B–E) The validation of four key genes expression paradigm in PRAD (n = 492) tumor and non-tumor (n = 152). (F–I) Survival analysis of four key genes associated with PCa using Kaplan Meier method, * Significant p-value < 0.05.
Genes 13 00655 g008
Table
Table 1. Information about the included GSEs in this study.
Table 1. Information about the included GSEs in this study.
Sample
(Accession No.)		Prostate CancerBenign Prostate HyperplasiaOrganismSample TypePlatformReferenceIncluded/
Excluded
GSE55945138Homo
sapiens		Radical prostatectomy tissueAffymetrix
GPL570		[17]Included
GSE10474944Homo
sapiens		Fine-Needle Aspiration tissueAffymetrix
GPL570		[18]Included
GSE466023614Homo
sapiens		Laser micro dissected tissueAffymetrix
GPL570		[19]Included
GSE325715939Homo
sapiens		Fresh frozen
tissue		Illumina
GPL6947		[20]Included
GSE14228848NilHomo
sapiens		TissueAgilent
GPL13264		[21]Excluded
GSE1557921NilHomo
sapiens		TissueAgilent
GPL28148		NAExcluded
GSE11315310NilHomo
sapiens		TissueGPL21825[22]Excluded
GSE134160164NilHomo
sapiens		Fresh frozen
tissue		Agilent GPL26898[23]Excluded
Table
Table 2. Top 10 highly connected up and downregulated gene.
Table 2. Top 10 highly connected up and downregulated gene.
StatusGene SymbolDegreeStatusGene SymbolDegree
UpregulatedMYC166DownregulatedEGFR190
CDK1111 VEGFA178
CCNB197STAT3117
EZH295CD44113
AURKA89PTEN110
UBE2C82VCL86
AURKB80IGF184
COL1A176CAV182
MKI6776KDR79
TOP2A75PIK3R172
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Khan, M.M.; Mohsen, M.T.; Malik, M.Z.; Bagabir, S.A.; Alkhanani, M.F.; Haque, S.; Serajuddin, M.; Bharadwaj, M. Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods. Genes 2022, 13, 655. https://doi.org/10.3390/genes13040655

AMA Style

Khan MM, Mohsen MT, Malik MZ, Bagabir SA, Alkhanani MF, Haque S, Serajuddin M, Bharadwaj M. Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods. Genes. 2022; 13(4):655. https://doi.org/10.3390/genes13040655

Chicago/Turabian Style

Khan, Mohd Mabood, Mohammad Taleb Mohsen, Md. Zubbair Malik, Sali Abubaker Bagabir, Mustfa F. Alkhanani, Shafiul Haque, Mohammad Serajuddin, and Mausumi Bharadwaj. 2022. "Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods" Genes 13, no. 4: 655. https://doi.org/10.3390/genes13040655

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop