Identification of Differentially Expressed Genes and Drug Targets in Prostate Cancer Using Next-Generation Sequencing

doi:10.21203/rs.3.rs-4988338/v1

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Identification of Differentially Expressed Genes and Drug Targets in Prostate Cancer Using Next-Generation Sequencing

https://doi.org/10.21203/rs.3.rs-4988338/v1

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Prostate cancer, a leading malignancy with significant impact on men’s health, was the focus of this study, which aimed to identify candidate genes through differential gene expression analysis using Galaxy, an open-source platform for analyzing next-generation sequencing data. RNA-Seq analysis was performed on several datasets from the GEO database, comparing nine tumor patient datasets with eight non-tumor datasets. This analysis revealed ten upregulated and ten downregulated genes with log2FC counts > 2.5 and p-values < 0.05. To further investigate these differentially expressed genes (DEGs), WebGestalt was used for comprehensive in silico analysis, visualizing enrichment via volcano plots. Additionally, protein-protein interaction (PPI) networks were constructed using STRING, identifying three gene modules and ten hub genes through Cytoscape cluster analysis. Molecular docking studies were then conducted on these hub genes using PyRx software, with protein structures retrieved from the Protein Data Bank (PDB). This included the addition of polar hydrogen atoms, the assignment of partial charges, and the removal of water molecules to prepare for efficient molecular docking. This research enhances our understanding of gene interactions and protein-phytochemicals binding mechanisms, thereby contributing to therapeutic advancements in the biopharmaceutical sector.

Prostate cancer

next-generation sequencing

differential gene expression (DEG)

Galaxy server

String

Prostate cancer (PCa) has the second-highest morbidity and death rates among males worldwide. Family genealogy, genetic makeup, and race are all known to increase the risk of prostate cancer (Wang et al. 2022). An increased risk of PCa is linked to aging. PCa patients are often older than 50, averaging 66 years old. In 2020, there were around 375,304 deaths from PCa-related causes and 1,414,259 new cases worldwide. Many microarray-based efforts have been conducted to determine the key genes associated with PCa. The examination of the genetic alterations underlying the start and course of PCa has become easier because of microarray technology. PCa is almost exclusively caused by adenocarcinomas (Wang et al. 2022). These malignancies originate from the gland cell. The risk of PCa increases, particularly after 50. About 60% of instances of PCa are diagnosed in people 65 years of age or older (Lee et al. 2007). Hereditary PCa is an uncommon type of illness that occurs in around 5% of cases. The cause of PCa is unclear, but genetics is certainly involved. Genetic prostate tumors are caused by gene mutations, or changes, that are handed down through a family from one generation to the next (Punnen and Cooperberg 2013). PCa is commonly associated with the typical Western diet. There is quite minimal evidence to support a link between trans-fat, saturated fat, or carbohydrate intake and PCa. Black men die due to prostate tumors at a higher rate than white men. PCa may not exhibit any signs at all in its early stages. It is a more advanced correlative sign and indications such as difficulty urinating, blood in the urine, blood in the semen, as well as decreased force in the urine stream, loss of weight, erectile challenges and bone discomfort. Studies have shown varying findings on the likelihood of developing PCa in obese individuals relative to those who are of a normal weight. Numerous differentially expressed genes (DEGs) have been identified among patients as determined by gene expression profiling (Annapurna et al. 2021). Yet, modern techniques demonstrate that an extensive number of genes were unrelated to each other. Amongst the screening methods are the digital rectal exam and the prostate-specific antigen (PSA) blood test of PCa. Now, radiation therapy, surgery, hormonal therapy, and constant surveillance are the treatments available for localized prostate cancer. It also includes more newly created therapies like targeted immunotherapy (Matsukawa et al. 2024).

An alarming number of people are developing PCa, which can result in a number of potentially fatal diseases. Identifying PCa genes is crucial for addressing the global epidemic and advancing effective treatment strategies. Large DNA fragments were found utilizing Next generation sequencing (NGS) technology, which was made possible by technical improvement and modernization. RNA sequencing has been extensively used in recent years to track alterations in the cellular transcriptome on a continuous basis (Chang et al. 2014). Using a galaxy platform, RNA-Seq aims to generate gene expression profiles by identifying genes or the accompanying molecular pathways and comprehending the genes that are differently expressed among two or more biological circumstances. Public databases are used to import the PCa dataset, which is used to find DEG associated with PCa (Wang et al. 2022). The objective is to discover significant genes related to PCa development to enhance the disease's detection, prognosis, as well as treatment.

NEXT-GENERATION RNA SEQUENCING

Next-generation RNA sequencing samples were retrieved from NCBI GEO Database from 15 different studies namely GSM5353230, GSM5353231, GSM5353232, GSM5353233, GSM5353234, GSM5353235, GSM5353236, GSM5353237, GSM5353238, GSM5353239, GSM5353240, GSE223241, GSM5353242, GSM5353243, GSM53532344 (Table 1). A total of 60 samples represents 30 cases and 30 controls of 7 samples were from tumor patients and 8 samples were from non-tumor patients. The datasets were imported into the Galaxy Server (https://usegalaxy.org.au/) using the tool faster download and extract reads in FASTQ format from NCBI SRA (Galaxy Version2.11.0 + galaxy0) (Jalili et al. 2020). The read quality check was performed by using the tool Fast QC read quality reports (Galaxy Version 0.73 + galaxy0). Sequence Mapping and Alignment were performed using HISAT2, a fast and sensitive alignment program (Galaxy Version 2.2.1 + galaxy1). The RNA sequence reads were mapped to reference human genome version hg38. Feature counts were used to measure gene expression in RNA-Seq data from BAM files (GalaxyVersion 2.0.1 + galaxy2). Annotations for gene regions were provided in the GTF format. Differential expression gene analysis was performed on two factors namely, tumor VS non tumor with Deseq2 (Galaxy Version 3.50.1 + galaxy0) (Jemal, et al. 2011). A well-liked resource for interpreting gene lists obtained from WebGestalt. WebGestalt supports 12 organisms, 342 gene identities, 155,175 functioning classifications, user-uploaded functioning databases, and more with the 2019 edition (Elizarraras et al. 2024). For each category in the search database, the volcano plot displays the log of the FDR against the enrichment ratio. Major categories are in the top sides. The dot's size and color correspond to the category's dimensions. A dot's details can be viewed by hovering over it, and clicking on it updates the section with more specific information. A switch button that the user presses allow them to pan and zoom the plot. The enriched subcategories have labels attached to them, and you can manually move the labels around with your mouse.

Table 1

Next-generation RNA sequencing samples from GEO database.
GEO Dataset	Sample Type
GSM5353230	NORMAL
GSM5353231	NORMAL
GSM5353234	NORMAL
GSM5353235	NORMAL
GSM5353238	NORMAL
GSM5353239	NORMAL
GSM5353241	NORMAL
GSM5353242	NORMAL
GSM5353232	TUMOR
GSM5353233	TUMOR
GSM5353236	TUMOR
GSM5353237	TUMOR
GSM5353240	TUMOR
GSM5353243	TUMOR
GSM5353244	TUMOR
GSM5353243	TUMOR

PROTEIN-PROTEIN INTERACTION NETWORK

Using PPI and eighty-seven Gene Ontology-ranked genes in order of preference, a network with PPI has been constructed using string (12.0). The web-based database STRING has around 24.6 million peptides and over 3.1 billion associations, which correspond to 5,090 different organisms (Szklarczyk et al. 2023). The relationship between genotypes, biologic systems, and gene expression is visualized via the Cytoscape software (Kohl et al. 2011). In the present research, the scale-free property of the network led to the elevated score of central nodes.

String was used to construct a PPI network for the 23 genes with a high confidence score > 0.900. Three gene modules were obtained from Cytoscape software. First gene module consists of 38 nodes and 308 edges in the network. Second gene module consists of 3 nodes and 3 edges. Third gene module consists of 3 nodes and 3 edges. Using a cluster analysis of filtering nodes, 10 hub node genes were identified. The hub genes were PLA2G2F, PLA2G2D, PLA2G1B, PLA2G10, PLA2G5, PLA2G3, LPCAT4, PLB1, PLA2G12A, PLA2G2E. The biopharmaceutical sector of today is characterized by complexity due to the market's increasing expectations for new treatment classes, enhanced specificity and safety, and more complex disease mechanisms. Maintaining this level of intricacy calls for a more thorough comprehension of therapeutic conduct. An exceptional way to investigate biological and physical chemical reactions at the atomic level is through molecular modeling techniques (Lobanov et al. 2020).

MOLECULAR DOCKING

The three-dimensional structure of the prostate cancer target proteins [PDB ID: 1BBC (PLA2G12A), 1KQU (PLA2G1B), 1KVO (LPCAT4), 1POD (PLA2G3), 1TRN (PLB1), 3U8H (PLA2G5), 5WZM (PLA2G2E), 5WZO (PLA2G2F), 5WZT(PLA2G2F), 8ERC (PLA2G10) was obtained from the Protein Data Bank (PDB) (Burley et al. 2021). The protein structure was prepared for docking using PyRx, which includes the addition of polar hydrogen atoms, assignment of partial charges, and removal of water molecules. A receptor grid box was generated around the active site of the proteins to define the search space for ligand binding (Dallakyan et al. 2015). Grid parameters, such as grid dimensions and spacing, were optimized based on the size and shape of the binding pocket (Table 1). In total nine phytochemicals Curcumin, Resveratrol, Quercetin, Epigallocatechin gallate, Sulforaphane, Lycopene, Anthocyanins, Capsaicin, Gingerol are sourced from and retrieved from the PubChem database (Hao et al. 2022; Kim et al. 2019). The phytochemicals were prepared in PDBQT format, ensuring the correct protonation states using PyRx. In PyRx, the docking algorithm primarily used is based on AutoDock Vina. The nine phytochemicals were docked against the prostate cancer target proteins 1BBC, 1KQU, 1KVO, 1POD, 1TRN, 3U8H, 5WZM, 5WZO, 5WZT and 8ERC. The toxicity profile for the nine phytochemicals Curcumin, Resveratrol, Quercetin, Epigallocatechin gallate, Sulforaphane, Lycopene, Anthocyanins, Capsaicin, Gingerol were predicted using the ProTox-II server (Banerjee et al. 2018).

DESEQ2

To gain a preliminary understanding of the mechanisms behind prostate cancer, 60 patients, 30 cases and 30 controls (9 tumor patients and 8 non-tumor patients) were chosen for further investigation and their corresponding id’s are SRR14712974, SRR14712975, SRR14712978, SRR14712979, SRR14712982, SRR14712985, SRR14712986, SRR14712972, SRR14712973, SRR14712976, SRR14712977, SRR14712980, SRR14712981, SRR14712983, SRR14712984 (Figs. 1 and 2). The volcano plot was used to investigate the differentially expressed genes between healthy and cancer samples from limma-voom. The volcano plot demonstrates the expressed fold change of genes in normal and tumor samples calculated with the degree of statistical significance in different samples (Fig. 3). A total of 45828 DEG’s with a threshold criterion of LogFC greater than and equal to 2.5 and p-value less than 0.05 as the cut-off point were diagnosed. Among them 18, 998 upregulated genes and 26, 830 downregulated genes. The Gene ontology resources showed fold enrichment pathways for up and downregulated genes. The integrated pathways from KEGG database for differentially expressed genes comprise of pathways in cancer, Ras signaling pathway, Lipid metabolism pathways (Fig. 4). The protein-protein interactions between DEG’s were derived from the STRING database. The PPI network was represented in the form of nodes and edges, where each node represented a protein product of single-gene and edges represented the protein-protein association. The network backbone of identified upregulated genes consisted of 57 nodes and 143 edges with an estimated clustering coefficient 0.242 (Fig. 5). Similarly, the network backbone of identified downregulated genes consisted of nodes and edges with an estimated clustering coefficient of 0.404. After string analysis, Cytoscape was used to visualize and identify the PPI network. MCODE plugin was used to identify the hub genes,and the parameters of DEG clustering and scoring were as follows: for cluster 1 MCODE score = 9.750, Degree Cut-off = 2, Node Score Cut-off = 0.2, k-score = 2, and Max. Depth = 100. It consists of 38 nodes and 308 edges (Fig. 6). For Cluster2, MCODE score = 8.048, Degree Cut-off = 2, Node Score Cut-off = 0.2, kscore = 2, and Max. Depth = 100 (Fig. 7). It consists of 3 nodes and 3 edges for cluster 3 MCODE score = 9.750, Degree Cut-off = 2, Node Score Cut-off = 0.2, k-score = 2, and Max. Depth = 100 (Fig. 8). It consists of 3 nodes and 3 edges. Using a cluster analysis of filtering nodes, 10 hub node genes were identified. The hub genes were PLA2G2F, PLA2G2D, PLA2G1B, PLA2G10, PLA2G5, PLA2G3, LPCAT4, PLB1, PLA2G12A, PLA2G2E (Fig. 9).

In the current study molecular docking was performed using PyRx software. The prostate cancer target proteins [PDB ID: 1BBC (PLA2G12A), 1KQU (PLA2G1B), 1KVO (LPCAT4), 1POD (PLA2G3), 1TRN (PLB1), 3U8H (PLA2G5), 5WZM (PLA2G2E), 5WZO (PLA2G2F), 5WZT(PLA2G2F), 8ERC (PLA2G10) were chosen for docking (Table 2). The nine phytochemicals: curcumin, resveratrol, quercetin, epigallocatechin gallate, sulforaphane, lycopene, anthocyanins, capsaicin, gingerol were docked against the prostate cancer ten target proteins. The curcumin shows better binding toward the active site of 1KQU and 5WZO with the docking score of -7.7 kcal/mol. The phytochemicals resveratrol, quercetin, epigallocatechin gallate shows better binding with 5WZM with the docking score of -8, -8.5 and − 9 kcal/mol respectively. Sulforaphane and lycopene show binding free energy of -4.0 and − 7.1 kcal/mol with 1POD and 3U8H. The phytochemicals anthocyanins, capsaicin and gingerol show greater affinity of -8.6, -7.6 and − 6.9 kcal/mol with 8ERC respectively (Table 3). The extent to which a chemical or specific combination of chemicals might harm an organism is known as toxicity. Estimating the toxicity of compounds ProTox-II makes it possible to make simple predictions for various toxicity levels. Lycopene is the lead chemical that demonstrated a low toxicological description, excellent docking result, and an excellent affinity for binding (Table 4).

Table 2

Active site of PCa target proteins.
PDB ID	Active site
1BCC	Blind docking
1KQU	Gly31, His47, Asp91, Gly29, Lys62, Val30, Asp48, Leu2
1KVO	Gly29, Asp91, His47, Tyr51, Cys44, Tyr21, Phe5, and Val30
1POD	Blind docking
1TRN	Ser195, His57, Asp102, Ser214, Trp215, Val213, Ser214, Gly193
3U8H	Gly31, Val30, Phe23, Gly22, Tyr21, Asp91, His6, Gly29, and Lys62
5WZM	Arg106, Tyr104, Gly102, Leu101, Lys15, and Trs211
5WZO	Arg92, Glu88, Arg52, Cys49, Leu53
5WZD	Arg212, Glu101, Gol212, Arg92
8ERC	Blind docking

Table 3

Molecular docking of prostate cancer target proteins with phytochemicals.
Docking Score (Kcal/mol)
Phytochemicals	Protein
Phytochemicals	1BBC	1KQU	1KVO	1POD	1TRN	3U8H	5WZM	5WZO	5WZT	8ERC
Curcumin	-7.3	-7.7	-7.4	-7.4	-6.7	-7.6	-7.2	-7.7	-7.5	-7
Resveratrol	-6.2	-7.4	-7.4	-6.7	-6.1	-6.9	-8	-7.2	-7.4	-7.7
Quercetin	-8.1	-8.3	-7.9	-7.2	-6.9	-8.1	-8.5	-8.2	-8.1	-8
Epigallocatechin gallate	-7.8	-8.7	-5.2	-6.6	-7.7	-8.8	-9	-8.6	-8.1	-8.1
Sulforaphane	-3.8	-3.9	-2.2	-4.0	-3.2	-3.8	-3.7	-3.7	-3.6	-3.5
Lycopene	-4.9	-6.8	-3.3	-5.6	-4.7	-7.1	-5.6	-5.9	-7	-6.5
Anthocyanins	-7.4	-8.0	-7.6	-7.2	-6.8	-7.6	-8	-7.7	-8	-8.6
Capsaicin	-6.5	-7.3	-4.4	-5.6	-6.3	-7.1	-6.4	-5.9	-6.8	-7.6
Gingerol	-6.2	-6.8	-4.6	-5.6	-5.8	-6.6	-6.6	-6.8	-6.8	-6.9

Table 4

Toxicity prediction of phytochemicals using PROTOX-II.
COMPOUND	PREDICTEDLD50	PREDICTED TOXCITY CLASS	AVERAGE SIMILIRITY	PREDICTION ACCURACY
CURCUMIN	2000mg/kg	4	100%	100%
RESVERATROL	1560mg/kg	4	69.97%	68.07%
QUERCETIN	159mg/kg	3	100%	100%
EPIGALLOCATECHIN GALLATE	1000mg/kg	4	100%	100%
SULFORAPHANE	1000mg/kg	4	42.25%	54.26%
LYCOPENE	5700mg/kg	6	79.06%	69.26%
ANTHOCYANINS	5000mg/kg	5	72.24%	69.26%
CAPSAICIN	47mg/kg	2	100%	100%
GINGEROL	250mg/kg	3	100%	100%

In this study, a comprehensive analysis was conducted to understand the molecular mechanisms underlying prostate cancer by examining differential gene expression between tumor and non-tumor samples. Using a volcano plot derived from limma-voom, a total of 45,828 differentially expressed genes (DEGs) were identified, with a stringent threshold of a log fold change (FC) greater than or equal to 2.5 and a p-value less than 0.05 (Ritchie et al. 2015). Among these, 18,998 genes were upregulated, while 26,830 were downregulated.

Gene Ontology (GO) and KEGG pathway analyses revealed that these DEGs are involved in critical pathways related to cancer, such as the Ras signaling pathway and lipid metabolism (Ashburner et al. 2000; Kanehisa et al. 2021). The Ras signaling pathway, in particular, is known to be pivotal in cancer development and progression, making it a significant focus in cancer research. Additionally, the involvement of lipid metabolism pathways suggests potential alterations in metabolic processes associated with cancer.

To further elucidate the interactions among DEGs, we utilized the STRING database to construct protein-protein interaction (PPI) networks. The PPI network for upregulated genes comprised 57 nodes and 143 edges, while the network for downregulated genes included a smaller number of nodes and edges with distinct connectivity patterns. The clustering coefficients for upregulated and downregulated networks were 0.242 and 0.404, respectively, indicating a higher degree of clustering among downregulated genes.

The MCODE plugin in Cytoscape identified several key clusters within these networks. Cluster 1, with a high MCODE score of 9.750, consisted of 38 nodes and 308 edges, suggesting a densely interconnected network. Clusters 2 and 3, though smaller, also showed significant interaction patterns, particularly around FTO and PNLIP in Cluster 2 and 3. Through cluster analysis 10 hub node genes were identified the hub genes were PLA2G2F, PLA2G2D, PLA2G1B, PLA2G10, PLA2G5, PLA2G3, LPCAT4, PLB1, PLA2G12A, PLA2G2E. This dense clustering underscores the complex and highly interconnected nature of the molecular changes associated with prostate cancer.

Molecular docking studies using PyRx software aimed to evaluate the interaction of nine phytochemicals with prostate cancer target proteins. They are 1BBC (PLA2G12A), 1KQU (PLA2G1B), 1KVO (LPCAT4), 1POD (PLA2G3), 1TRN (PLB1), 3U8H (PLA2G5), 5WZM (PLA2G2E), 5WZO (PLA2G2F), 5WZT(PLA2G2F), 8ERC (PLA2G10). The results demonstrated varied binding affinities across different target proteins. Curcumin exhibited the strongest binding affinity towards proteins 1KQU and 5WZO, with docking scores of -7.7 kcal/mol. Resveratrol, quercetin, and epigallocatechin gallate showed better binding to protein 5WZM with docking scores ranging from − 8 to -9 kcal/mol.

The binding affinities of sulforaphane and lycopene were moderate, with scores of -4.0 and − 7.1 kcal/mol, respectively. Notably, anthocyanins, capsaicin, and gingerol displayed considerable binding affinity with protein 8ERC, with scores of -8.6, -7.6, and − 6.9 kcal/mol, respectively. These findings suggest that these phytochemicals could have potential therapeutic effects in prostate cancer by interacting effectively with specific target proteins.

Toxicity prediction using ProTox-II indicated that lycopene is a lead compound due to its low toxicological profile coupled with excellent docking results and binding affinity. This highlights its potential as a safer therapeutic option with promising efficacy.

The integration of gene expression analysis, PPI network construction, and molecular docking provides a multifaceted understanding of the molecular mechanisms driving prostate cancer. The identification of key pathways and interactions, along with the promising results from phytochemical docking studies, suggests that targeted therapeutic approaches involving these compounds could be beneficial for drug design for prostate cancer.

Additionally, further exploration of the specific roles of the hub genes and their interactions within the identified clusters could provide deeper insights into the molecular underpinnings of prostate cancer and guide the development of novel therapeutic strategies. Our study highlights the importance of a multi-approach strategy in understanding prostate cancer mechanisms and emphasizes the potential of natural compounds in cancer therapy.

Our study has significantly advanced the understanding of prostate cancer by integrating differential gene expression analysis, protein-protein interaction (PPI) network construction, and molecular docking studies. By analyzing a dataset from 17 patients, we identified 45,828 differentially expressed genes, revealing crucial involvement of pathways such as Ras signaling and lipid metabolism. The PPI network analysis highlighted key hub genes including PLA2G2F, PLA2G2D, PLA2G1B, PLA2G10, PLA2G5, PLA2G3, LPCAT4, PLB1, PLA2G12A, PLA2G2E and uncovered densely interconnected clusters, revealing on the complex molecular interactions in prostate cancer. Molecular docking studies further identified several phytochemicals with potential therapeutic effects, with lycopene emerging as a lead candidate due to its low toxicity and strong binding affinity. Overall, these findings underscore the importance of a comprehensive, multi-approach strategy in elucidating prostate cancer mechanisms and highlight promising natural compounds that could inform future therapeutic developments.

PCa

Prostate cancer

PDB

Protein Data Bank

DEG

differential gene expression

PSA

Prostate-specific antigen

PPI

protein-protein interaction

log2FC

logchange

ACKNOWLEDGEMENTS

We thank the Department of Bioinformatics, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research for their computational facilities.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

FUNDING

No funding was received.

ETHICAL APPROVAL

Not applicable.

INFORMED CONSENT

Not applicable.

Author Contribution

J.J.B Designed the study; analyzed the data; contributed to data interpretation; reviewed the manuscript.C.P Performed the experiments; analyzed the data; wrote the manuscript.J.N.A Reviewed the manuscript and analyzed the data.D.J Conducted literature review; contributed to data interpretation; revised the manuscript.

Annapurna SD, Pasumarthi D, Pasha A, Doneti R, Sheela B, Botlagunta M, Lakshmi BV, Pawar SC (2021) Identification of differentially expressed genes in cervical cancer patients by comparative transcriptome analysis. BioMed Research International 2021:8810074
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29
Banerjee P, Eckert AO, Schrey AK, Preissner R (2018) ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 46:W257–W263
Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, Christie CH, Dalenberg K, Costanzo LD, Duarte JM, Dutta S, Feng Z, Ganesan S, Goodsell DS, Ghosh S, Green RK, Guranović V, Guzenko D, Hudson BP, Lawson CL, Liang Y, Lowe R, Namkoong H, Peisach E, Persikova I, Randle C, Rose A, Rose Y, Sali A, Segura J, Sekharan S, Shao C, Tao Y-P, Voigt M, Westbrook JD, Young JY, Zardecki C, Zhuravleva M (2021) RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res 49:D437–D451
Chang S, Boffetta P, Adami HO, Cole P, Mandel JS (2014) A critical review of the epidemiology of Agent Orange/TCDD and prostate cancer. Eur J Epidemiol 29:667–723
Dallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. Chemical biology: methods and protocols 243–250
Elizarraras JM, Liao Y, Shi Z, Zhu Q, Pico AR, Zhang B (2024) WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics. Nucleic Acids Res 52:W415–W421
Hao Q, Wu Y, Vadgama JV, Wang P (2022) Phytochemicals in inhibition of prostate cancer: evidence from molecular mechanisms studies. Biomolecules 12:1306
Jalili V, Afgan E, Gu Q, Clements D, Blankenberg D, Goecks J, Taylor J, Nekrutenko A (2000) The Galaxy Platform for Accessible, Reproducible and Collaborative Biomedical Analyses: 2020 Update. Nucleic Acids Res 48:8205–8207
Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011) Global cancer statistics. CA Cancer J Clin 61:69–90
Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M (2021) KEGG: integrating viruses and cellular organisms. Nucleic Acids Res 49:D545–D551
Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE (2019) PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 47:D1102–D1109
Kohl M, Wiese S, Warscheid B (2011) Cytoscape: software for visualization and analysis of biological networks. Nucleic Acids Res 696:291–303
Lee J, Demissie K, Lu SE, Rhoads GG (2007) Cancer incidence among Korean-American immigrants in the United States and native Koreans in South Korea. Cancer Control 14:78–85
Lobanov VS, Shenkin PS (2020) Modeling of molecular interactions in drug discovery: Current methods and applications. J Med Chem 63:5401–5422
Matsukawa A, Yanagisawa T, Bekku K, Parizi MK, Laukhtina E, Klemm J, Chiujdea S, Mori K, Kimura S, Fazekas T, Miszczyk M (2024) Comparing the performance of digital rectal examination and prostate-specific antigen as a screening test for prostate cancer: a systematic review and meta-analysis. Eur Urol Oncol 7:697–704
Punnen S, Cooperberg MR (2013) The epidemiology of high-risk prostate cancer. Curr Opin Urol 23:331–336
Ritchie ME, Phipson B, Wu DI, Hu Y, Law CW, Shi W, Smyth GK (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47–e47
Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, Bork P (2023) The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51:D638–D646
Wang L, Lu B, He M, Wang Y, Wang Z, Du L (2022) Prostate cancer incidence and mortality: global status and temporal trends in 89 countries from 2000 to 2019. Front Public Health 10:811044

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Identification of Differentially Expressed Genes and Drug Targets in Prostate Cancer Using Next-Generation Sequencing

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Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

NEXT-GENERATION RNA SEQUENCING

PROTEIN-PROTEIN INTERACTION NETWORK

MOLECULAR DOCKING

RESULTS

DESEQ2

DISCUSSION

CONCLUSION

Abbreviations

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1