World Journal of Oncology, ISSN 1920-4531 print, 1920-454X online, Open Access
Article copyright, the authors; Journal compilation copyright, World J Oncol and Elmer Press Inc
Journal website https://wjon.elmerpub.com

Original Article

Volume 17, Number 4, August 2026, pages 494-508

Integrated Transcriptomic Analysis Identifies Potential Biomarkers in Castration-Resistant Prostate Cancer

Figures

↓  Figure 1. Differential transcriptomic landscape distinguishing PNT2 and 22Rv1 cells. (a) Volcano plot illustrating global differential gene expression between the non-malignant prostate epithelial cell line PNT2 and the castration-resistant prostate cancer cell line 22Rv1. Each point represents an individual gene distributed according to log2 fold change (x-axis) and statistical significance expressed as −log10 (adjusted P value) (y-axis). Genes significantly upregulated in 22Rv1 are highlighted in red, whereas genes downregulated relative to PNT2 are shown in blue. (b) Heatmap of the most significantly DEGs between PNT2 and 22Rv1 cells based on normalized transcript abundance. Hierarchical clustering reveals distinct gene expression patterns separating the non-malignant and castration-resistant cellular states. Red indicates relatively higher expression, whereas blue indicates lower expression. (c) Focused heatmap of representative genes identified among the top DEGs, including MALAT1, FASN, PARP1, SET, ENSG00000214719, and IGF1, illustrating clear transcriptional divergence between the two cellular models. DEGs: differentially expressed genes.
Figure 1.
↓  Figure 2. Gene Ontology (GO) enrichment analysis of differentially expressed genes. (a) GO biological process enrichment analysis of DEGs identified between PNT2 and 22Rv1 cells. The bubble plot displays significantly enriched biological processes associated with transcriptional remodeling in the castration-resistant state. The x-axis represents fold enrichment of each GO term. Bubble size indicates the number of genes contributing to each biological process, and color intensity represents statistical significance expressed as −log10(FDR). (b) GO cellular component enrichment analysis highlighting the subcellular compartments associated with the identified DEGs. Enriched categories include extracellular vesicle–related structures, cytoskeletal components, chromosomal elements, and mitochondrial-associated compartments. Bubble size reflects gene count, and color intensity represents −log10(FDR). FDR: false discovery rate; DEGs: differentially expressed genes. BP: biological process; CC: cellular component.
Figure 2.
↓  Figure 3. Functional enrichment analysis of molecular functions and signaling pathways. (a) Gene Ontology (GO) molecular function enrichment analysis of DEGs between PNT2 and 22Rv1 cells. Significantly enriched functions include nucleic acid binding, RNA binding, enzyme binding, and catalytic activity acting on proteins. These enrichments indicate increased activity of regulatory complexes and transcriptional control mechanisms in the castration-resistant phenotype. (b) KEGG pathway enrichment analysis identifying signaling pathways associated with the observed transcriptional changes. Significantly enriched pathways include ribosome-associated pathways, metabolic pathways, and PI3K–Akt signaling. Bubble size indicates the number of genes associated with each pathway, and color intensity represents statistical significance (−log10(FDR)). FDR: false discovery rate; DEGs: differentially expressed genes; MF: molecular function; KEGG: Kyoto Encyclopedia of Genes and Genomes.
Figure 3.
↓  Figure 4. Gene co-expression network analysis of differentially expressed genes. The co-expression network was constructed from significantly differentially expressed genes identified in the transcriptomic comparison between PNT2 and 22Rv1 cells. Each node represents an individual gene, and edges represent significant correlation-based co-expression relationships. The network reveals a central regulatory module composed of AR, PARP1, KDM6B, BAZ2A, RANGAP1, NFAT5, and MAP4, which display multiple interaction links and high connectivity. The presence of this densely interconnected module highlights coordinated transcriptional regulation associated with the castration-resistant phenotype.
Figure 4.
↓  Figure 5. Clinical validation of candidate genes in prostate adenocarcinoma datasets. Boxplots showing the expression levels of (a) FASN, (b) PARP1, (c) ENSG00000214719, (d) SET, (e) MALAT1, and (f) IGF1 in prostate adenocarcinoma (PRAD) tumors (n = 492) and normal prostate tissues (n = 152) obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets via the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) platform. Expression values are presented as log2(TPM + 1). The central line represents the median, the box indicates the interquartile range, and whiskers represent the data distribution. TPM: transcripts per million.
Figure 5.

Tables

↓  Table 1. Top Differentially Expressed Genes Identified Between PNT2 and 22Rv1 Cells
 
Ensembl IDGene symbolLog2 fold changeAdjusted P value (FDR)Chromosomal locationGene type
ID: identifier; FDR: false discovery rate; lncRNA: long noncoding RNA.
ENSG00000251562MALAT117.601.34 × 10–1711q13.1lncRNA
ENSG00000169710FASN14.805.36 × 10–1717q25.3Protein-coding
ENSG00000143799PARP111.249.67 × 10–161q42.12Protein-coding
ENSG00000119335SET10.911.03 × 10–159q34.11Protein-coding
ENSG00000214719Unknown/lncRNA ENSG0000021471910.231.88 × 10–1517q11.2lncRNA
ENSG00000174227IGF1−7.595.31 × 10–1412q23.2Protein-coding

 

↓  Table 2. Gene Ontology (GO) Biological Process Enrichment Analysis of Differentially Expressed Genes Between PNT2 and 22Rv1 Cells
 
GO termBiological processFDRGene countPathway gene countFold enrichment
FDR: false discovery rate.
GO:0141187Nucleic acid biosynthetic process4.43 × 10–193344,9021.62
GO:0032774RNA biosynthetic process1.37 × 10–163194,7881.58
GO:0019219Regulation of nucleobase-containing compound metabolic process1.39 × 10–142904,3631.58
GO:0006996Organelle organization5.98 × 10–202883,9201.74
GO:0030154Cell differentiation1.03 × 10–82844,7821.41
GO:0048869Cellular developmental process1.03 × 10–82844,7831.41
GO:0009893Positive regulation of metabolic process6.42 × 10–162763,9881.64
GO:0006810Transport3.90 × 10−72764,7971.37
GO:0044085Cellular component biogenesis9.95 × 10–192753,7751.73
GO:0006950Response to stress3.21 × 10–82704,5451.41
GO:0051252Regulation of RNA metabolic process2.33 × 10–132694,0461.58
GO:0048731System development2.54 × 10–92634,2871.46
GO:0010604Positive regulation of macromolecule metabolic process6.42 × 10–162593,6591.68
GO:0051641Cellular localization1.10 × 10–122513,7431.59
GO:0006351DNA-templated transcription1.32 × 10–102513,9191.52
GO:0048583Regulation of response to stimulus4.67 × 10–42514,7041.27
GO:0009892Negative regulation of metabolic process7.50 × 10–202443,1151.86
GO:0022607Cellular component assembly9.49 × 10–142413,4681.65
GO:2001141Regulation of RNA biosynthetic process9.16 × 10–102403,7761.51
GO:0042221Response to chemical2.38 × 10–42404,4171.29

 

↓  Table 3. Gene Ontology (GO) Cellular Component Enrichment Analysis of Differentially Expressed Genes Between PNT2 and 22Rv1 Cells
 
GO termCellular componentFDRGene countPathway gene countFold enrichment
FDR: false discovery rate.
GO:0005654Nucleoplasm9.19 × 10–283484,6701.77
GO:0031982Vesicle2.70 × 10–122884,5711.50
GO:0005576Extracellular region1.74 × 10–52584,6751.31
GO:0005615Extracellular space3.67 × 10–122403,6191.57
GO:0031090Organelle membrane2.25 × 10–22164,3401.18
GO:0070062Extracellular exosome3.18 × 10–222042,3492.06
GO:0043230Extracellular organelle1.19 × 10–212042,3832.03
GO:0065010Extracellular membrane-bounded organelle1.19 × 10–212042,3832.03
GO:1903561Extracellular vesicle1.19 × 10–212042,3822.03
GO:0005856Cytoskeleton2.19 × 10–142002,6931.76
GO:0030054Cell junction6.39 × 10–131902,6051.73
GO:0005694Chromosome2.54 × 10–131672,1601.83
GO:0042995Cell projection1.36 × 10–61632,5721.50
GO:0120025Plasma membrane bounded cell projection1.30 × 10–61572,4491.52
GO:0031410Cytoplasmic vesicle2.79 × 10–21512,9411.22
GO:0097708Intracellular vesicle2.95 × 10–21512,9471.22
GO:1902494Catalytic complex2.74 × 10–91441,9901.72
GO:0015630Microtubule cytoskeleton2.53 × 10–131311,5322.03
GO:0099080Supramolecular complex2.29 × 10–101271,6181.86
GO:0005739Mitochondrion1.38 × 10–41252,0291.46

 

↓  Table 4. Gene Ontology (GO) Molecular Function Enrichment Analysis of Differentially Expressed Genes Between PNT2 and 22Rv1 Cells
 
GO termMolecular functionFDRGene countPathway gene countFold enrichment
FDR: false discovery rate; ATP: adenosine triphosphate.
GO:0003676Nucleic acid binding4.87 × 10–403684,4651.96
GO:0003723RNA binding2.53 × 10–502331,8862.93
GO:0003677DNA binding1.08 × 10–101912,7481.65
GO:0019899Enzyme binding2.01 × 10–171892,2961.95
GO:1901363Heterocyclic compound binding2.10 × 10–81712,5391.60
GO:0043168Anion binding8.33 × 10–71702,6711.51
GO:1901265Nucleoside phosphate binding3.28 × 10–91672,3981.65
GO:0000166Nucleotide binding5.51 × 10–91652,3801.65
GO:0097367Carbohydrate derivative binding2.75 × 10–61602,5211.51
GO:0017076Purine nucleotide binding4.43 × 10–81522,2021.64
GO:0032555Purine ribonucleotide binding1.78 × 10–71442,1021.63
GO:0032553Ribonucleotide binding2.56 × 10–71442,1191.61
GO:0035639Purine ribonucleoside triphosphate binding7.77 × 10–81432,0521.65
GO:0140096Catalytic activity acting on a protein3.40 × 10–21382,6241.25
GO:0098772Molecular function regulator activity1.31 × 10–21342,4571.29
GO:0044877Protein-containing complex binding2.66 × 10–131321,5252.05
GO:0030554Adenyl nucleotide binding1.66 × 10–71291,8171.68
GO:0042802Identical protein binding4.24 × 10–21282,4281.25
GO:0032559Adenyl ribonucleotide binding6.43 × 10–71211,7181.67
GO:0005524ATP binding2.82 × 10–71201,6731.70

 

↓  Table 5. KEGG Pathway Enrichment Analysis of Differentially Expressed Genes Between PNT2 and 22Rv1 Cells
 
KEGG pathway IDPathway nameFDRGene countPathway gene countFold enrichment
KEGG: Kyoto Encyclopedia of Genes and Genomes; ID: identifier; FDR: false discovery rate.
hsa01100Metabolic pathways3.13 × 10–3931,5561.42
hsa05022Pathways of neurodegeneration–multiple diseases6.50 × 10–6464782.28
hsa05171Coronavirus disease 2019 (COVID-19)7.96 × 10–15442344.46
hsa05014Amyotrophic lateral sclerosis3.77 × 10–8443672.85
hsa05010Alzheimer disease6.50 × 10–6403872.45
hsa05200Pathways in cancer3.98 × 10–3395291.75
hsa03010Ribosome3.63 × 10–16381585.71
hsa04714Thermogenesis7.77 × 10–9352343.55
hsa05020Prion disease1.10 × 10–6342752.93
hsa05012Parkinson disease5.53 × 10–6322682.83
hsa05415Diabetic cardiomyopathy3.79 × 10–8312043.61
hsa05016Huntington disease1.69 × 10–4313072.40
hsa05132Salmonella infection1.69 × 10–4272482.58
hsa04151PI3K-Akt signaling pathway1.59 × 10–2273621.77
hsa05131Shigellosis2.69 × 10–4262462.51
hsa05165Human papillomavirus infection1.07 × 10–3263321.86
hsa05205Proteoglycans in cancer5.16 × 10–5252032.92
hsa05208Chemical carcinogenesis–reactive oxygen species2.25 × 10–4252272.61
hsa04015Rap1 signaling pathway2.20 × 10–4242112.70
hsa05130Pathogenic Escherichia coli infection2.32 × 10–4232002.73