Identification of prostate cancer hub genes and therapeutic agents using bioinformatics approach

Identification of prostate cancer associated genes for diagnosis and prognosis: a modernized in silico approach

Prostate cancer (PCa) ranks as the second leading cause of cancer-related deaths in men. Diagnosing PCa relies on molecular markers known as diagnostic biomarkers, while prognostic biomarkers are used to identify key proteins involved in PCa treatments. This study aims to gather PCa-associated genes and assess their potential as either diagnostic or prognostic biomarkers for PCa. A corpus of 152,064 PCa-related data from PubMed, spanning from May 1936 to December 2020, was compiled. Additionally, 4199 genes associated with PCa terms were collected from the National Center of Biotechnology Information (NCBI) database. The PubMed corpus data was extracted using pubmed.mineR to identify PCa-associated genes. Network and pathway analyses were conducted using various tools, such as STRING, DAVID, KEGG, MCODE 2.0, cytoHubba app, CluePedia, and ClueGO app. Significant marker genes were identified using Random Forest, Support Vector Machines, Neural Network algorithms, and the Cox Proportional Hazard model. This study reports 3062 unique PCa-associated genes along with 2518 corresponding unique PMIDs. Diagnostic markers such as IL6, MAPK3, JUN, FOS, ACTB, MYC, and TGFB1 were identified, while prognostic markers like ACTB and HDAC1 were highlighted in PubMed. This suggests that the potential target genes provided by PubMed data outweigh those in the NCBI database.

Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer

Prostate cancer (PCa) is one of the most frequently diagnosed cancers among men in the world. Its prevention has been limited because of an incomplete understanding of how environmental exposures to chemicals contribute to the molecular pathogenesis of aggressive PCa. Environmental exposures to endocrine-disrupting chemicals (EDCs) may mimic hormones involved in PCa development. This research aims to identify EDCs associated with PCa hub genes and/or transcription factors (TF) of these hub genes in addition to their protein-protein interaction (PPI) network. We are expanding upon the scope of our previous work, using six PCa microarray datasets, namely, GSE46602, GSE38241, GSE69223, GSE32571, GSE55945, and GSE26126, from the NCBI/GEO, to select differentially expressed genes based on |log2FC| (fold change) ≥ 1 and an adjusted p-value < 0.05. An integrated bioinformatics analysis was used for enrichment analysis (using DAVID.6.8, GO, KEGG, STRING, MCODE, CytoHubba, and GeneMANIA). Next, we validated the association of these PCa hub genes in RNA-seq PCa cases and controls from TCGA. The influence of environmental chemical exposures, including EDCs, was extrapolated using the chemical toxicogenomic database (CTD). A total of 369 overlapping DEGs were identified associated with biological processes, such as cancer pathways, cell division, response to estradiol, peptide hormone processing, and the p53 signaling pathway. Enrichment analysis revealed five up-regulated (NCAPG, MKI67, TPX2, CCNA2, CCNB1) and seven down-regulated (CDK1, CCNB2, AURKA, UBE2C, BUB1B, CENPF, RRM2) hub gene expressions. Expression levels of these hub genes were significant in PCa tissues with high Gleason scores ≥ 7. These identified hub genes influenced disease-free survival and overall survival of patients 60-80 years of age. The CTD studies showed 17 recognized EDCs that affect TFs (NFY, CETS1P54, OLF1, SRF, COMP1) that are known to bind to our PCa hub genes, namely, NCAPG, MKI67, CCNA2, CDK1, UBE2C, and CENPF. These validated differentially expressed hub genes can be potentially developed as molecular biomarkers with a systems perspective for risk assessment of a wide-ranging list of EDCs that may play overlapping and important role(s) in the prognosis of aggressive PCa.

An integrative approach toward identification and analysis of therapeutic targets involved in HPV pathogenesis with a focus on carcinomas

BACKGROUND

Persistent infection of high-risk HPVs is known to cause diverse carcinomas, mainly cervical, oropharyngeal, penile, etc. However, efficient treatment is still lacking.

OBJECTIVE

Identify and analyze potential therapeutic targets involved in HPV oncogenesis and repurposing drug candidates.

METHODS

Integrative analyses were performed on the compendium of 1887 HPV infection-associated or integration-driven disrupted genes cataloged from the Open Targets Platform and HPVbase resource. Potential target genes are prioritized using STRING, Cytoscape, cytoHubba, and MCODE. Gene ontology and KEGG pathway enrichment analysis are performed. Further, TCGA cancer genomic data of CESC and HNSCC is analyzed. Moreover, regulatory networks are also deduced by employing NetworkAnalyst.

RESULTS

We have implemented a unique approach for identifying and prioritizing druggable targets and repurposing drug candidates against HPV oncogenesis. Overall, hundred key genes with 44 core targets were prioritized with transcription factors (TFs) and microRNAs (miRNAs) regulators pertinent to HPV pathogenesis. Genomic alteration profiling further substantiated our findings. Among identified druggable targets, TP53, NOTCH1, PIK3CA, EP300, CREBBP, EGFR, ERBB2, PTEN, and FN1 are frequently mutated in CESC and HNSCC. Furthermore, PIK3CA, CCND1, RFC4, KAT5, MYC, PTK2, EGFR, and ERBB2 show significant copy number gain, and FN1, CHEK1, CUL1, EZH2, NRAS, and H2AFX was marked for the substantial copy number loss in both carcinomas. Likewise, under-explored relevant regulators, i.e., TFs (HINFP, ARID3A, NFATC2, NKX3-2, EN1) and miRNAs (has-mir-98-5p, has-mir-24-3p, has-mir-192-5p, has-mir-519d-3p) is also identified.

CONCLUSIONS

We have identified potential therapeutic targets, transcriptional and post-transcriptional regulators to explicate HPV pathogenesis as well as potential repurposing drug candidates. This study would aid in biomarker and drug discovery against HPV-mediated carcinoma.

Identification of target and pathway of aspirin combined with Lipitor treatment in prostate cancer through integrated bioinformatics analysis

PURPOSE

Our previous studies have confirmed that aspirin combined with Lipitor inhibited the development of prostate cancer (PCa), but the mechanisms need to be comprehensively expounded. The study aims to screen out the hub genes of combination therapy and to explore their association with the pathogenesis and prognosis of PCa.

METHODS

Gene expressions were quantified by RNA sequencing (RNA-seq). Altered biological function, pathways of differentially expressed genes (DEGs), protein-protein interaction network, the filtering of hub genes, gene co-expression and the pathogenesis and prognosis were revealed by bioinformatics analysis. The correlation between hub gene expression and patient survival was validated by Kaplan-Meier. The effects of silent DNA replication and sister chromatid cohesion 1 (siDSCC1) combined with Lipitor and aspirin on DSCC1 expression, viability, invasion and migration of PCa cells were detected by qRT-PCR, Wound healing and transwell assays.

RESULTS

157 overlapped DEGs involved in FoxO, PI3K-Akt and p53 signaling pathways were identified. Ten hub genes (NEIL3, CDC7, DSCC1, CDC25C, PRIM1, MCM10, FBXO5, DTL, SERPINE1, EXO1) were verified to be correlated with the pathology and prognosis of PCa. DSCC1 silencing not only inhibited the viability, migration and invasion of PCa cells, but also strengthened the suppressing effects of Lipitor and aspirin alone or in combination on PCa cells.

CONCLUSION

The enrichment pathways and targets of Lipitor combined with aspirin in PCa are discovered, and DSCC1 silencing can potentiate the effect of Lipitor combined with aspirin in the treatment of PCa.

Based on network pharmacology and molecular docking to predict the mechanism of Huangqi in the treatment of castration-resistant prostate cancer

Background

As a kind of traditional Chinese medicine, HQ is widely mentioned in the treatment of cancerous diseases in China, which has been proven to have a therapeutic effect on cancerous diseases, such as prostate cancer. To predict the specific mechanism of HQ in the treatment of CRPC, we will conduct preliminary verification and discussion based on a comprehensive consideration of network pharmacology and molecular docking.

Methods

TCMSP was used to obtain the compounds and reach the effective targets of HQ. The targets of CRPC were reached based on GeneCards database and CTD database. GO and KEGG were utilized for the analysis of overlapping targets. The software of Openbabel was used to convert the formats of ligands and reporters. In addition, molecular docking studies were performed by using the software of Autodock Vina.

Result

It can be seen from the database results that there were 87 active compounds (20 key active compounds) in HQ, and 33 targets were screened out for CRPC treatment. GO and KEGG pathway enrichment analyses identified 81 significant GO terms and 24 significant KEGG pathways. There is a difference in terms of the expression of core protein between cancer patients and healthy people. The expression of core protein in patients also has an impact on the life cycle. The results of molecular docking showed that the docking activity of drug molecules and core proteins was better.

Conclusions

It is concluded from the results of this network pharmacology and molecular docking that HQ makes a multi-target and multi-biological process, and results in the multi-channel synergistic effect on the treatment of CRPC by regulating cell apoptosis, proliferation and metastasis, which still needs further verification by experimental research.

Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer

Prostate cancer (PCa) is one of the leading malignant tumors in US men. The lack of understanding of the molecular pathology on the risk of food supply chain exposures of environmental phenol (EP) and paraben (PB) chemicals limits the prevention, diagnosis, and treatment options. This research aims to utilize a risk assessment approach to demonstrate the association of EP and PB exposures detected in the urine samples along with PCa in US men (NHANES data 2005−2015). Further, we employ integrated bioinformatics to examine how EP and PB exposure influences the molecular pathways associated with the progression of PCa. The odds ratio, multiple regression model, and Pearson coefficients were used to evaluate goodness-of-fit analyses. The results demonstrated associations of EPs, PBs, and their metabolites, qualitative and quantitative variables, with PCa. The genes responsive to EP and PB exposures were identified using the Comparative Toxicogenomic Database (CTD). DAVID.6.8, GO, and KEGG enrichment analyses were used to delineate their roles in prostate carcinogenesis. The plug-in CytoHubba and MCODE completed identification of the hub genes in Cytoscape software for their roles in the PCa prognosis. It was then validated by using the UALCAN database by evaluating the expression levels and predictive values of the identified hub genes in prostate cancer prognosis using TCGA data. We demonstrate a significant association of higher levels of EPs and PBs in the urine samples, categorical and numerical confounders, with self-reported PCa cases. The higher expression levels of the hub genes (BUB1B, TOP2A, UBE2C, RRM2, and CENPF) in the aggressive stages (Gleason score > 8) of PCa tissues indicate their potential role(s) in the carcinogenic pathways. Our results present an innovative approach to extrapolate and validate hub genes responsive to the EPs and PBs, which may contribute to the severity of the disease prognosis, especially in the older population of US men.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC.

METHODS

Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein-protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.

RESULTS

A total of 197 DEGs were identified. The protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.

CONCLUSION

Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.

Gene Expression Analysis Reveals Key Genes and Signalings Associated with the Prognosis of Prostate Cancer

It is urgent to identify novel biomarkers for prostate cancer (PCa) prognosis and to understand the mechanisms regulating the tumorigenesis for PCa treatment. In this study, GSE17951 and TCGA were used to identify the differentially expressed genes (DEGs). Our study demonstrated that 1533 genes with increased expression and 2301 genes with decreased expression in PCa. Bioinformatics analysis data indicated that these up-regulated genes had an association with the modulation of mitotic nuclear division, sister chromatid cohesion, cell division, and cell cycle. Additionally, our results revealed downregulated genes took part in modulating extracellular matrix organization, angiogenesis, signal transduction, and Ras signaling pathway. Hub upregulated and downregulated PPI networks were identified by protein-protein interaction (PPI) network analysis and MCODE analysis. Of note, 12 cell cycle regulators, comprising CCNB1, CCNB2, PLK1, TTK, AURKA, CDC20, BUB1, PTTG1, CDC45, CDC25C, CCNA2, and BUB1B, were demonstrated to function crucially in PCa development. By detecting their expression in PCa cell lines, we confirmed that these cell cycle regulator expressions were heightened in PCa cells. GEPIA databases analysis showed that higher expression of these cell cycle regulators was correlated to shorter disease-free survival (DFS) time in PCa samples. Our findings collectively suggested targeting cell cycle pathways may offer novel prognosis and treatment biomarkers for PCa.

Network-Based Analysis Reveals Gene Signature in Tip Cells and Stalk Cells

BACKGROUND

Angiogenesis occurs during various physiological or pathological processes such as wound healing and tumor growth. Differentiation of vascular endothelial cells into tip cells and stalk cells initiates formation of new blood vessels. Tip cells and stalk cells are endothelial cells with different biological characteristics and functions.

OBJECTIVE

The aim of this study was to determine the mechanisms of angiogenesis by exploring differences in gene expression of tip cells and stalk cells.

METHODS

Raw data were retrieved from NCBI Gene Expression Omnibus (GSE19284). Data were reanalyzed using bioinformatics methods that employ robust statistical methods, including identification of differentially expressed genes (DEGs) between stalk and tip cells, weighted gene correlation network analysis (WGCNA), gene ontology and pathway enrichment analysis using DAVID tools, integration of protein-protein interaction (PPI) networks and screening of hub genes. DEGs of stalk and tip cells were grouped as dataset A. Gene modules associated with differentiation of stalk and tip cells screened by WGCNA were named dataset B. Further, we retrieved existing markers of angiogenesis from previous experimental studies on tip and stalk cells which we named dataset C. Intersection of datasets A, B and C was used as a candidate gene. Subsequently, we verified the results applying quantitative polymerase chain reaction (Q -PCR) to our clinical specimen. In general, the Q-PCR results coincide with the majority of the expression profile.

RESULTS

We identified five candidate genes, including ESM1，CXCR4，JAG1，FLT1 and PTK2 and two pathways, including Rap1 signaling pathway and PI3K-Akt signaling pathway in vascular endothelial cells that differentiate into tip cells and stalk cells using bioinformatic analysis.

CONCLUSION

Bioinformatics approaches provide new avenues for basic research in different fields such as angiogenesis. The findings of this study provide new perspectives and basis for the study of molecular mechanisms of vascular endothelial cell differentiation into stalk and tip cells. Genes and pathways identified in this study are potential biomarkers and therapeutic targets for angiogenesis in tumor.

Identification of UBE2C as hub gene in driving prostate cancer by integrated bioinformatics analysis

BACKGROUND

The aim of this study was to identify novel genes in promoting primary prostate cancer (PCa) progression and to explore its role in the prognosis of prostate cancer.

METHODS

Four microarray datasets containing primary prostate cancer samples and benign prostate samples were downloaded from Gene Expression Omnibus (GEO), then differentially expressed genes (DEGs) were identified by R software (version 3.6.2). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to identify the function of DEGs. Using STRING and Cytoscape (version 3.7.1), we constructed a protein-protein interaction (PPI) network and identified the hub gene of prostate cancer. Clinical data on GSE70770 and TCGA was collected to show the role of hub gene in prostate cancer progression. The correlations between hub gene and clinical parameters were also indicated by cox regression analysis. Gene Set Enrichment Analysis (GSEA) was performed to highlight the function of Ubiquitin-conjugating enzyme complex (UBE2C) in prostate cancer.

RESULTS

243 upregulated genes and 298 downregulated genes that changed in at least two microarrays have been identified. GO and KEGG analysis indicated significant changes in the oxidation-reduction process, angiogenesis, TGF-beta signaling pathway. UBE2C, PDZ-binding kinase (PBK), cyclin B1 (CCNB1), Cyclin-dependent kinase inhibitor 3 (CDKN3), topoisomerase II alpha (TOP2A), Aurora kinase A (AURKA) and MKI67 were identified as the candidate hub genes, which were all correlated with prostate cancer patient' disease-free survival in TCGA. In fact, only UBE2C was highly expressed in prostate cancer when compared with benign prostate tissue in TCGA and the expression of UBE2C was also in parallel with the Gleason score of prostate cancer. Cox regression analysis has indicated UBE2C could function as the independent prognostic factor of prostate cancer. GSEA showed UBE2C had played an important role in the pathway of prostate cancer, such as NOTCH signaling pathway, WNT-β-catenin signaling pathway.

CONCLUSIONS

UBE2C was pivotal for the progression of prostate cancer and the level of UBE2C was important to predict the prognosis of patients.

Dysregulation of miR-3607 predicts prognosis of hepatocellular carcinoma and regulates tumor cell proliferation, migration and invasion

Background

Hepatocellular carcinoma (HCC) is one of the most common global malignancies with increasing morbidity and mortality. The purpose of this study was to investigate the expression levels and prognostic value of microRNA-3607 (miR-3607) in patients with HCC.

Methods

The expression of miR-3607 was estimated by quantitative real-time RT-PCR. Survival analysis using the Kaplan-Meier method and Cox regression analysis was conducted to evaluate the prognostic value of miR-3607. The functional role of miR-3607 in HCC progression was further assessed using gain- and loss-of-function experiments. Bioinformatics analysis and a dual-luciferase reporter assay were used to explore the direct targets of miR-3607.

Results

miR-3607 expression was found to be significantly decreased in HCC tissues and cells compared with the matched tissues and cells (P < 0.001). The decreased expression of miR-3607 was associated with the patients’ tumor size and TNM stage (all P < 0.05). According to the survival curves, patients with low miR-3607 expression had poorer overall survival than those with high levels (log-rank P = 0.012). Moreover, the Cox analysis results indicated that miR-3607 expression was an independent prognostic factor for HCC. The results of cell experiments revealed that the overexpression of miR-3607 in HCC cells led to the inhibited cell proliferation, migration, and invasion. TGFBR1 was identified as a direct target of miR-3607.

Conclusion

The data of this study indicated that the decreased expression of miR-3607 in HCC predicts poor prognosis and the overexpression of miR-3607 in HCC cells can suppress the tumor progression by targeting TGFBR1. This study provides a novel insight into the prognosis and treatment of HCC, and miR-3607 serves as a candidate prognostic biomarker and therapeutic target of HCC.

Identification of Candidate Genes and Therapeutic Agents for Light Chain Amyloidosis Based on Bioinformatics Approach

Objective

Systemic amyloid light chain (AL) amyloidosis is a rare plasma cell disease. However, the regulatory mechanisms of AL amyloidosis have not been thoroughly uncovered, identification of candidate genes and therapeutic agents for this disease is crucial to provide novel insights into exploring the regulatory mechanisms underlying AL amyloidosis.

Methods

The gene expression profile of GSE73040, including 9 specimens from AL amyloidosis patients and 5 specimens from normal control, was downloaded from GEO datasets. Differentially expressed genes (DEGs) were sorted with regard to AL amyloidosis versus normal control group using Limma package. The gene enrichment analyses including GO and KEGG pathway were performed using DAVID website subsequently. Furthermore, the protein–protein interaction (PPI) network for DEGs was constructed by Cytoscape software and STRING database. DEGs were mapped to the connectivity map datasets to identify potential molecular agents of AL amyloidosis.

Results

A total of 1464 DEGs (727 up-regulated, 737 down-regulated) were identified in AL amyloidosis samples versus control samples, these dysregulated genes were associated with the dysfunction of ribosome biogenesis and immune response. PPI network and module analysis uncovered that several crucial genes were defined as candidate genes, including ITGAM, ITGB2, ITGAX, IMP3 and FBL. More importantly, we identified the small molecular agents (AT-9283, Ritonavir and PKC beta-inhibitor) as the potential drugs for AL amyloidosis.

Conclusion

Using bioinformatics approach, we have identified candidate genes and pathways in AL amyloidosis, which can extend our understanding of the cause and molecular mechanisms, and these crucial genes and pathways could act as biomarkers and therapeutic targets for AL amyloidosis.

Increased expression of CD81 is associated with poor prognosis of prostate cancer and increases the progression of prostate cancer cells in vitro

CD81, a member of the tetraspanin family, has been revealed to be upregulated and associated with prognosis in several types of cancer; however, this relationship has not been explored in prostate cancer. The present study aimed to investigate the prognostic significance and functional role of CD81 in prostate cancer. The expression of CD81 in prostate cancer tissues and cell lines was evaluated using qRT-PCR analysis. Kaplan-Meier survival analysis and Cox regression analysis were conducted to explore the prognostic significance of CD81. Cell experiments were used to explore the effects of CD81 on cell proliferation, migration, and invasion in prostate cell lines in vitro. The expression of CD81 was increased in both prostate cancer tissues and cell lines. Upregulation of CD81 was significantly associated with lymph node metastasis and TNM stage. Moreover, patients with high CD81 levels had poorer overall survival than those with lower levels. Additionally, tumor cell proliferation, migration, and invasion were inhibited by knockdown of CD81. The present results indicated that CD81 plays an oncogenic role in prostate cancer. Overexpression of CD81 may serve as a prognostic biomarker and therapeutic target and is involved in the progression of prostate cancer.

Screening and identification of key biomarkers in prostate cancer using bioinformatics

Prostate cancer (PCa) is the second most common cancer amongst males worldwide. In the current study, microarray datasets GSE3325 and GSE6919 from the Gene Expression Omnibus database were screened to identify candidate genes that are associated with the progression of PCa. A total of 273 differentially expressed genes (DEGs) were identified, which included 173 downregulated genes and 100 upregulated genes, and a protein-protein interaction network was constructed using Search Tool for the Retired of Interacting Genes. The enriched functions and pathways of the identified DEGs included cell adhesion, the negative regulation of cell proliferation, protein binding and focal adhesion. A total of 8 hub genes were identified, of which PDZ binding kinase, Krüppel-like factor 4, collagen type XII α-1 chain, RAP1A and RAP39B were indicated to be associated with the progression and recurrence of PCa. In conclusion, the DEGs and hub genes identified in the present study may aid in determining the molecular mechanisms associated with PCa carcinogenesis and progression.

Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods

Background

Prostate cancer (PCa) is a common urinary malignancy, whose molecular mechanism has not been fully elucidated. We aimed to screen for key genes and biological pathways related to PCa using bioinformatics method.

Methods

Differentially expressed genes (DEGs) were filtered out from the GSE103512 dataset and subjected to the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein–protein interactions (PPI) network was constructed, following by the identification of hub genes. The results of former studies were compared with ours. The relative expression levels of hub genes were examined in The Cancer Genome Atlas (TCGA) and Oncomine public databases. The University of California Santa Cruz Xena online tools were used to study whether the expression of hub genes was correlated with the survival of PCa patients from TCGA cohorts.

Results

Totally, 252 (186 upregulated and 66 downregulated) DEGs were identified. GO analysis enriched mainly in “oxidation-reduction process” and “positive regulation of transcription from RNA polymerase II promoter”; KEGG pathway analysis enriched mostly in “metabolic pathways” and “protein digestion and absorption.” Kallikrein-related peptidase 3, cadherin 1 (CDH1), Kallikrein-related peptidase 2 (KLK2), forkhead box A1 (FOXA1), and epithelial cell adhesion molecule (EPCAM) were identified as hub genes from the PPI network. CDH1, FOXA1, and EPCAM were validated by other relevant gene expression omnibus datasets. All hub genes were validated by both TCGA and Oncomine except KLK2. Two additional top DEGs (ABCC4 and SLPI) were found to be associated with the prognosis of PCa patients.

Conclusions

This study excavated the key genes and pathways in PCa, which might be biomarkers for diagnosis, prognosis, and potential therapeutic targets.