Establishment of two new predictive models for prostate cancer to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone (4–10 ng ml<sup>−1</sup>)

Liu, Jun; Wang, Zhiqian; Li, Min; Zhou, Mingyang; Yu, Yang; Zhan, Weiwei

doi:10.4103/aja.aja_46_19

Cited by 22 publications

(10 citation statements)

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“…At present, prostate specific antigen (PSA) has been the most routinely used biomarker for the screening and monitoring of PCa (Gandaglia et al, 2019). However, elevated PSA does not necessarily indicate PCa and often leads to false positive results as well as overdiagnosis since it can also be seen in other benign lesions such as prostatitis or benign prostatic hyperplasia (Inahara et al, 2006; Liu et al, 2019b). Therefore, excavating reliable and effective biomarkers and learning hub genes involved in the biological process (BP) of PCa is urgently needed.…”

Section: Introductionmentioning

confidence: 99%

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

Duan

Zeng

2019

PeerJ

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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.

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Section: Introductionmentioning

confidence: 99%

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

Duan

Zeng

2019

PeerJ

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“…On this issue, many scholars have tried to use regression analysis to construct models to predict PCa. Liu et al used PSA and PSA derivatives to build a prediction model of PCa in 197 patients with PSA in the gray area, which predicted the sensitivity and specificity of PCa to be 75.4% and 75.8%, respectively [ 18 ]. However, that study did not consider imageology examination.…”

Section: Discussionmentioning

confidence: 99%

“…NCS-PCa could be monitored closely without surgical intervention, which has a good prognosis and a low mortality rate, while high-risk PCa patients must be actively treated [ 37 , 38 ]. Therefore, accurate identification of low-risk PCa patients could reduce the risk of performing unnecessary invasive biopsies, without increasing the risk to patient health [ 18 ]. In the present study, we developed a satisfactory diagnostic model for predicting NCS-PCa and CS-PCa, whose sensitivity and specificity were 71.41% and 81.82%, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…In the present study, we developed a satisfactory diagnostic model for predicting NCS-PCa and CS-PCa, whose sensitivity and specificity were 71.41% and 81.82%, respectively. Compared with the model of Jun Liu et al, who just analyzed PSA and its derivatives for CS-PCa, our prediction model also included mpMRI and had a better detection value (AUC: 0.82 vs 0.87) [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

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Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Chen

et al. 2021

Med Sci Monit

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Background Two diagnostic models of prostate cancer (PCa) and clinically significant prostate cancer (CS-PCa) were established using clinical data of among patients whose prostate-specific antigen (PSA) levels are in the gray area (4.0–10.0 ng/ml). Material/Methods Data from 181 patients whose PSA levels were in the gray area were retrospectively analyzed, and the following data were collected: age, digital rectal examination, total PSA, PSA density (PSAD), free/total PSA (f/t PSA), transrectal ultrasound, multiparametric magnetic resonance imaging (mpMRI), and pathological reports. Patients were diagnosed with benign prostatic hyperplasia (BPH) and PCa by pathology reports, and PCa patients were separated into non-clinically significant PCa (NCS-PCa) and CS-PCa by Gleason score. Afterward, predictor models constructed by above parameters were researched to diagnose PCa and CS-PCa, respectively. Results According to the analysis of included clinical data, there were 109 patients with BPH, 44 patients with NCS-PCa, and 28 patients with CS-PCa. Regression analysis showed PCa was correlated with f/t PSA, PSAD, and mpMRI ( P <0.01), and CS-PCa was correlated with PSAD and mpMRI ( P <0.01). The area under the receiver operating characteristic curves of 2 models for PCa (sensitivity=73.64%, specificity=64.23%) and for CS-PCa (sensitivity=71.41%, specificity=81.82%) were 0.79 and 0.87, respectively. Conclusions The prediction models had satisfactory diagnostic value for PCa and CS-PCa among patients with PSA in the gray area, and use of these models may help reduce overdiagnosis.

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“…At present, prostate specific antigen (PSA) has been the most routinely used biomarker for the screening and monitoring of PCa (Gandaglia et al 2019). However, elevated PSA does not necessarily indicate PCa and often leads to false positive results as well as overdiagnosis since it can also be seen in other benign lesions such as prostatitis or benign prostatic hyperplasia (BPH) (Inahara et al 2006;Liu et al 2019b).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

2019

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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 UCSC 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'. KLK3, CDH1, KLK2, FOXA1, and EPCAM were identified as hub genes from the PPI network. CDH1, FOXA1, and EPCAM were validated by other relevant GEO 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.

show abstract

Establishment of two new predictive models for prostate cancer to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone (4–10 ng ml⁻¹)

Cited by 22 publications

References 5 publications

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

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

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

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Establishment of two new predictive models for prostate cancer to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone (4–10 ng ml−1)

Cited by 22 publications

References 5 publications

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

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

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

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Establishment of two new predictive models for prostate cancer to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone (4–10 ng ml⁻¹)