Erratum: Improvement of Prostate Cancer Diagnosis by Detecting PSA Glycosylation-Specific Changes: Erratum

miR-455-3p has superior diagnostic potential to PSA in peripheral blood for prostate cancer

BACKGROUND

Prostate-specific antigen (PSA) is commonly used as a biomarker to diagnose and predict the course of prostate cancer (PCa). However, PSA detection is susceptible to changes in the physiologic environment, which may lead to some misdiagnosis. Thus, it is crucial to find a novel diagnostic marker.

METHODS

We accessed microRNA (miRNA) expression datasets (GSE206793 and GSE112264) from the GEO database, analyzing peripheral blood samples from PCa patients. Differentially expressed miRNAs (DEmiRNAs) were identified using GEO2R. A specific miRNA, miR-455-3p, was pinpointed through rigorous analysis of clinical correlations and ROC curves. Peripheral blood samples from healthy individuals and PCa patients were subjected to qRT-PCR validation, aligning results with the GSE206793 dataset. The miRWalk database was utilized to predict downstream genes, while STRING facilitated the construction of a protein-protein interaction (PPI) network. KEGG pathway analysis enriched our understanding of potential molecular pathways.

RESULTS

We found that miR-455-3p was highly expressed in the peripheral blood of PCa patients with Gleason score (GS) ≥ 8, while independent of T stage, age and PSA. ROC analysis revealed a favorable diagnostic efficacy of miR-455-3p and AUC for the two datasets was respectively 0.943 and 0.847. The qRT-PCR assay also revealed consistent results. Interestingly, the PSA levels of P1 (GS = 5 + 4) and P6 (GS = 3 + 3) were respectively 3.38 and 4.45 ng/ml, while miR-455-3p was highly expressed in both, suggesting its low misdiagnosis. The speculation was validated in GSE206793 dataset. Finally, 9 potential targets of miR-455-3p were predicted. PPI network revealed PPP2R2A, ITGB1 and CDKN1A as key nodes. KEGG pathway analysis revealed that they were enriched in various cancers, biological processes and molecular signals.

CONCLUSION

Our study identifies miR-455-3p as a promising diagnostic marker for PCa, outperforming PSA in terms of specificity and sensitivity. The robustness of miR-455-3p, coupled with its potential downstream targets and associated pathways, highlights its clinical significance for improved PCa diagnosis and management.

Capitalizing glycomic changes for improved biomarker-based cancer diagnostics

Cancer serum biomarkers are valuable or even indispensable for cancer diagnostics and/or monitoring and, currently, many cancer serum markers are routinely used in the clinic. Most of those markers are glycoproteins, carrying cancer-specific glycan structures that can provide extra-information for cancer monitoring. Nonetheless, in the majority of cases, this differential feature is not exploited and the corresponding analytical assays detect only the protein amount, disregarding the analysis of the aberrant glycoform. Two exceptions to this trend are the biomarkers α-fetoprotein (AFP) and cancer antigen 19-9 (CA19-9), which are clinically monitored for their cancer-related glycan changes, and only the AFP assay includes quantification of both the protein amount and the altered glycoform. This narrative review demonstrates, through several examples, the advantages of the combined quantification of protein cancer biomarkers and the respective glycoform analysis, which enable to yield the maximum information and overcome the weaknesses of each individual analysis. This strategy allows to achieve higher sensitivity and specificity in the detection of cancer, enhancing the diagnostic power of biomarker-based cancer detection tests.

FCGR3A: A new biomarker with potential prognostic value for prostate cancer

To screen target gene cluster by bioinformatics analysis and verify them by in vitro experiment and clinicopathological correlation analysis. We try to find a new biomarker with prognostic value for prostate cancer (PCa). 42 candidate marker genes were constructed by protein protein interaction (PPI) network and enriched by KEGG pathway to find out the gene cluster we are interested in. Prognostic model was established to preliminarily analyze the prognostic value of this gene cluster in PCa, and Cox risk regression was used for comparative analysis. Immunohistochemistry was used to detect the expression of each gene in clinical tissue microarray. Finally, we analyzed the correlation between each gene and their clinicopathological features of PCa combined with TCGA clinical data. Based on the analysis of PPI and KEGG, we found the target gene cluster (FCGR3A, HAVCR2, CCR7 and CD28). Prognostic model analysis showed that this gene cluster had the ability to predict biochemical recurrence, and the survival rate and ROC analysis showed favorable prediction effect. Univariate Cox regression analysis showed that the risk scores of Gleason score (GS), T stage, N stage and PSA were significantly different (P<0.05), and the risk ratio of high expression was 2.30 times that of low expression (P=0.004). However, it was not statistically significant in multivariate Cox regression analysis (P>0.05). The results of tissue microarray showed that FCGR3A and HAVCR2 were highly expressed in PCa (P<0.01), while the expression of CCR7 and CD28 had no significant difference (P>0.05). Kaplan-Meier analysis showed that there was significant difference in BCR free survival of FCGR3A and HAVCR2 (FCGR3A, P=0.010; HAVCR2, P=0.018), while the expression of CCR7 and CD28 had no significant difference on the survival and prognosis of PCa patients (P>0.05). TCGA clinical data analysis found that the expression of FCGR3A had a unique correlation with the clinicopathological features of PCa, which was closely related to the tumor stage. The expression of FCGR3A is related to BCR free survival of PCa patients. Therefore, FCGR3A is a new biomarker with potential prognostic value of PCa.