Review Reports

Round 1

Reviewer 1 Report

Dada et al. investigated whether miRNA molecules in plasma extracellular vesicles could serve as molecular biomarkers for in vitro diagnosis of prostate cancer in a South African population. The authors utilized miRNA molecules from the TCGA database in prostate cancer and validated seven candidate miRNA molecules in plasma extracellular vesicles from prostate cancer patients and control groups. The results indicated that the ratio of miR-194-5p/miR-16-5p can be used as a biomarker to assess metastatic invasion in prostate cancer patients in the South African population. This is an interesting biomarker screening study, but the authors need to address the following issues to improve the quality of the paper:

1. The authors need to explain why the control group consisted of patients rather than healthy individuals.

2. In Figure 1A, it is observed that the sizes of extracellular vesicles from the BPH group and PCa group are not consistent. At least, provide data spectra of extracellular vesicle diameters to support this observation.

3. In Table 2, please avoid using "×" to represent the X symbol, and ensure consistency in displaying all data with two decimal places.

4. Whether the authors' research findings can be validated in the EVmiRNA or miRandola databases.

5. The authors should utilize ROC curves to analyze the sensitivity of the biomarkers.

 

Minor editing of English language required

Author Response

The replies are attached

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments for the authors:

1. There is no reference in 70, please provide it here.

2. In line 72, please provide some examples of cancer-related molecules

here.

3. In line 99, EVs were isolated using the Invitrogen Total Exosomes

Isolation Kit. Please specify if any specific procedure or

considerations were used for the isolation, as it would help in

understanding the isolation process in better sense.

4. how much EVs proteins were loaded on SDS-PAGE?. It

6. In this paragraph about small RNA sequencing in plasma EVs, all the

methods were performed using a kit, but there is a need to clarify certain

things, such as the type of EVs referred to in line 114, the specific

parameters assessed on the Agilent Technologies 2100 Bioanalyzer in

line 117, and it would be beneficial to provide a reference or specify the

version of the BBMap package used for merging the overlapping paired-

end reads in line 122.

7. The paragraph provides information on how to identify RNA and analyze

miRNA in lines 124-127. However, it requires some concise details and

explanations, such as how Oasis 2.0 is used for RNA analysis and the

relevance of tools like the Bioconductor package miRBaseConverter and

TMM scaling.

8. The TCGA prostatic tissue miRNA, which is mentioned in lines 129-135,

requires further information. For example, line 131 states the need to

elaborate on the guidelines and restrictions to better understand the

context in which the analysis was performed.

9. It is necessary to make the conclusion paragraph more informative

10. In the introduction, there is a need to provide some more information

about prostate cancer.

11. EVs includes both microvesicles and exosomes,usually, exosomes are in 40-15nm in size, and microvesicles also ranging from 30-1000nm. The mostly markers uesed here are for exosomes, How authors confirmed microvesicles.  

Overall, the work is very interesting. 

Some english grammer needs to be fixed  

Author Response

The replies are attached

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments to Authors

In this study of Temilola et al., the authors aimed to analyze the expression of microRNAs (miRNAs) contained in extracellular vesicles purified from the blood plasma of South African patients with different prostate cancer aggressiveness. The general idea is attractive, and several analyses were conducted with interesting results. However, the authors perform bioinformatic analyses that make it difficult to follow the aim of the work.

-          The Gleason scale score is an important starting point for the analyses performed in this work. In this regard, the authors should better contextualize in the introduction whether this clinical grading scale can explain the pathophysiological mechanisms associated with prostate cancer progression.

-          The authors begin with sequencing data performed on 3 patients with low Gleason scores and 3 patients with high Gleason scores. However, no patient with benign prostatic hyperplasia or healthy prostate is considered in this first analysis. The authors should explain excluding a control condition to compare the sequencing data since the miRNAs they find differentially expressed in patients with low and high Gleason scores may not necessarily differ in the absence of disease.

-          The characterization of EVs should be more precise. TEM images do not have a calibration bar, so it is difficult to know how they conclude the size of the vesicles. Also, by this technique, it is difficult to determine the size of the EVs because the way the sample is taken for microscopy could not be considered the total population of EVs in the plasma obtained from patients. The authors should consider another technique, such as Dynamic Light Scattering, to determine vesicle size or add it as a limitation of this study.

-          The authors need to explain better that the database they used as reference (TCGA-PRAD) is expression data of miRNAs in general and not contained in vesicles. This is important because when they describe that only 7 miRNAs are downregulated in both cohorts (Line 208-209), it is not clear that those two cohorts they refer to 1) differentially expressed miRNAs between low and high Gleason score of TGCA-PRAD and 2) differentially expressed miRNAs between low and high score from EVs purified from patient samples.

The authors perform several bioinformatics analyses, which could be important for elucidating the molecular mechanism of EVs in prostate cancer progression; however, these analyses are not crucial in the diagnostic context for identifying potential biomarkers. Therefore, these results are converted to an extensive list of genes as supplementary material.

 

-The order of the results also does not seem clear to understand which validations they finally decided to do experimentally.

-          Given that the authors have arrived at a list of 7 candidate miRNAs differentially expressed between low and high Gleason scores and that their expression is confirmed using the TGCA-PRAD base, it is unclear why, for the following interatomic analyses, the authors back to the complete list of 65 differentially expressed miRNAs before comparing with the TGCA-PRAD base. The authors should consider justifying this decision as it does not seem purposeful and confounds the logical order of the results. If the goal is to obtain a list of DEG miRNAs validated by TGCA-PRAD, it looks more straightforward to analyze the 185 deregulated miRNAs (Table S3) to identify target genes. In fact, from the list of 340 candidates (Table S6), they identified 112 candidate target genes from the 7 differentially expressed miRNAs between low and high Gleason scores (Table S7).

-          What is the purpose of reanalyzing the target genes for each of the seven miRNAs when they already have a pool of 112 target genes shared by all seven miRNAs? This change in approach confounds and detracts from the relevance of many of the previous results. 

-          The list of interactions classified as weak and strong does not seem to have relevance for this study for two reasons: 1) it is not specified which interactions are strong and weak and which criteria they took as a cutoff point, and 2) the list derived from the 51 miRNAs (Table S5) is not used in another analysis.

-          On the other hand, the list of interactions of the seven candidate miRNAs classified as weak and strong (Table S9) also does not specify which ones are of each type and the cutoff point.

-          At this point, I recommend considering only the 112 target genes shared by the seven miRNAs (Table S7) for the following analyses or the strong and weak interactions of the seven miRNAs after justifying the cutoff point criteria.

Most of the results are presented as supplementary tables. However, some results could be interestingly represented within the article graphically. Some of them are:

-          Differentially expressed miRNAs from high and low Gleason score

-          Wikipathways enrichment analysis for the 112 targeted genes by the seven candidate miRNAs

In the experimental validation, its mentioned miR-424 was included in the analyses. Is this a differentially expressed miRNA in any of your results?

Supplementary Figure 1 was not included, but from the description of the results, they mention not having found differences between groups. What is the purpose of continuing with the pairwise correlation analysis of miRNAs, and why was miR-16-5p used as an endogenous reference for the expression of miRNAs in extracellular vesicles? Continuing with a correlation analysis does not make much sense if no expression differences exist between groups.

In the description of the results in Supplementary Figure 1, absolute expression values are mentioned. Did you consider analyzing the relative expression values compared to the control group, in your case, patients with benign prostatic tumors?

Discussion

-          The authors did not perform any ROC curve analysis to determine the sensitivity, specificity, and predictive values of miRNAs as potential diagnostic biomarkers or the stratification of patients according to the aggressiveness of prostate cancer. Therefore, the discussion should be mainly oriented to the results obtained.

-          How can the discrepancy between the results of the seven differentially expressed candidate miRNAs by sequencing analysis and qPCR validation be explained?

-          Several bioinformatics analyses are not discussed in this section. What was the purpose of obtaining so many different lists of target genes?

-          The authors mention that the miR-194-5p/miR-16-5p ratio is a suitable ratio to represent differences in expression between groups, in favor that miR-16 has been previously reported as an endogenous marker to normalize qPCR results. However, miR-16-5p is one of the seven differentially expressed candidate miRNAs. This is counterproductive to be considered an expression reference value.

Minors

-The name miR-194-5p is duplicated (Line 274)

-The latest results described in Lines 297-299 do not refer to any figure or table.

-Add to Table S18 a column with the legend of the group to which each subject/ID belongs.

-Figure 3E describes miR-194-1 and miR-194-2. What is the difference between them?

 

Overall, there are significant areas to review in the results considered in this study, their interpretation and discussion, and minor comments to attend in general in the manuscript to conclude that on this initial state, the manuscript should be considered with significant comments to help the authors to produce a more impactful and reliable study.  

Author Response

The replies are attached

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors addressed the comments nicely, Therefore, I recommend it for publication in this journal.   

Reviewer 3 Report

The authors considered the comments and observations made to the manuscript; even the manuscript has been significantly improved. Therefore, the manuscript is ready for publication.