Title: Prognostic Assessment Capability of a Five-Gene Signature in Pancreatic Cancer: A
Machine Learning Based-Study
Version: 2 Date: 30 Jan 2023
Reviewer:  Jeong Hoon Lee
Reviewer's report:
Dear Authors, many thanks for the opportunity to read your valuable paper.
Pancreatic cancer is a type of cancer with very rare data and a very poor prognosis.
Survival analysis using mRNA expression in TCGA database has been performed quite a lot.
However, due to the absence of normal samples in the TCGA database,
comparative studies with cancer and normal samples from the same organ were lacking.
In this study, mRNA prognostic markers of pancreatic cancer were identified by merging
multiple databases.
This manuscript is very well written, easy to read and understand.
I have only to suggest to you some minor comments, and some my question.
1. Pege 5, 3.3. Removing batch effects.
Have you performed PCA on all 20,000 genes?
If possible, could you provide a figure utilizing umap rather than PCA?
And there are no Abbreviations for PCA.
Removing the batch effect is very important, but I am curious whether it is right or wrong that
there is no difference between the cancer sample and the normal sample in the PCA results.
2. Regarding Figure 1, 3 and 5, 6, can you make it a little prettier?
It is unfortunate that the size of the lines and dotted lines is unbalanced.
But the content of the w is very good.
In particular, kaplan meier is too long vertically.
3. Regarding fig 6 and fig 7.
When specificity increases, but there is a section where sensitivity increases.
This is very strange.
4. In a database where TNM stages are available, can you provide the results of coxph regression
analysis of 5 genes and TNM stages together?
TNM stage is one of the most powerful prognosis factors, but I wonder if gene expression can
complement TNM stage.
Title: Prognostic Assessment Capability of a Five-Gene Signature in Pancreatic Cancer: A
Machine Learning Based-Study
Version: 2 Date: 30 Jan 2023
Reviewer:  Jeong Hoon Lee
Reviewer's report:
Dear Authors, many thanks for the opportunity to read your valuable paper.
Pancreatic cancer is a type of cancer with very rare data and a very poor prognosis.
Survival analysis using mRNA expression in TCGA database has been performed quite a lot.
However, due to the absence of normal samples in the TCGA database,
comparative studies with cancer and normal samples from the same organ were lacking.
In this study, mRNA prognostic markers of pancreatic cancer were identified by merging
multiple databases.
This manuscript is very well written, easy to read and understand.
I have only to suggest to you some minor comments, and some my question.
1. Pege 5, 3.3. Removing batch effects.
Have you performed PCA on all 20,000 genes?
If possible, could you provide a figure utilizing umap rather than PCA?
And there are no Abbreviations for PCA.
Removing the batch effect is very important, but I am curious whether it is right or wrong that
there is no difference between the cancer sample and the normal sample in the PCA results.
2. Regarding Figure 1, 3 and 5, 6, can you make it a little prettier?
It is unfortunate that the size of the lines and dotted lines is unbalanced.
But the content of the w is very good.
In particular, kaplan meier is too long vertically.
3. Regarding fig 6 and fig 7.
When specificity increases, but there is a section where sensitivity increases.
This is very strange.
4. In a database where TNM stages are available, can you provide the results of coxph regression
analysis of 5 genes and TNM stages together?
TNM stage is one of the most powerful prognosis factors, but I wonder if gene expression can
complement TNM stage.
Please review this paper.
user
Title: Prognostic Assessment Capability of a Five-Gene Signature in Pancreatic Cancer: A
Machine Learning Based-Study
Version: 2 Date: 30 Jan 2023
Reviewer:  Jeong Hoon Lee
Reviewer's report:
Dear Authors, many thanks for the opportunity to read your valuable paper.
Pancreatic cancer is a type of cancer with very rare data and a very poor prognosis.
Survival analysis using mRNA expression in TCGA database has been performed quite a lot.
However, due to the absence of normal samples in the TCGA database,
comparative studies with cancer and normal samples from the same organ were lacking.
In this study, mRNA prognostic markers of pancreatic cancer were identified by merging
multiple databases.
This manuscript is very well written, easy to read and understand.
I have only to suggest to you some minor comments, and some my question.
1. Pege 5, 3.3. Removing batch effects.
Have you performed PCA on all 20,000 genes?
If possible, could you provide a figure utilizing umap rather than PCA?
And there are no Abbreviations for PCA.
Removing the batch effect is very important, but I am curious whether it is right or wrong that
there is no difference between the cancer sample and the normal sample in the PCA results.
2. Regarding Figure 1, 3 and 5, 6, can you make it a little prettier?
It is unfortunate that the size of the lines and dotted lines is unbalanced.
But the content of the w is very good.
In particular, kaplan meier is too long vertically.
3. Regarding fig 6 and fig 7.
When specificity increases, but there is a section where sensitivity increases.
This is very strange.
4. In a database where TNM stages are available, can you provide the results of coxph regression
analysis of 5 genes and TNM stages together?
TNM stage is one of the most powerful prognosis factors, but I wonder if gene expression can
complement TNM stage.
assistant