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The authors have conducted excellent work on this manuscript, incorporating feedback from reviewers to enhance the clarity and depth of the content. They have successfully elucidated the role of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma. Thanks to the thoughtful integration of the reviewers' suggestions.
[# PeerJ Staff Note - this decision was reviewed and approved by Brenda Oppert, a PeerJ Section Editor covering this Section #]
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The authors have addressed my comments and have improved the manuscript. They have also cited recent literature. It can be accepted after minor English corrections and improvement.
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Dear authors,
We kindly request that you carefully review the comments provided by the reviewers. Their valuable suggestions offer insights to enhance your manuscript. Incorporate their suggestions and carefully address all comments in your manuscript; it will significantly strengthen its content.
[# PeerJ Staff Note: It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors are in agreement that they are relevant and useful #]
Abstract:
1. The abstract should mention the datasets (GSE1987 and GSE18842) used as well as the validation experiments (TCGA database and qPCR), but it does not mention the machine learning techniques employed. It is recommended that the authors include information about the use of Random Forest and LASSO regression analysis.
Introduction:
1. The introduction provides detailed background information on lung cancer and lung adenocarcinoma, which is appropriate. However, the literature review could be further strengthened, especially with recent studies most relevant to the current research.
2. The authors state "yet a substantial number await clinical verification" (line 41). Please provide examples.
3. The paper primarily focuses on lung adenocarcinoma; why is there a discovery of DEGs across different cancer types [16]? (lines 55-56)
4. It is recommended that the authors further elaborate on how the current study differs from previous ones and the anticipated novel contributions.
Methods:
1. The authors should provide sufficient information to ensure the transparency and reproducibility of the research process. It is recommended to include version information for all software, databases, and analytical tools used, as well as a description of the statistical analysis methods, including the tests used, how P-values were determined, and any necessary corrections.
2. More information should be provided regarding the specific machine learning algorithms used to identify biomarkers and the rationale behind their selection. Additionally, including a description of the statistical methods used to assess the significance of the identified biomarkers will enhance the reproducibility and transparency of the study.
Results:
1. In Figure 1C: "Venn diagram highlighting 89 overlapping up-regulated DEGs in the GSE1987 and GSE18842 datasets. D: Venn diagram showing 190 overlapping down-regulated DEGs in GSE1987 and GSE18842 datasets," it is suggested that the authors use different colors to represent up- and down-regulated genes to more clearly and intuitively show the different trends.
2. The results section should provide more background information on the functional importance of the identified DEGs and key genes and their dysregulation in the progression of lung adenocarcinoma.
Discussion:
1. In the discussion, the results should be explained in more detail, especially regarding the specific roles and mechanisms of the identified biomarkers in the occurrence and development of lung adenocarcinoma. This will help readers better understand the significance of the study.
2. The authors should discuss in depth how the Random Forest and LASSO regression analysis helped to identify the key genes BUB1B, CENPF, and PLK1, and explain the potential roles of these genes in the development of lung adenocarcinoma.
3. The discussion should include a comparison with existing studies, especially regarding the known roles of these genes in lung adenocarcinoma.
4. The discussion should mention the choice and results of statistical tests, including P-values and effect sizes, so that readers can assess the significance of the results.
5. In addition to the identification of biomarkers, the potential applications of these biomarkers in the clinical diagnosis, prognosis assessment, and therapeutic guidance of lung adenocarcinoma should also be discussed. This will make the discussion more comprehensive and meaningful.
6. It is recommended that the authors propose future research directions based on the current study results, which will help to promote further development in the field.
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what are the novelty, limitations, and perspectives of this work? the discussion should include these.
please merely don't heap up the data, more comparison and interpretation.
The manuscript requires significant editorial assistance, focusing on both grammars as well the overall structure of the manuscript such as the ordering of the paragraphs, paragraph structure and sentence structure.
More backgrounds and interpretations are required in the introduction and discussion, especially the main and novel findings from this study.
Detailed description of all method sections is required.
could the authors provide more solid data to strengthen the conclusion of this work?
please pay attention to the format of the English text.
This research is interesting research that uses machine learning (ML) technology in the identification of lung adenocarcinoma. Some comments.
Abstract:
The aim of this research is broad and not clear. Hence, it is better to add modify and make the aim clear.
The line “The expression of genes increased by 89 and decreased by 190.” It is not clear. Need more explanation.
Introduction
It is better to add the prevalence of lung carcinoma and lung adenocarcinoma in Asia and in China.
It is better to add details on Machine learning and its importance in medicine.
There are some study already published recently.
doi: 10.1016/j.bbrep.2024.101693
doi: https://doi.org/10.1101/2023.11.25.568645
https://doi.org/10.1016/j.ajpath.2022.06.015
[# PeerJ Staff Note: It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors are in agreement that they are relevant and useful #]
Add the importance and significance of this study. What are the differences from the previous research?
Discussion
A previous study (doi: 10.1016/j.bbrep.2024.101693) found “SH3GL2 and MMP17 to be potential biomarkers for Lung adenocarcinoma. Why in this research these genes not detected as biomarkers?
[# PeerJ Staff Note: It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors are in agreement that they are relevant and useful #]
Comparison and discussion are needed with similar recent studies.
Add more on the limitations of machine learning in the detection of the genes.
In addition, English correction needs to be done in the Abstract and throughout the manuscript.
Significant improvement of the manuscript is needed.
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