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I can confirm the authors have addressed all the reviewers' comments. I have checked all the reviewers' concerns and checked the revisions made by the authors, and am happy with the revised version. This is ready for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
Authors have revised most issues in the study and discussed limitations, it could be accepted for publication.
Rigorous investigation performed to a high technical & ethical standard, methods described with sufficient detail & information to replicate.
All underlying data have been provided, the data could support their conclusion.
N/A
It is my opinion as the Academic Editor for your article - Analysis of Molecular Subtypes and Prognostic Signature of Senescence-Associated Secretory Phenotype in Pancreatic Cancer - that it requires some revision.
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
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The study of Yuewen Kuang et all, reported a well-designed and timely study that combines bioinformatics and functional validation to investigate the prognostic value of SASP-related genes in pancreatic cancer. The manuscript is scientifically sound and provides a theoretical foundation for further research on personalized therapeutic strategies.
no comment
I will suggest to expand the discussion on how combination therapies targeting SASP pathways could be translated into clinical practice.
-Improve language clarity throughout the manuscript with minor editing.
-SAPS genes resulted to be 83 in the results description and 66 in the workflow.
-Figure 1 workflow should be a schema and not steps of analysis with figures that are not readable.
-Enlarge Figure 1 caption.
-What is the reason of showing Figure 2B? Do you conclude anything from the chromosomal position?
-Lasso p-values should be corrected for multiple test.
-All figures must be in high resolution.
This study focuses on the senescence-associated secretory phenotype (SASP) in pancreatic cancer, conducting research from multiple aspects such as molecular subtype analysis, prognostic signature construction and validation, tumor mutation and immune microenvironment analysis, and functional verification of key genes. It aims to explore the mechanism of action and clinical application value of SASP in pancreatic cancer. The English expression of manuscript is suggested to be polished and improve the research logical.
The experimental design is reasonable and data presented in this study is sufficient, methods were described in details.
Conclusions are well stated,but some research is necessary for investigation.
This study focuses on the senescence-associated secretory phenotype (SASP) in pancreatic cancer, conducting research from multiple aspects such as molecular subtype analysis, prognostic signature construction and validation, tumor mutation and immune microenvironment analysis, and functional verification of key genes. It aims to explore the mechanism of action and clinical application value of SASP in pancreatic cancer. However, many background information should be provided to support their conclusion, the manuscripts was suggested to be thoroughly polished to improve the logical and readability. Many comments were listed as following:
Major revision：
1. In lines 70-71, the study divides PC patients from the TCGA-ICGC cohort into two SASP subtypes, but does not explain the classification basis, whether based on gene expression or mutation frequency, Fig 2E showed the PCA clarification of two clusters, circles or other indicators were suggested to be added, making more obvious distinguish distribution between cluster A and B.
2. The discrimination for results of manuscript seems too simple and many detailed information was missed. For example, Fig2H showed the GSVA results, while they didn’t mentioned the original of genes for GSVA analysis. Then Fig 2I showed the 381 DEGs between Cluster A and B, whether the GSVA results were generated based on these 381 DEGs? If so, the GSVA results should be followed as 2I.
3. Line 151-153：The GO and KEGG analysis should be clarified to make this sentence reasonable.
4. Univariate Cox regression identified 33 prognostic SASP genes in Fig 3A, LASSO regression derived a 7 gene signature to clarify into low- and high-risk patients. Why 7 were selected for clarification should be explained.
5. The low- and high-risk patients were used to further analysis, these patients were based on cluster A or B or A plus B? what’s the relationship between low/ high-risk patients with cluster A/B based on SASP? The difference phenotype of SASP in low-risk and high-risk pattern should be clearly mentioned to highlight the significance of SASP in tumorigenesis or risk.
6. In lines 159-161, the risk score formula is given, but the score range is not provided.
7. In lines 183-188, the results are analyzed, but the mechanism is not explained in combination with the characteristics of SASP clusters.
8. In lines 190-197, functional experiments verify the carcinogenic effect of ANGPTL4, but fail to explain the direct association between this gene and SASP. It is suggested to supple simple elucidation for ANGPTL4 in SASP and senescence.
9. Only the function of ANGPTL4 has been verified, while the functions of the other 6 genes (DKK1, EREG, IGFBP3, IL18, MMP13, PLAU) in the 7-gene prognostic signature constructed have not been verified through experiments, making it impossible to clarify whether these genes have similar carcinogenic or tumor-suppressive effects in the occurrence and development of pancreatic cancer.
10. In Fig 7, the author analyzed the role of ANGPTL4 in PC, but there are many errors in the figures. Many results mentioned about ANGPTL4 in manuscript were labeled with MMP13. Authors should check it carefully.
11. In Fig. 8, when verifying the function of ANGPTL4, it is only observed that it can promote the proliferation, migration and invasion of pancreatic cancer cells, but the molecular mechanism of its action has not been explored in depth. The senescent and SASP phenotype of PCs should be investigated to connect the associated between SASP with tumorigenesis.
12. In Figs. 1-8, the pictures are blurred when enlarged, making it difficult to see the bioinformatics part clearly.
Due to the lack of biological novelty, insufficient mechanistic validation, potential methodological issues, and overinterpretation of data, I do not recommend publication of this manuscript in its current form. A complete redesign with deeper experimental validation and clinically meaningful endpoints would be necessary for reconsideration.
Redundancy and Overstatement of Conclusions
The authors often overstate the clinical utility of their model and the biological importance of their SASP clusters without providing experimental causality or prospective validation.
1. Lack of Biological Novelty and Mechanistic Insight: The study presents a computational analysis of senescence-associated secretory phenotype (SASP) genes in pancreatic cancer, resulting in the identification of molecular subtypes and a prognostic signature. While SASP has been widely studied, the authors do not present sufficient mechanistic evidence to support the biological relevance or novelty of their findings. Many of the conclusions drawn remain correlative, without causal validation. ANGPTL4 is proposed as a key factor, yet its direct role in SASP-mediated pancreatic cancer progression is neither experimentally nor mechanistically elucidated.
2. Over-reliance on Public Databases and Inadequate Experimental Validation: The prognostic model is constructed using TCGA and ICGC datasets, and although it is statistically supported, the results lack independent validation in external clinical cohorts. Moreover, wet-lab validation is limited to ANGPTL4 functional assays, which do not directly test SASP involvement.
1. Methodological Concerns and Potential Overfitting
The manuscript describes the use of LASSO Cox regression and consensus clustering to generate subtypes and a 7-gene risk model. However:
1.There is no rigorous assessment of overfitting, especially given the modest AUC values (~0.6–0.7).
2.Batch effect correction, data normalization, and training/test splits are inadequately documented or justified.
3.The GSVA pathway enrichment results are mentioned without orthogonal validation.
2. Insufficient Clarity in Model Construction and Clinical Translation
The risk model is not explained with sufficient clinical interpretability. There is no evidence that the model offers added prognostic value over existing clinical parameters such as AJCC staging, CA19-9 levels, or molecular features (e.g., KRAS/TP53 status).
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