
==== Front
Int J Surg
Int J Surg
JS9
International Journal of Surgery (London, England)
1743-9191
1743-9159
Lippincott Williams & Wilkins Hagerstown, MD

IJS-D-24-01865
10.1097/JS9.0000000000001654
00082
3
Correspondence
Letter to the Editor ‘Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study’
Deng Dawei PhD ddwtougao@163.com
a
Zhang Chuan PhD bcbyzc@qq.com

Yi Pengsheng PhD a*15208207079@163.com

Yang Hanfeng PhD b*yhf5@nsmc.edu.cn

a Department of Hepato-biliary-pancreas, Affiliated Hospital of North Sichuan Medical College
b Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, People’s Republic of China
* Corresponding authors. Address: Department of Hepato-biliary-pancreas, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, People’s Republic of China. E-mail: 15208207079@163.com (P. Yi), and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, People’s Republic of China. E-mail: yhf5@nsmc.edu.cn (H. Yang).
9 2024
20 5 2024
110 9 59015902
6 5 2024
8 5 2024
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/

OPEN-ACCESSTRUE
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pmc Dear Editor,

We read the recent high-quality research article titled ‘Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study’ by Xu et al.1 with great interest. Previous studies have suggested a close association between S100 family genes and inflammatory diseases of the pancreas, as well as with tumor-related conditions2,3. However, there has been a lack of robust bioinformatic studies to support these findings. This study fills a gap in the current field, particularly highlighting the association between the S100 gene family and pancreatic cancer. Future research will aim to target core molecules within the S100 family to improve the prognosis of pancreatic cancer patients. Nonetheless, we have additional insights worth considering, which may further optimize research protocols and offer greater benefits for pancreatic cancer patients in the future.

Firstly, the authors employed the LASSO regression method to construct a pancreatic adenocarcinoma prognostic model related to S100 family genes, which is innovative to some extent; however, LASSO regression may not necessarily be the optimal modeling approach. Previously, Liu et al.4 proposed a highly informative machine learning network to assist in the development of prognostic models. Within this network, 10 mainstream machine learning algorithms can be integrated, including but not limited to survival support vector machine (survival-SVM), random survival forest (RSF), supervised principal components (SuperPC), Lasso, Ridge, partial least squares regression for Cox (plsRcox), stepwise Cox, generalized boosted regression modeling (GBM), elastic network (Enet), and CoxBoost. During the development of this prognostic model, utilizing the aforementioned machine learning methods to train pancreatic cancer cohorts separately may lead to an improved prognostic model scheme.

Secondly, all sequencing data of pancreatic cancer patients in this study were obtained from public databases. Although the authors made every effort to integrate data from different databases to maximize the sample size for analysis, the prognostic model undoubtedly lacked prospective validation in a clinical real-world setting. Compared to retrospective analyses from databases, results from prospective cohorts may be more authentic, and therefore this aspect warrants attention. In addition, the clinical data provided by these public databases are often quite limited. Only a small portion of samples include relatively complete clinical information, while most samples lack comprehensive and reliable clinical data, which is an objective phenomenon unrelated to the authors. Due to the lack of corresponding clinical information, a stratified analysis of the S100 family prognostic model for pancreatic cancer, such as a stratified analysis for populations receiving neoadjuvant chemotherapy or one based on geographical characteristics of populations, cannot be conducted.

Thirdly, with the rapid development of bioinformatics, there have been several similar reports on the creation of pancreatic cancer prognostic models. For example, models based on types of cell death (apoptosis, necroptosis, ferroptosis, and pyroptosis), immune cell infiltration characteristics (such as neutrophils and T cells), or cellular metabolism pathways (glycolysis, amino acid metabolism, and fatty acid metabolism) have been constructed. If the authors compared their independently developed prognostic model with other available models, it could further highlight the clinical application potential of this study.

Fourthly, pancreatic cancer is often diagnosed at an advanced stage due to the lack of clear clinical features and effective early screening methods. This leads to a loss of surgical options for most patients, which is very unfortunate. Currently, the identification of molecular markers for the early detection of pancreatic cancer is eagerly anticipated. In this study, the authors established prognostic molecular markers for pancreatic cancer based on expression profiles of the S100 gene family. Whether these markers can be applied to the early detection of pancreatic cancer remains unknown, and large-scale multicentre clinical studies are anticipated to overcome this challenge.

In conclusion, the authors have highlighted a close association between S100 family genes and pancreatic cancer by integrating transcriptome and single-cell sequencing data. We look forward to future molecular biology studies elucidating the specific roles and mechanisms of S100 family genes in pancreatic cancer.

Ethical approval

Not applicable.

Consent

Not applicable.

Sources of funding

Not applicable.

Author contribution

D.D. and C.Z.: conception and manuscript writing; P.Y. and H.Y.: manuscript revising. All authors were involved in the final approval of the manuscript.

Conflicts of interest disclosure

There are no conflicts of interest.

Research registration unique identifying number (UIN)

Not applicable.

Guarantor

Dawei Deng.

Data availability statement

Not applicable.

Provenance and peer review

Our paper was not invited.

Acknowledgements

Not applicable.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Published online 20 May 2024
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References

1 Xu ZJ Li JA Cao ZY . Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study. Int J Surg 2024. [Epub ahead of print].
2 Xiang H Guo F Tao X . Pancreatic ductal deletion of S100A9 alleviates acute pancreatitis by targeting VNN1-mediated ROS release to inhibit NLRP3 activation. Theranostics 2021;11 :4467–4482.33754072
3 Wu Y Zhou Q Guo F . S100 proteins in pancreatic cancer: current knowledge and future perspectives. Front Oncol 2021;11 :711180.34527585
4 Liu Z Liu L Weng S . Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer. Nat Commun 2022;13 :816.35145098
