
==== Front
J Transl Med
J Transl Med
Journal of Translational Medicine
1479-5876
BioMed Central London

39075572
5510
10.1186/s12967-024-05510-2
Letter to the Editor
Letter: Sample preparation before sequencing and bioinformatics analysis method selection are more important than the results
Tan Guigeng
Shao Yuanyuan
http://orcid.org/0000-0002-7510-6032
Zhu Zhiguo zgzhudr@163.com
zhuzhiguo@mail.jnmc.edu.cn

grid.449428.7 0000 0004 1797 7280 Department of Urology, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, Shandong 272007 China
29 7 2024
29 7 2024
2024
22 69911 7 2024
14 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
http://dx.doi.org/10.13039/501100002858 China Postdoctoral Science Foundation 2023M731309 Zhu Zhiguo http://dx.doi.org/10.13039/501100021171 Basic and Applied Basic Research Foundation of Guangdong Province 2020A1515110946 Zhu Zhiguo PhD Research Foundation of Affiliated Hospital of Jining Medical University2022-BS-001 Zhu Zhiguo Research Fund for Academician LinHe New MedicineJYHL2022FMS09 Zhu Zhiguo Jining Key Research and Development Foundation2023YXNS023 Zhu Zhiguo issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcDear editor

I read Marianna Talia et al.’s study [1] with great interest. Data sources for the study include RNA-seq data from clinical samples (20 invasive mammary ductal carcinomas and 20 prostate adenocarcinomas) and public datasets (TCGA, METABRIC, AFFYMETRIX, GEO). In this study, authors identified two cancer-associated fibroblasts (CAFs)-related gene signatures for breast and prostate cancer. The CAFs-related gene signatures could serve as an effective survival predictor for patients with breast and prostate cancer. Despite the strengths of this study, several underlying concerns, particularly the experimental design, require further elucidation. We believe that these problems are common but neglected and need to be paid attention to by researchers.

Firstly, the clinical sample’s information needs to be further detailed. This includes not only the clinical pathological characteristics of the patients, but also the way these samples were processed. We found that the authors mixed the 20 breast or prostate cancer CAFs collected into 3 sequencing samples. However, how the technical and biological replicates of the experiment were performed was not disclosed. Unlike the stability of DNA, RNA has strong temporal and spatial properties. Different sample mixing schemes and RNA extraction schemes have a great impact on RNA sequencing results [2]. Figure 1 demonstrated three possible sample processing methods. Obviously, different sample processing methods will inevitably produce different results. Based on our previous experience in extracting CAFs from renal clear cell carcinoma, CAFs extracted from tumor tissues are sufficient for subsequent RNA sequencing. Biological replicates are important in most RNA-seq experiments. Therefore, the act of combining 20 samples into 3 samples greatly weakened the value of the study.

Fig. 1 Three possible sample processing methods. (A) 20 CAFs samples were mixed and RNA was extracted. The RNA sample divided into 3 sequencing samples The experiment was performed with only technical replicates. (B) Considering the long sample collection time, researchers may extract RNA in batches. However, since the number of samples (n = 20) is an even number, the amount of CAFs samples contained in each sequencing sample is different. (C) RNA was extracted from each CAFs sample separately. The 20 RNA samples were mixed and divided into 3 sequencing samples

Secondly, grouping settings during analysis. We are very confused about why two different CAFs (breast cancer vs. prostate cancer) are compared in the RNA-seq analysis. The article describes that “810 genes were found up-regulated and 1181 genes were found down-regulated in breast with respect to prostate CAFs”. This analysis seems arbitrary and without reason. These differentially expressed genes cannot be considered as tumor-specific CAFs genes. Our analysis should be logical and explainable. In most cases, the differentially expressed genes come from the comparison between CAFs and adjacent normal fibroblasts [3]. ‘FindAllMarkers’ function (for scRNA-seq) and Weighted Gene Co-expression Network Analysis (WGCNA, for RNA-seq) can be used to identify marker genes. In addition, authors intersected genes in CAFs with differentially expressed genes (tumor vs. normal) in TCGA dataset. Using WGCNA to identify key genes originating from CAFs in TCGA dataset seems to be a better choice [4, 5].

Thirdly, the purpose of conducting such research is to obtain a risk model with potential clinical application value. For example, Yan-Jie Zhong et al. [5] establish a CAFs-based risk model: CAFs score = exp (EVA1A) * 0.447 - exp (APBA2) * -0.438 + exp (LRRTM4) *0.284 - exp (GOLGA8M) *1.183 + exp (BPIFB2) *0.870. This risk model could serve as an effective survival predictor for patients with intrahepatic cholangiocarcinoma. In this study, the authors simply provided two lists of genes, with which we can differentiate patients into two groups with different prognoses. This gene list is difficult to quantify and therefore difficult to apply in clinical practice. We also cannot further evaluate its predictive value through a time-dependent ROC analysis.

This letter is not intended to provide a critique of an interesting study. We only want to draw attention to the importance of sample preparation before sequencing and bioinformatics analysis method selection.

Acknowledgements

Not applicable.

Author contributions

All authors contributed equally. All authors read and approved the final manuscript.

Funding

This research was funded by China Postdoctoral Science Foundation (2023M731309), Guangdong Basic and Applied Basic Research Foundation (2020A1515110946), PhD Research Foundation of Affiliated Hospital of Jining Medical University (2022-BS-001), Research Fund for Academician LinHe New Medicine (JYHL2022FMS09) and Jining Key Research and Development Foundation (2023YXNS023).

Data availability

Not applicable.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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