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

39285433
5585
10.1186/s12967-024-05585-x
Letter to the Editor
Harnessing sample preparation for RNA-sequencing toward a reliable bioinformatics analysis
Talia Marianna 1
Cesario Eugenio 2
Cirillo Francesca 1
Scordamaglia Domenica 1
Di Dio Marika 1
Zicarelli Azzurra 1
Mondino Adelina Assunta 1
Occhiuzzi Maria Antonietta 1
De Francesco Ernestina Marianna 3
Belfiore Antonino 4
Miglietta Anna Maria 5
Di Dio Michele 6
Capalbo Carlo 17
Maggiolini Marcello marcello.maggiolini@unical.it

1
Lappano Rosamaria rosamaria.lappano@unical.it

1
1 https://ror.org/02rc97e94 grid.7778.f 0000 0004 1937 0319 Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Rende, Italy
2 https://ror.org/02rc97e94 grid.7778.f 0000 0004 1937 0319 Department of Cultures, Education and Society, University of Calabria, 87036 Rende, Italy
3 https://ror.org/04vd28p53 grid.440863.d 0000 0004 0460 360X Department of Medicine and Surgery, University of Enna “Kore”, 94100 Enna, Italy
4 https://ror.org/03a64bh57 grid.8158.4 0000 0004 1757 1969 Endocrinology, Department of Clinical and Experimental Medicine, University of Catania, Garibaldi-Nesima Hospital, 95122 Catania, Italy
5 grid.413811.e Breast and General Surgery Unit, Annunziata Hospital Cosenza, 87100 Cosenza, Italy
6 grid.413811.e Division of Urology, Department of Surgery, Annunziata Hospital, 87100 Cosenza, Italy
7 grid.413811.e Complex Operative Oncology Unit, Annunziata Hospital Cosenza, 87100 Cosenza, Italy
16 9 2024
16 9 2024
2024
22 84631 7 2024
4 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.
http://dx.doi.org/10.13039/501100005010 Associazione Italiana per la Ricerca sul Cancro IG n. 27386 Lappano Rosamaria issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcDear Editor,

We aim to clarify the issues raised by Tan et al. [1] regarding our recent publication “Cancer-associated fibroblasts (CAFs) gene signatures predict outcomes in breast and prostate tumor patients” [2], which provided changes in the transcriptomic milieu of these malignancies largely occurring in women and men, respectively [3]. We appreciated that the letter of Tan and colleagues dealt with our experimental model and data, giving us the opportunity to further detail the evidence shown.

The first issue was related to the patient’s characteristics and sample processing before RNA-sequencing (RNA-seq) analysis. Regarding the patient data, we selected 20 female patients with luminal invasive breast cancer characterized by estrogen receptor (ER)-positivity, human epidermal growth factor receptor 2 (HER2)-negativity and Ki67 ≥ 30% as well as 20 male patients with prostate cancer characterized by Gleason score at least of 8 or PSA > 20 mg/l. Concerning the sample processing method, we specified the experimental procedure in the “cell cultures” paragraph of the Methods section of our manuscript [2]. In particular, we stated that breast and prostate CAFs were isolated respectively from 20 mammary ductal carcinomas and 20 prostate adenocarcinomas. Then, a unique population of breast and prostate CAFs was obtained by pooling the 20 isolated cell cultures of each tumor type. From the single population of breast and prostate CAFs, we obtained three biological replicates, as previously recommended for the RNA-seq experiments [4]. Next, the RNA extraction from each biological replicate was performed by employing the same sample processing method.

As it concerns the grouping settings of our analysis, we aimed to identify the differences in the gene expression profiles occurring in CAFs of the two malignancies considered, similarly to previous studies that evaluated different types of tumors by RNA-seq analyses [5]. Therefore, comparing the differentially expressed genes in CAFs from breast and prostate cancer, we uncovered the unique molecular signatures and pathways that may trigger the action of CAFs in these malignancies. Our data may also help in pinpointing potential specific biomarkers and therapeutic targets of the breast and prostate tumor microenvironment, thereby supporting the improvement of personalized medicine and targeted strategies.

We esteemed the suggestion of Tan and colleagues [1] regarding the use of specific tools that may allow the identification of molecular and biological functions of the differentially expressed genes. However, we preferred the use of the enrichment analyses rather than to explore gene correlation patterns. Additionally, we clarify that our study did not compare tumor versus normal TCGA samples, as stated in the letter of Tan and colleagues. Instead, we specifically compared breast versus prostate samples of the TCGA dataset patients, thereafter we intersected the identified genes with those obtained in CAFs.

Next, we remark that the cumulative impact of the genes belonging to the identified signatures was robustly held by k-means analysis. Importantly, we demonstrated that a common prognosis characterizes patients clustered according to comparable gene expression patterns. The reliability of our analysis was further validated through a classification task, which provided attribute usage for each gene along with metrics for accuracy, recall and precision. The attribute usage percentage served as a quantitative measure of each gene’s influence in patient classification, thereby assigning varying degrees of importance to the different genes. This metric reflects the frequency by which each gene is employed as a decision criterion in our predictive models, thus showing its significance toward patient outcomes. We also appreciated the suggestion of Tan and colleagues regarding the time-dependent ROC analysis. Considering that our clustering approach inherently stratifies patients on the basis of gene expression patterns leading to distinct groups with built-in prognostic differences, we believe that a time-dependent ROC analysis is not suitable for our current workflow. Of note, we carried out our tests by (i) exploiting the k-cross validation, (ii) computing multiple evaluation metrics (accuracy, precision and recall) and (iii) showing the confusion matrices for the different cases. This methodology guarantees a statistically robust estimation of classification model performances as well as a complete explainability in terms of true positive, true negative, false positive and false negative rates. Overall, we recognize the value of the analyses proposed by Tan et al., therefore it would be useful to take into consideration their suggestions for subsequent studies.

Acknowledgements

Not applicable.

Author contributions

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

Funding

Fondazione AIRC supported E.M.D.F. (Start-Up Grant 21651), A.B. (IG n. 23369), R.L. (IG n. 27386). Ministero della Salute (Italy) supported A.B., M.M. and R.L. (RF-2019-12368937). Ministero dell’Università e Ricerca supported E.M.D.F. (Prin 2022 PNRR P2022MALRP), A.B. (Prin 2022 2022Y79PT4), M.M. (Prin 2022 2022Y79PT4) and R.L. (Prin 2022 202282CMEA; Prin 2022 PNRR P2022MALRP). This work was also funded by: (1) The Next Generation EU - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009; (2) The National Plan for NRRP Complementary Investments - project n. PNC0000003 - AdvaNced Technologies for Human-centrEd Medicine (ANTHEM); (3) The Next Generation EU - Project Age-It: “Ageing Well in an Ageing Society” [DM 1557 11.10.2022]; (4) POS RADIOAMICA project funded by the Italian Minister of Health (CUP: H53C22000650006); (5) POS CAL.HUB. RIA project funded by the Italian Minister of Health (CUP H53C22000800006); (6) Proof of Concept (PoC) - Patent Enhancement Program Unical Pathways (UP) (CUP C28H23000330002).

Data availability

Not applicable.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All the authors agree to publish this paper.

Competing interests

Not applicable.

Publisher’s Note

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

Marianna Talia, Francesca Cirillo, Domenica Scordamaglia contributed equally to this work. Marcello Maggiolini and Rosamaria Lappano are co-last authors.
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