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. 2023 Feb 3;18(2):e0281252. doi: 10.1371/journal.pone.0281252

The impact of variance in carnitine palmitoyltransferase-1 expression on breast cancer prognosis is stratified by clinical and anthropometric factors

Ryan Liu 1,2, Shyryn Ospanova 3, Rachel J Perry 1,*
Editor: Divyansh Agarwal4
PMCID: PMC9897541  PMID: 36735704

Abstract

CPT1A is a rate-limiting enzyme in fatty acid oxidation and is upregulated in high-risk breast cancer. Obesity and menopausal status’ relationship with breast cancer prognosis is well established, but its connection with fatty acid metabolism is not. We utilized RNA sequencing data in the Xena Functional Genomics Explorer, to explore CPT1A’s effect on breast cancer patients’ survival probability. Using [18F]-fluorothymidine positron emission tomography-computed tomography images from The Cancer Imaging Archive, we segmented these analyses by obesity and menopausal status. In 1214 patients, higher CPT1A expression is associated with lower breast cancer survivability. We confirmed a previously observed protective relationship between obesity and breast cancer in pre-menopausal patients and supported this data using two-sided Pearson correlations. Taken together, these analyses using open-access databases bolster the potential role of CPT1A-dependent fatty acid metabolism as a pathogenic factor in breast cancer.

Introduction

Breast cancer is the leading cause of cancer mortality in women [1]. Over two million new cases of breast cancer are diagnosed each year, accounting for 24% of total new cancer cases and 15% of total cancer deaths in women. There are many pathogenic and permissive mechanisms underlying tumorogenesis, and metabolic reprogramming is among these emerging mechanisms. Metabolic reprogramming explores how metabolic shifts activate pathways generating and utilizing substrates such as fatty acids and glucose [2]. These pathways provide the energy required for cancer cells to proliferate, migrate, and invade. Although, since the pioneering work of Otto Warburg, the cancer metabolism field has primarily focused on glucose as the most important substrate for rapidly proliferating tumor cells, the role of fatty acids in breast cancer metabolism is far less well understood. It is of particular importance to define how fatty acid metabolism affects tumor metabolism and, ultimately, growth in tumors such as breast cancer that exist in an adipocyte-enriched milieu, because the surrounding lipids are expected to heavily influence the composition of the tumor microenvironment.

Fatty acids are a nutrient-dense source of energy for cancer cells, and under certain conditions may be required for tumor cell survival [3]. Relatedly, lipids are a critical component of cell membranes, so it is not surprising that fatty acid oxidation is an increasingly well-established factor promoting cancer metastasis [4]. Carnitine palmitoyl transferase 1A (CPT1A) resides in the outer mitochondrial membrane [5] and is the first rate-limiting enzyme in fatty acid oxidation, via its role in mediating fatty acid entry into the mitochondria [2]. Increased CPT1 expression and/or activity would therefore be expected to contribute to increased cancer proliferation and, in turn, mortality, but, to our knowledge, this hypothesis remains surprisingly unproven. Increasing availability of multi-Omics data highlights the opportunity for prognostic analyses such as these [6, 7].

Proliferation rate is a prognostic marker for breast cancer patients, and it can be measured by positron emission tomography-computed tomography (PET-CT) with [18F]-fluorothymidine (18F-FLT) in humans [8]. 18F-FLT is a substance uptaken by cells in the S-phase of the cell cycle. Increased proliferative activity is seen as increased 18F-FLT uptake in breast cancer since high 18F-FLT uptake is observed in tissues presenting high mitotic activity. Therefore, increased tumor 18F-FLT uptake is generally viewed as a poor prognostic marker. We demonstrated in an analysis of PET-CT images from The National Cancer Imaging Archive (TCIA) that body mass index (BMI) is paradoxically negatively correlated with 18F-FLT uptake in premenopausal breast cancer patients but positively correlated with 18F-FLT uptake in postmenopausal breast cancer patients, as confirmed in the literature [9]. BMI is a powerful metric for cancer research, and we considered data from patients across a comprehensive range of body size classes based on BMI: 1—cachexia (BMI below 18.5; although no patients in the current study qualified for this category), 2—healthy weight (BMI of 18.5 to 24.9), 3—overweight (BMI of 25.0 to 29.9), and 4—obese (BMI of or above 30.0) [10].

Given the relatively small sample size of 18F-FLT scans available in the TCIA dataset (33 total), RNA Sequencing Visualization was used to confirm the image analysis results [11]. We employed the Xena Functional Genomics Explorer to understand how CPT1A expression may predict breast cancer prognosis, through the lens of clinical and anthropometric data. This is a unique aspect of the current study, as the literature has not yet explored how fatty acid metabolism predicts outcomes in breast cancer in the context of obesity and menopausal status.

This study employed a multimodal approach to understand how fatty acid metabolism and obesity status affect tumor thymidine uptake and proliferation. We confirmed prior evidence of the paradoxical relationship between obesity and breast cancer and the detrimental nature of CPT1A expression on prognoses [1, 12]. In addition, we stratified patients with high and low expression of CPT1A and analyzed how menopausal status affected the survival rate of each, imparting a new perspective on an explored topic. These results add to our understanding of how body composition and fatty acids can alter the metabolism of breast cancer, highlighting a compelling need for future mechanistic studies.

Materials and methods

RNA sequencing visualization

RNA sequencing data from the TCGA Breast Cancer (BRCA) data set was uploaded into the UCSC Xena Functional Genomics Browser’s Visualization suite. The dataset used can be found here: https://xenabrowser.net/datapages/?dataset=TCGA.BRCA.sampleMap%2FBRCA_clinicalMatrix&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443. All 1,247 samples were included in column A. CPT1A was added into column B as a genomic data type, and gene expression and somatic mutation datasets were selected to be analyzed. A Kaplan Meier plot was generated from column B, with overall survival chosen as the dependent variable. 1214 of the 1247 total samples contained data on the gene expression level of CPT1A, and these samples were used to generate the KM plot. Two groups of gene expression levels were set, with 602 samples considered to have a low expression level of CPT1A (<10.83 FPKM) and 612 samples considered to have a high expression level of CPT1A (> = 10.83 FPKM). 10.83 FPKM was the cutoff level for CPT1A gene expression, as the samples were divided at the median. Lastly, the custom survival time cutoff was set to 8605 days after the first treatment. A KM plot was then generated from column C, with overall survival again chosen as the dependent variable. 790 out of the 1247 total samples contained data on mutations in the CPT1A gene, and these samples were used to generate the KM plot. The custom survival time cutoff was set to 8605 days after the first treatment.

Menopausal status was added as a phenotypic data type in the same Visualization suite. Two Kaplan Meier plots were generated from the CPT1A gene expression and menopausal status data: one depicting the correlation between menopausal status and survival probability in samples with high expression levels of CPT1A (> = 10.83 FPKM) and the correlation between menopausal status and survival probability in samples with low expression levels of CPT1A (<10.83 FPKM). A new subgroup column was created: gene expression levels greater than or equal to 10.83 FPKM from column B were picked as the samples of interest and were filtered to keep those samples. A KM plot was generated for column C, with overall survival chosen as the dependent variable. 612 of the 1247 samples matched the criteria of high expression level of CPT1A (> = 10.83 FPKM), and 545 of the 612 samples contained data on menopausal status. 514 of the 545 samples had clear distinctions between pre-menopausal and post-menopausal, and were used to generate the KM plot. 123 samples had pre-menopausal status, and 391 samples had post-menopausal status. The survival time cutoff was set to 3461 days after the first treatment. The current sample filter was cleared, and a new subgroup column was created: gene expression levels less than 10.83 FPKM from column B were picked as the samples of interest and filtered to keep those samples. A KM plot was generated for column C, with progression during disease-free interval chosen as the dependent variable. 606 of the 1247 samples matched the criteria of low expression level of CPT1A (<10.83 FPKM), and 552 of the 606 samples contained data on menopausal status. 506 of the 552 samples had clear distinctions between pre-menopausal and post-menopausal, and were used to generate the KM plot. 136 samples had pre-menopausal status, and 370 samples had post-menopausal status. The survival time cutoff was set to 3663 days after the first treatment.

Using the Model organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) database, CPT1A data in humans was entered and transferred into the Genotype-Tissue Expression (GTEx) Portal [13–15]. The dataset used can be found here: https://gtexportal.org/home/gene/CPT1A. From these data, CPT1A expression level in different tissue types was examined.

PET-CT image analysis

The scans used for the image analysis were provided by TCIA. The dataset can be found here: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=30671268. All patients with an 18F-FLT PET-CT scan, height, weight, and menopausal status were studied, and 18 pre-menopausal patients and 15 post-menopausal patients fulfilled these criteria. If multiple scans were available, the earliest one was used to minimize the effect of neoadjuvant chemotherapy on the tumor.

PET and CT images were uploaded into Fiji ImageJ with a PET-CT Viewer. Using the brown fat ROI tool, the region of interest was drawn and projected through a range of slices. Fixed-volume spheres were drawn to measure mean thymidine uptake in tumor tissue, where only SUV parameters (2–15) and the “any” voxel criteria were selected. Lean body mass-corrected standardized uptake values (SUV) were calculated. The primary endpoint was SUV (g/mL) of tumors correlated to obesity status. Among 33 patients, BMI ranged from 19.0 to 33.3 kg/m2 (mean [SD] = 27.2 [4.00]).

Data were analyzed using GraphPad Prism, and a two-sided Pearson correlation was performed. It was found that none of the analyses were normally distributed. Under this condition, a Mann-Whitney Test was performed on each analysis to determine significance. Statistical significance was defined as P values less than .05. Marginally significant values were determined as P values greater than .05 but less than .10, as none of the analyses produced significant results.

Results

Survival probability By CPT1A expression level and presence of mutations

Patients with a high expression level of CPT1A had a lower survival rate (median survival time of 3,500 days) than those with low expression levels of CPT1A (median survival time of 4,200 days) (Fig 1A). Survival probability reached 0% for the high expression group at 7,500 days. In addition, patients with CPT1A mutation(s) had a lower survival rate (median survival time of 1,100 days) than those without mutation(s) in CPT1A (median survival time of 4,300 days) (Fig 1B). At 3,200 days, the survival probability reached 0% for the group with a CPT1A mutation.

Fig 1. Days after initial treatment vs. survival probability.

Fig 1

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Correlations between days after initial treatment and survival probability in breast cancer patients (A) with high (> = 10.83 RKMP) vs. low (<10.83 RKMP) expression levels of CPT1A and (B) with or without mutations in CPT1A.

Correlations between obesity status and tumor SUV mean by menopausal status

Tumor SUVmean is insignificantly (approaching statistical significance) negatively correlated with obesity status in pre-menopausal breast cancer patients (Fig 2A). In post-menopausal breast cancer patients, tumor SUVmean is insignificantly positively correlated with obesity status (Fig 2B).

Fig 2. Obesity status vs. lean body mass-corrected thymidine uptake.

Fig 2

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Correlations between obesity status and lean body mass-corrected thymidine uptake value (SUVmean) in breast cancer patients separated by (A) pre-menopausal status and (B) post-menopausal status. A two-sided Pearson correlation and Mann-Whitney test was performed, and the corresponding r value determined statistical significance. SUV—standardized uptake value.

SUVmean for tumor, adipose tissue, and organs by menopausal status

Tumor SUVmean in post-menopausal patients is significantly higher than tumor SUVmean in pre-menopausal patients (Fig 3). This same relationship is shown in heart SUVmean, and the opposite relationship is shown in kidney SUVmean.

Fig 3. Lean body mass-corrected mean standardized uptake value (SUVmean) in tumor; subcutaneous adipose tissue (SAT); visceral adipose tissue (VAT) at the level of the S2 vertebrae; liver; heart; and kidney separated by menopausal status (n = 33).

Fig 3

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Survival probability for patients with high CPT1A expression by menopausal status

Pre-menopausal breast cancer patients with a high expression level of CPT1A had a higher survival rate than their post-menopausal counterparts (Fig 4). Peri-menopausal patients had a 100% survival probability throughout the study.

Fig 4. Days after initial treatment vs. survival probability.

Fig 4

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Correlations between days after initial treatment and survival probability in breast cancer patients with a high expression level of CPT1A (> = 10.83 RKMP) separated by menopausal status.

Survival probability for patients with low CPT1A expression by menopausal status

Pre-menopausal breast cancer patients with a high expression level of CPT1A had a higher survival rate than their post-menopausal counterparts until the two groups’ intersection at 3663 days (Fig 5). Peri-menopausal patients had the highest survival probability at 3663 days.

Fig 5. Days after initial treatment vs. survival probability.

Fig 5

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Correlations between days after initial treatment and survival probability in breast cancer patients with a low expression level of CPT1A (<10.83 RKMP) separated by menopausal status.

Human expression of CPT1A by tissue type

Breast tissue had the fourth-highest median expression of CPT1A, preceded by organs of the gastrointestinal tract, including the colon, small intestine, and stomach, which are all adipose tissue-enriched microenvironments (Fig 6). Breast tissue was followed by kidney and liver tissue. Brain, blood, and pancreas tissue showed insignificant differences in CPT1A expression.

Fig 6. Transcripts per million (TPM) of CPT1A separated by tissue types in which CPT1A is present.

Fig 6

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Discussion

CPT1A is upregulated in breast cancer, worsening prognoses for high-risk patients. Although the role of obesity and menopausal status on breast cancer outcome has been studied before, we aimed to examine the lesser-known relationship between fatty acid metabolism and breast cancer outcome in the context of obesity and menopausal status. This study analyzes breast cancer patients in general, regardless of subtype, as there was limited data on subtype in both the image analysis and RNA sequencing visualization databases. CPT1A is upregulated in breast cancer, and inhibiting CPT1A activates cell apoptosis and suppresses cancer cell invasion [1]. Further, CPT1A is overexpressed in breast cancer tissues compared to normal breast tissues. This relationship is most strongly demonstrated in triple-negative breast cancer. We also observe an association between CPT1A expression and lymph node status, tumor size, tumor burden, histological grading, human epidermal growth factor receptor 2 (HER2) status, and molecular subtype. Fig 1A confirms these results and implies that 10.83 FPKM is the determining CPT1A expression level that produces significant differences in survival rate. Although previous studies have concluded that overexpression of CPT1A leads to poor breast cancer prognosis, to our knowledge no study has defined the deciding expression level: the expression level in which levels above or below it are predicted to result in significant differences in survival.

Fig 1B is consistent with the idea that CPT1A drives breast cancer metabolism [1]. A prominent clinical feature of CPT1A mutations in humans is elevated free carnitine levels [16], increasing the rate of fatty acid metabolism and energy for cancer cells to use and increasing proliferation and mortality. The connection between CPT1A mutations and breast cancer outcome is a gap in the literature, and this study explores the trend and the possible mechanisms behind it.

The image analysis portion of this study, represented in Fig 2A, strongly tended to confirm the paradoxical relationship between obesity and breast cancer in pre-menopausal women [12]. Increased serum estrogen levels are associated with poor breast cancer prognosis. In pre-menopausal women, estrogen is produced in the ovaries, and the low estradiol levels in patients are caused by negative feedback in the hypothalamic-pituitary axis suppressing ovarian function. On the other hand, our data from Fig 2B do not show a significant relationship between obesity and breast cancer in post-menopausal women. Estrogen synthesis in post-menopausal women occurs in adipose tissue, elevating estradiol levels in obese women compared to non-obese women. Although this relationship has been studied before, instead of using BMI as a continuous variable, this study uses obesity status, defined by BMI, as a categorical variable. BMI can reveal how height and weight affect breast cancer, but obesity status more explicitly represents how obesity affects outcome [17]. The analysis producing Fig 4 was not separated by obesity status, but post-menopausal patients still had a higher SUVmean than pre-menopausal patients. This served as a control for the image analysis data and showed that the bias towards post-menopausal patients should be further investigated. The comparably small sample size in the image analysis was limited by the number of eligible 18F-FLT scans on TCIA. The results approached statistical significance but, likely due to a type 2 error in the setting of limited available data, did not reach significance and would benefit from future follow-up studies with more samples. It should also be noted that our data on the interaction of menopausal status and BMI on breast cancer prognosis cannot distinguish between effects of age and of hormones per se. As aging is associated with increased ectopic lipid accumulation and increased de novo lipogenesis even independent of body weight, the effect of age could certainly confound the findings of the current study [18, 19]. However, due to the limited data available, we were not able to assess the impact of age as a continuous variable on the interaction between BMI, CPT1, and breast cancer outcomes.

To corroborate the image analysis results, we further explore the relationship between CPT1A and breast cancer, through the lens of the relationship between CPT1A and obesity. Past studies have found that CPT1A expression is positively correlated with BMI in humans [20]. Using RNA sequencing visualization, we filtered patients with high expression of CPT1A, using 10.83 FPKM as the determining level again, and explored the survival probabilities of patients in each menopausal stage within the expression level range, as portrayed by Fig 4. This study adds a new perspective to an established trend by breaking down the analysis of CPT1A and breast cancer by obesity and menopausal status. Interestingly, peri-menopausal patients with high CPT1A expression had a 100% survival probability across the study. This could be due to the small sample size in the peri-menopausal group (19 total), but it should be further explored. Fig 5 served as the control and increased confidence in the high CPT1A expression group data.

CPT1A is found in the liver, pancreas, kidney, brain, blood, and embryonic tissues [5]. The results of Fig 6 are partially expected because adipocytes are the predominant cell population in the breast, where they can secrete significant amounts of fatty acids as metabolic substrates for tumors [21]. Therefore, high CPT1A expression, which potentially leads to the high absorption of those fatty acids by the tumor, will take advantage of those fatty acid mobilizations, promoting increased cancer proliferation. Obesity, results in increased breast cancer incidence and breast tumor size, leading to an increased rate of metastasis formation and elevated mortality [2]. This effect potentially increases the number of fatty acid mobilizations for breast cancer cells to utilize. Previous studies have established that CPT1A is expressed in breast tissue, but this study compares this expression to other tissue types in which CPT1A is also highly expressed. Breast tissue is revealed as the tissue type with the highest CPT1A expression level, a feature not previously identified.

Conclusion

In summary, fatty acid metabolism with respect to CPT1A was detrimental to breast cancer outcomes. We separated analyses by obesity and menopausal status, confirming the existing literature and presenting a new angle of breast cancer metabolism. PET and CT images were acquired through TCIA and analyzed in Fiji ImageJ, while RNA sequencing data was visualized in the Xena Functional Genomics Explorer, MARRVEL, and GTEx Portal. Although limited by the number of eligible 18F-FLT scans, we highlighted the importance of fatty acid metabolism in the study of breast cancer metabolism. Prospective studies should break down these analyses using cancer subtype, operative status, and more samples to better portray the effect of dysregulated fatty acid metabolism on breast cancer outcomes.

Acknowledgments

We are grateful to Dr. Gang Peng in the Yale Cancer Center for his helpful input on the statistical analyses performed here.

Data Availability

These are third-party data and not the property of the authors. All data are available through The Cancer Imaging Archive and The Cancer Genome Atlas. The links to the specific datasets are as follows: https://xenabrowser.net/datapages/?dataset=TCGA.BRCA.sampleMap%2FBRCA_clinicalMatrix&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 https://gtexportal.org/home/gene/CPT1A https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=30671268.

Funding Statement

This study was supported in part by a Pilot Grant from the Yale Cancer Center, and by a dkNET Summer of Data student fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Jianhong Zhou

22 Nov 2022

PONE-D-22-26128The Impact of Variance in Carnitine Palmitoyltransferase-1 Expression on Breast Cancer Prognosis is Stratified by Clinical and Anthropometric FactorsPLOS ONE

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We will update your Data Availability statement to reflect the information you provide in your cover letter.

4. Please ensure that you refer to Figure 3 in your text as, if accepted, production will need this reference to link the reader to the figure.

5. Please upload a new copy of Figure 6 as the detail is not clear. Please follow the link for more information: " ext-link-type="uri" xlink:type="simple">https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/" https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/

6. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Ryan Liu and authors in the manuscript “The Impact of Variance in Carnitine Palmitoyltransferase-1 Expression on Breast Cancer Prognosis is Stratified by Clinical and Anthropometric Factors” addressed the connection between breast cancer prognosis and fatty acid metabolism in cancer patients. CPT1A transports long-chain fatty acid to mitochondria for beta-oxidation and gene expression of this enzyme in the breast cancer tissue was used as a measurement of fatty acid metabolism rate within the cancer. To study this relationship, authors worked with available public databases and archive such as RNA-seq data and 18F-fluorothymidine PET-CT images and analyzed the data in relation to obesity and menopausal status using appropriate statistical methods. It is very interesting study, and their findings have significant impacts in the cancer metabolism field. In addition, the novelty of work is that authors skillfully combined research tools such as Fiji ImageJ, Xena Functional Genomics Explorer, MARRVEL, and GTEx Portal.

Despite the above, there are some major points that I would like to point out:

1. 10.83 FPKM is used as cut off level for CPT1A gene expression. How did authors determine this cut off level? It was not explained in the Method section.

2. Figures need to be referred in the Results, not in the Discussion.

3. In the sentence, the results on Figure 1A seems to be described in opposite way than the actual data.

4. Figure 4 on Page 6 and Figure 5 on Page 7 need to be described better and correct way. For these data set, could age rather than menopausal status play a role?

5. In Figure 6, lower CPT1A expression in brain parts is not surprising, because CPT1C is the major isoform in brain. However, the highest expression of CPT1A is observed in GI-tract (colon, duodenum, and small intestine). Can authors add these tissue in the Figure 6 in other to compare with breast tissue?

6. On page 9, the statement “these results are consistent with the fact that CPT1A is a major regulator of breast cancer” needs to be re-written. To make this statement, CPT1A activity assay or at least protein expression need to be determined. Authors for their interpretation, used only RNA-seq data and these data or actual gene expression of CPT1A is not confirmed by quantitative PCR in the tissue. Since metabolism in breast cancer cells not fully understood yet, a statement such as “a major regulator” should be avoided.

Minor points

1. On page 2, reference 3 was cited as “we demonstrated…” However, these authors were not listed as authors in the #3 manuscript. Please clarify this reference.

2. On page 2, “understanding” should be replaced by “understand”.

3. In the Figure 2, should it be simpler and easier to understand if words “normal healthy, overweight and obese” included instead of 2,3,4 numbers?

4. Number of participants (n) should be added in the legends for Figure 2 and 3.

5. Tissue names in Figure 6 on Page 8 are too small and not readable. Can authors replace this with higher resolution figure?

Reviewer #2: The authors analyzed the role of CPT1A differentially expression on Breast Cancer Prognosis concrning Clinical and Anthropometric Factors. The method is approperiatly designed and the manuscript is well presented. The results were validated using different measurement including CT scan and RNA-seq analysis. The work may gain so many interests, however, I have minor concerns:

- The figures are fuzzy. vector-based or high quality figures are required for the final version.

- The authors may refer to recent breast cancer progression works in the literature such as PMID: 35205681 and PMID: 34997055.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2023 Feb 3;18(2):e0281252. doi: 10.1371/journal.pone.0281252.r002

Author response to Decision Letter 0

13 Dec 2022

We thank the editors and reviewers for their time spent on this manuscript. Below please find our responses to all comments, queries, and concerns.

Journal comments:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Thank you for providing the links to the style templates. We have followed these requirements carefully.

2. Thank you for stating the following in the Acknowledgments Section of your manuscript:

“This study was supported in part by a Pilot Grant from the Yale Cancer Center, and by a dkNET Summer of Data student fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors declare that no conflicts of interest exist.”

We note that you have provided additional information within the Acknowledgements Section that is not currently declared in your Funding Statement. Please note that funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

“This study was supported in part by a Pilot Grant from the Yale Cancer Center, and by a dkNET Summer of Data student fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

The funding statement is correct as written. We have now removed any funding-related text from the manuscript.

3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

We have now provided links to the original data sets in the Materials and Methods section as well as in the Data Availability statement.

4. Please ensure that you refer to Figure 3 in your text as, if accepted, production will need this reference to link the reader to the figure.

In the revised version, we have ensured that all figures are referenced in the Results section.

5. Please upload a new copy of Figure 6 as the detail is not clear. Please follow the link for more information: https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/" https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/

A new copy of Figure 6 has now been uploaded. We hope that it will now meet the technical requirements of the journal.

6. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

The reference list has been reviewed to ensure that it is complete and correct.

Reviewer comments:

Reviewer 1: Ryan Liu and authors in the manuscript “The Impact of Variance in Carnitine Palmitoyltransferase-1 Expression on Breast Cancer Prognosis is Stratified by Clinical and Anthropometric Factors” addressed the connection between breast cancer prognosis and fatty acid metabolism in cancer patients. CPT1A transports long-chain fatty acid to mitochondria for beta-oxidation and gene expression of this enzyme in the breast cancer tissue was used as a measurement of fatty acid metabolism rate within the cancer. To study this relationship, authors worked with available public databases and archive such as RNA-seq data and 18F-fluorothymidine PET-CT images and analyzed the data in relation to obesity and menopausal status using appropriate statistical methods. It is very interesting study, and their findings have significant impacts in the cancer metabolism field. In addition, the novelty of work is that authors skillfully combined research tools such as Fiji ImageJ, Xena Functional Genomics Explorer, MARRVEL, and GTEx Portal.

We thank the reviewer for considering this a “very interesting study,” and asserting that “the novelty of [this] work is that [we] skillfully combined research tools such as Fiji ImageJ, Xena Functional Genomics Explorer, MARRVEL, and GTEx Portal.” We are delighted that this reviewer considers this a “very interesting study, and [our] findings have significant impacts in the cancer metabolism field.”

Specific Points

1. 10.83 FPKM is used as cut off level for CPT1A gene expression. How did authors determine this cut off level? It was not explained in the Method section.

The reviewer makes a great point, and the manuscript added the explanation: “10.83 FPKM was the cutoff level for CPT1A gene expression, as the samples were divided at the median.” The protocol in UCSC Xena determined this.

2. Figures need to be referred in the Results, not in the Discussion.

This helpfully suggested change has been made. We have removed all figure descriptions from the Discussion and only reference figures when necessary to convey the point expanded upon in the Discussion.

3. In the sentence, the results on Figure 1A seems to be described in opposite way than the actual data.

We appreciate the reviewer for pointing out this mistake. It has been corrected: “Patients with a high expression level of CPT1A had a lower survival rate (medial survival time of 3,500 days) than those with low expression levels of CPT1A (medial survival time of 4,200 days).”

4. Figure 4 on Page 6 and Figure 5 on Page 7 need to be described better and correct way. For these data set, could age rather than menopausal status play a role?

We thank the reviewer for pointing out these errors. They have been corrected: “Pre-menopausal breast cancer patients with a high expression level of CPT1A had a higher survival rate than their post-menopausal counterparts” and “Pre-menopausal breast cancer patients with a high expression level of CPT1A observed a higher survival rate than their post-menopausal counterparts until the two group’s intersection at 3663 days.”

We agree with the reviewer that age could play a role in the outcomes in this data set. We are not able to specifically test this question because of limited available data, but have added a comment on this important point in the Discussion:

“It should also be noted that our data on the interaction of menopausal status and BMI on breast cancer prognosis cannot distinguish between effects of age and of hormones per se. As aging is associated with increased ectopic lipid accumulation and increased de novo lipogenesis even independent of body weight, the effect of age could certainly confound the findings of the current study. However, due to the limited data available, we were not able to assess the impact of age as a continuous variable on the interaction between BMI, CPT1, and breast cancer outcomes.”

5. In Figure 6, lower CPT1A expression in brain parts is not surprising, because CPT1C is the major isoform in brain. However, the highest expression of CPT1A is observed in GI-tract (colon, duodenum, and small intestine). Can authors add these tissue in the Figure 6 in other to compare with breast tissue?

This is an important point that has been added in Figure 6. Breast tissue expression of CPT1A is now compared with blood, colon, kidney, liver, pancreas, small intestine, and stomach tissue.

6. On page 9, the statement “these results are consistent with the fact that CPT1A is a major regulator of breast cancer” needs to be re-written. To make this statement, CPT1A activity assay or at least protein expression need to be determined. Authors for their interpretation, used only RNA-seq data and these data or actual gene expression of CPT1A is not confirmed by quantitative PCR in the tissue. Since metabolism in breast cancer cells not fully understood yet, a statement such as “a major regulator” should be avoided.

The reviewer brings up a valid point. We have replaced the claim with a less definitive statement: “Figure 1b is consistent with the idea that CPT1A drives breast cancer metabolism.”

7. On page 2, reference 3 was cited as “we demonstrated…” However, these authors were not listed as authors in the #3 manuscript. Please clarify this reference.

This helpful suggestion has been incorporated, by removing “we demonstrated” and replacing it with “as demonstrated in the literature.”

8. On page 2, “understanding” should be replaced by “understand”.

This helpful correction has been made.

9. In the Figure 2, should it be simpler and easier to understand if words “normal healthy, overweight and obese” included instead of 2,3,4 numbers?

The reviewer makes a great point. Figure 2 now has qualitative x-axis labels.

10. Number of participants (n) should be added in the legends for Figure 2 and 3.

This helpful suggestion has been incorporated.

11. Tissue names in Figure 6 on Page 8 are too small and not readable. Can authors replace this with higher resolution figure?

The figures have been replaced with their high-resolution counterparts.

Reviewer 2: The authors analyzed the role of CPT1A differentially expression on Breast Cancer Prognosis concrning Clinical and Anthropometric Factors. The method is approperiatly designed and the manuscript is well presented. The results were validated using different measurement including CT scan and RNA-seq analysis.

We thank Reviewer 2 for stating that “the method is appropriately designed and the manuscript is well presented.” We are pleased that the reviewer values the validation of the conclusions.

Specific Points

1. The figures are fuzzy. vector-based or high quality figures are required for the final version.

The figures have been replaced with their high-resolution counterparts.

2. The authors may refer to recent breast cancer progression works in the literature such as PMID: 35205681 and PMID: 34997055.

We have reviewed the useful literature mentioned, and thank the reviewer for the suggestion. These papers have now been cited in the Introduction.

Attachment

Submitted filename: Response to Reviewers PONE-D-22-26128.docx

Click here for additional data file. (31.9KB, docx)

Decision Letter 1

Divyansh Agarwal

19 Jan 2023

The Impact of Variance in Carnitine Palmitoyltransferase-1 Expression on Breast Cancer Prognosis is Stratified by Clinical and Anthropometric Factors

PONE-D-22-26128R1

Dear Dr. Perry,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Divyansh Agarwal

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Ryan Liu and authors have addressed most of the comments in the revision of their manuscript “The Impact of Variance in Carnitine Palmitoyltransferase-1 Expression on Breast Cancer Prognosis is Stratified by Clinical and Anthropometric Factors”.

Though, a few more minor concerns I would like to point out:

1. In the Discussion, instead of citing just Figure 1b, I like the actual sentence that has been removed in the revised version. In continuation of the sentence, how about this idea: “These results are consistent with the idea that breast cancer metabolism could be (might be?) more dependent on CPT1a-mediated mitochondrial fatty acid oxidation”?

2. In the end of the Discussion, the last sentence “Breast tissue is revealed as the tissue type with the highest CPT1A expression level, a feature not previously identified”, are the authors talking about normal breast tissue or breast cancer tissue? Since Figure 6 is so blurry, I really cannot read that 3 words. At least, based on the updated version of Figure 6, they should say “one of the highest…”.

3. Does number of participants n=33 in the legend for Figure 3 mean 33 for each groups or total for 2 groups? It needs to be clarified.

4. Tissue names in Figure 6 are still blurry and difficult to read. Can authors include tissue names in the figure legend (for example name them in order from left to right)?

Reviewer #2: I think the reviewers have done fair efforts addressing reviewers comments. I was wish for more literature. but overall 21 papers in the reference is fine.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: ABEDALRHMAN ALKHATEEB

**********

Acceptance letter

Divyansh Agarwal

23 Jan 2023

PONE-D-22-26128R1

The Impact of Variance in Carnitine Palmitoyltransferase-1 Expression on Breast Cancer Prognosis is Stratified by Clinical and Anthropometric Factors

Dear Dr. Perry:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Dr. Divyansh Agarwal

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: Response to Reviewers PONE-D-22-26128.docx

    Click here for additional data file. (31.9KB, docx)

    Data Availability Statement

    These are third-party data and not the property of the authors. All data are available through The Cancer Imaging Archive and The Cancer Genome Atlas. The links to the specific datasets are as follows: https://xenabrowser.net/datapages/?dataset=TCGA.BRCA.sampleMap%2FBRCA_clinicalMatrix&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 https://gtexportal.org/home/gene/CPT1A https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=30671268.

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