A Novel Ferroptosis-Related Signature for Prediction of Prognosis, Immune Profiles and Drug Sensitivity in Hepatocellular Carcinoma Patients

Round 1

Reviewer 1 Report

In this manuscript, by analyzing Hepatocellular carcinoma (HCC) patient data from multiple databases, the authors claim they have found a new Ferroptosis-related signature to predict the prognosis and HCC patients. Specifically, the authors have found that HCC patients in a high-risk group with high ferroptosis-related signature had worse prognosis results than others without the high ferroptosis-related signature.

 

This is an interesting finding and could be helpful for HCC prognosis and drug use prediction. However, the following issues should be addressed before being accepted for publishing.

 

Major: 

Does Ferroptosis-related signature only high in some high-risk HCC patients, or could it be high in low-risk HCC patients as well? If it could be high in some low-risk HCC patients, that means the authors' method should be used in addition to other existing classification methods.

Minor:

1, Figure 6 resolution is too low to read.

2, There are several word spacing issues in the title, abstract, and the main context.

3, There are several typos. For example, I believe it should be nFRGS, but not nnFRGs at line 12.

Author Response

Dear editors and reviewers,

Thank you very much for your attention and the reviewers’ comments on our paper submitted to Current Oncology (manuscript ID: curroncol-1870399). We have revised the manuscript according to your kind advice and the reviewers’ comments. We have also proof-read the manuscript carefully to minimize grammatical errors. All the authors have read and approved the revised version of manuscript, and agreed to the authorship. We sincerely hope this manuscript will be finally acceptable to be published on Current Oncology.

Thanks very much for all your help and looking forward to hearing from you soon.

 

Sincerely,

Jing Tao, M.D., Ph.D.

Associate Professor

Department of Pancreatic Surgery, Renmin Hospital, Wuhan University

Here below is our description on revision according to the reviewers’ comments.

Reviewer #1:In this manuscript, by analyzing Hepatocellular carcinoma (HCC) patient data from multiple databases, the authors claim they have found a new Ferroptosis-related signature to predict the prognosis and HCC patients. Specifically, the authors have found that HCC patients in a high-risk group with high ferroptosis-related signature had worse prognosis results than others without the high ferroptosis-related signature.

This is an interesting finding and could be helpful for HCC prognosis and drug use prediction. However, the following issues should be addressed before being accepted for publishing.

1. Does Ferroptosis-related signature only high in some high-risk HCC patients, or could it be high in low-risk HCC patients as well? If it could be high in some low-risk HCC patients, that means the authors' method should be used in addition to other existing classification methods.

Response: Thanks for your kind suggestion. We have carefully checked the data and further validated the significant upregulation of the expression of the 7 genes comprising the nFRGs in the high-risk group in both the training set and the validation cohort (p<0.001). We have performed the annotation in Figure3A and Figure4A (detailed in Figure 3A and Figure 4A).

2. Figure 6 resolution is too low to read.

Response: Thanks for your kind suggestion. Considering the layout of the article figures, we placed Figure 6 from the original draft into Figure 5. We have enhanced the resolution of Figure 5, and improved the quality of other figures.

3. There are several word spacing issues in the title, abstract, and the main context.

Response: Thanks for your kind suggestion. We have read the manuscript carefully and adjusted the word spacing to make it meet the article publication requirements (detailed in revised manuscript).

4. There are several typos. For example, I believe it should be nFRGS, but not nnFRGs at line 12.

Response: Thanks for your kind suggestion. We have checked the manuscript throughout carefully again, and revised the grammar, equation, and typesetting errors, etc.

Author Response File: Author Response.pdf

Reviewer 2 Report

Overall, this is a well-written manuscript and a lot of effort has been done to provide results using multiple techniques.

However, in the past few years, there have been several similar studies in which researchers have worked on identifying Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. Thus, the work lacks novelty.

 

Following are some suggestions,

 

Please provide the Liver sample distribution for the study/differential analysis  i.e. How many control samples were used and what was their source?

In addition to 377 tcga (usa) samples, ICGC has two more cohorts, 260(Jp) and 369 (Fr) samples. Which one is used for the validation and why only 370 and 231 are reported in the manuscript?   How much do final results changes after including the missing samples?

 

How ‘Coefi’ was defined in the risk score, for each gene from lasso analysis?

 

How DSS, PFI, and DFI plots look for validation cohort?

 

The overall AUCs are not great. It would be great if the authors could provide couple of comparative tables, including the signatures from multiple studies and along with their predictive powers (in tcga) cohort.

Author Response

Dear editors and reviewers,

Thank you very much for your attention and the reviewers’ comments on our paper submitted to Current Oncology (manuscript ID: curroncol-1870399). We have revised the manuscript according to your kind advice and the reviewers’ comments. We have also proof-read the manuscript carefully to minimize grammatical errors. All the authors have read and approved the revised version of manuscript, and agreed to the authorship. We sincerely hope this manuscript will be finally acceptable to be published on Current Oncology.

Thanks very much for all your help and looking forward to hearing from you soon.

 

Sincerely,

Jing Tao, M.D., Ph.D.

Associate Professor

Department of Pancreatic Surgery, Renmin Hospital, Wuhan University

Here below is our description on revision according to the reviewers’ comments.

Reviewer2#: Overall, this is a well-written manuscript and a lot of effort has been done to provide results using multiple techniques.

1.In the past few years, there have been several similar studies in which researchers have worked on identifying Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. Thus, the work lacks novelty.

Response: Thanks for your kind suggestion. We have read the manuscript carefully and added some necessary data and algorithms, such as Igvor210 cohort and GSE104580.Our results suggested that nFRGs performed excellently in predicting prognoses and played a key role in assessing immunotherapy response, TACE efficacy and drug sensitivity in two subgroups of HCC patients. Although there are some previous studies exploring the role of ferroptosis-related gene signature in prognosis on HCC, our study has the following novel features compared with the previous studies. Firstly, nFRGs may perform better in predicting prognosis of HCC compared with other promising gene signatures (detailed in Figure 6). Moreover, our study demonstrated that patients in the high-risk group are more likely to benefit from immunotherapy from more different perspectives, such as using data from TIDE, IPS, TIS, CD8A, STAT1, Igvor210 cohort and mutations (detailed in Figure 10 and discussion). Compared to previous studies, our data are more informative and evidential. More importantly, we used the data from GSE104580 to preliminarily explore the value of nFRGs to assess TACE (detailed in Figure 12 A-B), which has been little discussed in previous studies. Our study suggests that patients in the low-risk group may benefit more from TACE treatment. Altogether, we believe that this study is somewhat innovative as well as clinically applicable.

2.Please provide the Liver sample distribution for the study/differential analysis  i.e. How many control samples were used and what was their source?

In addition to 377 tcga (usa) samples, ICGC has two more cohorts, 260(Jp) and 369 (Fr) samples. Which one is used for the validation and why only 370 and 231 are reported in the manuscript?   How much do final results changes after including the missing samples?

Response: Thank for your kind suggestion. We have checked the data. We found that 370 and 231 samples reported in original manuscript were not entirely accurate. Thus, we have repeated the statistical analysis. In the TCGA-LIHC, 374 samples from the tumor group and 50 samples from the normal group were included. Of these 374 tumor samples, a total of 368 samples with survival data were included. In the ICGC database, a total of 232 samples out of 240 of these tumor samples had survival data. We only use samples with survival data in the construction and validation of nFRGs, so we included 368 samples in the training set data (TCGA) and 232 samples in the validation data set (ICGC-JP) (detailed in Results).

For the differential analysis, 374 tumor samples and 50 normal samples were used in the TCGA-LIHC, while 240 tumor samples and 202 normal samples were used in the ICGC database.

Furthermore, for those samples that do not contain survival data (survival time and survival status), we cannot use the data from missing samples to construct, validate of nFRGs, and perform subsequent analyses. Thus, we cannot estimate the effect of these missing samples on the final results

3.How ‘Coefi’ was defined in the risk score, for each gene from lasso analysis?

Response: Thanks for your kind suggestion. The Confi of the seven genes that make up the nFRGs were screened and calculated by the LASSO algorithm. We have added the description in “”Methods and materials”.

4.How DSS, PFI, and DFI plots look for validation cohort?

Response: Thanks for your kind suggestion. We checked the data and found that the clinical data of ICGC-JP (validation cohort) did not contain information on DSS, DFI, PFI, etc., so we were unable to perform the relevant analysis.

5.The overall AUCs are not great. It would be great if the authors could provide couple of comparative tables, including the signatures from multiple studies and along with their predictive powers (in tcga) cohort.

Response: Thanks for your kind suggestion. nFRGs have AUC values of 0.787 and 0.751 in TCGA and ICGC databases, respectively, both close to 0.8. According to your suggestion, we compared nFRGs with other promising gene signatures, such as ferroptosis, cuproptosis-, pyroptosis, inflammatory response and metabolism. Since some studies are illustrating the AUC of the nonogram based on gene signatures, we cannot directly compare the AUC of these gene signatures. It has been reported in the literature [1-2] that decision curve analysis (DCA ) seems to be more suitable than ROC curves to compare the clinical value of multiple models. Therefore, we applied DCA to compare the clinical value between different gene signatures. As showed in Figure 5, nFRG was superior to other genes signatures in predicting the prognoses in patients with HCC. Thus, we think that nFRGs performed excellently for prediction of the prognoses in patients with HCC.

1.Kerr KF, Brown MD, Zhu K, Janes H. Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use. J Clin Oncol. 2016 Jul 20;34(21):2534-40. doi: 10.1200/JCO.2015.65.5654.

2.Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, Ma ZL, Liu ZY. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol. 2016 Jun 20;34(18):2157-64. doi: 10.1200/JCO.2015.65.9128.

Round 2

Reviewer 1 Report

Most of my concerns are being addressed.