Immunological and prognostic significance of novel ferroptosis-related genes in soft tissue sarcoma.

BACKGROUND: Ferroptosis has exhibited great potential in the treatment of cancer and has gained widespread attention in soft tissue sarcoma (STS). The aim was to explore the immunological and prognostic significance of novel ferroptosis-related genes in STS.
METHODS: We identified ferroptosis-related differentially expressed genes (DEGs) in STS to construct the networks of enrichment analysis and protein-protein interaction. Subsequently, hub genes with prognostic significance were localized and a series of prognostic and immune analyses were performed.
RESULTS: 40 ferroptosis-related DEGs were identified, of which HELLS, STMN1 EPAS1, CXCL2, NQO1, and IL6 were classified as hub genes and were associated with the prognosis in STS patients. In the results of the immune analysis, PDCD1, CTLA4, TIGIT, IDO1 and CD27 exhibited consistent intense correlations as immune checkpoint genes, as well as macrophage, neutrophil, cytotoxic cell, dendritic cell, interdigitating dendritic cell and plasmacytoid dendritic cell as immune cells. EPAS1 and HELLS might be independent prognostic factors for STS patients, and separate prognostic models were constructed by using them.
CONCLUSIONS: We recognized novel ferroptosis-related genes with prognostic value in STS. Furthermore, we searched out potential immune checkpoints and critical immune cells.

Introduction

Soft tissue sarcoma (STS) is a group of malignant tumors originating from mesenchymal tissue and containing multiple histological subtypes [1]. The prognosis of partial STS is poor with no effective treatment and the precise prediction of the prognosis for STS patients is a challenging topic [2]. The previous view was that immunotherapy was unpromising in STS, but this has been reversed in recent years [3].

Ferroptosis is an emerging phenotype of regulated cell death (RCD) which relies on reactive oxygen species deposition mediated by iron catalysis and lipid peroxidation [4]. Ferroptosis performs an essential role in the initiation, progression and prognosis of multiple diseases [5]. Meanwhile, ferroptosis has exhibited great potential in the treatment of cancer and has gained widespread attention in STS as well [6]. Recent studies have revealed that ferroptosis and tumor immunity can be mutually regulated [7, 8].

In the present study, differentially expressed genes (DEGs) were identified through the Gene Expression Omnibus (GEO) database, the FerrDb database, the Immunology Database and Analysis Portal (ImmPort) database, and the networks of enrichment analysis and protein-protein interaction (PPI) were constructed. Prognostic and immune analyses were performed through the Cancer Genome Atlas (TCGA) database. The aim was to explore the immunological and prognostic significance of novel ferroptosis-related genes in STS.

Materials and methods

Data sources

We downloaded RNA-seq data from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) in the GSE21122, GSE6481 and GSE2719 datasets, and all three datasets were from the GPL96 platform. Selected samples from GSE21122 included leiomyosarcoma (26), dedifferentiated liposarcoma(46), myxoid liposarcoma (20), pleomorphic liposarcoma (23), myxofibrosarcoma (31), pleomorphic fibrosarcoma (3), normal human fat (9); selected samples from GSE6481 included synovial sarcoma (16), malignant peripheral nerve sheath tumor (3); selected samples from GSE2719 included gastrointestinal stromal tumor (2), round cell tumor (4). In total, from the GSE21122, GSE6481 and GSE2719 datasets, we selected 174 STS samples covering 10 subtypes as the experimental group and 9 normal human fat samples as the control group for difference analysis. Furthermore, we chose the GSE63157 dataset for external validation of the gene prognostic value. We downloaded RNA-seq and clinical data from the TCGA database (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) for 263 samples, including leiomyosarcoma (105), dedifferentiated liposarcoma (59), undifferentiated pleomorphic sarcoma (51), myxofibrosarcoma (25), synovial sarcoma (10), malignant peripheral nerve sheath tumor (9), desmoid tumor (2), unclassified sarcoma (2). RNA-seq data in FPKM format was converted to TPM format and log2 transformed. We downloaded the lists of 259 ferroptosis-related genes and 2498 immune-related genes from the FerrDb database (http://www.zhounan.org/ferrdb) [9] and ImmPort database (https://immport.niaid.nih.gov) [10], respectively. All material was sourced from public databases and did not involve informed consent from participants.

Data pre-processing and differential analysis

We downloaded the GSE2719, GSE6481, and GSE21122 datasets by the GEOquery package of R [11]. Probes with one probe corresponding to more than one molecule were removed, when probes corresponding to the same molecule were encountered, and only the probe with the highest signal value was retained. For the filtered data, we used the ComBat function of the sva package to remove inter-batch differences, box plots to present the normalization result, principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) plots to present the clustering result (S1A–S1F Fig). Differential analysis was carried out by the limma package [12] and visualized using the ggplot2 package and ComplexHeatmap package [13]. The adjusted p value (false discovery rate, FDR) < 0.05 and | log fold change (FC)| > 1 for the DEGs were set as screening conditions.

Functional enrichment analysis and PPI networks construction

Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were implemented through the clusterProfiler package of R [14]. FDR < 0.05 for the enriched item was considered statistically significant. After predicting the interactions between DEGs in the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://string-db.org/) [15] by setting the combined score > 0.4, the PPI networks were built using Cytoscape [16] and cytoHubba [17] respectively.

Hub genes identification and prognostic models construction

Through the TCGA database, high expression and low expression groups were divided by the median of DEGs expression and survival analysis was performed with the survival package of R [18]. DEGs with potential prognostic significance were identified as hub genes by log-rank analysis and visualization was achieved through the survminer package. The Wilcoxon rank sum test was chosen for correlation analysis of hub genes expression with clinical variables, and the ggplot2 package was used for visualization. All clinical variables of STS were integrated into univariate Cox regression, parameters were included in overall survival (OS) and progression free survival (PFS), and variables that were significant for univariate analysis were integrated into multivariate Cox regression. After evaluating significant variables in the multivariate analysis by the timeROC package, they were incorporated into a nomogram to construct the model [19]. The population of the model was 263 patients with well-defined STS, from the TCGA database and screened with corresponding clinical information, and the model was validated by a calibration curve, with visualization implemented through the rms package. The results were considered statistically significant at p < 0.05.

Immune analysis

Through the TCGA database, high expression and low expression groups were classified according to the upper and lower quartiles of DEGs expression and the GSVA package of R accompanied by Spearman correlation test was applied for immune analysis [20]. 7 popular immune checkpoint genes (ICGs) [21, 22] and 24 immune cells composing the main tumor immune microenvironment [23] were included by applying the CIBERSORT deconvolution algorithm, and the ggplot2 package was used to construct co-expression plots. The results were considered statistically significant at p < 0.05.

Statistical analysis

Statistical analysis relied on R software (version 3.6.3) and Cytoscape software (version 3.8.2).

Results

Ferroptosis-related DEGs identification in STS

A total of 927 DEGs for STS were identified in 183 samples from the GSE21122, GSE6481 and GSE2719 datasets, including 345 for up-regulation and 582 for down-regulation. The volcano plot covered all genes in differential analysis (Fig 1A) and the heatmap displayed the top 20 DEGs for each of up-regulation and down-regulation (Fig 1B). Among them, a total of 40 genes were associated with ferroptosis (Table 1), including 6 for up-regulation and 34 for down-regulation (Fig 1C).

Fig 1

Identification of ferroptosis-related DEGs in STS.

(A) The volcano plot of all genes. (B) The heatmap of the top 20 DEGs for each of up-regulation and down-regulation. (C) The Venn diagram of the intersection among up-regulated DEGs, down-regulated DEGs and ferroptosis-related genes.

Table 1

Ferroptosis-related DEGs in STS.

No.	Genes	Expression	FDR	log FC
1	CDKN2A	Up-regulation	<0.001	2.012
2	HELLS	Up-regulation	<0.001	2.015
3	GDF15	Up-regulation	0.020	1.447
4	STMN1	Up-regulation	<0.001	1.162
5	RRM2	Up-regulation	<0.001	2.550
6	AURKA	Up-regulation	<0.001	1.447
7	PGD	Down-regulation	0.001	-1.294
8	ACO1	Down-regulation	<0.001	-1.714
9	GABARAPL1	Down-regulation	<0.001	-1.521
10	EGFR	Down-regulation	0.002	-1.189
11	CDO1	Down-regulation	<0.001	-2.184
12	EPAS1	Down-regulation	<0.001	-1.969
13	HILPDA	Down-regulation	0.007	-1.002
14	LPIN1	Down-regulation	<0.001	-1.003
15	TLR4	Down-regulation	<0.001	-1.037
16	AKR1C1	Down-regulation	<0.001	-2.875
17	AKR1C3	Down-regulation	<0.001	-2.294
18	GCLC	Down-regulation	<0.001	-1.694
19	NQO1	Down-regulation	<0.001	-2.045
20	MT1G	Down-regulation	<0.001	-1.565
21	SCD	Down-regulation	<0.001	-2.246
22	CDKN1A	Down-regulation	<0.001	-1.196
23	PRDX6	Down-regulation	<0.001	-1.083
24	PLIN2	Down-regulation	0.018	-1.054
25	ZFP36	Down-regulation	<0.001	-2.141
26	CAV1	Down-regulation	<0.001	-1.255
27	PTGS2	Down-regulation	<0.001	-1.866
28	DUSP1	Down-regulation	<0.001	-1.566
29	NCF2	Down-regulation	0.002	-1.049
30	BNIP3	Down-regulation	0.001	-1.602
31	PCK2	Down-regulation	<0.001	-1.341
32	TXNIP	Down-regulation	<0.001	-1.333
33	IL6	Down-regulation	<0.001	-2.806
34	CXCL2	Down-regulation	<0.001	-3.999
35	MAP3K5	Down-regulation	<0.001	-1.599
36	SLC2A3	Down-regulation	0.008	-1.129
37	ACSF2	Down-regulation	<0.001	-1.181
38	TF	Down-regulation	<0.001	-3.001
39	ATF3	Down-regulation	<0.001	-1.931
40	GPX4	Down-regulation	<0.001	-1.325

DEGs, differentially expressed genes; STS, soft tissue sarcoma; FC, fold change.

Ferroptosis-related DEGs enrichment analysis

After conducting enrichment analysis on the 40 ferroptosis-related DEGs, the top 5 enriched entries and pathways were obtained to construct the GO enrichment network (Fig 2A) and the KEGG enrichment network (Fig 2B) respectively. GO analysis indicated that these genes functioned in response to metalion (GO: 0010038), response to corticosteroid (GO: 0031960), response to nutrient levels (GO: 0031667), response to oxidative stress (GO: 0006979) and reactive oxygen species metabolic process (GO: 0072593). KEGG analysis suggested that corresponding genes were significantly associated with glutathione metabolism (hsa00480), FoxO signaling pathway (hsa04068), HIF-1 signaling pathway (hsa04066), legionellosis (hsa05134) and Kaposi sarcoma-associated herpesvirus infection (hsa05167).

Fig 2

Enrichment analysis of ferroptosis-related DEGs in STS.

(A) The network of GO enrichment analysis for the top 5 entries. (B) The network of KEGG enrichment analysis for the top 5 pathways.

PPI networks construction

Interactions of ferroptosis-related DEGs in STS were predicted by STRING and a PPI network covering 38 nodes and 97 edges was structured by Cytoscape (Fig 3A). Subsequently we used cytoHubba to further identify the top 25 genes and build a 25-node, 69-edge PPI network (Fig 3B).

Fig 3

PPI networks of ferroptosis-related DEGs in STS.

(A) The PPI network covering 38 nodes and 97 edges using Cytoscape. (B) The PPI network covering 25 nodes and 69 edges using cytoHubba.

Hub genes identification

Survival analysis revealed the potential prognostic value of HELLS, STMN1 in up-regulation DEGs and EPAS1, CXCL2, NQO1, IL6 in down-regulation DEGs, with high expression of HELLS, STMN1 and low expression of EPAS1, CXCL2, NQO1, IL6 suggesting a short OS in STS patients (Fig 4A). Accordingly, these 6 genes were identified as hub genes for further study and the association between hub genes and STS clinical variables was analyzed (Fig 4B). Compared to male patients with STS, female patients exhibited high expression of HELLS, STMN1, NQO1 and low expression of EPAS1. Compared to other histological types, leiomyosarcoma showed high expression of HELLS, EPAS1, NQO1 and low expression of IL6. Besides, HELLS and STMN1 was highly expressed in STS metastatic patients compared to non-metastatic patients.

Fig 4

Clinical relevance of hub genes.

(A) K-M curves of hub genes expression. (B) Box plots of hub genes expression and clinical variables.

Hub genes GSEA analysis

The 263 STS samples from TCGA database were divided into low expression and high expression groups based on the median of hub gene expression respectively for GSEA analysis. GSEA manifested significant differences in enrichment of MSigDB Collection (FDR < 0.05) and significant-enriched gene sets were ranked based on normalized enrichment score (NES) values. The top-two most significant-enriched gene sets for HELLS were G alpha signaling events and olfactory transduction (Fig 5A). The top-two most significant-enriched gene sets for STMN1 were signaling by Rho GTPases and processing of capped intron-containing pre-mRNA (Fig 5B). The top-two most significant-enriched gene sets for EPAS1 were M-phase and metabolism of amino acids and derivatives (Fig 5C). The top-two most significant-enriched gene sets for CXCL2 were neuronal system and neuroactive ligand receptor interaction (Fig 5D). The top-two most significant-enriched gene sets for NQO1 were signaling by interleukins and Leishmania infection (Fig 5E). The top-two most significant-enriched gene sets for IL6 were signaling by interleukins and GPCR-ligand binding (Fig 5F).

Fig 5

GSEA analysis of hub genes.

(A) HELLS. (B) STMN1. (C) EPAS1. (D) CXCL2. (E) NQO1. (F) IL6.

EPAS1 and HELLS might be independent prognostic factors for STS patients

Clinical variables for STS and the expression of 6 hub genes were included in univariate Cox regression analysis and those factors of significance were further subsumed into multivariate Cox regression analysis. The results revealed that when the prognostic indicator was OS, high grade residual tumor, metastasis, positive margin status, high expression of HELLS and STMN1, low expression of EPAS1, CXCL2, NQO1, IL6 were associated with poor prognosis. Furthermore, residual tumor, metastasis status, margin status, EPAS1 expression might be independent prognostic factors for OS in STS patients (Table 2). When the prognostic indicator was PFS, high grade residual tumor, metastasis, positive margin status, HELLS high expression were associated with poor prognosis. Residual tumor, metastasis status, margin status, HELLS expression might be independent prognostic factors for PFS in STS patients (Table 3).

Table 2

Univariate and multivariate Cox regression analysis to identify prognostic factors for OS in patients with STS.

Variables	Total(N)	Univariate analysis		Multivariate analysis
		Hazard ratio (95% CI)	p-value	Hazard ratio (95% CI)	p-value
Age (>60 vs. < = 60)	263	1.285 (0.864–1.911)	0.216
Gender (Male vs. Female)	263	0.905 (0.607–1.349)	0.623
Race (White vs. Others)	254	0.725 (0.350–1.501)	0.386
Histological type (Leiomyosarcoma vs. Others)	263	0.913 (0.611–1.363)	0.656
Radiation therapy (Yes vs. No)	257	0.864 (0.557–1.339)	0.513
Residual tumor (R2 vs. R0&R1)	235	8.365 (3.972–17.617)	<0.001	22.480 (6.480–77.987)	<0.001
Metastasis status (Yes vs. No)	179	2.888 (1.762–4.732)	<0.001	3.493 (1.852–6.585)	<0.001
Margin status (Positive vs. Negative)	213	1.957 (1.215–3.151)	0.006	1.879 (1.054–3.350)	0.032
HELLS (High vs. Low)	263	1.883 (1.250–2.836)	0.002	1.272 (0.603–2.683)	0.527
STMN1 (High vs. Low)	263	1.859 (1.242–2.783)	0.003	0.844 (0.416–1.712)	0.638
EPAS1 (Low vs. High)	263	1.627 (1.093–2.424)	0.017	2.698 (1.347–5.406)	0.005
CXCL2 (Low vs. High)	263	1.625 (1.089–2.425)	0.017	1.142 (0.531–2.455)	0.735
NQO1 (Low vs. High)	263	1.504 (1.009–2.242)	0.045	1.307 (0.737–2.319)	0.360
IL6 (Low vs. High)	263	1.624 (1.085–2.432)	0.018	1.191 (0.557–2.544)	0.652

OS, overall survival; STS, soft tissue sarcoma.

Table 3

Univariate and multivariate Cox regression analysis to identify prognostic factors for PFS in patients with STS.

Variables	Total(N)	Univariate analysis		Multivariate analysis
		Hazard ratio (95% CI)	p-value	Hazard ratio (95% CI)	p-value
Age (>60 vs. < = 60)	263	0.938 (0.675–1.305)	0.706
Gender (Male vs. Female)	263	1.092 (0.785–1.520)	0.600
Race (White vs. Others)	254	1.155 (0.605–2.203)	0.662
Histological type (Leiomyosarcoma vs. Others)	263	1.101 (0.790–1.536)	0.570
Radiation therapy (Yes vs. No)	257	1.124 (0.788–1.602)	0.519
Residual tumor (R2 vs. R0&R1)	235	4.230 (2.140–8.360)	<0.001	4.985 (1.811–13.723)	0.002
Metastasis status (Yes vs. No)	179	7.294 (4.700–11.318)	<0.001	6.672 (4.087–10.894)	<0.001
Margin status (Positive vs. Negative)	213	2.176 (1.493–3.173)	<0.001	2.497 (1.551–4.021)	<0.001
HELLS (High vs. Low)	263	1.549 (1.111–2.160)	0.010	1.707 (1.040–2.803)	0.035
STMN1 (High vs. Low)	263	1.368 (0.981–1.908)	0.064
EPAS1 (Low vs. High)	263	1.053 (0.757–1.464)	0.760
CXCL2 (Low vs. High)	263	1.017 (0.731–1.413)	0.922
NQO1 (Low vs. High)	263	1.343 (0.965–1.870)	0.081
IL6 (Low vs. High)	263	1.303 (0.937–1.813)	0.116

PFS, progression free survival; STS, soft tissue sarcoma.

Validation of EPAS1 and HELLS prognostic value

Predictive efficacy of EPAS1 and HELLS for prognosis was internally verified using time-dependent receiver operating characteristic (ROC) curves in TCGA database (Fig 6A and 6B). Subsequently, predictive efficacy of EPAS1 and HELLS for prognosis was externally validated using time-dependent ROC curves in GEO database, which exhibited similar prognostic value (Fig 6C and 6D).

Fig 6

Validation of EPAS1 and HELLS prognostic value.

(A) The ROC curve of EPAS1 predicting OS for STS patients in TCGA database. (B) The ROC curve of HELLS predicting PFS for STS patients in TCGA database. (C) The ROC curve of EPAS1 predicting OS for STS patients in GEO database. (D) The ROC curve of HELLS predicting PFS for STS patients in GEO database.

Construction and evaluation of prognostic models for STS patients

The statistically significant results of the multivariate Cox regression analysis were used to construct the separate nomogram for prediction models of OS (Fig 7A) and PFS (Fig 7B) in STS patients. For both patients with primary STS and metastatic STS, the indicators for each nomogram were derived from the primary tumor foci. The C-indexes for OS and PFS model were 0.756 (0.719–0.794) and 0.782 (0.756–0.808) respectively. Calibration curves for the models of OS (Fig 7C) and PFS (Fig 7D) confirmed the consistency of the predicted prognosis with the actual outcome.

Fig 7

Visualization of prognostic prediction models in STS.

(A) The nomogram for predicting OS. (B) The nomogram for predicting PFS. (C) The calibration curve to evaluate the OS nomogram. (D) The calibration curve to evaluate the PFS nomogram.

Association of hub genes expression and ICGs

CXCL2 and IL6 were shown to be immunologically relevant in 6 hub genes (Fig 8A). We correlated hub genes with ICGs and presented the results in co-expression heatmaps. CXCL2 and IL6 showed consistent results, with both CXCL2 and IL6 positively linked to the expression of PDCD1, CTLA4, TIGIT, IDO1 and CD27 (Fig 8B and 8C). Consistency of results and significant association with ICGs were not demonstrated in HELLS, STMN1, EPAS1 and NQO1 (Fig 8D–8G).

Fig 8

Analysis of ICGs in STS.

(A) The Venn diagram of the intersection between hub genes and immune-related genes. The correlation of ICGs with the expression of CXCL2 (B), IL6 (C), HELLS (D), STMN1 (E), EPAS1 (F), NQO1 (G).

Association of hub genes expression and immune cells infiltration

6 hub genes were subsequently correlated with 24 immune cells in the tumor microenvironment (Fig 9A). In addition to CXCL2 and IL6, we observed that HELLS was also strongly associated with immune cells and exhibited the consistent result with CXCL2 and IL6. CXCL2 and IL6 with down-regulated in STS were significantly positively related to macrophage, neutrophil, cytotoxic cell, dendritic cell (DC), interdigitating dendritic cell (iDC), plasmacytoid dendritic cell (pDC) (all r > 0.3) (Fig 9B and 9C), and HELLS with up-regulated in STS was comparatively negatively correlated with macrophage, neutrophil, cytotoxic cell, DC, iDC, pDC (all r < -0.3) (Fig 9D) Consistency of results and significant association with immune cells were not demonstrated in STMN1, EPAS1 and NQO1.

Fig 9

Analysis of immune cells in STS.

(A) The correlation of 6 hub genes expression with 24 immune cells. (B) The correlation of CXCL2 expression with 6 immune cells. (C) The correlation of IL6 expression with 6 immune cells. (D) The correlation of HELLS expression with 6 immune cells.

Discussion

STS is a set of heterogeneous malignancies involving over 100 different histological types, with widely varying treatment outcomes [24]. In general, current therapies are only effective in a small proportion of STS, with limited efficacy in most STS and even recurrence in more than 50% of patients [25]. Although it was considered that STS was extremely insensitive to immune responses in the past, which precluded the application of immunotherapy to STS, recent studies have demonstrated a large degree of immune heterogeneity within the subclass of STS and some positive responses to immunotherapy have also been reported in successive clinical trials [26, 27]. Partial STS subtypes, including dedifferentiated liposarcoma, leiomyosarcoma, embryonal rhabdomyosarcoma and undifferentiated pleomorphic sarcoma, have been identified as featuring high levels of immune cells infiltration and ICGs expression, and exhibit a potentially active reaction to immune checkpoint inhibitors (ICIs) therapy [3]. Consequently it is essential to locate critical ICGs and immune infiltration factors adapted to STS.

Currently, the availability of immunotherapy alone is severely limited in patients with most tumor types. Since extensive crossover between immunotherapy and non-apoptotic RCD mechanisms has been detected, non-apoptotic cancer cell death accompanied by immunomodulation is considered an exceedingly promising strategy for cancer treatment [28]. Ferroptosis, a neoteric form of RCD with unique biological and morphological features, has been shown to interact with the tumor immune response and can influence immunotherapeutic efficacy on the one hand [8], and in turn is regulated by immune cells on the other [7]. In the present study, based on ferroptosis-related genes in STS, we identified potential ICGs including PDCD1, CTLA4, TIGIT, IDO1 and CD27, which might serve as important targets for immunotherapy. In addition, we explored a group of closely related immune cells including macrophage, neutrophil, cytotoxic cell, DC, iDC and pDC, which might act as pivotal regulators in the immune microenvironment of STS. Interestingly, we observed high concordance of immune analysis results for HELLS with CXCL2 and IL6, revealing for the first time a possible immunological effect of HELLS in tumor.

Among dedifferentiated liposarcoma, undifferentiated pleomorphic sarcoma and leiomyosarcoma, it has been confirmed that tumors with high immunogenic gene profiles are accompanied by high levels of PDCD1 expression [29]. PD-1, as the most researched immune checkpoint, is encoded by PDCD1 and also occupies an important position in STS study. More than half of the samples in a STS cohort had positive expression of PD-1 on immune cells [30], and PD-1 expression is also generally considered to be associated with the prognosis of STS patients [31, 32]. Moreover, CTLA4, IDO1 and other ICGs have demonstrated varying degrees of value for STS management [3]. In terms of immune cells, macrophage has been established as a significant player in several sarcoma types [33], with the modification of the macrophage phenotype from tumor-promoting to tumor-suppressing regarded as a promising option for STS treatment [34]. And a range of immunotherapies targeting DC, iDC and pDC may be well tolerated in patients with refractory STS due to their excellent immunological response and safety profile, as well as offering the opportunity to prevent recurrence of sarcoma [35]. On balance, for most STS subtypes, immunotherapy may be required novel regimens and combinations [34].

In addition, we substantiated that EPAS1 and HELLS might act as independent prognostic predictors of STS, leading to the construction of two efficient prognostic models. For both patients with primary STS and metastatic STS, the indicators for each nomogram were derived from the primary tumor foci. However, the obtained model still needs to be further verified in an independent cohort. The expression of 6 hub genes was discovered to be associated with survival during the model construction, with EPAS1, STMN1, CXCL2, NQO1 being identified for the first time in STS. EPAS1 is a diver of ferroptosis [36], compared to normal tissue, which is expressed at lower levels in most human STS [37]. Zhu et al. found that up-regulation of EPAS1 significantly enhanced the growth inhibition of gastric adenocarcinoma and that targeting EPAS1 might be an alternative therapeutic approach for cancer [38]. Relatively, HELLS, NQO1 are suppressors of ferroptosis [39, 40]. Law et al. suggested that HELLS mediated epigenetic silencing of various cancer suppressor genes and evidenced in hepatocellular carcinoma that its overexpression potentiated tumor cell migration and proliferation [41]. Huang et al. identified high expression and prognostic impact of HELLS in STS samples [42], which also underpinned our findings. In the TCGA database of STS samples, GSEA indicated that NQO1 was closely connected to interleukin-related signaling pathways. NQO1 has been confirmed to interact with interleukins in a variety of cancers, thereby affecting the inflammatory response and participating in the immune regulation associated with the tumor microenvironment [43, 44]. As for STMN1, CXCL2 and IL6, they are currently treated as biomarkers of ferroptosis and their expression is monitored for down-regulation once ferroptosis occurs [45, 46]. STMN1 is commonly recognized as an oncogene, and its up-regulation is tightly linked to the malignant behaviour and poor prognosis of various tumors [47]. In leiomyosarcoma, STMN1 has also been characterised by high expression and can be a sensitive biomarker with strong diagnostic efficacy [48, 49]. Our study revealed the potential immunological relevance and clinical value of these novel ferroptosis-related genes, which might contribute to the precise treatment and prognostic prediction of patients with STS.

Conclusions

In conclusion, we identified novel ferroptosis-related genes with prognostic value in STS. Furthermore, we searched out potential immune checkpoints and critical immune cells.

Evaluation of data pre-processing from the GEO database.

Comparison of box plots (A-B), PCA plots (C-D) and UMAP plots (E-F) before and after data pre-processing.

(TIF)

Click here for additional data file.

20 Oct 2021

PONE-D-21-24357Immunological and prognostic significance of novel ferroptosis-related genes in soft tissue sarcomaPLOS ONE

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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: Partly

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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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

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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

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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: In this manuscript Zhao et coworkers performed an in silico analysis of RNAseq data from a very large series of well characterized soft tissue sarcoma (STS) samples spanning different subtypes with the aim to explore the immunological and prognostic value of ferroptosis-related genes, in particular of 6 genes (HELLS, STMN1, EAPS1, CXCL2, NQO1, IL6).

The here presented data are interesting, but the manuscript is too descriptive and, in some points, confused. In particular, authors should address the following major issues:

-Authors should clearly specify which samples they compared to identify genes differentially express in STS. Did they compare tumor vs normal tissues?

-Authors should add in the main text a table reporting the name of the 40 ferroptosis-related genes along with their fold change.

-Results reported in figure 4B are not commented in the text. Authors should discuss the differential expression of the 6 hub genes between male and female, leiomyosarcoma and other STS subtypes, different disease progression.

-Authors should comment and discuss gene sets differentially enriched in STS TCGA samples divided on the basis of expression of the 6 hub genes.

-Paragraphs entitled ‘Association of hub genes expression and ICGs’ and ‘Association of hub genes expression and immune cells infiltration’ should be moved at the end of Results section.

-Authors should specify which deconvolution tool they used to estimate population of immune cells infiltrating the tumors.

-‘Immunohistochemical validation’ paragraph should be removed, as in my opinion it does not represent a validation of results obtained by in silico analysis.

-Extensive editing of English language is required.

Minor issues:

-In Figure 2, names of GO and KEGG networks are unreadable. May be authors should add them in the bar graph.

-In Figure 3, color legend is missing. Please, add it.

Reviewer #2: Thank you for putting together this comprehensive manuscript that identifies several novel targets for immunotherapy agents derived from the ferroptosis pathway in sarcoma.

It will be interesting to how you intend to translate these findings into clinical trials.

**********

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.

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: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

1 Nov 2021

Dear editor,

Thank you very much for your letter and advice. We have revised the paper, and would like to re-submit it for your consideration. We have addressed the comments raised by the reviewers, and the amendments are highlighted in red in Revised Manuscript with Track Changes.

We hope that the revision is acceptable, and we look forward to hearing from you soon.

With best wishes,

Yours sincerely,

We would like to express our sincere gratitude to the reviewers for their constructive and positive comments. It is an honor for our manuscript to receive such rigorous and meticulous guidance. Explain accordingly one by one:

Reviewer #1 comments:

-Authors should clearly specify which samples they compared to identify genes differentially express in STS. Did they compare tumor vs normal tissues? Thank you for your insightful suggestion. We have added a detailed explanation and red-flagged it in the method section of the article.

-Authors should add in the main text a table reporting the name of the 40 ferroptosis-related genes along with their fold change. This is a great recommendation. We have added Table 1 to show that 40 ferroptosis-related genes.

-Results reported in figure 4B are not commented in the text. Authors should discuss the differential expression of the 6 hub genes between male and female, leiomyosarcoma and other STS subtypes, different disease progression. Thanks for the comment. We have added a detailed explanation of the clinical relevance of the hub gene in the results section of the article and have highlighted it in red.

-Authors should comment and discuss gene sets differentially enriched in STS TCGA samples divided on the basis of expression of the 6 hub genes. Thanks for your reminder. We have added a description of the GSEA results in the results section of the article and discussed the relevant results in the discussion section. All of the above changes are highlighted in red in the article.

-Paragraphs entitled ‘Association of hub genes expression and ICGs’ and ‘Association of hub genes expression and immune cells infiltration’ should be moved at the end of Results section. Thank you for your suggestion. They have been moved at the end of Results section.

-Authors should specify which deconvolution tool they used to estimate population of immune cells infiltrating the tumors. Thanks for your reminder. We have added the relevant information and red-flagged it in the method section of the article.

-‘Immunohistochemical validation’ paragraph should be removed, as in my opinion it does not represent a validation of results obtained by in silico analysis. Thanks for the comment. ’Immunohistochemical validation’ paragraph has been removed.

-Extensive editing of English language is required. Thank you for your insightful suggestion. In response to the language issues in the article, we have carefully reviewed and gone through extensive revisions. Subsequently, we found multiple English language professionals to review and correct the grammar and vocabulary in our article. Thank you again for your reminder.

-In Figure 2, names of GO and KEGG networks are unreadable. May be authors should add them in the bar graph. Thank you for your suggestion. Since the names of both KEGG and GO analysis results are too long, and the size of Figure is limited to the extent that they cannot be shown clearly in the bar chart. Therefore, we chose to provide additional descriptions of the names and numbers of the KEGG and GO analysis results in the Results section of the article, which are highlighted in red.

-In Figure 3, color legend is missing. Please, add it. Thanks for the comment. We have added them in the Figure 3.

Reviewer #2 comments:

Thank you for putting together this comprehensive manuscript that identifies several novel targets for immunotherapy agents derived from the ferroptosis pathway in sarcoma. It will be interesting to how you intend to translate these findings into clinical trials. Thanks for your reminder. This is one of the few comprehensive analyses for soft tissue sarcoma (STS) with large samples, multiple subtypes and multiple databases. Ferroptosis, immune checkpoint, immune cell and prognostic model were integrated. Initially, we identified novel ferroptosis-related genes with prognostic value in STS. Furthermore, we searched out potential key immune checkpoints and immune cells, and revealed for the first time their possible immune relevance. Ultimately, we constructed two efficient predictive models for prognosis of STS patients.

The results of our current study are a guide to translate and carry out subsequent experiments. The first is to carry out relevant experiments around ferroptosis, and to carry out ROS, GPX4, and Fe level measurements in STS samples around the six ferroptosis-related hub genes we identified, and to verify the relationship with related pathway molecules based on the results of enrichment analyses to find upstream and downstream mechanisms. Second, subsequent experiments can be centered on precise immunotherapy of STS. Since the original view was that immunotherapy for STS was almost useless, immunotherapy for STS is now possible with the discovery of novel RCD mechanisms such as ferroptosis. Due to the heterogeneity of STS, immunotherapy around STS requires a very high degree of precision. It is crucial to clarify the subtypes of STS that are sensitive to immunotherapy and to specify the immune cells, immune checkpoint genes, and reciprocal genes involved in STS immunotherapy. The immune cells and immune checkpoint genes identified in our current study with the broad category of STS are theoretically applicable to immunological studies of all STS subtypes. In addition, we found the immunogenicity of HELLS for the first time, and HELLS plays an important role in immunological studies of STS as a ferroptosis-related gene, which also has an innovative guiding effect on the development of related experiments. The final aspect involved is the validation of the clinical data, and the 2 prognostic models we designed were applicable to cohort studies of all STS subtypes. Of course to the extent that the bioinformatics analysis we performed only served as a guide, the definitive conclusions need to be validated by a large number of follow-up experiments.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

15 Nov 2021

PONE-D-21-24357R1Immunological and prognostic significance of novel ferroptosis-related genes in soft tissue sarcomaPLOS ONE

Dear Dr. Cui,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Authors did a great job in addressing comments from a reviewer. The manuscript has been furtherly reviewed by a bioinformatician and comments provided. Also, some methodological comments on nomograms need to be addressed.

Please submit your revised manuscript by Dec 30 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Sandro Pasquali, M.D., Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

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.

Additional Editor Comments:

Regarding the generation of prognostic nomograms, some clarifications and modifications are needed. The authors should consider the following publication "Kattan MW, Hess KR, Amin MB, et al;members of the AJCC Precision MedicineCore. American Joint Committee on Cancer acceptance criteria for inclusion of riskmodels for individualized prognosis in th epractice of precision medicine. CA Cancer J Clin. 2016;66:370–374." as a guidence to develop and present their nomograms. In this article there is checklist, which should be followed. For instance, it is unclear which is the patient population (e.g. primary or metastatic tumour) that was used to generate the nomograms and it is unclear if nomograms were validated. Although I understand that Authors do not aim at targeting inclusion in AJCC staging, if the authors cannot match the required quality standards for their nomograms it would be probably better to exclude them from the current article.

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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 #3: (No Response)

**********

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: (No Response)

Reviewer #3: Yes

**********

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

Reviewer #1: (No Response)

Reviewer #3: 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: (No Response)

Reviewer #3: 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: (No Response)

Reviewer #3: 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: The authors have addressed all my questions and made the necessary changes to the manuscript. I have no further comments.

Reviewer #3: Authors describe a panel of genes, related to ferroptosis able to have a significative discrimination power in prognostic and immunological model of soft tissue sarcomas.

This article has an innovative character and could contribute to knowledge of soft tissue sarcoma.

Minor reviews:

1) Authors should specify which metric they used for ranking genes in GSEA.

2) Authors correctly used ComBat algorithm to eliminate batch effects and, for this reason, they should make explicit in supplementary figure B and C which samples belonged to the same dataset (using different symbols or colors) to highlight the dataset independence (technical variability) from the biological variability.

3) Authors should maintain the same color scheme: in figure 1B the legend bar (add a title for describing) shows positive magnitudo of fold change in red color, and blue for negative (and do the same also for high and low expression in survival analysis), while in the volcano plot (figure 1A) the color scheme is inverted. To avoid any doubt change consistently also the color of the circles in the Venn diagram (figure 1C). Explicit also the type of p-value correction in y-label in volcano plot.

**********

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 #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

17 Nov 2021

Dear editor,

Thank you very much for your letter and advice. We have revised the paper, and would like to re-submit it for your consideration. We have addressed the comments raised by the reviewers, and the amendments are highlighted in red in Revised Manuscript with Track Changes.

We hope that the revision is acceptable, and we look forward to hearing from you soon.

With best wishes,

Yours sincerely,

Editor Comments:

Please review your reference list to ensure that it is complete and correct. Thanks for the comment. We have checked the reference list and made sure it is complete and correct.

Regarding the generation of prognostic nomograms, some clarifications and modifications are needed. Thank you for the reminder. Your suggestion is very meaningful. We have conducted a self-review based on the checklist according to your guidance. We have added and highlighted the missing descriptions in the manuscript, such as the patient population that generated the nomogram. We found that we met most of the requirements in the checklist, except for our study which may lack further validation. On the one hand, our study is one of the few that covers almost all common subtypes of soft tissue sarcoma and is therefore highly universal; however, on the contrary, validation of such a wide range of subtype models may not be so easy to achieve. In our study, we performed C-indexes and Calibration curves validation. Of course we clearly recognize that this is not sufficient and we have added a description of the shortcomings of the constructed nomogram in the discussion section, but we believe that our nomogram may be of some value as a guide and reference for subsequent related studies. Also, we fully understand your suggestion to remove the nomogram part, and we will comply with your suggestion to remove this section if you still feel that it is not necessary to keep it.

Reviewer #3 Comments:

We would like to express our sincere gratitude to the reviewer for the constructive and positive comments. It is an honor for our manuscript to receive such rigorous and meticulous guidance. Explain accordingly one by one:

1.Authors should specify which metric they used for ranking genes in GSEA. Thank you for your insightful suggestion. We have added a description of the GSEA ranking methodology in the results section.

2.They should make explicit in supplementary figure B and C which samples belonged to the same dataset. Thank you for your valuable comments. We have considered what you said before, but because the sample size included in our study was too large, adding 2 legends to one figure would have made the images very cluttered. After much testing and comparison, we finally chose the optimal approach and recreated the Figure S1. We divided the original image into 2 images and used different legend for each image.

3.Authors should maintain the same color scheme. Thanks for your reminder. We recreated the Figure 1 to ensure that the legend colors of the positive and negative expressions of the volcano plot, heat map, venn diagram, and survival curves remain consistent. We also clarified the description of the Y-label of the volcano plot. Also we added the missing legend to the heat map.

Submitted filename: Response to Reviewers v2.docx

Click here for additional data file.

15 Dec 2021

PONE-D-21-24357R2Immunological and prognostic significance of novel ferroptosis-related genes in soft tissue sarcomaPLOS ONE

Dear Dr. Cui,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.Please ensure that your decision is justified on PLOS ONE’s publication criteria and not, for example, on novelty or perceived impact.

Please submit your revised manuscript by Jan 29 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Sandro Pasquali, M.D., Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

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.

Additional Editor Comments:

Thank you for addressing reviewers and editor comments in your revised manuscript. Considering that nomograms are not validated, they should not be considered conclusive and therefore the statement in the conclusion of abstract and manuscript about nomogram should be removed. Also, can you specify whether nomograms were generated on primary or metastatic or both tumours and patients? This should clearly stated in the results section and discussion.

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

18 Dec 2021

Dear editor,

Thank you very much for your letter and advice. We have revised the paper, and would like to re-submit it for your consideration. We have addressed the comments and the amendments are highlighted in red in Revised Manuscript with Track Changes.

We hope that the revision is acceptable, and we look forward to hearing from you soon.

With best wishes,

Yours sincerely,

Editor Comments:

- Please review your reference list to ensure that it is complete and correct.

Thanks for the comment. We have checked the reference list and made sure it is complete and correct.

Additional Editor Comments:

- The statement in the conclusion of abstract and manuscript about nomogram should be removed.

Thank you for your suggestion. We have removed the statement of nomogram in the conclusion section.

- Can you specify whether nomograms were generated on primary or metastatic or both tumours and patients? This should be clearly stated in the results section and discussion.

Thank you for your reminder. We have clarified this issue in the result section and in the discussion section.

Submitted filename: Response to Reviewers v3.docx

Click here for additional data file.

20 Dec 2021

Immunological and prognostic significance of novel ferroptosis-related genes in soft tissue sarcoma

PONE-D-21-24357R3

Dear Dr. Cui,

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,

Sandro Pasquali, M.D., Ph.D.

Academic Editor

PLOS ONE

24 Dec 2021

PONE-D-21-24357R3

Immunological and prognostic significance of novel ferroptosis-related genes in soft tissue sarcoma

Dear Dr. Cui:

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.

If we can help with anything else, please email us at plosone@plos.org.

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

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Sandro Pasquali

Academic Editor

PLOS ONE