# Identification of Ferroptosis-Related Gene Prognostic Signature and HSF1 for Reversing Doxorubicin and Gemcitabine Resistance in Uterine Carcinosarcoma

Authors: Shuxia Han; Qing Liu; ZhiJuan Yang; JingWen Ma; Dan Liu; Caiping Yan; Duoxian Liang
Journal: Disease Markers
 (2022)

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## Abstract

Purpose. Iron metabolism and ferroptosis play crucial roles in the pathogenesis of cancer. In this study, we aim to study the role of ferroptosis-related genes (FRGs) in uterine carcinosarcoma (UCS) and identify potential target for UCS. Methods. Prognostic differentially expressed FRGs were identified of in the TCGA cohort. Integrated analysis, cox regression, and the least absolute shrinkage and selection operator (LASSO) methods of FRGs were performed to construct a multigene signature prognostic model. Moreover, a dataset from Gene Expression Omnibus (GEO) served as an external validation. HSF1 was knockdown in MES-SA and FU-MMT-1 cells, and cell viability, lipid ROS, and intracellular iron level were detected when combined with doxorubicin or gemcitabine. Result. Five FRGs were selected to construct a prognostic model of UCS. The group with high-risk signature score exhibited obviously lower overall survival (OS) than the group with low risk signature score in both TCGA and validated GEO cohorts. Multivariate Cox regression analysis further indicated that the risk score was an independent factor for the prognosis of UCS patients. The high-risk group of UCS has a higher sensitivity in the treatment of doxorubicin and gemcitabine. Knocking down of HSF1 in MES-SA and FU-MMT-1 cells was more sensitive to doxorubicin and gemcitabine via increasing ferroptosis. Conclusions. The five FRGs risk signature prognostic model having a superior and drug sensitivity predictive performance for OS in UCS, and HSF1 is a potential marker sensitive to doxorubicin and gemcitabine in UCS patients.


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## Body

## 1. Introduction

Uterine carcinosarcomas (UCS) are aggressive mesenchymal tumors, which account for 3-4% of all uterine malignant neoplasia [1]. They are a heterogeneous group of tumors that include several histologic types. The most common among them is leiomyosarcoma (around 60%) followed by endometrial stromal tumors (6-20% of UCS) and undifferentiated sarcomas (5%) [2]. Most UCS occur in women over 40 years of age. Patients often present with pelvic mass or abnormal uterine bleeding [3]. As most patients are asymptomatic at the time of diagnosis, therefore, they usually have poor prognosis. The heterogeneous of UCS is the critical factor for the different treatment response and prognosis [4]. However, there is an urgent need to explore molecular mechanisms and identify of novel biomarkers in the treatment and prognosis of UCS.Ferroptosis is a programmed, iron-dependent cell death driven by an accumulation of lipid peroxides. It differs from autophagy, apoptosis, and other regulated cell deaths [5]. The morphology of mitochondria undergoes dramatic changes during ferroptosis, including the loss of mitochondria crista, mitochondrial shrinkage with increased membrane density, and outer mitochondrial membrane rupture [6]. In recent years, ferroptosis has emerged as a promising treatment option for cancer therapy, especially in cancers resistant to conventional therapies [7]. Several studies have suggested the use of ferroptosis-related gene (FRG) signature as a prognostic feature for hepatocellular carcinoma [8]. However, the characterization of FRGs in progression of UCS and their prognostic potential for UCS needs further investigation.Herein, we established a FRG signature prognostic model and validated this model showing a superior accuracy in both TCGA and GEO cohorts. Moreover, the FRG model can predict the drug susceptibility treatment with imatinib, doxorubicin, and gemcitabine in UCS. We also found heat shock transcription factor-1 (HSF1) is a critical marker for the sensitivity for doxorubicin and gemcitabine UCS cells.


## 2. Materials and Methods

### 2.1. The Data Collection

The mRNA expression profiles and corresponding clinical information were downloaded from the TCGA database (containing 85 UCS samples) and GTEx database (containing 78 normal samples). Moreover, we used GEO (GSE119043) database (including 50 samples) as validated cohort. The total of 259 FRGs had been found in the database (http://www.zhounan.org/ferrdb/operations/download.html). All the databases we selected are public, and this study strictly followed database access policies and publishing guidelines, without requiring ethical approval from a local ethics committee.


### 2.2. Screening and Identification of FRG Signals Related with UCS Prognosis

The mRNA expression profiles from TCGA and GTEx database were matched with the FRGs. The Limma-R software package was used to identify the ferroptosis-related differentially expressed genes (DEGs) between uterine carcinosarcoma tissues and adjacent nontumor tissues, withfalsedetectionrate<0.05. Using the univariate Cox analysis of overall survival (OS) to detect the FRGs of prognostic potential through the “survival” R package. The overlapping prognostic DEG was included in the Lasso-Cox regression using the “glmnet” R package. According to the minimum criteria, the penalty parameter (λ) was tenfold cross validation. A risk score was calculated for each patient based on the expression of each gene and the corresponding regression coefficient, the formula was riskscore=correspondingregressioncoefficient×expressionofeachgene. The UCS patients were assigned into two parts (low- and high-risk groups) according to the optimal cutoff value of the signature score. PCA was based on the expression of characteristic genes in the TCGA database and is performed by using the “prcomp” functionality of the “stats” R package. Besides, T-SNE was performed with “Rtsne” R software package to observe the distribution of the low- and high-risk groups.


### 2.3. Building and Assessing the Model of the FRGs

Compared to other clinical features in the TCGA and GTEx database, univariate and multivariate Cox regression analyses were performed to determine whether risk score was an independent prognostic predictor of OS. Building a prediction nomogram based on independent predictors by using “rms” R package. Time-dependent receiver-operating characteristic (ROC) curve was used to assess the predictive ability of the nomogram through the “timeROC” R package. Using the UCS patients from GEO database to validate the predictive model about FRGs, which utilized the same formula as that in the GETx and TCGA database.


### 2.4. Analyzing Function Enrichment and Predicting Immunotherapy Response

An interactive network of overlapping prognostic DEGs was generated by using a string database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Genome Ontology (GO) were analyzed. The BH method adjustedP value. The activity of 13 immune-related pathways was evaluated by single-sample gene collection enrichment analysis (ssGSEA) in “gsva” R software package, and the infiltration fraction of 16 immune cells was calculated.


### 2.5. Predicting the Potential of Small Molecule Drugs

The limma software package was used to screen the differentially expressed genes (DEG) of two tumor subtypes (thefalsediscoveryrate<0.05, ∣foldchange∣≥1). The upregulated genes and downregulated genes were input to CMap database (http://portals.broadinstitute.org/cmap/). Using the enrichment value and P value to predict the potential of small molecule drugs for UCS patients.


### 2.6. Predicting the Sensitivity of Drugs

The Genomics of Drug Sensitivity in Cancer database was used to assess the sensitivity of chemotherapeutic agents by using the Genomics of Drug Sensitivity in Cancer database (GDSC;https://www.cancerrxgene.org/). Using the pRRophetic package to calculate the half maximal inhibitory concentration (IC50) of chemotherapeutic agents.


### 2.7. Cell Culture and Lentiviral Transduction

MES-SA and FU-MMT-1 cells were obtained from ATCC (Manassas, VA, USA). MES-SA cells were grown in McCoy’s 5a with 10% fetal bovine serum (FBS), and FU-MMT-1 cells were grown in RPMI-1640 medium with 10% FBS. The medium was supplemented with 100 U/ml of penicillin and streptomycin. The following plasmids were used to transfect in 293T cells and product lentivirus: HSF1 shRNA pLKO.1 plasmids (Thermo Fisher; TRCN0000007480, TRCN0000007484), pLKO.1 empty vector, pLKO.1 GFP shRNA, pCMV-R8.72 lentiviral packaging, and pCMV-VSV-G envelope plasmid (Addgene; plasmid ID 10878, 30323, 22036, and 8454). Stable HSF1 knockdown MES-SA and FU-MMT-1 cells were screened by puromycin.


### 2.8. Cell Viability and Colony Formation Assay

Cell viability was evaluated with a cell counting kit-8 (#C0038, Beyotime, China) according to manufacturer’s instructions. For the colony formation assays, 100 cells were seeded into 12-well plates and cultured for 21 d. Then, clones were then fixed and stained with 0.5% crystal violet. The colonies were counted, and the numbers recorded.


### 2.9. Western Blot Analysis

Proteins in cells were extracted, and electrophoretic separation was performed by SDS-polyacrylamide gel. Proteins in gel were transferred to nitrocellulose membrane and subsequently incubated with the primary antibody (HSF1 (D3L8I) Rabbit mAb #12972, CST;β-actin (13E5) Rabbit mAb #4970, CST). After incubation with peroxidase-conjugated secondary antibodies, the signals were visualized by enhanced chemiluminescence (Pierce, Rockford, IL, USA, #32106) according to manufacturer’s instructions.


### 2.10. Iron Assay

The relative iron concentration in cells was detected via the Iron Assay Kit (#ab83366, Abcam). In the assay, ferric carrier protein dissociates ferric iron into a solution in the presence of acid buffer. After reduction to the ferrous form (Fe2+), iron reacts with Ferene S to produce a stable colored complex and give absorbance at 593 nm.


### 2.11. Lipid ROS Assay

Fluorometric analysis of lipid ROS production was carried out by dye C11-BODIPY kit (#D-3861, Life Technologies) according to the manufacturer’s instructions. Oxidation of the polyunsaturated butadienyl portion of the dye resulted in a shift of the fluorescence emission peak from ~590 nm to ~510 nm.


### 2.12. Statistical Analysis

All statistical analyses were processed by the R software. The two-sidet-test was used to compare gene expression between UCS tissue and nearby nontumor tissue. Kaplan-Meier analysis and log-rank test were used to compare the OS of each group. Comparing the immune pathway or cell ssGSEA scores in the low- and high-risk groups by Mann-Whitney test. No special requirements, P values was two-side, and all P values < 0.05 was considered statistically significant.


## 2.1. The Data Collection

The mRNA expression profiles and corresponding clinical information were downloaded from the TCGA database (containing 85 UCS samples) and GTEx database (containing 78 normal samples). Moreover, we used GEO (GSE119043) database (including 50 samples) as validated cohort. The total of 259 FRGs had been found in the database (http://www.zhounan.org/ferrdb/operations/download.html). All the databases we selected are public, and this study strictly followed database access policies and publishing guidelines, without requiring ethical approval from a local ethics committee.


## 2.2. Screening and Identification of FRG Signals Related with UCS Prognosis

The mRNA expression profiles from TCGA and GTEx database were matched with the FRGs. The Limma-R software package was used to identify the ferroptosis-related differentially expressed genes (DEGs) between uterine carcinosarcoma tissues and adjacent nontumor tissues, withfalsedetectionrate<0.05. Using the univariate Cox analysis of overall survival (OS) to detect the FRGs of prognostic potential through the “survival” R package. The overlapping prognostic DEG was included in the Lasso-Cox regression using the “glmnet” R package. According to the minimum criteria, the penalty parameter (λ) was tenfold cross validation. A risk score was calculated for each patient based on the expression of each gene and the corresponding regression coefficient, the formula was riskscore=correspondingregressioncoefficient×expressionofeachgene. The UCS patients were assigned into two parts (low- and high-risk groups) according to the optimal cutoff value of the signature score. PCA was based on the expression of characteristic genes in the TCGA database and is performed by using the “prcomp” functionality of the “stats” R package. Besides, T-SNE was performed with “Rtsne” R software package to observe the distribution of the low- and high-risk groups.


## 2.3. Building and Assessing the Model of the FRGs

Compared to other clinical features in the TCGA and GTEx database, univariate and multivariate Cox regression analyses were performed to determine whether risk score was an independent prognostic predictor of OS. Building a prediction nomogram based on independent predictors by using “rms” R package. Time-dependent receiver-operating characteristic (ROC) curve was used to assess the predictive ability of the nomogram through the “timeROC” R package. Using the UCS patients from GEO database to validate the predictive model about FRGs, which utilized the same formula as that in the GETx and TCGA database.


## 2.4. Analyzing Function Enrichment and Predicting Immunotherapy Response

An interactive network of overlapping prognostic DEGs was generated by using a string database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Genome Ontology (GO) were analyzed. The BH method adjustedP value. The activity of 13 immune-related pathways was evaluated by single-sample gene collection enrichment analysis (ssGSEA) in “gsva” R software package, and the infiltration fraction of 16 immune cells was calculated.


## 2.5. Predicting the Potential of Small Molecule Drugs

The limma software package was used to screen the differentially expressed genes (DEG) of two tumor subtypes (thefalsediscoveryrate<0.05, ∣foldchange∣≥1). The upregulated genes and downregulated genes were input to CMap database (http://portals.broadinstitute.org/cmap/). Using the enrichment value and P value to predict the potential of small molecule drugs for UCS patients.


## 2.6. Predicting the Sensitivity of Drugs

The Genomics of Drug Sensitivity in Cancer database was used to assess the sensitivity of chemotherapeutic agents by using the Genomics of Drug Sensitivity in Cancer database (GDSC;https://www.cancerrxgene.org/). Using the pRRophetic package to calculate the half maximal inhibitory concentration (IC50) of chemotherapeutic agents.


## 2.7. Cell Culture and Lentiviral Transduction

MES-SA and FU-MMT-1 cells were obtained from ATCC (Manassas, VA, USA). MES-SA cells were grown in McCoy’s 5a with 10% fetal bovine serum (FBS), and FU-MMT-1 cells were grown in RPMI-1640 medium with 10% FBS. The medium was supplemented with 100 U/ml of penicillin and streptomycin. The following plasmids were used to transfect in 293T cells and product lentivirus: HSF1 shRNA pLKO.1 plasmids (Thermo Fisher; TRCN0000007480, TRCN0000007484), pLKO.1 empty vector, pLKO.1 GFP shRNA, pCMV-R8.72 lentiviral packaging, and pCMV-VSV-G envelope plasmid (Addgene; plasmid ID 10878, 30323, 22036, and 8454). Stable HSF1 knockdown MES-SA and FU-MMT-1 cells were screened by puromycin.


## 2.8. Cell Viability and Colony Formation Assay

Cell viability was evaluated with a cell counting kit-8 (#C0038, Beyotime, China) according to manufacturer’s instructions. For the colony formation assays, 100 cells were seeded into 12-well plates and cultured for 21 d. Then, clones were then fixed and stained with 0.5% crystal violet. The colonies were counted, and the numbers recorded.


## 2.9. Western Blot Analysis

Proteins in cells were extracted, and electrophoretic separation was performed by SDS-polyacrylamide gel. Proteins in gel were transferred to nitrocellulose membrane and subsequently incubated with the primary antibody (HSF1 (D3L8I) Rabbit mAb #12972, CST;β-actin (13E5) Rabbit mAb #4970, CST). After incubation with peroxidase-conjugated secondary antibodies, the signals were visualized by enhanced chemiluminescence (Pierce, Rockford, IL, USA, #32106) according to manufacturer’s instructions.


## 2.10. Iron Assay

The relative iron concentration in cells was detected via the Iron Assay Kit (#ab83366, Abcam). In the assay, ferric carrier protein dissociates ferric iron into a solution in the presence of acid buffer. After reduction to the ferrous form (Fe2+), iron reacts with Ferene S to produce a stable colored complex and give absorbance at 593 nm.


## 2.11. Lipid ROS Assay

Fluorometric analysis of lipid ROS production was carried out by dye C11-BODIPY kit (#D-3861, Life Technologies) according to the manufacturer’s instructions. Oxidation of the polyunsaturated butadienyl portion of the dye resulted in a shift of the fluorescence emission peak from ~590 nm to ~510 nm.


## 2.12. Statistical Analysis

All statistical analyses were processed by the R software. The two-sidet-test was used to compare gene expression between UCS tissue and nearby nontumor tissue. Kaplan-Meier analysis and log-rank test were used to compare the OS of each group. Comparing the immune pathway or cell ssGSEA scores in the low- and high-risk groups by Mann-Whitney test. No special requirements, P values was two-side, and all P values < 0.05 was considered statistically significant.


## 3. Results

### 3.1. Identifying the FRGs Associated with the UCS Prognosis in the TCGA and GTEx Database

A total of 31 ferroptosis-related differentially expressed genes associated with UCS prognosis were picked out (Figure1(a)). The heatmap showed 5 genes (PGD, GPT2, HSF1, ISCU, and PLIN2) were different expression in UCS tissue compared with normal tissue (Figure 1(b)). From the univariate Cox regression analysis, we could know that 5 FRGs (PGD, HSF1, ISCU, PLIN2, GPT2) were associated with OS of the UCS patients. Four of which were risk genes (HR>1) and one gene (ISCU) was the protective gene (HR<1) for the prognosis of UCS (Figure 1(c)). Figure 1(d) showed the correlation between 5 FRGs.Figure 1
Identification of the FRGs in the TCGA and GTEx cohort. (a) Venn diagram to identify differentially expressed genes between the FRGs that were correlated with OS. (b) The two overlapping genes were all upregulated, and three genes was downregulated in tumor tissue. (c) Forest plots showing the results of the univariate Cox regression analysis between gene expression and OS in the US. (d) The correlation network of candidate genes. The correlation coefficients are represented by different colors.
(a)(b)(c)(d)


### 3.2. Building and Validating the FRG Model of UCS

A five FRGs (PGD, HSF1, ISCU, PLIN2, and GPT2) prognostic model related with OS for UCS patients was built using LASSO Cox regression analysis in the TCGA and GTEx cohort. Our prognostic model constructed using 5 FRGs is as follows:RiskScore=0.181×expressionofPGD+0.523×expressionofHSF1+−0.355×expressionofISCU+0.346×expressionofPLIN2+0.229×expressionofGPT2 . The UCS patients were divided into two groups (low- and high-risk groups) on the basis of the optimal cut-off value of the signature score in the TCGA and GTEx cohort (Figure 2(a)). Patients in the high-risk group died earlier and were less survive probability compared to patients in the low-risk group (Figures 2(b) and 2(e)). Through the PCA and t-SNE analysis, we found that the two risk groups presented a reliable discrete distribution (Figures 2(c) and 2(d)). Besides, the 1-year and 3-year AUC value for the FRG signature were 0.746 and 0.693 in the TCGA and GTEx database (Figure 2(f)).Figure 2
Prognostic analysis of the five-gene signature model in the TCGA and GEO cohort. (a) The distribution and median value of the risk scores in the TCGA cohort. (b) The distributions of OS status, OS, and risk score in the TCGA cohort. (c) PC plot of the TCGA cohort. (d) t-SNE analysis of the TCGA cohort. (e) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the TCGA cohort. (f) The AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the TCGA cohort. (g) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the GEO (GSE119043) cohort. (h) The AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the GEO (GSE119043) cohort.
(a)(b)(c)(d)(e)(f)(g)(h)The same as the result in the TCGA and GTEx cohort, patients in the high-risk group were less survive probability than patients in the low-risk group (Figure2(g)). At the same time, the 1-year and 3-year AUC value for the FRG signature were 0.722 and 0.765 in the GEO cohort (Figure 2(h)).


### 3.3. Functional Analysis in the TCGA and GTEx Cohort

We used the FRGs to explore the underlying molecular mechanism of the model by GO enrichment and KEGG pathway analysis in the two risk groups. For GO enrichment analysis, the FRGs were highly associated with muscle system process, muscle contraction, and muscle tissue/organ development in term of biological process. In cellular component and molecular function, the FRGs are highly related to contractile fiber, myofibril, and actin binding (Figure3(a)). Interestingly, the FRGs were mostly enriched in the pathway associated with muscle contraction in the TCGA and GTEx cohort, such as calcium signaling pathway, vascular smooth muscle contraction, and regulation of actin cytoskeleton (Figure 3(b)).Figure 3
Representative results of GO, KEGG, and ssGSEA score anlysis. (a, b) The most significant or shared GO enrichment and KEGG pathways in the TCGA cohort are displayed. (c, d) The scores of 16 immune cells and 13 immune-related functions are displayed in boxplots.
(a)(b)(c)(d)To explore the relationship between immune status and risk score, we used ssGSEA to quantitatively analyze the enrichment score of related immune cell subgroups or related pathways in the TCGA and GTEx cohort. There are significant differences in some related immune cell subgroups or related pathways, such as DCs, mast cells, neutrophils, NK cells, Th2 cells, APC costimulation, cytolytic activity, T cell costimulation, and type II IFN response (Figures3(c) and 3(d)). The risk score of the mast cells, neutrophils, NK cells, Th2 cells, APC costimulation, cytolytic activity, T cell costimulation, and type II IFN response was higher in the low-risk group compared to the high-risk group (Figures 3(c) and 3(d)). Only DCs, the risk score was higher in the high-risk group (Figure 3(c)).


### 3.4. Predicting the Potential of Small Molecule Drugs

Two genes (PGD, GPT2) were upregulated and three genes (HSF1, ISCU, and PLIN2) were downregulated. Based on them, we used CMap database to analyze and predict the potential of small molecule drugs for UCS patients. We could know that some small molecule drugs, such as trimethoprim, imatinib, ciclopirox, and PF-01378883-00, were predicted for the therapy of UCS patients (from Table1).Table 1
Prediction of potential of small molecule drugs in UCS.
Cmap nameMeanEnrichmentPChlorhexidine-0.32-0.720.00372Trimethoprim0.3670.6950.00653Imatinib0.4520.9410.00658Diethylstilbestrol-0.33-0.6060.01273Ciclopirox0.480.7060.0153PF-01378883-000.4350.6980.0171911-Deoxy-16,16-dimethylprostaglandin E20.3180.6830.0217Ramipril0.3720.6690.02656Trolox C0.1480.6430.03937Alpha-yohimbine-0.419-0.7210.04479


### 3.5. Immune Checkpoint Expression Pattern and Sensitivity of Chemotherapy Drugs for UCS Patients

Six important immune checkpoints (PD1, PDL1, PDL2, CTLA4, CD80, and CD86) were expressed in the UCS patients in TCGA and GTEx cohort. The result showed that the expression of immune checkpoints PDL1 and PDL2 were higher in the high-risk group compared to low-risk group (Figures4(a)–4(f)).Figure 4
Predictions of the immunotherapy markers and sensitivity of chemotherapy drugs in high-risk group and low-risk group of UCS patients. (a–f) The violin plots present the expression of 6 principal immune checkpoint molecules, namely, PDL1, PDL2, CTLA4, CD80, CD86, and PD1 in the TCGA cohort. (g–k) Box plots for the estimated IC50 of chemotherapy drugs between two subtypes, imatinib, cisplatin, docetaxel, doxorubicin, and gemcitabine in the ICGC cohort, respectively.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)At present, chemotherapy drugs, such as doxorubicin and gemcitabine, are the main chemotherapeutic agents for UCS. Interestingly, the IC50 value of imatinib was higher in high-risk group compared with the low-risk group in UCS patients (P=0.00044) (Figure 4(g)). However, the calculated IC50 value of doxorubicin (P=0.019) and gemcitabine (P=2.5e−05) was lower in the high-risk group (Figures 4(j) and 4(k)). This result implied the high-risk group was more sensitive to doxorubicin and gemcitabine; the low-risk group was more sensitive to imatinib.


### 3.6. Targeting HSF1 Reverse Resistance of Doxorubicin and Gemcitabine in UCS Cells

As high HSF1 expression has the highest HR value in the FRGs signature of UCS, herein, we hypothesized HSF1 is correlated with the drug sensitivity of doxorubicin and gemcitabine in UCS. Therefore, we firstly knockdown the HSF1 in MES-SA and FU-MMT-1 cells (Figure5(a)). Interestingly, the cell viability and clone formation assay results showed that cell HSF1 knockdown significant inhibits growth of MES-SA and FU-MMT-1 cells and promotes more sensitive to the treatment of doxorubicin and gemcitabine (Figures 5(b)–5(i)). These results further confirmed the HSF1 is a potential target for reversing drug resistance of doxorubicin and gemcitabine in UCS.Figure 5
Targeting HSF1 enhance sensitivity of doxorubicin and gemcitabine in MES-SA and FU-MMT-1 cells. (a) Western blot was used to detect the HSF1 expression after knockdown in MES-SA and FU-MMT-1 cells. (b, c) Cell viability were detected when HSF1 knockdown combined with treatment with doxorubicin in MES-SA and FU-MMT-1 cells. (d, e) Cell viability were detected when HSF1 knockdown combined with treatment with gemcitabine in MES-SA and FU-MMT-1 cells. (f–i) Colony forming ability were detected when HSF1 knockdown combined with treatment with gemcitabine or doxorubicin in MES-SA and FU-MMT-1 cells (∗ vs. control group, P<0.05; # vs. gemcitabine 1 μM or doxorubicin 1 μM group, P<0.05).
(a)(b)(c)(d)(e)(f)(g)(h)(i)


### 3.7. Targeting HSF1 Sensitive to Doxorubicin and Gemcitabine via Ferroptosis in UCS

As HSF1 is a FRG and shows as a biomarker for reverse drug resistance in UCS, we detected the lipid ROS and iron levels after HSF1 knockdown or doxorubicin or gemcitabine treatment. Firstly, knockdown HSF1 significantly enhanced the lipid ROS level in MES-SA and FU-MMT-1 cells (Figures6(a) and 6(b)), when combing the treatment of HSF1 knockdown and doxorubicin or gemcitabine, the lipid ROS level is more higher than the ctrl and HSF1 knockdown groups (Figures 6(a) and 6(b)). At last, the HSF1 knockdown also increased the intracellular iron level. And intracellular iron level also increased when combing the treatment of HSF1 knockdown and doxorubicin or gemcitabine (Figures 6(c) and 6(d)). Therefore, these result revealed that targeting HSF1 reverses drug resistance of doxorubicin and gemcitabine in UCS via ferroptosis pathway.Figure 6
Inhibition of HSF1 expression increased gemcitabine- or doxorubicin-induced ferroptosis. (a, b) Analysis of intracellular lipid ROS when HSF1 knockdown combined with treatment with gemcitabine or doxorubicin in MES-SA and FU-MMT-1 cells. (c, d) Analysis of intracellular iron levels when HSF1 knockdown combined with treatment with gemcitabine or doxorubicin in MES-SA and FU-MMT-1 cells.
(a)(b)(c)(d)


## 3.1. Identifying the FRGs Associated with the UCS Prognosis in the TCGA and GTEx Database

A total of 31 ferroptosis-related differentially expressed genes associated with UCS prognosis were picked out (Figure1(a)). The heatmap showed 5 genes (PGD, GPT2, HSF1, ISCU, and PLIN2) were different expression in UCS tissue compared with normal tissue (Figure 1(b)). From the univariate Cox regression analysis, we could know that 5 FRGs (PGD, HSF1, ISCU, PLIN2, GPT2) were associated with OS of the UCS patients. Four of which were risk genes (HR>1) and one gene (ISCU) was the protective gene (HR<1) for the prognosis of UCS (Figure 1(c)). Figure 1(d) showed the correlation between 5 FRGs.Figure 1
Identification of the FRGs in the TCGA and GTEx cohort. (a) Venn diagram to identify differentially expressed genes between the FRGs that were correlated with OS. (b) The two overlapping genes were all upregulated, and three genes was downregulated in tumor tissue. (c) Forest plots showing the results of the univariate Cox regression analysis between gene expression and OS in the US. (d) The correlation network of candidate genes. The correlation coefficients are represented by different colors.
(a)(b)(c)(d)


## 3.2. Building and Validating the FRG Model of UCS

A five FRGs (PGD, HSF1, ISCU, PLIN2, and GPT2) prognostic model related with OS for UCS patients was built using LASSO Cox regression analysis in the TCGA and GTEx cohort. Our prognostic model constructed using 5 FRGs is as follows:RiskScore=0.181×expressionofPGD+0.523×expressionofHSF1+−0.355×expressionofISCU+0.346×expressionofPLIN2+0.229×expressionofGPT2 . The UCS patients were divided into two groups (low- and high-risk groups) on the basis of the optimal cut-off value of the signature score in the TCGA and GTEx cohort (Figure 2(a)). Patients in the high-risk group died earlier and were less survive probability compared to patients in the low-risk group (Figures 2(b) and 2(e)). Through the PCA and t-SNE analysis, we found that the two risk groups presented a reliable discrete distribution (Figures 2(c) and 2(d)). Besides, the 1-year and 3-year AUC value for the FRG signature were 0.746 and 0.693 in the TCGA and GTEx database (Figure 2(f)).Figure 2
Prognostic analysis of the five-gene signature model in the TCGA and GEO cohort. (a) The distribution and median value of the risk scores in the TCGA cohort. (b) The distributions of OS status, OS, and risk score in the TCGA cohort. (c) PC plot of the TCGA cohort. (d) t-SNE analysis of the TCGA cohort. (e) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the TCGA cohort. (f) The AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the TCGA cohort. (g) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the GEO (GSE119043) cohort. (h) The AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the GEO (GSE119043) cohort.
(a)(b)(c)(d)(e)(f)(g)(h)The same as the result in the TCGA and GTEx cohort, patients in the high-risk group were less survive probability than patients in the low-risk group (Figure2(g)). At the same time, the 1-year and 3-year AUC value for the FRG signature were 0.722 and 0.765 in the GEO cohort (Figure 2(h)).


## 3.3. Functional Analysis in the TCGA and GTEx Cohort

We used the FRGs to explore the underlying molecular mechanism of the model by GO enrichment and KEGG pathway analysis in the two risk groups. For GO enrichment analysis, the FRGs were highly associated with muscle system process, muscle contraction, and muscle tissue/organ development in term of biological process. In cellular component and molecular function, the FRGs are highly related to contractile fiber, myofibril, and actin binding (Figure3(a)). Interestingly, the FRGs were mostly enriched in the pathway associated with muscle contraction in the TCGA and GTEx cohort, such as calcium signaling pathway, vascular smooth muscle contraction, and regulation of actin cytoskeleton (Figure 3(b)).Figure 3
Representative results of GO, KEGG, and ssGSEA score anlysis. (a, b) The most significant or shared GO enrichment and KEGG pathways in the TCGA cohort are displayed. (c, d) The scores of 16 immune cells and 13 immune-related functions are displayed in boxplots.
(a)(b)(c)(d)To explore the relationship between immune status and risk score, we used ssGSEA to quantitatively analyze the enrichment score of related immune cell subgroups or related pathways in the TCGA and GTEx cohort. There are significant differences in some related immune cell subgroups or related pathways, such as DCs, mast cells, neutrophils, NK cells, Th2 cells, APC costimulation, cytolytic activity, T cell costimulation, and type II IFN response (Figures3(c) and 3(d)). The risk score of the mast cells, neutrophils, NK cells, Th2 cells, APC costimulation, cytolytic activity, T cell costimulation, and type II IFN response was higher in the low-risk group compared to the high-risk group (Figures 3(c) and 3(d)). Only DCs, the risk score was higher in the high-risk group (Figure 3(c)).


## 3.4. Predicting the Potential of Small Molecule Drugs

Two genes (PGD, GPT2) were upregulated and three genes (HSF1, ISCU, and PLIN2) were downregulated. Based on them, we used CMap database to analyze and predict the potential of small molecule drugs for UCS patients. We could know that some small molecule drugs, such as trimethoprim, imatinib, ciclopirox, and PF-01378883-00, were predicted for the therapy of UCS patients (from Table1).Table 1
Prediction of potential of small molecule drugs in UCS.
Cmap nameMeanEnrichmentPChlorhexidine-0.32-0.720.00372Trimethoprim0.3670.6950.00653Imatinib0.4520.9410.00658Diethylstilbestrol-0.33-0.6060.01273Ciclopirox0.480.7060.0153PF-01378883-000.4350.6980.0171911-Deoxy-16,16-dimethylprostaglandin E20.3180.6830.0217Ramipril0.3720.6690.02656Trolox C0.1480.6430.03937Alpha-yohimbine-0.419-0.7210.04479


## 3.5. Immune Checkpoint Expression Pattern and Sensitivity of Chemotherapy Drugs for UCS Patients

Six important immune checkpoints (PD1, PDL1, PDL2, CTLA4, CD80, and CD86) were expressed in the UCS patients in TCGA and GTEx cohort. The result showed that the expression of immune checkpoints PDL1 and PDL2 were higher in the high-risk group compared to low-risk group (Figures4(a)–4(f)).Figure 4
Predictions of the immunotherapy markers and sensitivity of chemotherapy drugs in high-risk group and low-risk group of UCS patients. (a–f) The violin plots present the expression of 6 principal immune checkpoint molecules, namely, PDL1, PDL2, CTLA4, CD80, CD86, and PD1 in the TCGA cohort. (g–k) Box plots for the estimated IC50 of chemotherapy drugs between two subtypes, imatinib, cisplatin, docetaxel, doxorubicin, and gemcitabine in the ICGC cohort, respectively.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)At present, chemotherapy drugs, such as doxorubicin and gemcitabine, are the main chemotherapeutic agents for UCS. Interestingly, the IC50 value of imatinib was higher in high-risk group compared with the low-risk group in UCS patients (P=0.00044) (Figure 4(g)). However, the calculated IC50 value of doxorubicin (P=0.019) and gemcitabine (P=2.5e−05) was lower in the high-risk group (Figures 4(j) and 4(k)). This result implied the high-risk group was more sensitive to doxorubicin and gemcitabine; the low-risk group was more sensitive to imatinib.


## 3.6. Targeting HSF1 Reverse Resistance of Doxorubicin and Gemcitabine in UCS Cells

As high HSF1 expression has the highest HR value in the FRGs signature of UCS, herein, we hypothesized HSF1 is correlated with the drug sensitivity of doxorubicin and gemcitabine in UCS. Therefore, we firstly knockdown the HSF1 in MES-SA and FU-MMT-1 cells (Figure5(a)). Interestingly, the cell viability and clone formation assay results showed that cell HSF1 knockdown significant inhibits growth of MES-SA and FU-MMT-1 cells and promotes more sensitive to the treatment of doxorubicin and gemcitabine (Figures 5(b)–5(i)). These results further confirmed the HSF1 is a potential target for reversing drug resistance of doxorubicin and gemcitabine in UCS.Figure 5
Targeting HSF1 enhance sensitivity of doxorubicin and gemcitabine in MES-SA and FU-MMT-1 cells. (a) Western blot was used to detect the HSF1 expression after knockdown in MES-SA and FU-MMT-1 cells. (b, c) Cell viability were detected when HSF1 knockdown combined with treatment with doxorubicin in MES-SA and FU-MMT-1 cells. (d, e) Cell viability were detected when HSF1 knockdown combined with treatment with gemcitabine in MES-SA and FU-MMT-1 cells. (f–i) Colony forming ability were detected when HSF1 knockdown combined with treatment with gemcitabine or doxorubicin in MES-SA and FU-MMT-1 cells (∗ vs. control group, P<0.05; # vs. gemcitabine 1 μM or doxorubicin 1 μM group, P<0.05).
(a)(b)(c)(d)(e)(f)(g)(h)(i)


## 3.7. Targeting HSF1 Sensitive to Doxorubicin and Gemcitabine via Ferroptosis in UCS

As HSF1 is a FRG and shows as a biomarker for reverse drug resistance in UCS, we detected the lipid ROS and iron levels after HSF1 knockdown or doxorubicin or gemcitabine treatment. Firstly, knockdown HSF1 significantly enhanced the lipid ROS level in MES-SA and FU-MMT-1 cells (Figures6(a) and 6(b)), when combing the treatment of HSF1 knockdown and doxorubicin or gemcitabine, the lipid ROS level is more higher than the ctrl and HSF1 knockdown groups (Figures 6(a) and 6(b)). At last, the HSF1 knockdown also increased the intracellular iron level. And intracellular iron level also increased when combing the treatment of HSF1 knockdown and doxorubicin or gemcitabine (Figures 6(c) and 6(d)). Therefore, these result revealed that targeting HSF1 reverses drug resistance of doxorubicin and gemcitabine in UCS via ferroptosis pathway.Figure 6
Inhibition of HSF1 expression increased gemcitabine- or doxorubicin-induced ferroptosis. (a, b) Analysis of intracellular lipid ROS when HSF1 knockdown combined with treatment with gemcitabine or doxorubicin in MES-SA and FU-MMT-1 cells. (c, d) Analysis of intracellular iron levels when HSF1 knockdown combined with treatment with gemcitabine or doxorubicin in MES-SA and FU-MMT-1 cells.
(a)(b)(c)(d)


## 4. Discussion

In this study, five ferroptosis-related differentially expressed genes were picked out to build the prognostic model of OS for UCS patients based on LASSO Cox regression analysis. Then, we used GEO database to validate the model. Besides, GO enrichment and KEGG pathway analysis were done between UCS patients and normal control in TCGA and GTEx cohort. The KEGG result showed that the FRGs were mostly enriched in the pathway associated with muscle contraction, such as calcium signaling pathway and regulation of actin cytoskeleton. The GO analysis result showed that FRGs were highly associated with muscle system process, muscle contraction, contractile fiber, myofibril, and actin binding. All of them indicated that FRGs had relationship with the development of UCS, which also showed that FRGs maybe serve as a potential biomarker for UCS. Afterward, we analyzed the enrichment score of related immune cell subgroups or related pathways. The result showed that there were significant differences in some aspects, such as DCs, mast cells, neutrophils, and NK cells. We could know that some small molecule drugs, such as trimethoprim, imatinib, and ciclopirox, may have potential role in treating UCS. For sensitivity of chemotherapy drugs of UCS patients, the high-risk group was more sensitive to doxorubicin and gemcitabine, and the low-risk group was more sensitive to imatinib. At last, we identified a critical marker, HSF1, not only relate to the drug sensitivity of doxorubicin and gemcitabine, but also the ferroptosis pathway in UCS cells. Therefore, for the first time, our results provided a FRG signaling model that can be used to predict prognosis, immunotherapy response, and chemotherapeutic sensitivity of UCS.At present, the UICC-AJCS standard staging is mainly used for prognostic staging of UCS. However, the standard staging of UICC-AJCS has some shortcomings when applied to UCS, that is, although the standard staging of UICC-AJCS includes tumor size, degree of differentiation, and depth of invasion, it lacks the information of tumor origin site or histological type and does not take into account details of local invasion or regional spread during surgery. Therefore, a new and reliable prognostic model of OS is important for UCS patients. In this study, we identified FRGs associated with UCS prognosis and built a prognostic model of OS with FRGs for UCS patients. After validation, the 3-year AUC value for the FRG prognostic model was 0.765 in the GEO cohort.The five FRGs (PGD, HSF1, ISCU, PLIN2, and GPT2) were identified to build a prognostic model. PGD, also called phosphoglycerate dehydrogenase, which is the first rate-limiting enzyme for serine synthesis and often overexpresses in human tumor [9]. It maybe promote the development of the tumor by activating serine synthesis. For iron-sulfur cluster assembly enzyme ISCU, studies have shown that inhibition of ISCU is closely associated with poor prognosis [10]. HSF1 has a systematic effect on the tumor proteome. It suppresses amyloidosis by tumour-suppressive, keeping the cancer protein stable to support the malignant state [11]. PLIN2 may be associated with development of renal cancer, glioblastoma multiforme, and bladder and prostate cancer [12–14]. However, how these FRDEG genes affect the prognosis of UCS patients needs further exploration and research.Our study also found that some immune checkpoints PDL1 and PDL2 were related with FRGs signature. For PD-L2, it has been thought to be only present in dendritic cells and macrophages [15]. However, some studies have shown that PD-L2 is also present in somatic tissue and cancer [15]. There is a speculation that it might play a role in the progression of UCS. Current studies have shown the potential vulnerability of UCS to immunotherapy, so only a few studies have elucidated the inhibitory effect of PD-1/PD-L1 in the treatment of uterine leiomyosarcoma [16]. At the same time, although PD-L1 expression is associated with poor prognosis in non-small-cell lung cancer (NSCLC), renal cell carcinoma (RCC), prostate cancer, and colorectal cancer, we found that PD-L1 expression is associated with better prognosis in uterine carcinosarcoma [17–19]. Since FRG signaling can predict the expression of immune checkpoints in UCS, our results also found that FRG signaling could predict the immunotherapeutic response for UCS patients. Therefore, our findings suggested that FRG signaling may be a potential biomarker for predicting UCS immunotherapy.The sensitivity of chemotherapeutic agents for UCS patients also can be predicted in the FRG model. The result showed that the high-risk group was more sensitive to doxorubicin and gemcitabine; the low-risk group was more sensitive to imatinib. Doxorubicin is used to treat a variety of cancers, such as stomach, breast, ovarian, and uterine cancers [20]. It interferes with DNA replication by inserting DNA molecules and alkylation, inhibiting the activity of topoisomerase II, leading to DNA double-strand breaks, and thus, interfering with the process of cancer development [21]. Besides, doxorubicin is a way for the treatment of advance UCS [22]. Some current studies also show that gemcitabine together with doxorubicin is promising for the cure of leiomyosarcoma that is a type of uterine carcinosarcoma [23]. What role does imatinib play in uterine carcinosarcoma needs to further explore. All in all, for the first time, we provide a relatively reliable and new model to predict chemotherapeutic drugs sensitivity for UCS patients.Finally, as the HSF1 has the highest HR value in the FRGs model; therefore, we hypothesized the HSF1 could be a potential target for reversing drug resistance in UCS, which included doxorubicin and gemcitabine. As doxorubicin and gemcitabine are main drugs for the chemotherapy in UCS, but the drug resistance still was serious problem for UCS patients [24]. We found knocked down HSF1 in MES-SA and FU-MMT-1 cells was more sensitive to doxorubicin and gemcitabine via increasing ferroptosis. Previous studies found that inhibition of HSF1 can increase erastin-induced ferroptosis [25], which implied HSF1 is a critical target for regulating ferroptosis. Our study further firstly found that knocking down HSF1 can enhanced sensitivity of doxorubicin or gemcitabine treatment in UCS cells. And we also confirmed that the ferroptosis may be a critical pathway for the HSF1 involving the sensitivity of UCS cells when treated by doxorubicin or gemcitabine, but further in-depth mechanisms still needed to be cleared.


## 5. Conclusion

In brief, the five FRG risk signature prognostic model having a superior and drug sensitivity predictive performance for OS in UCS and HSF1 is a potential marker sensitive to doxorubicin and gemcitabine in UCS patients.



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*Source: 6400227-2022-01-12.xml*