Ferroptosis‐related gene CHAC1 is a valid indicator for the poor prognosis of kidney renal clear cell carcinoma

Abstract To evaluate the validity of CHAC1 for predicting the prognosis of kidney renal clear cell carcinoma (KIRC) and to explore its therapeutic potential for KIRC, we conducted several bioinformatic analyses using the sequencing data and clinical information derived from online databases. We found CHAC1 is down‐regulated in KIRC samples when compared with normal samples but up‐regulated in KIRC samples with relatively higher malignancy and later stages. Univariate cox analysis and multivariate cox regression analysis were conducted and the results revealed up‐regulated CHAC1 is an independent risk factor for poor prognosis of KIRC. Further, the nomogram model based on the result of multivariate cox regression analysis was constructed and effectively predicted patients' 1‐year, 3‐year and 5‐year survival respectively. The correlation analyses showed CHAC1 is associated with the immune pathway markers of memory B cell, natural killer cell and type1 T helper cell as well as the checkpoint genes like ADORA2A, CD200, CD44, CD70, HHLA2, NRP1, PDCD1LG2 and TNFRSF18. Furthermore, experiments in vitro indicated CHAC1 could induce cell death in KIRC cell lines but had limited influence on cell migration and cell invasion. In conclusion, CHAC1 is found a valid indicator for poor prognosis of kidney renal clear cell carcinoma.


| INTRODUC TI ON
Renal cell carcinoma (RCC) is the most common solid lesion in kidney and accounts for approximately 90% of kidney malignancies 1 and 3% of all cancers. 2 The EAU guideline indicated that the incidence of renal cell carcinoma is increasing yearly with an annual increase of about 2% during the last two decades. 2 Among all histological subtypes of RCC, kidney renal clear cell carcinoma (KIRC) is the most common one and accounts for about 75% of all RCC. 2 There is no doubt the investigation of KIRC is of great clinical importance.
Cancer cells are frequently found to be characterized with metabolic abnormalities. And reprogramming of metabolism is associated with tumour progression, the adaption to stress and anti-tumour therapies. 3,4 For example, dysregulated glutathione (GSH) metabolism has been found playing a vital role in the initiation, progression and drug resistance of various kinds of malignant tumour. 5 Glutathione is the most abundant thiol in living cells 6 and acts as a reactive oxygen species (ROS) scavenger to prevent ROS damage from important cellular components. 7 Previous researches have demonstrated that the depleting of GSH is associated with the accumulation of ROS and the induction of ferroptosis, a new form of iron-dependent, programmed cell death, 8 which has been found involved in the development of kidney diseases, especially in KIRC. 9 For example, Heike Miess's research indicated that the glutathione redox system is essential to prevent ferroptosis in KIRC. 10 Wu and his colleagues successfully constructed a new survival model for predicting the prognosis of KIRC using 32 ferroptosis-related genes in his research. 11 By bioinformatic analysis of KIRC sequencing data and clinical information derived from The Cancer Genome Atlas (TCGA) database, we identified the ferroptosis-related gene ChaC Glutathione Specific Gamma-Glutamylcyclotransferase 1 (CHAC1), which had also been mentioned in Wu's research, is significantly down-regulated in KIRC samples when compared with normal samples. Given the known function of CHAC1 in GSH degradation and ferroptosis activation, 11 we speculated that CHAC1 may play a role in the initiation or the progress of KIRC and the differential expression of CHAC1 might be a valid indicator for predicting the prognosis of KIRC.
To confirm our speculation, we conducted several bioinformatic analyses using the R scripts and website tools to investigate the clinicobiological function of CHAC1 in KIRC as well as the therapeutic potential of CHAC1 in KIRC. Furthermore, we performed experiments in vitro to demonstrate the function of CHAC1 in kidney cancer cell lines using overexpression vector of CHAC1. The results of current research would provide new strategies for predicting the prognosis of KIRC and explore the therapeutic potential of CHAC1 for KIRC.

| Data collection and processing
Pan-cancer sequencing data and the RNA sequencing data of KIRC as well as its corresponding clinical information of 531 KIRC samples and 72 normal samples were downloaded from the TCGA database and processed using Bioconductor package in R statistical environment. 12,13 Differentially expressed genes were identified using the Bioconductor package of edgeR with criteria of |log 2 foldchange(log 2 FC)|>2 and adjusted P-value (adj.P) <.05. Predicted neoantigens for TCGA samples were obtained through The Cancer Immunome Atlas (TCIA). 14 The tumour mutation burden (TMB) and microsatellite instability (MSI) were calculated as the total mutation incidences per million base pair and the number of insertion or deletion events that occurred in repeating sequences of genes respectively. 15

| Immune cell infiltration and tumour microenvironment analysis
The database-derived website tool-Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) 16

| Cell culture and samples
KIRC cell lines 786-0 and CAKI-1, renal tubular epithelial cell HK-2 were stored in our laboratory. Cells were cultured according to the culture methods described in ATCC website respectively. Tissue samples of KIRC and pericarcinous tissues were collected from patients who received surgical treatment for KIRC in our institution.
Extracted tissues were stored in −80°C or in 4% paraformaldehyde for further researches. The research was approved by the ethics committee of Shanghai General Hospital and all patients signed the informed consent.

| Transfection
Overexpression vector of CHAC1 and its negative control (NC) vector were constructed by Gene Pharma. Vectors were transfected using lipo3000 reagent (Invitrogen) according to the manufacturer's protocol. Cells were incubated for 48 hours before further researches.

| CCK-8 assay
KIRC cell lines 786-0 and CAKI-1 transfected with overexpression vector of CHAC1 or its negative control vector were seeded into 96-well plates with a density of 1000 cells/well. After culture for 24 hours, 10 μL CCK-8 reagent was added to each well as scheduled.
The optical density was measured after 2 hours incubation.

| Cell migration and invasion assay
Transwell chambers (Corning Incorporated) with a pore size of 8 mm was used for cell migration and invasion assays. About 5 × 10 4 786-0 and CAKI-1 cells transfected with overexpression vector of CHAC1 or its negative control vector were seeded into the upper chamber.
These cells were cultured with serum-free medium. And medium containing 20% FBS was added to the lower chamber served as a chemoattractant. After incubation for 48 hours, 4% paraformaldehyde was used for fixing. Cells that migrated or invaded to the lower surface were stained in 10% crystal violet and counted under highpower fields microscopically.

| Quantitative real-time PCR (qRT-PCR)
Total RNA of cells and samples were extracted using TRIzol reagent (Thermo Fisher Scientific). RNA reverse transcription was performed using a PrimeScript™ RT reagent Kit with gDNA eraser (Takara) and quantitative real-time PCR was performed using TBGreen ® Premix Ex Taq™ (Takara). The data were normalized using GAPDH levels and further analysed by the 2−ΔΔCT method.

| Western blotting
Cells and samples were lysed using RIPA lysis buffer containing 1/100 PMSF (Roche). Total proteins were quantified using BCA protein assay kit (Pierce). Protein samples were resolved by 10% SDS-PAGE gel and transferred to polyvinylidene difluoride membrane.
And the stripes were incubated with primary antibodies against CHAC1 (Proteintech) and ACTB (abcam) at 4°C overnight, followed by incubating with a peroxidase-conjugated goat anti-rabbit IgG antibody (CST) for 2 hours at room temperature. Immunopositively bands were analysed using a FluorChem M system (ProteinSimple).

The quantification of western blot images was conducted using
Image J (Rawak Software Inc) and the targeted protein intensity was normalized with ACTB.

| Immunohistochemistry
After antigen retrieval, samples were blocked in 10% BSA and incubated with primary rabbit antibodies against CHAC1 (Proteintech) for 30 minutes followed with incubation of biotinylated secondary antibodies (CST) for 30 minutes. Vectastain Elite ABC (Vector Laboratories) was added for 30 minutes and the reaction developed with 3,39-diaminobenzidine DAB peroxidase substrate before counterstaining with hematoxylin. The expression of CHAC1 in KIRC samples and para-carcinoma tissues was calculated using Image J (Rawak Software Inc).

| Statistics
All statistical data analyses and figures were carried out using SPSS 25.0, GraphPad Prism 6.0 and R scripts/Bioconductor packages. Briefly, the Mann-Whitney U test was used to compare the expression of CHAC1 between groups. The Kaplan-Meier analysis was used to construct the survival curves of KIRC after dividing these patients into high and low risk according to the expression of CHAC1. Univariate cox analysis and multivariate cox regression analysis were performed to investigate whether CHAC1 can effectively predict the prognosis of KIRC. Moreover by the R "rms" package, we constructed a nomogram-based model to predict patients' survival. The calibration curves and receiver operating characteristic curves (ROC) were conducted to verify the validity of the nomogram model. The spearman correlation test was used to assess the correlation between CHAC1 expression and targets of interest, such as neoantigens, MSI, TMB, mismatch repair (MMR) genes and methylation transferases. All statistical results with P < .05 were considered statistically significant.

| The differential expression of CHAC1 in KIRC
We compared the expression of CHAC1 in different types of tumour samples and their corresponding normal samples using pancancer sequencing data derived from TCGA database. We found

| Establish of predict model for the prognosis of KIRC
Univariate and multivariate cox regression analysis were used to identify the factors associated with overall survival (OS) of KIRC.
The univariate cox analysis revealed that age (P < .001), grade

| Correlation analyses of CHAC1 and MMR genes as well as methylation transferases in KIRC
To investigate the link between CHAC1 expression and tumorigenesis mechanisms, we examined the relationship between CHAC1 and

| Gene set enrichment analysis of CHAC1 in KIRC
We used the Gene Set Enrichment Analysis to identify potential signaling pathways might be activated or inhibited because of the

| Evaluate the function of CHAC1 in vitro
By qRT-PCR and western blot, we confirmed the mRNA and protein expression of CHAC1 is down-regulated in KIRC cell lines 786-0 and CAKI-1 when compared with renal tubular epithelial cell HK-2 ( Figure 6A). Furthermore, we investigated the mRNA and protein expression of CHAC1 using qRT-PCR, western blot and immunohistochemistry in KIRC samples and the pericarcinous tissues collected in our institution. The results of qRT-PCR and western blot revealed the mRNA and protein expression of CHAC1 is down-regulated in KIRC tissues when compared with corresponding pericarcinous tissues. However, among these tumour samples with different T stages and total stages, we found KIRC samples extracted from higher T stage or higher total stage usually exhibit relatively higher CHAC1 mRNA or protein expression ( Figure 6E). The result from immunohistochemistry was similar with the results of qRT-PCR and western blot though no statistic difference about the staining of CHAC1 was noticed between the KIRC samples with T1 stage and T3 stage ( Figure 6F). CHAC1 overexpression vector was used to investigate the function of CHAC1 in KIRC cell lines. The transfection efficiency was verified using qRT-PCR and western blot ( Figure 6B). The results of CCK-8 revealed that over-expression of CHAC1 significantly induced cell death ( Figure 6C). However, the results from cell migration and invasion assays showed over-expression of CHAC1 had limited influence on cell migration and invasion in KIRC cell lines ( Figure 6D).

F I G U R E 4 Correlation analyses
of CHAC1 and MMR genes as well as methylation transferases in KIRC. A, The correlation analysis of CHAC1 expression and MMR genes. B, The correlation analysis of CHAC1 expression and methylation transferases. (*P < .05; **P < .01; ***P < .001)

| D ISCUSS I ON
Kidney renal clear cell carcinoma was found featured with the GSH metabolism abnormalities and highly sensitive to the depletion of GSH. 20 It has been proved that the depletion of GSH is associated with the ROS accumulation and ferroptosis activation. 8,9 CHAC1 is a newly discovered endoplasmic reticulum inducible gene, 21 involved in the γ-glutamyl cycle that can degrade glutathione 22,23 and promote cell apoptosis or ferroptosis. 24,25 Previous research has also demonstrated that CHAC1 is differentially expressed in KIRC. 11 However, to our knowledge, there have been no researches investigating the value of CHAC1 in the prognosis or therapeutic potential in KIRC.
By differential genes expression analysis, we found CHAC1 is down-regulated in KIRC samples when compared with normal kidney samples. But when we compared the expression of CHAC1 in KIRC samples with different grades and stages, we noticed CHAC1 is up-regulated in relatively higher malignancy or later stage of KIRC.
These controversial findings obfuscate the role of CHAC1 in the initiation or the progress of KIRC. By reviewing existing literature, we noticed elevated expression of CHAC1 or its splicing variants could predict poor outcomes in uveal melanoma patients 26  It is reported dysregulated GSH metabolism widely exist in malignant tumours. [28][29][30] And Heike Miess has demonstrated that higher levels of GSH were detected in higher malignancy and later stage of KIRC. 10 As a result, we speculated the up-regulated CHAC1 in relatively higher malignancy or later stage of KIRC noticed in our research might be the feedback of high levels of GSH and further researches are still needed to reveal the function of CHAC1 in KIRC.
To explore the potential of CHAC1 for the immunotherapy of KIRC, correlation analyses were conducted and no clear association between CHAC1 and immunotherapy related features such as neoantigens, MSI, TMB and tumour microenvironment was no- We further studied the role of CHAC1 in tumorigenesis by investigating the possible correlation between CHAC1 and MMR genes as well as methylation transferases. And the results showed CHAC1 is associated with the methylation transferase DNMT2. To our knowledge, the correlation between these two genes has been barely studied. Gene Set Enrichment Analysis was also conducted and the result indicated CHAC1 may influence KIRC progression through the pathway of the cardiac muscle contraction, proteasome and glycosaminoglycan biosynthesis chondroitin sulfate. Although no researches on these correlation have been conducted.
To sum up, in current research, we found CHAC1 is downregulated in KIRC samples when compared with normal samples.
However, among KIRC samples with different grades and stages, CHAC1 is up-regulated in relatively higher malignancy and later stage of KIRC. The survival curve and multivariate cox regression analysis indicated up-regulated CHAC1 is an independent risk factor for poor prognosis of KIRC. A nomogram model based on the result of multivariate cox regression analysis was constructed and effectively predicted patients survival rate.

CO N FLI C T O F I NTE R E S T
The authors declare that they have no conflict of interest.