Ferroptosis-related lncRNA signature robustly predicts the prognosis and immunotherapy efficiency of clear cell renal cell carcinoma

doi:10.21203/rs.3.rs-1703263/v1

Download PDF

Research Article

Ferroptosis-related lncRNA signature robustly predicts the prognosis and immunotherapy efficiency of clear cell renal cell carcinoma

https://doi.org/10.21203/rs.3.rs-1703263/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background:

Ferroptosis is a new type of iron- and reactive oxygen species-dependent cell death, which is observed in a variety of disease conditions and plays an important role in cancer. However, studies on ferroptosis-related long noncoding RNAs (FerLncRNAs) in clear cell renal cell carcinoma (ccRCC) are limited. The purpose of this study was to investigate the potential prognostic value of FerLncRNAs and their relationship with the immune microenvironment and immunotherapy response of ccRCC.

Methods: RNA sequencing data of 526 patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database. The patients with ccRCC in TCGA were randomly divided (1:1) into a training and testing cohort. ICGC and GEO databases were used for validation. Screening for FerLncRNAs was performed using Pearson’s correlation analysis with the reported ferroptosis-related genes. A FerLncRNA signature was constructed using univariate, LASSO, and multivariate Cox regression analyses in the training cohort. Internal and external datasets were performed to verify the FRlncRNA signature. Four major FRlncRNAs were verified through in vitro experiment.

Results: A FerLncRNA signature (LINC00894, DUXAP8, LINC01426, PVT1, PELATON, LINC02609, and MYG1-AS1) was established. Patients in the high-risk group had a worse prognosis than those in the low-risk group. Analysis of the area under the time-dependent ROC curve (AUC) confirmed the predictive accuracy of the signature was better than the traditional evaluation factors; the AUC value for 5-year survival reached 0.772 in the total cohort. Multivariate Cox regression analysis showed that the risk score was an independent prognostic indicator. A nomogram was established based on the risk signature and clinical features to accurately predict the survival of patients with ccRCC. Four major FRlncRNAs (LINC00894, LINC01426, PVT1, and DUXAP8) were verified with the prognosis of ccRCC in the GEPIA and K-M Plotter databases, their expressions were validated by realtime PCR. Gene set enrichment analysis revealed the immune and tumor-related pathways in the individuals from the two risk groups. The immune cell infiltration landscapes and expression levels of immune checkpoints showed significantly different between the low- and high-risk groups. Finally, TIDE and ImmuCellAI analyses showed that patients in the low-risk group were more inclined to respond to immunotherapy than patients in the high-risk group. In addition, drug sensitivity analysis indicated that our risk signature had potential predictive value for chemotherapeutics and targeted therapy.

Conclusions: Our study established a novel FerLncRNA signature with robust predictive ability for survival and efficiency in predicting the response to immunotherapy, chemotherapy and targeted therapy in ccRCC patients. Its component FerLncRNAs may serve as therapeutic targets for ccRCC.

ferroptosis

lncRNA

clear cell renal cell carcinoma

immune microenvironment

immune checkpoint

treatment

Renal cell carcinoma (RCC) is the 9th most common cancer in men and 14th most common cancer in women, and the global increase in RCC morbidity and mortality has caused significant concerns [1]. Despite the recent rapid development and widespread clinical use of antiangiogenic agents and immune checkpoint inhibitors (ICIs) for the treatment of cancer, including RCC, the median overall survival (OS) rate of patients with RCC remains unsatisfactory [2]. Therefore, it is important to explore the RCC pathogenesis and mechanisms of development and find new therapeutic targets. Clear cell RCC (ccRCC) is the most common type of RCC, accounting for approximately 75–80% of all cases of RCC [1]. Recent studies have shown that ccRCC cells are susceptible to ferroptosis, a new form of iron-dependent programmed death that is morphologically, genetically, and chemically distinct from other forms of cell death [2]. Ferroptosis is characterized by lipid peroxidation, iron accumulation, and glutathione deprivation [3], and is closely related to the occurrence and development of cancer. Activation of the process of ferroptosis in cancer cells is a novel strategy for treating cancer, especially in drug-resistant cases, after conventional treatment [2].

Long noncoding RNAs (lncRNAs) are noncoding RNAs greater than 200 nucleotides in length. Studies have shown that lncRNAs play important roles in epigenetic regulation, as well as in the regulation of the cell cycle and differentiation, and these molecules have become a focus of genetic research [4]. Although lncRNAs do not encode proteins, they have several specific functions, such as transcriptional regulation, mRNA processing, and posttranscriptional regulation of mRNA [5]. Studies have also shown that lncRNAs play a role in regulating ferroptosis [5, 6]; however, the impact of ferroptosis on lncRNAs-dependent ccRCC progression, immune microenvironment and immunotherapy response has been scarcely studied.

In this study, we aimed to investigate the ferroptosis-related genes (FRGs) and FerLncRNAs in ccRCC. We established a ferroptosis-related lncRNA prognostic signature to predict the individual prognosis of ccRCC. To validate the prognostic signature, we investigated its efficiency and accuracy in the training, testing, total cohorts in TCGA database, as well as ICGC database. The results obtained with GEPIA, and K-M Plotter data supported the predictive ability of the major lncRNAs in risk signature. In addition, immune cell infiltration and check point expressions associated with this signature were explored. Our study demonstrated and proved that the prognostic signature can be applied in the clinical prognosis of ccRCC patients. Importantly, our study provided a new approach for predicting the response to treatment, including immunotherapy, chemotherapy and targeted therapy, in ccRCC patients.

Data source

The RNA-seq (FPKM) data for ccRCC samples (n = 539) and adjacent nontumorous kidney samples (n = 72) were retrieved from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov). Corresponding clinicopathological characteristics of patients with ccRCC (n = 526), including OS, were also obtained from the TCGA database. Based on the patient ID number, we matched their transcriptome data with clinical information and excluded the data for patients who did not match. Thus, we obtained complete gene expression profiles of 526 patients with ccRCC. Using the R package "caret," all patients with ccRCC were randomly divided into two cohorts: training and test. The specific clinical parameters for both cohorts, as well as for the entire TCGA cohort, are shown in Table 1. There were no significant differences in the clinical characteristics of patients between the training and test cohorts.

From the Gene Expression Omnibus(GEO) database (http://www.ncbi.nlm.nih.gov/geo/), three ccRCC datasets (GSE15641, GSE46699, and GSE40435) were selected for bioinformatics analysis. The expression data of 91 ccRCC patients from the ICGC database (https://dcc.icgc.org/analysis) were obtained for the external validation of the risk signature.

Table 1

Clinical characteristics and related cohort grouping of 526 patients with ccRCC.

Variates	Type	Overall cohorts (n=526)	Training cohorts (n=264)	Testing cohorts (n=262)	P value
age	<=60years	264(50.19%)	135(51.14%)	129(49.24%)	0.7275
	>60years	262(49.81%)	129(48.86%)	133(50.76%)
gender	Female	183(34.79%)	97(36.74%)	86(32.82%)	0.3944
	Male	343(65.21%)	167(63.26%)	176(67.18%)
grade	G1-G2	239(45.44%)	124(46.97%)	115(43.89%)	0.631
	G3-G4	279(53.04%)	137(51.89%)	142(54.2%)
	Unknown	8(1.52%)	3(1.14%)	5(1.91%)
stage	I-II	318(60.46%)	164(62.12%)	154(58.78%)	0.4872
	III-IV	208(39.54%)	100(37.88%)	108(41.22%)
M	M0	418(79.47%)	207(78.41%)	211(80.53%)	0.0709
	M1	78(14.83%)	36(13.64%)	42(16.03%)
	Unknown	30(5.7%)	21(7.95%)	9(3.44%)
N	N0	238(45.25%)	117(44.32%)	121(46.18%)	0.9085
	N1	16(3.04%)	8(3.03%)	8(3.05%)
	Unknown	272(51.71%)	139(52.65%)	133(50.76%)
T	I-II	336(63.88%)	177(67.05%)	159(60.69%)	0.1535
	III-IV	190(36.12%)	87(32.95%)	103(39.31%)

Identification of FerLncRNAs

lncRNAs and protein-coding genes were classified using the Ensembl human genome browser GRCh38.p13 (http://asia.ensembl.org/index.html) [7]. A total of 239 FRGs were identified in the Ferritin Database (http://www.zhounan.org/ferrdb/operations/download.html) [8]. Pearson’s correlation coefficients were then calculated to determine the correlation between the expression of FRGs and the corresponding lncrnas. Ferroptosis-associated lncRnas were identified based on p < 0.001 and the absolute value of the Pearson’s correlation coefficient greater than 0.35.

Construction and validation of a prognostic FerLncRNA signature

Screening for differentially expressed FRGs and lncRNAs between tumor and nontumor tissues was performed using the R package “edgeR.” The screening criteria were a false discovery rate of <0.001 and |log₂FC|≥2, LncRAN with expression value of 0 was deleted. Next, the differentially expressed lncRNAs were analyzed by univariate Cox regression analysis with OS to identify the prognostic lncRNAs in the entire cohort. The univariate variables with P-values of <0.05 were included in the least absolute shrinkage and selection operator (LASSO) analysis, which was used to further select useful predictive features to avoid overfitting of the model. Subsequently, a multivariate Cox regression analysis of candidate ferroptosis-associated lncRNAs was performed to assess their contribution as prognostic factors for OS in the training group. Finally, seven best ferroptosis-associated lncRNAs were identified for the prognostic model. The risk score of each patient was calculated based on this prognostic signature according to the normalized expression levels of FerLncRNAs and corresponding regression coefficients as follows:

The patients in the training cohort were divided into low- and high-risk groups based on the median risk score, and OS was compared between the groups using the Kaplan–Meier method and log-rank test. The area under the time-dependent ROC curve (AUC) was then determined using the "survivalROC" R package to assess the predictive accuracy of lncRNA features associated with ferroptosis. To validate this prognostic model, the risk score for each patient in the test cohorts, overall cohorts, and ICGC cohorts was calculated using the same way to confirm the stability of the established model.

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) enrichment analysis of differentially expressed FRGs

The “clusterProfiler” R package was used for the enrichment analysis of differentially expressed genes based on KEGG and GO pathway annotations.

Construction of an lncRNA–mRNA Coexpression network

To demonstrate the correlation between FerLncRNAs and their corresponding mRNAs, we mapped an mRNA–lncRNA coexpression network using the Cytoscape software (version 3.8.2, Mac OSX, http://www. cytoscape.org/). A Sankey diagram was then plotted to further demonstrate the degree of correlation between FerLncRNAs and their corresponding mRNAs.

Principal Component Analyses

We utilized the principal component analysis (PCA) to reduce the dimension and visualize the renal cancer patients with different risk values.

Immunity analysis and gene expression

The infiltration of immune cells was evaluated using the CIBERSORT tool [9]. Immune and stromal cell scores were calculated using the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm. The Tumor Immune Estimation Resource (TIMER), CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC algorithms were compared to assess cellular components or cell immune responses between the high- and low-risk groups based on the FerLncRNA signature. The differences in immune responses under different algorithms were visualized using a heatmap. In addition, single-sample GSEA (ssGSEA) was used to quantify the tumor-infiltrating immune cell types between the two groups and to assess their immune function. Potential immune checkpoints were retrieved from previous literature.

Cell Culture and Treatment

Human renal proximal tubule epithelial cells (HK-2 cells) and renal clear cell carcinoma cell lines (786-0, and Caki-1) were acquired from the American Type Culture Collection (ATCC, Manassas, VA, United States). Cell incubation was done at 37°C in a humid 5% CO₂ environment and was routinely cultured in RPMI 1640 or DEM supplemented with 10% fetal bovine serum (Invitrogen, Carlsbad, CA, United States), in which Caki-1 cells were maintained in McCoy's 5A (modified) medium (Gibco, USA).

RNA Extraction, Reverse Transcription, and Quantitative Real-time PCR (qRT-PCR)

Total RNA extraction micro-Kit (RNT411-03, Guangdong, China) extracts total RNA from the above cells, and then uses a spectrophotometer to detect the concentration and quality of total RNA. Then, cDNA was synthesized using random primers (hexamers) and oligo 18dT and SuperScript II Reverse Transcriptase (Invitrogen). qRT-PCR was performed on a Roche LightCycler 480 sequence detection system, under the following conditions: pre denaturation at 95 ° C for 30 seconds, denaturation at 95 ° C for 40 cycles for 5 seconds, annealing and extension at 60 ° C for 30 seconds. Human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as the constitutive control, 2^-ΔΔ CT function is used to calculate gene expression levels. The following primer sequences were used for qRT-PCR: PVT1, (forward) 5′-CCTGGTGAAGCATCTGATGCACG-3′ and (reverse) 5′-GCCAGGCTTTGTGGCACACGC-3′; LINC00894, (forward) 5′-GCAGGGTCTCTTGAGTTCCT-3′, and (reverse) 5′-TTCCTCAAGCTTCTCCAGGG-3′; DUXAP8, (forward) 5′-AGGATGGAGTCTCGCTGTATTGC-3′, and (reverse) 5′-GGAGGTTTGTTTCTTCTTTTTT-3′; LINC01426, (forward) 5′-CGCACCCAGATACTTTTCGT-3′, and (reverse)5′-GCCGTTGAGGTTGTCGTAAT-3′.

Prediction of immunotherapy response

TIDE (http://tide.dfci.harvard.edu/) algorithms were used to predict immune checkpoint response inhibitors in the high-risk and low-risk group. Immune Cell Abundance Identifier (ImmuCellAI) is a computational method published in 2020 to predict the response to immune checkpoint blockade based on the abundance of immune cells, particularly different T cell subsets[10]. The abundance of infiltrating immune cells was calculated by ImmuCellAI and used to develop the response prediction model. The immunotherapy response prediction model was developed using a support vector machine with the radial basis function kernel.

Assessment of the sensitivity of chemotherapy and molecular drugs

To estimate the risk score in predicting the response to chemotherapy and molecular drugs, the “pRRophetic” R package was applied to calculate the half-maximal inhibitory concentration (IC50) of samples between the low-risk and high-risk groups. The IC50 between the low- risk and high-risk groups was compared by the Wilcoxon signed-rank test.

Statistical analysis

The Wilcoxon test was used to compare the proportions of tumor-infiltrating immune cells and the expression levels of immune checkpoint molecules between the high- and low-risk groups. Spearman’s correlation analysis was used to analyze the correlations between tumor-infiltrating immune cells. Differences in the proportions of clinical features were analyzed using a chi-squared test. Univariate Cox regression, LASSO, and multivariate Cox regression analyses were performed to determine independent prognostic factors for OS. The predictive accuracy of the OS prognostic model was assessed by a time-dependent AUC analysis. All statistical analyses were performed using the R software (version 4.1.0) and RStudio (version 2021.09.1 Build 372 for macOS). Statistical significance was defined as a two-tailed P-value of <0.05.

Identification of Prognostic Differentially Expressed FerLncRNAs in Patients with ccRCC

The overall workflow of this study is shown in Figure S1. Analysis of RNA-seq data from patients with ccRCC resulted in the identification of 14,056 lncRNAs. FerLncRNAs were identified using 239 downloaded FRGs [8]. The expressions of 3,012 FerLncRNAs were found to be correlated (|R| > 0.35 and p <0.001) with the expression of FRGs. 77 FRGs were differentially expressed between benign and malignant tumor tissues.

Enrichment analysis of differentially expressed FRGs in ccRCC

Based on the GO analysis at the biological process level, differentially expressed FRGs were mainly enriched in response to hypoxia, response to decreased oxygen levels, and response to oxygen levels; at the cellular component level, differentially expressed FRGs were mainly enriched in the apical part of the cell, apical plasma membrane, and basolateral plasma membrane; and at the molecular function level, differentially expressed FRGs were mainly enriched in iron ion binding, oxidoreductase activity, acting on single donors with the incorporation of molecular oxygen, and acting on NADPH (Figure 1A). At these three levels, the top six enriched terms and the specific genes involved are presented in Figure 1B.

Based on the KEGG pathway analysis, the differentially expressed FRGs were mainly enriched in the HIF-1 signaling pathway, ferroptosis, microRNA in cancer, the PPAR signaling pathway, bladder cancer, autophagy, programmed death ligand 1 (PD-L1) expression, and the PD-1 checkpoint pathway (Figure 1C).

Construction of the Prognostic Model in the Training Cohorts

The correlation between differentially expressed FerLncRNAs and patient survival information was evaluated using univariate Cox analysis in training cohorts, which identified 30 prognostic lncRNAs in the training cohort. Because of the large number, LASSO analysis was conducted to avoid overfitting of the model (Figure 2A). Eventually, the following 10 FerLncRNAs were selected: LINC00894, DUXAP8, LINC01426, PVT1, MIR155HG , LINC01355, PELATON, LINC02609, MYG1-AS1, and PRKAR1B-AS1.

To further evaluate the significance of these differentially expressed FerLncRNAs in normal and malignant tissues and their prognostic significance in the model construction, multivariate Cox regression analysis was performed in the training group, which revealed that seven FerLncRNAs were independent prognostic factors for patients with ccRCC. Therefore, a seven-FerLncRNA signature was constructed to predict the OS of each patient with ccRCC. The risk score was calculated using the following equation:

Based on the median risk score, patients were divided into a high- and low-risk group. Kaplan–Meier curves demonstrated that the high-risk group had a worse prognosis in the training cohort (Figure 2B). The accuracy of the prognostic signature was evaluated by the ROC curve, the AUC values of 1, 3, 5, 7, and 10 years of overall survival (OS) were 0.896, 0.793, 0.801, 0.823, and 0.967, respectively (Figure 2C). As to the 5-year AUC values of prognostic signature compared with other clinicopathological factors (age, gender, grade, stage, T, M, and N), the prognostic signature has the highest values (Figure 2D). The heatmap showed remarkable differences in the expression of seven FerLncRNAs between the high-risk group and the low-risk group (Figure 2E), the scatter plot indicated that ccRCC patients with a high risk score had a lower survival rate than those with a low-risk score (Figure 2F). Moreover, the distribution map of the risk score was consistent with the categorization of patient groups (Figure 2G).

Validation of the Prognostic Model

To validate the predictive capacity of the FRlncRNA signature, risk scores of patients were calculated in the testing cohorts and overall cohorts, and patients were classified into the low- risk group and the high-risk group based on the median risk scores in the training cohorts. The Kaplan-Meier survival analysis of OS in the testing and overall cohorts demonstrated that these results were consistent with the training cohorts (all P<0.001, Figure 3A, G). The 5-year ROC curves of testing cohorts (AUC = 0.739) and overall cohorts (AUC = 0.772) demonstrated that the FRlncRNA signature has a better predictive capability compared with other clinicopathological factors (Figure 3B, H, respectively). The AUC values of 1-, 3-, 5-, 7-, and 10- years of OS were 0.599, 0.634, 0.739, and 0.838 in testing cohorts (Figure 3C), and the the AUC values of 1-, 3-, 5-, 7-, and 10-years of OS were 0.739, 0.712, 0.772, and 0.909 in overall cohorts (Figure 3I), these results further validate the prognostic signature. The consistent expression profiles of seven FRlncRNAs in the training cohorts are shown in the heatmaps (testing cohorts, Figures 3D; overall cohorts, Figure 3J). The survival rate of high-risk group was lower than that of low-risk group, and the risk score distribution map confirmed that the risk score of high-risk group was higher (testing cohorts, Figures 3EF; overall cohorts, Figure 3KL).

In addition, ICGC cohorts were used to evaluate the constructed model, which showed consistent expression profiles of the risk FRlncRNAs, and a good predictive capability of OS for patients with ccRCC (Figure S2).

These results show that, compared with other prediction models reported in the recent studies with ferroptosis in ccRCC, our FRlncRNAs prediction model has great advantages and clinical operability with less LncRNA number and the highest 5-year AUC value[11-14] (Table S1). Therefore, it can be used as a good index to predict the prognosis of ccRCC patients.

Taken together, our data suggested that the FRlncRNA signature showed a stable prognostic-predictive power.

Principal Component Analysis (PCA) and Stratified Survival Analysis of Clinicopathological Characteristics

The PCA schematic diagram shows two different risk levels of ccRCC patients in entire gene expression, ferroptosis genes expression, ferroptosis-related differentially expressed lncRNAs expression, and seven lncRNAs risk models (Figure 4A-D, respectively).

To assess the predictive ability of FRlncRNA signature and its stability in predicting OS in high-risk and low-risk groups, we performed stratified survival analysis of clinicopathological factors including age (<=60 years vs. >60 years), grade (Grade 1-2 vs. Grade 3-4), gender (Male vs. Female), stage (Stage I-II vs. Stage III-IV), T (T1-2 vs. T3-4), M (M1 vs. M0). The results of Kaplan-Meier survival analysis including different clinical factors further showed that OS in high-risk group was worse than that in low-risk group (all P< 0.01) (Figures 4E).

Kaplan-Meier curves (D) indicated the survival outcomes of high- and low-risk ccRCC patients stratified according to the age (<=60 years vs. >60 years), grade (Grade 1-2 vs. Grade 3-4), gender (Male vs. Female), stage (Stage I-II vs. Stage III-IV), T (T1-2 vs. T3-4), M (M1 vs. M0), respectively (all p<0.01).

Correlation between the FerLncRNA prognostic signature and Clinicopathological features

Strong correlations were observed between the risk scores and clinicopathological characteristics (stage, grade, T, M, and survival status) with ccRCC (Figure 5A), that is, as the stage, grade, metastasis and mortality increased, the risk score also gradually increased (Figure 5B, all P<0.001).

Construction and Evaluation of the Prognostic Nomogram

To determine whether the risk score was an independent prognostic factor in patients with ccRCC, univariate and multivariate Cox regression analyses were performed using clinical characteristics of the patients and their risk scores. The results demonstrated that the risk score was an independent prognostic factor (P<0.05) (Figure 6AB). Then, using the clinicopathological characteristics, including age, grade, stage and risk score, the nomogram was constructed using the "rms" package in R to predict the 1-, 3-, 5-, and 10-year OS of patients with ccRCC (Figure 6C). The results of the multivariate ROC curve showed that the 5-year AUC value of nomogram was 0.788, which was higher than that of the age (0.557), grade (0.675), and stage (0.725), indicating that the nomogram had the ability of accurate prediction for survival outcomes of ccRCC (Figure 6D). The time-dependent AUC analysis showed that the prognostic value of the nomogram was significantly higher than that of age, stage, and grade over a time span of 1 to 10 years (Figure 6E).

We used the calibration curve to observe whether the actual prognostic value was consistent with the predicted value of the nomogram and found that the calibration curves of 1-, 3-, 5-, and 10-year survival rates were consistent with the nomogram (Figure 6F). The DCA curves also showed that the nomogram had a favorable prognostic effect and a better clinical value than stage (Figure 6G). The clinical influences of the risk score for ccRCC patients in the training, and testing cohorts are showed in Figure S3.

CA curves for the nomogram and stage.

Functional enrichment analysis of the risk signature

To explore the biological functions associated with the risk signature, the differentially expressed genes between the high- and low-risk groups were used to perform GO and KEGG analysis. GO analysis consisted of molecular function (MF) analysis mainly including antigen biding, immunoglobulin receptor binding; cellular component (CC) analysis mainly containing immunoglobulin complex, external side of plasma membrance; biological process (BP) analysis mainly including B cell receptor signaling pathway, complement activation, and phagocytosis (Figure 7A). The KEGG pathway enrichment analysis displayed that cytokine receptor interaction, IL-17 signaling pathway, and NF-kappa B signaling pathway were enriched (Figure 7B). The “pathway-gene clustering” for GO (Figure 7C) and KEGG enrichment analysis (Figure 7D) were plotted.

Construction of a lncRNA–mRNA Coexpression network

We first explored the correlation between the seven FerLncRNAs, Figure 8A shows that most of our FerLncRNAs are positively correlated with each other. There are 28 ferroptosis-related genes associated with 7 FerLncRNAs. Figure 8B Sanky diagram indicates that there is a wide and complex correlation between them. To explore the potential roles of the seven FerLncRNAs in ccRCC, a lncRNA–mRNA coexpression network that contained 35 lncRNA–mRNA pairs was constructed using Cytoscape (Figure 8C). The correlation of FerLncRNAs and 28 FRGs were plotted (Figure 8D).

External Verification of the major genes

By inquiring the relevant literatures and online database[15-18], we decided to select four FRlncRNAs (LINC00894, LINC01426, PVT1, and DUXAP8) for further investigation. In ICGC database, the expression of LINC00894, LINC01426, PVT1, and DUXAP8 were obviously elevated in ccRCC compared with normal kidney tissues (Figure 9A-D, respectively, all P<0.05). The expression trends were also observed as to LINC00894, LINC01426, and PVT1 which were further validated with ccRCC cohorts from GEO database (GSE15641, GSE46699, GSE40435, Figure 9E-G, respectively, all P<0.01).

Further, we used two databases (GEPIA and K-M plotter) to explore four FRlncRNAs including expression levels, correlation with stage, and survival results. The expression levels of four FRlncRNAs were similar as validated by ICGC and GEO databases (Figure 9H-K). except LINC00894 (Figure 9L), the expression levels of LINC01426, PVT1, DUXAP8 increased gradually with the increase of stages (Figure 9M-O, respectively, all P<0.05), suggesting these FRlncRNAs were correlated with ccRCC progression. High expression levels of four FRlncRNAs were related to worse OS (Figure 9P-S, all P<0.05). similar results were also obtained in 530 ccRCC patients from the K-M plotter database (Figure 9T-W).

In vitro Experimental Verification of the Major Genes

The expression levels of four FRlncRNAs were verified by qRT- PCR in the normal and tumor cells (Figure 10). The results showed that the overall trend in the expression levels of all four FRlncRNAs increased obviously in ccRCC cell lines (Caki-1, and 786-O) compared with normal renal proximal tubule epithelial cells (HK-2), which are consistent with our previous bioinformatics analysis based on public database.

Immune landscape of the ccRCC Microenvironment

Functional enrichment analysis suggested that a number of biological functions associated with the FerLncRNAs were involved in immune responses. Further, based on the results of immunotyping of pancancer in the literature[19], we compared the relationship between risk score and immunotyping of ccRCC, and found that there were significant differences between the existing immune subtypes C1, C2, C3, and C6 and risk score (Figure 11A). Therefore, we consider that there is a potential correlation between our risk score and the immune infiltration response of ccRCC.

We further investigated the correlation of the risk score with the immune landscape of the ccRCC microenvironment. The high-risk group showed significantly higher immune, and ESTIMATE scores than those in the low-risk group (Figure 11B). The heatmap of immune responses based on the TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC algorithms is shown in Figure 11C; Based on this analysis, different algorithms indicated considerable differences between the two groups in terms of their immune infiltration functions. These findings fully confirmed that our FerLncRNA signature was strongly related to immune cell infiltration in ccRCC.

The results of ssGSEA analyses of ccRCC, which showed the correlation between immune cell subpopulations and related functions, revealed that APC co-stimulation, check point, cytolytic activity, HLA, inflammation promoting, T-cell co-hibition and costimulation, and Type I&II IFN response were significantly increased compared with low-risk group (Figure 11D, all P<0.05).

Given the importance of checkpoint inhibitor-based immunotherapies, we further explored the differences in the expression of immune checkpoints between the two groups. Substantial differences were found in the expression of CD80, CD28, CTLA4, IDO2, PDCD1, and many other important indicators between the two groups of patients, nearly all these checkpoints were elevated in high risk group (Figure 11E, all P<0.05). These results suggest that our risk score may have a potential correlation with the patient's response to immunotherapy.

Predicting sensitivity of chemotherapy and response to immunotherapy in patients with ccRCC

We evaluated the relationship between the risk signature and the sensitivity to chemotherapy and targeted therapy drugs for ccRCC patients by “pRRophetic” R package. Our results showed that, a significant difference was found between the two risk subgroups in the estimated IC50 values of 76 types of chemotherapy agents (including Etoposide, 5-Fluorouracil, Sorafenib, AKR inhibitor VIII, et al. all p<0.05, Table 2). The IC50 values of DMOG, AKT inhibitor VIII, Ruxolitinib, Lapatinib, and Rapamycin were obviously lower in samples of the low-risk group than in those of the high-risk group (Table 2). However, interestingly, there are still 43 drugs with low expression of IC50 in the high-risk group, the high-risk group demonstrated much higher sensitivity to the ROCK inhibitor (GSK429286A), HDAC inhibitor (Tubastatin A, MS-275), Raf inhibitor (Sorafenib), Aurora kinase inhibitor (VX-680), PDK-1 inhibitor (OSU-03012), Vinorelbine, Etoposide, 5-Fluorouracil than those of the low risk group (Figure 12A). These results indicated that the risk score had potential predictive significance for chemotherapy and targeted therapy.

Finally, we evaluated the potential immunotherapy response in each patient by TIDE and ImmuCellAI algorithms. The results demonstrated that, patients in low-risk group were has better immunotherapy response (Figure 12B), low-risk group patients were more likely to respond to immune checkpoint blockade (25%) than were patients in the high-risk group (10%) (Figure 12C). In addition, the risk score was lower in the responders than in the non-responders (Figure 12D). Taken together, these results indicated that the prognostic risk signature could predict the potential response to immunotherapy in ccRCC patients.

Table 2.

Risk score and chemotherapy drugs sensitivity in ccRCC patients.

Drug	Correlation Coefficient	P-value for correlation	IC50 value (median ± SD)		P-value for sensitivity
Drug	Correlation Coefficient	P-value for correlation	Low risk group	High risk group	P-value for sensitivity
GSK429286A	-0.4116847	9.03E-23	5.202±0.593	4.873±0.81	2.75E-15
Pyrimethamine	-0.4008079	1.22E-21	4.282±0.925	3.537±1.098	5.02E-15
Tubastatin A	-0.3752999	7.17E-19	4.841±0.655	4.487±0.983	1.27E-12
GNF-2	-0.3697642	1.74E-18	2.509±0.212	2.381±0.277	2.36E-11
OSU-03012	-0.3339629	3.60E-15	2.148±0.87	1.739±0.957	1.02E-09
AUY922	-0.3162495	1.11E-13	-3.092±0.575	-3.337±0.566	3.26E-10
BI-2536	-0.3140472	1.67E-13	-2.082±0.534	-2.324±0.618	6.02E-08
YM155	-0.299257	2.41E-12	-1.343±6.914	-3.863±6.499	4.06E-07
MS-275	-0.2928415	8.04E-12	1.669±1.596	0.922±1.778	9.92E-08
FTI-277	-0.2830392	3.80E-11	2.161±0.229	2.048±0.286	2.43E-09
CGP-60474	-0.2801646	6.08E-11	-2.236±0.752	-2.437±0.688	1.05E-07
WZ3105	-0.2796805	9.98E-11	1.393±1.891	0.666±2.211	2.43E-06
Vinorelbine	-0.2763082	1.13E-10	-3.305±1.291	-3.819±1.425	1.45E-06
LAQ824	-0.272809	1.98E-10	-2.507±0.854	-2.854±0.942	3.77E-07
BMS-754807	-0.2660241	5.69E-10	0.838±0.636	0.546±0.762	1.88E-07
Phenformin	-0.2656272	6.05E-10	7.963±0.91	7.547±1.068	2.32E-06
GW-2580	-0.2635171	1.25E-09	5.759±0.924	5.502±1.334	2.09E-06
NSC-207895	-0.2633884	8.52E-10	4.27±0.915	3.947±0.989	1.34E-05
Zibotentan	-0.2611228	1.20E-09	5.567±0.209	5.478±0.219	5.62E-07
Salubrinal	-0.2593582	2.01E-09	4.363±1.25	3.719±1.492	7.17E-06
CP466722	-0.2555132	2.76E-09	3.095±1.137	2.625±1.333	3.18E-06
NG-25	-0.2477181	8.52E-09	3.007±0.845	2.73±1.088	1.07E-05
JNK-9L	-0.2440906	1.42E-08	-0.144±0.531	-0.34±0.6	1.10E-05
JW-7-24-1	-0.2338737	5.75E-08	1.775±0.72	1.479±0.903	6.94E-06
PHA-665752	-0.2262784	1.56E-07	2.864±0.212	2.798±0.271	6.67E-06
JQ12	-0.2214814	2.88E-07	1.688±1.216	1.353±1.331	2.88E-05
A-443654	-0.2167294	5.21E-07	-0.803±0.312	-0.871±0.366	0.00069563
Etoposide	-0.2154125	6.12E-07	1.737±0.967	1.368±1.227	3.73E-05
Lisitinib	-0.2140062	7.45E-07	2.292±0.59	2.001±0.836	1.15E-06
Mitomycin C	-0.198886	4.30E-06	-0.598±0.849	-0.928±0.934	9.40E-06
5-Fluorouracil	-0.1917897	9.45E-06	3.674±1.028	3.368±1.114	6.08E-06
KIN001-135	-0.1808101	3.03E-05	3.889±0.253	3.793±0.325	0.00061634
Doxorubicin	-0.1787203	3.75E-05	-1.719±0.627	-1.916±0.81	0.00113553
AZ628	-0.1781504	0.00029364	5.71±2.579	4.896±2.837	0.00297656
TAK-715	-0.1702494	0.00010176	4.657±1.055	4.349±1.277	0.00130195
Crizotinib	-0.1637402	0.00030632	3.094±0.88	2.851±1.197	0.01034253
LY317615	-0.160018	0.0002287	3.346±0.612	3.082±0.679	1.57E-05
AS605240	-0.1598432	0.00028584	3.831±1.434	3.449±1.679	0.00634062
Sorafenib	-0.1433182	0.00118607	2.799±1.447	2.534±1.68	0.02333714
CP724714	-0.1393836	0.00150345	4.617±0.785	4.516±0.91	0.01613482
TL-1-85	-0.1369686	0.00170886	3.66±1.051	3.47±1.234	0.00884412
VX-680	-0.1223163	0.00496696	1.107±0.813	0.936±0.998	0.01145153
GW843682X	-0.1101968	0.01143801	-2.886±1.025	-2.965±1.119	0.02462889
GSK1904529A	0.10348973	0.01758529	2.261±0.283	2.287±0.289	0.02385087
CGP-082996	0.12168176	0.00532862	1.451±3.49	1.893±3.837	0.04197339
Dasatinib	0.136895	0.0018856	-0.708±3.034	0.201±3.198	0.01358105
Z-LLNle-CHO	0.13805049	0.00150446	0.263±1.627	0.403±1.507	0.01753678
Rapamycin	0.15665587	0.00031024	-2.749±1.604	-2.523±1.713	0.00827571
Lapatinib	0.15866254	0.0002588	2.268±0.832	2.431±0.835	0.00580935
Shikonin	0.16179519	0.00019418	-0.997±3.32	-0.591±3.634	0.00529048
XMD8-85	0.16653263	0.00014891	0.466±8.166	1.222±8.87	0.00678214
Ruxolitinib	0.17062802	8.53E-05	3.906±0.472	4.039±0.556	0.0114337
Midostaurin	0.17622863	4.82E-05	-0.858±2.128	-0.446±2.277	0.00351609
BX-912	0.177726	4.15E-05	2.573±1.062	2.858±1.306	0.00592782
Obatoclax Mesylate	0.18180522	2.73E-05	-1.836±2.237	-1.316±2.363	0.00139064
GSK-650394	0.18184426	2.72E-05	3.063±0.854	3.357±1.044	0.00263797
KIN001-102	0.18465183	2.03E-05	2.466±0.682	2.636±0.839	0.00197243
Bryostatin 1	0.18612326	2.38E-05	-3.542±1.523	-3.184±1.709	0.00529447
XL-184	0.18723462	1.54E-05	2.219±1.298	2.493±1.425	0.00124207
WZ-1-84	0.1894432	1.27E-05	3.504±1.102	3.763±1.103	0.00109396
QS11	0.19118071	1.01E-05	2.886±0.968	3.157±1.181	0.00151151
Bleomycin	0.19127995	1.02E-05	0.006±3.981	0.776±4.472	0.00150329
AS601245	0.1931847	8.11E-06	1.962±0.409	2.066±0.438	0.00075864
CMK	0.19339315	7.93E-06	1.154±2.384	1.664±2.546	0.00048235
Parthenolide	0.19787401	5.13E-06	2.076±1.924	2.608±2.121	0.00108445
Saracatinib	0.20334878	9.68E-06	1.193±2.263	2.109±2.329	0.00163244
NSC-87877	0.2054239	3.32E-06	4.11±1.689	4.579±1.875	0.00287139
FR-180204	0.2073988	1.61E-06	4.913±0.32	4.971±0.349	0.00070296
Pazopanib	0.20879199	1.36E-06	2.636±1.269	2.982±1.427	0.00019755
FMK	0.21931092	3.78E-07	4.629±0.954	4.927±1.068	0.00014473
XMD14-99	0.22055458	7.39E-07	3.562±1.773	4.14±1.951	0.00092496
CAL-101	0.2394262	3.25E-08	3.432±1.979	4.083±2.134	5.39E-05
Thapsigargin	0.24828123	7.87E-09	-5.281±3.865	-4.504±4.747	4.48E-06
ZSTK474	0.25063554	5.61E-09	0.436±1.358	0.964±1.538	1.49E-05
AKT inhibitor VIII	0.2598575	1.45E-09	2.277±0.286	2.367±0.286	1.41E-07
DMOG	0.27301586	1.91E-10	5.339±1.995	6.165±2.518	5.66E-07

Ferroptosis is a nonapoptotic form of programmed cell death. The main causes of ferroptosis are iron-dependent accumulation of reactive oxygen species (ROS) and the consumption of plasma membrane polyunsaturated fatty acids. Morphological features of cellular ferroptosis include a normal nucleus, an increased membrane density, a ruptured outer membrane, and atrophic or deficient mitochondria [20]. When the concentration of ROS exceeds the el2imination capacity of the antioxidant system, ROS can oxidize unsaturated fatty acids in the cell membrane to form lipid peroxides, which directly or indirectly damage the structure and function of cells [6]. Ferroptosis can promote tumorigenesis and cancer progression by inducing gene mutations and epithelial–mesenchymal transition and implicating other mechanisms [21]. Therefore, ferroptosis-related biomarkers can potentially serve as diagnostic biomarkers and therapeutic targets for ccRCC. An in-depth understanding of these biomarkers is expected to provide a breakthrough in the molecular mechanism of the occurrence and development of ferroptosis-related ccRCC.

In recent years, it has become increasingly clear that lncRNAs are involved in cell differentiation, cell cycle regulation, stem cell pluripotency, and the maintenance of various biological processes, including nerve growth differentiation and tumorigenesis, among others [22]. In the field of oncogenesis, lncRNAs play complex roles as oncogenes and also tumor repressors [23]. lncRNAs localize to chromatin, interact with proteins, and target RNAs by forming proliferation circuits, tumor suppressor circuits, viability circuits, motility circuits, and crosstalks between different mechanisms to promote cancer phenotypes [22, 24]. To date, an increasing number of studies have demonstrated that many lncRNAs are widely involved in tumorigenesis and the development of ccRCC. In recent years, studies have shown that lncRNAs are involved in the regulation of ferroptosis. It has been found that in tumors, cytoplasmic lncRNA P53RRA is downregulated and interacts with Ras GTPase-activating protein-binding protein 1 (G3BP1) to transfer p53 from the G3BP1 complex, which results in p53 retention in the nucleus and leads to cell cycle arrest, ferroptosis, and apoptosis [5]. The nuclear LINC00618 lncRNA accelerates ferroptosis in an apoptosis-dependent manner. Thus, ferroptosis- and apoptosis-related lncRNAs are critical to leukemogenesis and chemotherapy [25]. lncRNA OIP5-AS1 promotes prostate cancer progression and resistance to ferroptosis via miR-128-3p/SLC7A11 signaling [26].

Our research was based on the screening of TCGA data for FRG-related lncRNAs to find differentially expressed lncRNAs that could be used for the prognosis of patients with ccRCC. Survival analysis allowed the construction of a seven-lncRNA-based prediction model that could strongly predict the prognosis of patients with ccRCC in training cohorts, and this risk signature has been validated in testing and total cohorts, as well as the ICGC database. The risk signature was shown to be an independent risk factor for the prognosis that correlated with clinical indicators, including the stage, grade, and other characteristics of the patients, and had a high predictive AUC value. Next, we constructed a nomogram including the risk score to calculate the 1-, 3-, 5-, and 10-year survival rates in ccRCC patients, which has a better sensitivity compared with traditional stage standard. After inquiring the relevant literatures, we selected four lncRNAs (LINC00894, LINC01426, PVT1, and DUXAP8) for further investigation. The expression and clinical significances of these four lncRNAs were validated in ICGC, GEO, GEPIA and K-M plotter databases, further proved that these four lncRNAs were high expressed in tumor cells and correlated with stage and worse prognosis. qRT-PCR was applied to these four lncRNAs in ccRCC cells and norm kidney cells, and found that all those lncRNAs were obviously upregulated in tumor cells. Based on the functional annotation and pathway enrichment analysis of the risk signature, we noticed that a lot of immune-related pathways were enriched. Hence, we explored the correlation of risk signature with immune microenvironment landscape, and found considerable differences between the high and low risk groups in terms of their immune infiltration functions. We noticed that susbstantially differences were found in the expression of immune checkpoints expression in high and low risk groups in ccRCC patients, which suggest that our risk score may have a potential correlation with the patient's response to immunotherapy.

With the in-depth study of tumor immunology and molecular biology, immunotherapy provides a new perspective for tumor treatment. At present, the immune checkpoint inhibitors used to treat advanced renal cell carcinoma, such as Keytruda and Opdivo, can enhance the immune response against renal cell carcinoma by blocking PD-1. However, in contrast to most other types of anti-PD-1 responsive solid tumors, a high infiltration by CD8 + T cells in ccRCC patients has been previously associated with a worse prognosis[27, 28]. This is different from the common paradigm that the tumor is infiltrated by a large number of immune cells, forming an “infiltrated” or "hot" environment in the tumor, which will better respond to PD-1 blockade[28, 29]. This result was also observed in our study, that is, the prognosis of patients in the high-risk group was poor, but their immune function and immune checkpoint gene expression were increased, the predictive immunotherapy response by TIDE and ImmuCellAI showed that the low-risk groups patients could be benefited. Furthermore, by “pRRophetic” algorithm, we identified 76 types of chemotherapy agents which could be useful in treatment of ccRCC patients.

Currently, four FRlncRNAs (LINC00894, LINC01426, PVT1, and DUXAP8) have been reported to be related to oncogenesis and development. LINC00894 enhances cell proliferation and invasion by binding with miR-429 to mediate ZEB1 expression in breast cancer[15]; LINC01426 modulates CTBP1/miR-423-5p/FOXM1 axis via interacting with IGF2BP1 to aggravate ccRCC progression[16]; LncRNA DUXAP8 upregulates FOXM1 expression by sponging miR-485-5p and interacting with the FUS, thus facilitates multiple malignant phenotypes and resistance to PARP inhibitor in hepatocellular carcinoma[17]; LncRNA PVT1 promotes gemcitabine resistance via activating Wnt/β-catenin and autophagy pathway through modulating the miR-619- 5p/Pygo2 and miR-619-5p/ATG14 axes in pancreatic cancer[18]. Studies on the other lncRNAs also proved their functions in oncogenesis, such as knockdown of lncRNA PELATON enhanced sensitivity to ferroptosis inducers to inhibit cell proliferation and invasion in glioblastoma cells[30]; LINC02609 was positively correlated with late stage, grade, and distant metastasis in ccRCC[31], and associated with OS in soft tissue sarcoma[32]. Token together, the above results further confirmed the accuracy of our risk score, however, the potential exclusive functions of PELATON, LINC02609, and MYG1-AS1 in the tumorigenesis, development and prognosis of ccRCC remained unclear and further investigations are still needed.

There are some limitations in this study. First, our research was based on retrospective data available in the TCGA public database. This FerLncRNA prediction model and its clinical utility need to be further verified using multicenter, prospective, real-world data. Second, our research only revealed the relationship between FerLncRNAs and the TIME, while potential mechanisms and specific clinical applications need to be further explored experimentally.

ccRCC, Clear cell renal cell carcinoma;

AUC, Area under the time-dependent ROC curve;

LASSO, Least absolute shrinkage and selection operator;

GSEA, Gene set enrichment analysis;

GO, Gene Ontology;

KEGG, Kyoto Encyclopedia of Genes and Genomes;

LncRNA, Long noncoding RNA;

OS, Overall survival;

ROS, Reactive oxygen species;

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

This study analyzed publicly available datasets, which can be found here: TCGA (https://portal.gdc.cancer.gov/), ICGC (https://dcc.icgc.org/analysis), GEO(http://www.ncbi.nlm.nih.gov/geo/, GSE15641, GSE46699, and GSE40435), GEPIA (http://gepia.cancer-pku.cn), K-M Plotter (http://www.kmplot.com/), or be available from the corresponding author upon reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

The present study was supported by the Guide project for the Natural Science Foundation of Liaoning Province (No. 2019-ZD-0796), the 345 Talent Project of Shengjing Hospital of China Medical University.

Authors' contributions

Gang Liu designed the study. Lincheng Ju, and Yaxing Shi contributed to the data collection and statistical analysis. Lincheng Ju wrote the manuscript. Lincheng Ju and Gang Liu secured funding support and critically reviewed the manuscript. All authors read and approved the final manuscript.

Acknowledgements (optional)

Not applicable.

Nabi, S., et al., Renal cell carcinoma: a review of biology and pathophysiology. F1000Res, 2018. 7: p. 307.
Chen, X., et al., Ferroptosis: machinery and regulation. Autophagy, 2020: p. 1-28.
Stockwell, B.R., et al., Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. Cell, 2017. 171(2): p. 273-285.
Zhai, W., et al., A positive feed-forward loop between LncRNA-URRCC and EGFL7/P-AKT/FOXO3 signaling promotes proliferation and metastasis of clear cell renal cell carcinoma. Mol Cancer, 2019. 18(1): p. 81.
Mao, C., et al., A G3BP1-Interacting lncRNA Promotes Ferroptosis and Apoptosis in Cancer via Nuclear Sequestration of p53. Cancer Res, 2018. 78(13): p. 3484-3496.
Xie, B. and Y. Guo, Molecular mechanism of cell ferroptosis and research progress in regulation of ferroptosis by noncoding RNAs in tumor cells. Cell Death Discov, 2021. 7(1): p. 101.
Cunningham, F., et al., Ensembl 2019. Nucleic Acids Res, 2019. 47(D1): p. D745-D751.
Zhou, N. and J. Bao, FerrDb: a manually curated resource for regulators and markers of ferroptosis and ferroptosis-disease associations. Database (Oxford), 2020. 2020.
Newman, A.M., et al., Robust enumeration of cell subsets from tissue expression profiles. Nat Methods, 2015. 12(5): p. 453-7.
Miao, Y.R., et al., ImmuCellAI: A Unique Method for Comprehensive T-Cell Subsets Abundance Prediction and its Application in Cancer Immunotherapy. Adv Sci (Weinh), 2020. 7(7): p. 1902880.
Zhou, Z., et al., Identification and Validation of a Ferroptosis-Related Long Non-Coding RNA (FRlncRNA) Signature to Predict Survival Outcomes and the Immune Microenvironment in Patients With Clear Cell Renal Cell Carcinoma. Front Genet, 2022. 13: p. 787884.
Chen, X., et al., Analysis of Ferroptosis-Related LncRNAs Signatures Associated with Tumor Immune Infiltration and Experimental Validation in Clear Cell Renal Cell Carcinoma. Int J Gen Med, 2022. 15: p. 3215-3235.
Xing, X.L., et al., Development and validation of ferroptosis-related lncRNAs prognosis signatures in kidney renal clear cell carcinoma. Cancer Cell Int, 2021. 21(1): p. 591.
Bai, Z., et al., A Novel Prognostic Ferroptosis-Related Long Noncoding RNA Signature in Clear Cell Renal Cell Carcinoma. J Oncol, 2022. 2022: p. 6304824.
Meng, D.F., H. Shao, and C.B. Feng, LINC00894 Enhances the Progression of Breast Cancer by Sponging miR-429 to Regulate ZEB1 Expression. Onco Targets Ther, 2021. 14: p. 3395-3407.
Jiang, Y., et al., LINC01426 contributes to clear cell renal cell carcinoma progression by modulating CTBP1/miR-423-5p/FOXM1 axis via interacting with IGF2BP1. J Cell Physiol, 2021. 236(1): p. 427-439.
Hu, Y., et al., lncRNA DUXAP8 Facilitates Multiple Malignant Phenotypes and Resistance to PARP Inhibitor in HCC via Upregulating FOXM1. Mol Ther Oncolytics, 2020. 19: p. 308-322.
Zhou, C., et al., LncRNA PVT1 promotes gemcitabine resistance of pancreatic cancer via activating Wnt/beta-catenin and autophagy pathway through modulating the miR-619-5p/Pygo2 and miR-619-5p/ATG14 axes. Mol Cancer, 2020. 19(1): p. 118.
Thorsson, V., et al., The Immune Landscape of Cancer. Immunity, 2018. 48(4): p. 812-830 e14.
Jiang, N., et al., Progress in understanding the role of lncRNA in programmed cell death. Cell Death Discov, 2021. 7(1): p. 30.
Tang, D., et al., Ferroptosis: molecular mechanisms and health implications. Cell Res, 2021. 31(2): p. 107-125.
Fang, Y. and M.J. Fullwood, Roles, Functions, and Mechanisms of Long Non-coding RNAs in Cancer. Genomics Proteomics Bioinformatics, 2016. 14(1): p. 42-54.
Goodall, G.J. and V.O. Wickramasinghe, RNA in cancer. Nat Rev Cancer, 2021. 21(1): p. 22-36.
Schmitt, A.M. and H.Y. Chang, Long Noncoding RNAs in Cancer Pathways. Cancer Cell, 2016. 29(4): p. 452-463.
Wang, Z., et al., A Nuclear Long Non-Coding RNA LINC00618 Accelerates Ferroptosis in a Manner Dependent upon Apoptosis. Mol Ther, 2021. 29(1): p. 263-274.
Zhang, Y., et al., LncRNA OIP5-AS1 inhibits ferroptosis in prostate cancer with long-term cadmium exposure through miR-128-3p/SLC7A11 signaling. Ecotoxicol Environ Saf, 2021. 220: p. 112376.
Fridman, W.H., et al., The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol, 2017. 14(12): p. 717-734.
Braun, D.A., et al., Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med, 2020. 26(6): p. 909-918.
Chen, D.S. and I. Mellman, Elements of cancer immunity and the cancer-immune set point. Nature, 2017. 541(7637): p. 321-330.
Fu, H., et al., LncRNA PELATON, a Ferroptosis Suppressor and Prognositic Signature for GBM. Front Oncol, 2022. 12: p. 817737.
Su, Y., et al., Construction of Competitive Endogenous RNA Network and Verification of 3-Key LncRNA Signature Associated With Distant Metastasis and Poor Prognosis in Patients With Clear Cell Renal Cell Carcinoma. Front Oncol, 2021. 11: p. 640150.
He, R.Q., et al., Prediction of clinical outcome and survival in soft-tissue sarcoma using a ten-lncRNA signature. Oncotarget, 2017. 8(46): p. 80336-80347.

No competing interests reported.

FigureS1.jpg
Figure S1. The flowchart of this study.
FigureS2.jpg
Figure S2. Validation of the FerLncRNAs signature for 91 ccRCC patients in ICGC database. A. Heatmap of FRlncRNA expression profiles showed the expression of FRlncRNAs in high-risk and low-risk groups in the ICGC database;B. Risk score distribution plot showed the distribution of high-risk and low-risk in the ICGC database;C. Scatter plot showed the correlation between the survival status and risk score in the ICGC database;D. ROC curves and their AUC values showed 1-,and 5-year predictions in the ICGC database.
FigureS3.jpg
Figure S3. Identification of independent prognostic variables in training and testing cohortsUnivariate (A) and multivariate (B) Cox survival analysis showed that the risk signature was an independent prognostic factor, in training cohorts;Univariate (C) and multivariate (D) Cox survival analysis showed that the risk signature was an independent prognostic factor, in testing cohorts.
TableS1.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Ferroptosis-related lncRNA signature robustly predicts the prognosis and immunotherapy efficiency of clear cell renal cell carcinoma

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1