IL2RG’s Expression and its associated Long Non-Coding RNAs signature in ccRCC: Development, Validation, and Im-plications for Diagnosis,Prognosis,and Treatment

doi:10.21203/rs.3.rs-5310495/v1

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IL2RG’s Expression and its associated Long Non-Coding RNAs signature in ccRCC: Development, Validation, and Im-plications for Diagnosis,Prognosis,and Treatment

https://doi.org/10.21203/rs.3.rs-5310495/v1

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Background: Interleukin-2 Receptor Subunit Gamma (IL2RG) has been implicated in various cancers, but its role in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to explore IL2RG expression, its relationship with IL2RG -related lncRNAs (IRLs).

Methods: qRT-PCR and IHC were used to assess IL2RG expression in ccRCC tissues and cell lines. Pearson correlation analysis identified IRLs related to IL2RG, and LASSO regression was applied to develop a prognostic model. We also conducted drug sensitivity analysis.

Results: IL2RG was significantly upregulated in ccRCC tissues and correlated with advanced clinical stages (p<0.001). High IL2RG expression was linked to worse overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) (p<0.05). A 6-IRLs signature was identified, and the resulting model accurately predicted survival outcomes. Immune-related pathways were enriched in high-risk patients, and drug sensitivity analysis indicated that high-risk patients were more responsive to sunitinib and temsirolimus.

Conclusions: IL2RG and its related 6-IRLs are potential biomarkers for ccRCC progression. The 6-IRLs model provides a robust tool for predicting prognosis and guiding therapeutic decisions.

Clear Cell Renal Cell Carcinoma (ccRCC)

IL2RG

Long Non-Coding RNAs (lncRNAs)

Biomarkers

Immune Response

Tumor microenvironment(TME)

Renal cell carcinoma (RCC) is a highly malignant tumor originating from the renal tubular epithelium system[1]. In developed countries, the annual incidence of RCC continues to rise[2]. Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype, accounting for approximately 70-80% of all renal cancer cas-es[3]. It is notorious for its aggressive behavior and high metastatic potential, making treatment and management particularly challenging[4]. Clinically, ccRCC is often diagnosed at an advanced stage due to the lack of overt symptoms in its early phases. Treatment options for ccRCC include surgical interven-tions such as radical nephrectomy[5]. However, advanced or metastatic ccRCC often precludes surgical options, necessitating targeted therapies, immuno-therapies, or a combination of both. Yet, the tumor microenvironment is characterized by significant immune suppression, leading to poor prognoses[6]. Therefore, unraveling the complex molecular mechanisms underlying ccRCC, identi-fying novel biomarkers for early detection, and exploring new therapeutic targets to enhance treatment efficacy and overcome resistance are pressing issues.

Interleukin-2 Receptor Subunit Gamma (IL2RG) encodes the γ subunit of the interleukin-2 receptor, a key component of multiple cytokine receptor complexes, including IL-2, IL-4, IL-7, IL-9, IL-15, and IL-21[7,8]. Located on the X chromosome, mutations in IL2RG are linked to immunodeficiency disorders like severe combined immunodeficiency (SCID), highlighting its essential role in immune function[9]. Recent studies have identified IL2RG's significant role in the tumor microenvironment (TME), which includes tumor cells, immune cells, and extracellular components[10,11]. In ccRCC, the TME is rich in immune cells, such as T cells and natural killer (NK) cells, which affect tumor progression and response to therapy. IL2RG may influence the immune status of the TME by modulating immune cell development and function.

For example, IL2RG expression correlates with immune cell infiltration in breast cancer, and its downregulation can suppress T cell activity, aiding tumor evasion of immune surveillance[12,13]. The interaction between IL2RG and the IL-15 signaling pathway is vital for NK cell function, impacting both tumor progression and prognosis[14]. Furthermore, IL2RG has emerged as a potential target for ccRCC immunotherapy, serving as a biomarker for treatment efficacy and patient outcomes[15]. Research on IL2RG is crucial for advancing personalized immunotherapy.

Long non-coding RNAs (lncRNAs) are over 200 nucleotides long and play various roles in regulating gene expression without encoding proteins[16]. They influence transcription and post-transcriptional processes by acting as molecular sponges for miRNAs and modulating mRNA stability[17]. lncRNAs are increasingly recognized for their roles in cancer and immune disorders[18]. While IL2RG's function in the immune system is well-studied, research on IL2RG-related lncRNAs is still limited.

In this study, we first employed bioinformatics analysis using R software to ex-amine the TCGA-KIRC database, investigating the expression differences of the IL2RG gene between ccRCC and normal tissues, as well as its correlation with clinicopatho-logical features and prognosis. Subsequently, we assessed the mRNA and protein ex-pression levels of IL2RG in ccRCC and adjacent normal tissues through qRT-PCR and IHC experiments. Finally, we developed a signature comprising 6 IL2RG-related lncRNAs (IRLs) and evaluated its diagnostic, prognostic, and therapeutic value for ccRCC. Key lncRNAs were further validated using qRT-PCR. The results indicate that this signature holds significant potential for clinical application, providing guidance for precise diagnosis and offering new perspectives on immunotherapy for ccRCC.

Data acquisition, processing, and clinical sample collection.

IL2RG pan-cancer analysis was performed using the GEPIA2 database [19] (http://gepia2.cancer-pku.cn/). TCGA datasets were sourced from The Cancer Genome Atlas (TCGA) [20], with the transcriptomic gene expression matrix from the TCGA-KIRC project in transcripts per million (TPM) format, including RNA sequencing data from 533 ccRCC and 72 normal kidney tissues. Inclusion criteria were: ① kidney as the primary site; ② availability of complete lncRNA sequencing data and clinical information. Logarithmic transformation was applied for normalization, and the "limma" package in R software was used to correct the gene expression matrix. Tumors were classified into stages I-IV according to the TNM system, reflecting size and spread, and graded 1–4 (G1-G4) based on the ISUP grading system, indicating increasing malignancy. Samples with insufficient details were excluded.

Fifty specimens were collected from the Department of Urology at the First Hospital of Shanxi Medical University between January and October 2023 from patients undergoing radical nephrectomy for clear cell renal carcinoma (ccRCC). Exclusion criteria included preoperative treatments and incomplete clinical information. Specimens were stored at -80°C after immersion in liquid nitrogen. The study was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (No. 2021K034, April 26, 2021).

Analysis of the clinical correlations of IL2RG expression.

To identify the differential expression levels of IL2RG in ccRCC tissues compared to normal kidney tissues, the "limma" package in R software was employed. P < 0.05 was considered statistically significant. The R packages "pheatmap" and "boxplot" were uti-lized to display the expression levels and differences of IL2RG, while the "survival" package was used to assess the prognostic value of the IL2RG gene, with prognostic outcomes including Overall Survival (OS), Disease-Specific Survival (DSS), and Pro-gression-Free Interval (PFI).

Screening of potential regulatory IRLs

We retrieved 3,178 immune-related genes from the Immport database [21] and the InnateDB database [22]. A Pearson correlation analysis identified genes associated with IL2RG (r > 0.8, P < 0.001), resulting in 26 immune genes. We then analyzed lncRNAs co-expressed with these genes (r > 0.5, P < 0.001) and used the "limma" package to assess lncRNA differential expression between 72 normal kidney tissues and 533 ccRCC tissues. Co-clustering analysis was conducted with "limma" and "ConsensusClusterPlus," visualized using "survminer," "survival," and "pheatmap," including survival curves and heatmaps of clinical-pathological features.

Construction of the IRLs signature

To establish a robust prognostic model, we randomly divided 530 ccRCC patients with survival data into training and testing cohorts at a 1:1 ratio, with the features listed in Table S1. We constructed an lncRNA risk score formula using least absolute shrinkage and selection operator (LASSO) regression analysis and selected the optimal λ with the minimal bias in partial likelihood to determine the most suitable genes. The formula is as follows: Risk Score = Ʃ (Exp[lncRNA] × coef[lncRNA]), where coef[lncRNA] denotes the regression coefficient and Exp[lncRNA] reflects the expression level of the corre-sponding lncRNA. Each sample was classified into high-risk or low-risk groups based on the median risk score. An IRLs prognostic model was established using the training set, and a correlation heatmap was generated. Validation was performed using the testing set and the entire dataset, with differences in clinical characteristics between the total dataset, training set, and validation set assessed via chi-square tests, with P < 0.05 indicating statistical significance.

Evaluation of the Prognostic Model's Efficacy and Clinical Value

Using the median risk score of each sample as a threshold, the samples were cat-egorized into low-risk and high-risk groups, and the OS between these groups was compared using the log-rank test. Principal Component Analysis (PCA) was employed to assess the clustering capability of the IRL model risk scores. Combining risk scores with clinical data such as age, sex, tumor grade, and stage of ccRCC patients, Cox re-gression analysis was performed to determine whether the model could serve as an independent prognostic factor.

Construction of a nomogram

We constructed a nomogram using the "rms" package in R software to predict the survival rates of ccRCC patients at 1, 3, and 5 years. The reliability and accuracy of the nomogram were assessed through calibration curves, Receiver Operating Characteristic (ROC) curves, and Decision Curve Analysis (DCA).

The relationship between the IRLs model risk score and immune analysis

To investigate the pathway differences between high-risk and low-risk groups, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis [23]was performed based on risk scores, employing Gene Set Enrich-ment Analysis (GSEA). In R software, we utilized the "GSVA" package to compute the activity of 13 immune-related pathways through single-sample GSEA (ssGSEA). Im-mune scoring, including ESTIMATE score, immune score, and stromal score, was con-ducted to assess variations in immune infiltration levels between the groups. The CIBERSORT algorithm was employed to calculate immune cell infiltration levels, and Pearson correlation analysis was used to explore the relationship between risk scores and immune cell infiltration levels.

The relationship between IRLs model risk scores and drug sensitivity.

Based on the constructed scoring model, we employed the "pRRophetic" package in R to predict the half-maximal inhibitory concentration (IC50) of targeted drugs commonly used in the treatment of ccRCC. Subsequently, we visualized the drug sensitivity data using the "ggplot2" package and compared the IC50 values between the high-risk and low-risk groups using the two-sample Wilcoxon test to evaluate the correlation between drug sensitivity and risk scores. Statistical significance was set at P < 0.05.

Cell culture

Human renal epithelial cells (293T) and human ccRCC cells (786O, 769P, ACHN) were purchased from the Shanghai Cell Bank of the Institute of Life Sciences. These cells were cultured in DMEM (Procell, China) supplemented with 10% fetal bovine serum (Gibco, USA) and 1% penicillin/streptomycin (Solarbio, China). All cells were main-tained at 37°C in a 5% CO₂ incubator and kept at 80%-90% confluence. The culture me-dium was changed every 2–3 days to ensure optimal cell growth.

Quantitative real-time polymerase chain reaction(qRT-PCR)

The tissue samples were stored in a cryogenic freezer. First-Strand cDNA for qPCR was prepared according to the manufacturer's instructions, and subsequent qPCR reactions were conducted using the appropriate kits. Specifically, the First-Strand cDNA Synthesis SuperMix for qPCR kit and the Green qPCR SuperMix kit were both procured from TransGen Biotech (Beijing, China).GAPDH was used as an internal control,The primer sequences are as follows:GAPDH-F: 5ʹ- GCTCTCTGCTCCTCCTGTTC − 3ʹ; GAPDH-R:5ʹ-ACGACCAAATCCGTTGACTC − 3ʹ。IL2RG-F༚5ʹ- GTGCAGCCACTATCTATTCTCTG-3ʹ;IL2RG-R༚5ʹ-GTGAAGTGTTAGGTTCTCTGGAG-3ʹ。LINC00944-F༚5ʹ-GTGGAGAAGATGAGGAAACTGAGGAA-3ʹ;LINC00944-R༚5ʹ-AAGGGATGATGGAAATAAGCAAGAAG-3ʹ。AC016773.2-F: 5ʹ-TCAGTCCTCCAACTTTGTTCTTCTCC-3ʹ; AC016773.2-R: 5ʹ-CACACTTACCTCTTCCTCCACTCCAC-3ʹ。LINC02446-F༚5ʹ-ACATAAGGTGTAAACCAAGTAACCAA-3ʹ༛LINC02446-R༚5ʹ-CCAAAAATAAGAAAAGAGTAAAAAAA-3ʹ。LINC02328-F༚5ʹ-TGTGGAAAGTAGTTTGGCGATTTGTC-3ʹ༛LINC02328-R༚5ʹ-TGGATTCTGTATTAGGGTATGGGCTC-3ʹ。U62317.2-F༚5ʹ-TTTCTGGATTGTAGTTCTAATTGGGT-3ʹ༛U62317.2-R༚5ʹ-TGTTTTTGAGTTGTTATCATTTGCCT-3ʹ。KIF1C-AS1-F༚5ʹ-CCTCAATCTGCGACACATCTTCTACC-3ʹ༛KIF1C-AS1-R༚5ʹ-TCTAACCCTCCTTCTGCTCTTCCTTT-3ʹ。Gene relative expression levels were calculated using the 2^-ΔΔCt method.

Immunohistochemistry(IHC)

The tissues were processed through paraffin embedding, sectioning, deparaffinization, rehydration, antigen retrieval, and endogenous peroxidase blocking. Subsequently, the sections were stained with IL2RG antibody (abcam, USA) diluted to 1:300, followed by a secondary antibody. Color development was achieved using diaminobenzidine (DAB), and counterstaining was performed with hematoxylin to assess the protein expression of IL2RG.

Statistical analysis

Statistical analyses were performed using R software (version 4.1.3) and GraphPad Prism (version 9.0). The two-sample Wilcoxon rank-sum test assessed differences in IL2RG mRNA and associated lncRNA expression. Univariate Cox regression analyzed the prognostic value of key genes and lncRNAs. LASSO regression identified crucial lncRNAs related to IL2RG, while Pearson correlation tests explored relationships among clinical features, risk scores, immune checkpoint inhibitors, and immune infiltration levels. Survival analysis employed the Kaplan-Meier method. Inter-group differences were assessed using t-tests, and variance analysis compared differences among three or more groups. Statistical significance was defined as P < 0.05 (*: P < 0.05, **: P < 0.01, ***: P < 0.001).

The expression levels of IL2RG and their relationship with the clinical and pathological characteristics.

First, we conducted a pan-cancer analysis of IL2RG and found that IL2RG expres-sion was dysregulated in the majority of cancers. Specifically, IL2RG was upregulated in BRCA, CHOL, HNSC, KIRC, PCPG, and STAD, and downregulated in COAD, KICH, LUAD, and LUSC (Fig. 1A). The varied expression profile of IL2RG across different tumors suggests its involvement in tumorigenesis and progression through distinct mechanisms.

Subsequently, we analyzed the expression of IL2RG in ccRCC concerning its clin-ical and pathological relevance. We observed that IL2RG expression levels were signif-icantly associated with T stage, N stage, M stage, AJCC stage, and WHO/ISUP grade (P < 0.001). Moreover, logistic regression analysis revealed a positive correlation be-tween IL2RG expression and ccRCC progression (Fig. 1B-D), though no association was found with age or gender (Figure S1). Kaplan–Meier survival analysis indicated that high IL2RG expression was correlated with poorer OS, DSS, and PFI (P < 0.05, Fig-ure 1E-I).

Using qRT-PCR, we assessed IL2RG mRNA expression in samples (n = 50) and cell lines, with results presented as 2-ΔΔCt. We then performed IHC to examine IL2RG pro-tein expression in ccRCC tissues. The results demonstrated that IL2RG mRNA expres-sion was significantly higher in ccRCC tissues compared to normal tissues (P < 0.05, Fig. 2A). Similarly, IL2RG mRNA levels in 786O, 769P, and ACHN cells were signif-icantly elevated compared to 293T cells (Fig. 2B). IHC results further revealed that IL2RG protein was predominantly localized in the cell membrane and cytoplasm of ccRCC cells (Fig. 2C), and its expression was significantly higher in ccRCC tissues compared to normal tissues (P < 0.0001, Fig. 2D).

These findings suggest that IL2RG may play a role in ccRCC tumor progression and could serve as a potential prognostic biomarker for ccRCC patients.

Critical IRLs Screening.

Through the ImmPort and InnateDB databases, we identified 3,178 im-mune-related genes in humans, and using Pearson correlation analysis, we filtered 67 genes that were highly correlated with IL2RG (r > 0.8, P < 0.001) (Table S2). Ultimately, 26 immune genes significantly associated with IL2RG were confirmed (Figure S2A). The results revealed that IL2RG-related immune genes were generally dysregulated in ccRCC (Figure S2B). We further generated a heatmap, visually demonstrating the up-regulation of these 26 genes in ccRCC tissues (Figure S2C), suggesting that these genes may play pivotal roles in the initiation and progression of ccRCC.

Subsequently, we continued with Pearson correlation analysis to explore the as-sociation between these genes and lncRNAs (r > 0.8, P < 0.001), identifying key lncRNAs co-expressed with IL2RG and associated with prognosis (Figure S3A). The analysis revealed 63 IRLs that were closely related to patient survival outcomes and showed statistical significance (Table S3). We then constructed a forest plot for the top 20 lncRNAs positively correlated with prognosis (Figure S3B). Through the creation of heatmaps and differential expression plots, we filtered the top 20 lncRNAs most strongly associated with survival and prognosis. The results demonstrated significant differences in the expression of these lncRNAs between groups (Figure S3C-D).

Consensus Clustering based on IRLs.

Based on the expression profiles of 63 key immune-related lncRNAs (IRLs), we conducted consensus clustering analysis on 530 ccRCC patients. The analysis showed a low cumulative distribution function (CDF) value at k = 2 (Figure S4A-B). We found significant associations between clusters and clinical parameters, including TNM stage, histological grade, and clinical staging (Figure S4C). Prognosis analysis revealed significant differences among patient clusters (P < 0.001, Figure S4D). Using the CIBERSORT algorithm, we assessed immune cell infiltration, noting differences in B cells, CD8 + T cells, resting CD4 + memory T cells, monocytes, and M2 macrophages (Figure S4E). The ESTIMATE algorithm indicated significant differences in immune and microenvironment scores (P < 0.001), though no difference was observed in stromal scores (Figure S4F). These findings suggest that clustering based on key IRLs correlates with clinical characteristics and prognosis, highlighting potential biomarkers for ccRCC diagnosis and evaluation.

Development of a prognostic model based on IRLs.

In this study, 530 ccRCC patients were randomly divided into a training cohort (266 cases) and a testing cohort (264 cases). We performed LASSO regression analysis on the 63 IRLs, constructing a prognostic model comprising 6-IRLs (Fig. 3A-C). The formula for calculating the risk score of the model is: Risk score = (LINC00944 × 0.066) + (AC016773.2 × 0.100) + (LINC02446 × 0.002) + (LINC02328 × 0.018) + (U62317.2 × 0.001) + (KIF1C-AS1 × 0.041). We then plotted the forest plots of univariate and multivariate Cox regression analyses for these 6-IRLs (Figure S5). Using this formula, we calculated each patient’s risk score and categorized them into low-risk and high-risk groups based on the median risk score. Survival analysis of the total cohort, training set, and validation set revealed that the survival time of the high-risk group was significantly shorter than that of the low-risk group (Fig. 3D-F), with this difference being statis-tically significant (P < 0.05). Furthermore, ROC curve analysis demonstrated that the 6-IRL model exhibited high accuracy in predicting survival rates (Fig. 3G-I). Survival status curves and scatter plots indicated that as the risk score increased, patient sur-vival time significantly decreased (Fig. 3J-L). t-SNE analysis also revealed a marked distinction between the high-risk and low-risk groups (Fig. 3M-O).

Subsequently, we analyzed the expression of the risk score in ccRCC based on clinical and pathological data from the TCGA database (Figure S6A). We found that the risk score was significantly associated with T stage, M stage, AJCC stage, and WHO/ISUP grade, with higher risk scores correlating with more advanced T stage, M stage, AJCC stage, and WHO/ISUP grade (Figure S6B-E) (P < 0.05), while no correlation was found with age, gender, or N stage (Figure S7). Univariate and multivariate Cox regression analyses indicated that the 6-IRLs model could serve as an independent prognostic factor, with the results supported by both the validation set and the total TCGA cohort (Figure S8). This suggests that the scoring model holds promise as a crucial indicator for clinical prognostic assessment in ccRCC patients.

The stratified prognostic value of the 6-IRL signature.

We further evaluated the prognostic predictive capability of the 6-IRLs signature across various clinical strata, including gender (female vs. male), age (≤ 65 years vs. >65 years), AJCC stage (I–II vs. III–IV), ISUP grade (I–II vs. III–IV), T stage (T1–2 vs. T3–4), M stage (M0 vs. M1), and N stage (N0 vs. N1). The results revealed a significant difference in survival prognosis between high-risk and low-risk patients in most clinical groups, with high-risk patients exhibiting notably poorer outcomes (Figure S9). However, in subgroups of patients aged > 65 years, with ISUP grade I–II, M1, and N1, the survival differences were not statistically significant (Figure S10).

Development and validation of the prognostic nomogram.

We integrated multiple clinical factors closely associated with patient prognosis, including gender, AJCC stage, ISUP grade, and the 6-IRL signature, to develop a prac-tical clinical prediction tool aimed at enhancing the accuracy of survival predictions for ccRCC patients. This predictive model, represented by a nomogram, estimates the 1-year, 3-year, and 5-year survival probabilities of ccRCC patients (Fig. 4A). We subsequently validated the model’s performance in predicting survival at these time points using calibration curves (Fig. 4B), which demonstrated outstanding accuracy. Additionally, we employed decision curve analysis (DCA) to further assess the clinical utility of the tool. The DCA results indicated that the nomogram provided greater net benefits and a wider range of threshold probabilities in predicting 1-year, 3-year, and 5-year survival. Compared to the 6-IRL signature alone, the nomogram exhibited supe-rior clinical applicability (Fig. 4C-F).

immune Analysis via 6-IRLs Markers.

According to the results of Gene Set Enrichment Analysis (GSEA), we observed significant enrichment differences in multiple KEGG pathways between the high-risk and low-risk groups (Fig. 5A). In the high-risk group, the T-cell receptor (TCR) sig-naling pathway and Toll-like receptor (TLR) signaling pathway exhibited positive en-richment scores, conversely, the glutathione metabolism pathway, fatty acid metabolism, and TGF-β signaling pathway were significantly enriched in the low-risk group,furthermore, the P53 signaling pathway and autophagy regulation also showed differential enrichment across the risk spectrum, hinting at a complex regulatory network involving tumor suppression and autophagic processes that may influence the phenotypic transition between high-and low-risk groups.

Subsequently, we delved deeper into the enrichment levels and activities of im-mune cells, relevant pathways, and their functions in ccRCC. The results revealed sig-nificant differences in immune marker expression between the low-risk and high-risk groups (Fig. 5B). Further analysis of immune checkpoint expression between the two groups indicated substantial differences across several immune checkpoint mole-cules (Fig. 5C). Additionally, we evaluated the correlation between risk scores and immune cell infiltration. The analysis showed a positive correlation between risk scores and Memory B cells, Neutrophils, M1 macrophages, CD8 + T cells, Regulatory T cells, and Follicular helper T cells. In contrast, M2 macrophages, resting Mast cells, na-ive B cells, Monocytes, Eosinophils, activated Dendritic cells, and resting Dendritic cells were negatively correlated with risk scores (Fig. 5D-G, Figure S11).

Drug sensitivity analysis.

The sensitivity analysis of commonly used targeted drugs revealed that the high-risk group exhibited greater sensitivity to Sunitinib and Temsirolimus (Figure S12A, C), while the low-risk group demonstrated higher sensitivity to Sorafenib and Pazopanib (Figure S12B, E). Conversely, Axitinib showed no significant difference be-tween the two groups (p = 0.55) (Figure S12D). These results suggest a correlation be-tween risk group stratification and drug sensitivity. Furthermore, the 6-IRLs signature could serve as an independent predictor of drug efficacy, potentially holding signifi-cant clinical value.

Clinical sample validation.

The qRT-PCR results indicated that the relative expression levels of 6-lncRNAs were significantly elevated in ccRCC tumor tissues compared to adjacent normal tissues, with all differences being statistically significant (Fig. 6A-F) (P < 0.05). These ex-perimental findings corroborate our bioinformatics analysis, reaffirming the accuracy of our study through the confirmed expression levels of the 6-lncRNAs.

Renal cell carcinoma (RCC), or kidney cancer, mainly consists of clear cell carcinoma (ccRCC), which accounts for 80% of cases [24]. ccRCC often presents subtle symptoms, leading to late diagnoses and a five-year survival rate under 10% for metastatic cases [25]. Therefore, new diagnostic methods and improved survival rates are crucial. Interactions between tumor cells and immune components in the tumor microenvironment (TME) shape immune responses and tumor growth [26]. Studying immune factors in the TME is vital for effective immunotherapies.

IL2RG is a single-pass transmembrane protein that activates downstream signaling pathways, especially the JAK/STAT pathway, crucial for immune regulation [27]. Its role in tumor immune modulation has become clearer, as IL2RG mediates IL-2 signaling, enhancing the activation and function of immune cells in the tumor microenvironment [28]. The interaction between IL-2 and IL2RG boosts T cell and NK cell cytotoxicity against tumor cells [29]. However, melanoma cells can suppress IL2RG signaling, leading to immune evasion and promoting tumor growth [30]. Long non-coding RNAs (lncRNAs) significantly regulate tumor initiation and progression, and while they are emerging as important clinical biomarkers, reports on immune-related lncRNAs (IRLs) in ccRCC prognosis are limited. Thus, developing a prognosis model based on IRLs from the TCGA database and exploring new biomarkers and therapies offers significant potential for ccRCC treatment.

In this study, we investigated the role of IL2RG in ccRCC as a potential prognostic biomarker. Our pan-cancer analysis revealed IL2RG dysregulation in various cancers, indicating its involvement in tumorigenesis. In ccRCC tissues and cells, qRT-PCR and IHC assays demonstrated significant IL2RG upregulation compared to normal tissues, highlighting its role in ccRCC progression. We found that IL2RG expression correlated with tumor progression, including T, N, and M staging, suggesting its importance in ccRCC onset and prognosis. Additionally, we identified significant correlations between IL2RG and several immune-related genes, with 26 key immune genes dysregulated in ccRCC, emphasizing IL2RG's interaction with the tumor immune microenvironment. Our clustering of ccRCC patients using 63 key immune-related lncRNAs (IRLs) showed that grouping into two clusters based on these IRLs is feasible, further supporting their potential as therapeutic targets.

We identified 63 key immune-related lncRNAs (IRLs) and constructed a prognostic model using six IRLs (LINC00944, AC016773.2, LINC02446, LINC02328, U62317.2, KIF1C-AS1), highlighting the significance of lncRNAs in ccRCC. Liu et al. suggested LINC00944 as a prognostic biomarker [31], while Chen et al. found that its knockdown reduces proliferation and migration and enhances Akt phosphorylation [32]. Additionally, LINC00944 is associated with tumor-infiltrating T lymphocytes and apoptotic pathways [33]. AC016773.2 is linked to m6A methylation [34]. LINC02446 is involved in epithelial-mesenchymal transition (EMT) in bladder cancer and regulates the mTOR pathway, impacting proliferation and metastasis [35, 36]. However, the roles of LINC02328, U62317.2, and KIF1C-AS1 remain underexplored and need further study.

Subsequently, this prognostic model was validated in both the testing cohort and the TCGA cohort, with the scoring formula serving as a reliable prognostic indicator. Analysis of various clinical parameters in relation to the 6-IRLs signature revealed sig-nificant differences between high- and low-risk groups, underscoring its clinical pre-dictive value. The 6-IRLs signature was found to be closely associated with overall sur-vival (OS). We constructed a nomogram for risk scoring, predicting the 1-, 3-, and 5-year survival rates of ccRCC patients, with calibration and decision curve analyses confirming its high accuracy and sensitivity.

GSEA analysis of TME-related pathways revealed distinct enrichment patterns between high- and low-risk ccRCC groups. High-risk patients showed increased activity in innate immune pathways, while low-risk patients favored metabolic homeostasis, suggesting that targeted therapies for specific pathways may benefit these subtypes. Additionally, our findings emphasize the significant impact of IRLs on ccRCC's immune landscape. Consensus clustering based on IRL expression identified two distinct ccRCC subtypes, each with unique immune cell infiltration patterns. Differential infiltration of CD8 + T cells, M1 and M2 macrophages, B cells, and regulatory T cells highlights the link between immune response and lncRNA expression, with these cell types playing crucial roles in tumor suppression and immune evasion.

Our study revealed that high-risk patients' TME is characterized by increased immunosuppressive cells, like regulatory T cells, linked to poor prognosis and immunotherapy resistance [37]. The enrichment of TLRs and TCRs signaling pathways indicates an active yet dysregulated immune response, with heightened TLR activity reflecting a stronger inflammatory response [38], while aberrant TCR activation suggests T cell exhaustion [39]. This TME may promote tumor progression rather than effective anti-tumor immunity, highlighting the importance of immune pathways for improving high-risk patient prognosis through targeted immunotherapies.

In contrast, the low-risk group's enrichment in glutathione metabolism and TGF-β signaling pathways suggests stronger antioxidant and immunosuppressive capabilities [40, 41]. This quiescent immune state may help maintain tissue homeostasis and prevent tumor progression, contributing to better prognosis in ccRCC.

Overall, the immune microenvironment's heterogeneity underscores the need for personalized immunotherapy strategies. Targeting specific pathways, like enhancing CD8 + T cell activity or modulating macrophage polarization, could improve outcomes for high-risk patients based on their lncRNA profiles.

Our findings also highlight the crucial role of IRLs in clinical treatment. We found a significant correlation between the IRL gene risk model and drug sensitivity, indicating that high-risk patients are more sensitive to Sunitinib and Temsirolimus, while low-risk patients respond better to Sorafenib and Pazopanib. This suggests that IRL signatures could serve as predictive biomarkers, guiding targeted treatment selections based on the molecular mechanisms influenced by these lncRNAs.

Integrating IRL gene risk assessments into clinical practice could facilitate precision medicine for ccRCC, enabling personalized treatment plans. The IRL-based prognostic model developed here offers a valuable tool for risk stratification and therapeutic decision-making, emphasizing the role of IRLs in immune regulation, prognosis, and treatment outcomes.

However, further validation in larger cohorts is needed, along with mechanistic studies to clarify these lncRNAs' roles in immune responses and drug sensitivity. Integrating multi-omics data, including epigenomics and proteomics, could provide deeper insights into how these lncRNAs mediate tumor-immune interactions.

Disclosure:

Author Contributions: Weijing Hu: Writing – original draft,Conceptualization, Methodology, Validation Yongquan Chen: Conceptualization ,Software, Data Curation Xiaoling Guo: Investigation Xiaosong Wang: Investigation Dongwen Wang: Resources, Project administration Bo Wu: Writing - Review & Editing，Supervision，Project administration

Funding: This work was supported by the Beijing Bethune Charitable Foundation, Special Research Fund for Urological Oncology [grant numbers mnzl202029]; the Research Project Supported by Shanxi Scholarship Council of China [grant number 2021–160];.

Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by t the Ethics Committee of the First Hospital of Shanxi Medical University (No. 2021K034,2021-04-26).

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest: The authors declare no conflicts of interest

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No competing interests reported.

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IL2RG’s Expression and its associated Long Non-Coding RNAs signature in ccRCC: Development, Validation, and Im-plications for Diagnosis,Prognosis,and Treatment

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Abstract

Figures

Introduction

Material and Methods

Screening of potential regulatory IRLs

Construction of the IRLs signature

Evaluation of the Prognostic Model's Efficacy and Clinical Value

Construction of a nomogram

The relationship between the IRLs model risk score and immune analysis

Cell culture

Quantitative real-time polymerase chain reaction(qRT-PCR)

Immunohistochemistry(IHC)

Statistical analysis

Results

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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