Th2-MPP Cells Shape an Immunosuppressive Microenvironment in ccRCC: Development of a Prognostic Signature and Functional Validation of ITPKA

Th2-MPP Cells Shape an Immunosuppressive Microenvironment in ccRCC: Development of a Prognostic Signature and Functional Validation of ITPKA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Th2-MPP Cells Shape an Immunosuppressive Microenvironment in ccRCC: Development of a Prognostic Signature and Functional Validation of ITPKA Yuanfa Feng, Zeheng Tan, Biyan Wen, Zhenjie Wu, Haiyin Xiao, YiZe Li, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9010954/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Clear cell renal cell carcinoma (ccRCC), frequently characterized by the infiltration of type II T helper (Th2) cells, necessitates the development of reliable prognostic models and the discovery of new biomarkers to advance personalized treatment approaches. Th2-multipotent progenitor (Th2-MPP) cells, a recently identified subset of T-cells, have been linked to chronic type II inflammation. Methods: Multiple machine learning algorithms and their combinations were utilized to establish a robust Th2-MPP-related score (T2Ms) for predicting prognosis and response to PD-1 blockade therapy in ccRCC patients. Functional enrichment analysis and the TimiGP algorithm were used to investigate the potential mechanisms of T2Ms in ccRCC, and integrative analysis was employed to identify key T2Ms-associated genes. Results: Pan-cancer analysis revealed elevated expression of Th2-MPP signature genes in malignant ccRCC. A stable T2Ms scoring system was constructed and validated using three independent cohorts. T2Ms accurately predicted patient prognosis and response to PD-1 blockade therapy. Core biological processes associated with Th2-MPP were linked to chronic type II inflammatory responses. ITPKA was identified as a key T2Ms-related gene and an independent predictor of poor prognosis, with high expression correlating with inferior response to PD-1 blockade. The consistent upregulation of ITPKA at the protein level, corroborating our mRNA findings, prompted us to further investigate its functional role in ccRCC cell lines. Conclusion: This study suggests that Th2-MPP cells may be enriched in ccRCC and provides a Th2-MPP-based signature for predicting prognosis and therapeutic response to PD-1 inhibition. ITPKA was identified as a critical factor in the T2Ms model and as a potential biomarker for tumor-associated Th2-MPP cells. machine learning type 2 helper T multipotent progenitor cell clear cell renal cell carcinoma prognosis bioinformatics ITPKA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Renal cell carcinoma (RCC) ranks among the top ten most commonly diagnosed malignancies in the United States [1]. Clear cell RCC (ccRCC) is the most common pathological subtype, accounting for over 70% of cases [2]. It is a malignant neoplasm originating from the renal tubular epithelium, making up 80-90% of renal malignancies [3]. Over the past decade, the therapeutic landscape for ccRCC has shifted considerably with the integration of immune checkpoint inhibitor (ICI) therapy into adjuvant treatment regimens. However, the pronounced heterogeneity of the ccRCC tumor microenvironment (TME) limits clinical benefit to only a subset of patients [4]. CD4+ T helper (Th) cells are broadly classified into Th1 and Th2 subsets, which exist in a dynamic equilibrium maintained by the reciprocal inhibition of their signature cytokines [5]. Perturbations in the cytokine milieu within the TME can disrupt this homeostasis, often skewing the balance from a Th1- toward a Th2-dominant response [6]. This imbalance is recognized as a determinant factor in the development of malignant tumors [7]. In RCC, Th2 cells are notably more abundant than other T-cell subsets and their infiltration levels correlate positively with poor patient outcomes [8]. A study by Radomir Kratchmarov et al. identified a population of Th2 cells co-expressing TCF7 and LEF1 in chronic type 2 inflammatory diseases, naming them Th2 multipotent progenitor (Th2-MPP) cells [9]. These cells possess the capacity for self-renewal and differentiation into effector cells, regulatory T (Treg) cells, and follicular helper T cells. Whether Th2-MPP cells are present in cancers, and their significance in tumorigenesis and progression, along with the underlying mechanisms, remain to be elucidated. This study aims to investigate the expression of Th2-MPP cells in cancer (specifically ccRCC) and assess their impact on prognosis and response to immunotherapy through bioinformatic analyses; it further seeks to explore the potential mechanisms by which this cell population may contribute to immune evasion in ccRCC. 2. Methods 2.1 Ethical considerations All data utilized in this study were obtained from publicly available datasets. No ethical approval was required. 2.2 Data resources The RNA-sequencing data and curated clinical phenotypes for 33 prevalent cancer types were sourced from The Cancer Genome Atlas (TCGA) pan-cancer data, available for download from the Pan-Cancer Atlas Hub at University of California Santa Cruz (UCSC) Xena dataset (https:xenabrowser.net). The transcriptome data for Th2-MPP characteristic regulator in 33 tumor types, along with relevant paracancerous tissues, were extracted for further analysis. Additionally, gene expression profiles and clinical information for the E-MTAB-1980 cohort were obtained from the ArrayExpress website (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1980/), and expression data for GSE167573 was sourced from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). Furthermore, normalized transcriptomic and clinical data for ccRCC patients treated with Nivolumab (anti-PD-1) therapy [10], as reported in a published article, were utilized to enhance the clinical significance of this study. 2.3 Cell lines The human ccRCC cell line 786-O was obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). Cells were maintained in RPMI-1640 medium (MA0215, Meilunbio, Dalian, China) supplemented with 10% fetal bovine serum, penicillin, and streptomycin, and cultured at 37°C in a 5% CO₂ incubator. 2.4 Analysis of differential expression and survival in pan-cancer for regulators of Th2-MPP cell characteristics The Wilcoxon signed-rank test was used to identify differences in the expression of LEF1 and TCF7 between normal and tumor tissues across various types of cancer. The prognostic significance of Th2-MPP cell characteristic regulators in pan-cancer was assessed through Univariate Cox regression analysis and visualized using a forest plot. Overlapping candidates from different analyses were identified using the Venny 2.1.0 online platform. 2.5 Patient clustering using Gaussian mixture model (GMM) Patients within the Kidney renal clear cell carcinoma (KIRC) cohort were grouped into three clusters using the GMM analysis with the R package mclust. Kaplan-Meier (KM) analysis was conducted to compare survival outcomes between these groups using the survival package. 2.6 Characterization of the immune landscape in the patient clusters Single-sample gene set enrichment analysis (ssGSEA) was employed to characterize the immune landscape within the three patient clusters. 2.7 Analysis of immune-cell interactions enrichment The association between immune-cell relative abundance and prognosis was determined for the high-risk group using TimiGP [11], a robust computational method for inferring cell-cell interactions within the TME. This analysis involved twenty-two cell types reported by Bindea et al [12]. 2.8 Weighted gene coexpression network analysis (WGCNA) Differentially expressed genes (DEGs) between clusters 1 and 2 were first identified from the TCGA-KIRC cohort using a threshold of |log₂ fold change (logFC)| > 1. Based on these DEGs, a weighted gene co-expression network was constructed using the WGCNA package [13], with a soft-thresholding power of 7. Network connectivity was assessed via the topological overlap matrix (TOM), and modules were subsequently identified through hierarchical clustering. The relationships between each module and immune cell infiltration, Th2-MPP characteristic regulators, as well as clinical parameters, were then evaluated and visualized in a heatmap. 2.9 Development of a Th2-MPP cell-related signature (T2Ms) for predicting outcomes in patients with ccRCC through machine learning-based integration In order to create a consistent T2Ms for accurate outcome prediction in patients with ccRCC, 10 machine-learning algorithms and 63 algorithm combinations were integrated. The 10 machine-learning algorithms utilized in this study included elastic network (Enet), random survival forest (RSF), generalized boosted regression modeling (GBM), the least absolute shrinkage and selection operator (Lasso), Ridge, CoxBoost, partial least squares regression for Cox (plsRcox), stepwise Cox, survival support vector machine (survival-SVM), and supervised principal components (SuperPC). Prognostic DEGs in the TCGA-KIRC cohort were identified using univariate Cox regression. Subsequently, the prognostic DEGs were subjected to the 63 algorithm combinations to construct prediction models based on leave-one-out cross-validation (LOOCV) in the TCGA-KIRC cohort. Harrell’s concordance index (C-index) was calculated in three validation datasets, including the TCGA dataset, E-MTAB-1980 dataset, and the GSE167573 dataset. Finally, the model with the highest average C-index was chosen for use in the study. 2.10 Evaluation of the T2Ms The TCGA-KIRC cohort was divided into high risk score and low risk score groups based on the optimal cutoff value. Univariate Cox regression was performed to assess the prognostic significance of T2Ms in the three cohorts. K-M survival analysis and receiver operating characteristic (ROC) analysis were conducted to evaluate the accuracy of T2Ms in predicting patient outcomes. 2.11 Enrichment analysis DEGs between the high-risk and low-risk groups of T2Ms were identified using the R package ‘limma’, with thresholds of log2 fold change (FC) > 1 and adjusted P < 0.05. Subsequently, gene ontology (GO) enrichment analysis was conducted to explore the primary biological processes (BP) associated with immune activity and cancer using the R packages ‘ggplot2' and ‘GOplot’. Validation of the GO analysis results was achieved through Gene Set Enrichment Analysis (GSEA). 2.12 Prediction of drug sensitivity associated with the signature The "oncoPredict" R language package was utilized to predict the half-maximal inhibitory concentration (IC50) of anti-cancer drugs for each patient in the TCGA-KIRC database. The Genomics of Drug Sensitivity in Cancer (GDSC, https:www.cancerrxgene.org) and the Cancer Therapeutics Response Portal (CTRP, https:portals.broadinstitute.orgctrp.v2.1) databases served as the training sets. The calcPhenotype function was then employed to predict the IC50 of different drugs for each TCGA-KIRC patient. Subsequently, the Pearson correlation analysis was used to determine the correlations between the risk score of each patient and the IC50 of different drugs. 2.13 Immunohistochemistry (IHC) assay A tissue microarray containing 90 cases of ccRCC and paired adjacent normal tissues (Cat#HKidE180Su04, OUTDO BIOTECH, Shanghai, China) was used for the IHC assay. Detail information of the tissue microarray was displayed on the website (https://www.superchip.com.cn/biology/tissue.html). The ITPKA protein expression in kidney tissues was detected by the antibody (1:1,200; bs-17184R, BIOSS, Beijing, China). Patients in the TMA cohort were divided into high expression and low expression of ITPKA groups according the median ccRCC IHC score = 5.5. 2.14 Human ITPKA-specific siRNA and negative control siRNA were purchased from Tsingke Biotechnology Co., Ltd. (Beijing, China). Western blot assay was conducted 72 h after transfection to test the transfection efficacy. 2.15 Western blot Analysis Protein expression levels were determined by Western blot analysis following a standard protocol. Membranes were probed with primary antibodies against ITPKA (1:1,000; bs-17184R, BIOSS, Beijing, China), PD-L1 (1:1000, PTM-5075, PTM BIO, Hangzhou, China), and GAPDH (1:5,000; 60004-1-Ig, Proteintech, Chicago, IL, USA), the latter serving as an internal loading control. Band intensities were quantified using ImageJ software (National Institutes of Health, USA), and the relative expression of each target protein was normalized to that of GAPDH. 2.16 Colony formation assay We performed colony formation assays to evaluate long-term proliferative ability. Briefly, 786-O cells were plated at 2.5×10³ per well in 12-well plates and cultured for 7 days. To maintain knockdown efficiency throughout the experiment, we re-transfected the cells every 72 hours. 2.17 Cell proliferation assay Cell proliferation was measured using the CCK-8 kit (MA0218, Meilunbio) following the manufacturer’s instructions. We seeded 786-O cells at 1×10³ per well in 96-well plates. Absorbance at 450 nm was recorded every 24 hours up to 96 hours using a microplate reader, and growth curves were plotted from the data. 2.18 Statistical analysis All statistical analyses were performed using R software (version 4.3.0). Continuous variables are presented as the mean ± standard deviation (mean ± SD). The statistical significance of quantitative data was determined using Student’s t test or analysis of variance (ANOVA). A threshold of P < 0.05 was considered to be statistically significant. 3. Results 3.1 The Th2-MPP characteristic regulators exhibit differential mRNA expression and prognostic value in pan-cancer To comprehensively analyze the transcriptome expression pattern of Th2-MPP regulators in pan-cancer, data from TCGA, which includes 33 common cancer types, were utilized. Figure 1A and B illustrate the significant differential expression of LEF1 and TCF7 in multiple cancer types, including UCEC, THCA, STAD, READ, PRAD, PCPG, PAAD, LUSC, LUAD, LIHC, KIRP, KIRC, KICH, HNSC, ESCA, COAD, CHOL, and BRCA. Survival analysis revealed that LEF1 expression levels were associated with OS in ACC, UCEC, KIRP, LUSC, KIRC, BRCA, GBM, LGG, UVM, and THYM, while TCF7 expression levels were associated with relapse-free survival in PAAD, KIRC, LGG, PCPG, and THYM (Figure 1C and D). The Th2-MPP regulators showed a significant role in KIRC based on the intersection of the differential expression analysis and Cox regression analysis (Figure 1E). 3.2 Clustering ccRCC patients associated with Th2-MPP characters Based on the Bayesian Information Criterion (BIC) change curve, the VEV model was selected as the optimal fit for the GMM analysis (Figure 2A). The TCGA-KIRC cohort was divided into three clusters based on the expression of Th2-MPP regulators (Figure 2B). OS survival analysis indicated that Th2-MPP cluster 1 and Th2-MPP cluster 2 exhibited significant differences (Figure 2C). The expression of TCF7 and LEF1 also showed significant differences in the three Th2-MPP clusters (Figure 2D). The abundance of immune cells and expression of checkpoints were significantly lower in cluster 1 compared to cluster 2 (Figure 2E and F). To dissect the immune landscape of cluster 1, we employed the ssGSEA database and TimiGP analysis, which revealed a series of cell-cell interactions predominantly directed toward Th2 cells (Figure 2G). Specifically, we identified interactions from ten immune subsets to Th2 cells, including immature dendritic cells (iDCs), mast cells, T central memory (Tcm) cells, CD8+ T cells, B cells, natural killer (NK) cells, eosinophils, neutrophils, T follicular helper (Tfh) cells, and T helper (Th) cells. Subsequently, a directed interaction network was formed based on these cell-cell interactions (Figure 2H). DC cells and iDC cells displayed the largest outdegree, while Th2 cells exhibited large indegrees. The bottom layer (indegree = 0) comprised DC cells, mast cells, and macrophage cells, which were linked to a favorable prognosis. Conversely, the top layer (outdegree = 0) consisted of cell types associated with an unfavorable prognosis such as Th2 cells. The favorability score was then calculated (Figure 2I). DC cells demonstrated the highest favorable score, indicating a positive prognostic association and potential anti-tumor function. On the other hand, Th2 cells showed the highest unfavorable score, signifying a negative prognostic association and a possible pro-tumor function. 3.3 Identification of the suitable module associated with Th2-MPP cell The DEGs between cluster 1 and 2 were computed and visualized in a volcano plot (Figure 3A). Subsequently, the 436 most variable genes were retained for WGCNA. All 321 samples from the selected GMM clusters passed quality control, as hierarchical clustering detected no outliers (Figure 3B). To construct a scale-free co-expression network, we selected a soft-thresholding power of β = 7, where the scale-free topology fit index plateaued above 0.85 and mean connectivity decreased to an appropriate level (Figure 3C). Finally, the adjacency matrix was converted into a TOM to reduce the impact of spurious correlations. Hierarchical clustering based on the TOM then grouped genes with similar expression patterns into modules, as depicted in the clustering dendrogram (Figure 3D). To identify modules significantly associated with immune characteristics and clinical traits, we computed correlations between module eigengenes (MEs) and these phenotypic data. As illustrated in Figure 3E, the turquoise module exhibited significant negative correlations with tumor stage, Th2 cell infiltration, and the expression of LEF1 and TCF7. 3.4 Establishment and Integration of a Consensus T2Ms using Machine Learning Utilizing the expression profiles of Th2-MPP in the turquoise module, a machine learning-based approach was employed to develop a precise and robust T2Ms. Using the TCGA-KIRC cohort as the training set, 63 prediction models were fitted within a LOOCV framework, and their performance was evaluated by calculating the C-index across three independent datasets: TCGA-KIRC, E-MTAB-1980, and GSE167573. Among all candidate models, the combination of LASSO regression and RSF emerged as the optimal approach, achieving the highest average C-index of 0.832 (Figure 4A). In Lasso regression, the optimal λ was determined when the partial likelihood of deviance reached the minimum value (Figure 4B-C). Subsequently, the RSF algorithm was utilized to pinpoint core genes for T2Ms construction, with the prediction error rate stabilizing after the construction of 1000 survival trees (Figure 4D). Variable importance (VIMP) scores were calculated for all genes incorporated in the tree-building process, where higher scores indicated greater contributions to overall survival prediction (Figure 4E). This process led to the identification of a final set of 8 core genes (SLC16A12, ITPKA, CUBN, EMX2, IGF2BP2, WDR72, CRYL1, and ALDH6A1) for inclusion in the T2Ms. 3.5 Assessment of the precision and resilience of T2Ms Across all three independent cohorts: TCGA-KIRC, E-MTAB-1980, and GSE167573, patients stratified into the high-risk group based on the optimal cutoff value exhibited significantly poorer OS (Figure 4F-H). Time-dependent ROC analysis demonstrated that the T2Ms signature achieved robust predictive performance, with area under the curve (AUC) values exceeding 0.7 for 1-, 3-, and 5-year OS in each cohort (Figure 4I-K). 3.6 Immune Function and Cell Interactions of T2Ms Differential gene expression analysis revealed significant differences between high-risk and low-risk groups of T2Ms, as illustrated in a volcano plot (Figure 5A). GSEA identified key immune-related biological processes, including chronic inflammatory response, Tregs cell differentiation, and type 2 immune response (Figure 5B). Additionally, the abundance of immune cells in the high-risk group of T2Ms from the TCGA-KIRC cohort was significantly higher compared to the low-risk group (Figure 5C). Furthermore, the expression of immune checkpoints was found to be significantly higher in the low-risk group of T2Ms compared to the high-risk group (Figure 5D). To evaluate the predictive value of T2Ms in the NIHMS1611472 cohort treated with PD-1 blockade, we performed Kaplan-Meier survival analysis and tumor response assessment, which revealed that patients with high risk scores had significantly poorer overall survival (Figure 5E). The abundance of immune cells in the high-risk group of T2Ms in the PD-1 blockade-treated cohort was also significantly higher in the low-risk group (Figure 5F). Utilizing the ssGSEA database and TimiGP analysis with high-T2Ms patients in the NIHMS1611472 cohort, we identified the top 10 cell-cell interactions, including neutrophil cells → Tcm cells, neutrophil cells → Th2 cells, mast cells → T cells, neutrophil cells → CD8+ T cells, neutrophil cells → T cells, mast cells → tumor cells, mast cells → Tcm cells, neutrophil cells → macrophage cells, neutrophil cells → Tfh cells, and neutrophil cells → tumor cells (Figure 5G). Subsequently, a directed interaction network was constructed based on these cell-cell interactions (Figure 5H). Notably, mast cells exhibited the largest outdegree, while T cells had prominent indegrees. The bottom layer comprised mast cells, neutrophil cells, effective memory T (Tem) cells, eosinophil cells, and DC cells, which were associated with a favorable prognosis. Conversely, the top layer included cell types such as Th2 cells, cytotoxic cells, and T cells, which were associated with an unfavorable prognosis. The favorability score was calculated to further assess the prognostic association of these cell types (Figure 5I). Mast cells demonstrated the highest favorable score, indicating a positive prognostic association and potential anti-tumor function, while Th2 cells exhibited one of the highest unfavorable scores, suggesting a poor prognosis and potential pro-tumor functions. 3.7 Predicting drug sensitivity linked to the signature Pearson correlation analysis was utilized to identify the most relevant compounds for T2Ms, and the top 10 drugs with the highest correlation to T2Ms in both databases are depicted in Figure 6A and B. In the CTRP database (Figure 6C), 5 compounds (BCL-LZH-4, cimetidine, cucurbitacin I, PF-4800567 hydrochloride, and tubastain A) demonstrated higher IC50 values in the high T2Ms group, while 5 compounds (BRD-K99584050, niclosamide, fluvastatin, pevonedistat, NSC23766) showed higher IC50 values in the low T2Ms group. Similarly, in the GDSC database (Figure 6D), 6 compounds (SB5051241194, Ibrutinib1799, AZD37591915, OF-11853, LY21097611852, and Osimertinib1919) exhibited higher IC50 values in the high T2Ms group, whereas 4 compounds (Topotecan1808, AZD77621022, ERK66041714, and ULK149891733) showed higher IC50 values in the low T2Ms group. 3.8 ITPKA may play a crucial role in relation to T2Ms When compared to the mRNA expression of T2Ms in normal kidney tissues, SLC16A12, EMX2, IGF2BP2, WDR72, CRYL1, and ALDH61A were found to be downregulated, while ITPKA was upregulated in ccRCC (Figure 7A). There was no significant difference observed in CUBN. Subsequently, the AUC values of T2Ms in the diagnosis of ccRCC were calculated (Figure 7B). Regarding the relationship among the 8 genes of T2Ms, it was demonstrated that ITPKA had a strong positive correlation with other genes (Figure 7C). Univariate Cox regression analysis showed that ITPKA and IGF2BP2 were unfavorable prognostic factors for OS (Figure 7D). Patients were divided into two groups based on the expression of ITPKA determined by the optimal cutoff point, and it was found that patients with higher ITPKA expression had worse OS survival rates (HR = 1.90, P = 0.002) in the NIHMS1611472 cohort (Figure 7E). The immune infiltration was more abundant in the high-expression group of ITPKA compared to the low-expression group (Figure 7F). Utilizing the TimiGP analysis in high-ITPKA group, the top10 cell-cell interactions were identified as Th1 cells → Tcm cells, Th1 cells → Th2 cells, Th1 cells → T cells, Th1 cells → macrophage cells, Tcm cells → macrophage cells, cytotoxic cells → Th1 cells, Th1 cells → tumor cell, neutrophil cells → Th1 cells, NK cells → Mast cells, eosinophil cells → Tcm cells (Figure 7G). Subsequently, a directed interaction network was constructed based on the cell-cell interactions (Figure 7H). The bottom layer (indegree = 0) consisted of neutrophil cells, Tem cells, and NK cells, which tended to be associated with a favorable prognosis. In contrast, the top layer (outdegree = 0) contained cell types associated with an unfavorable prognosis, including T cells, Th2 cells, and tumor cells. The favorability score was calculated, with neutrophil cells showing the highest favorable score, indicating a positive prognostic association and a potential role in tumor suppression, while tumor cells showed the highest unfavorable score with the opposite role of neutrophil cells (Figure 7I). 3.9 ITPKA overexpression in ccRCC correlates with advanced stage and promotes tumor growth via PD-L1 regulation A TMA cohort was used to validate the expression of ITPKA in renal tissues through an IHC assay (Figure 8A). In our TMA cohort, ITPKA was over-expressed in ccRCC compared with normal tissues. ITPKA was upregulated in ccRCC of ≥T2 stage. Overexpressed ITPKA was detected in ccRCC of ≥ clinical stage 2 (Figure 8B). The patients with high expression of ITPKA tended to have a poor survival time after surgery, though the difference did not reach a statistical threshold (Figure 8C). siRNAs were used to knock down the expression of ITPKA in the 786-O cell line. The transfection efficiency was measured by WB, and we showed that si-1 and si-3 significantly interfered with the expression of ITPKA (Figure 8D, P< 0.01). The growth curve suggested that the knockdown of ITPKA inhibited the growth of the 786-O cell line (Figure 8E, P< 0.01). Dampened expression of ITPKA reduced the colony formation ability (Figure 8F). We found that the knockdown of ITPKA resulted in decreased expression of PD-L1 (Figure 8G, P< 0.01). 4. Discussion ccRCC is a highly immunogenic tumor characterized by substantial immune cell infiltration within its TME [14]. In 2019, PD-(L)1 ICI combination therapy was formally approved as a first-line treatment [15-17]; however, only approximately half of patients derive clinical benefit [18]. Despite the development of various predictive biomarkers aimed at enhancing the accuracy of immunotherapy benefit prediction in ccRCC patients [19-21], a widely accepted comprehensive predictive framework remains elusive. Our team has previously reported distinct predictive signatures: one based on tumor-infiltrating lymphocyte (TIL)-associated long non-coding RNAs (lncRNAs), revealing the critical impact of TIL-tumor cell interactions on disease progression [22]; and another based on stressed T cell (Tstr)-associated messenger RNAs (mRNAs), highlighting the significant influence of Tstr-tumor cell interactions on patient prognosis and ICI efficacy [23]. These studies collectively underscore the pivotal role of immune cells in ccRCC. Th2-MPP cells, characterized by co-expression of TCF7 and LEF1, are enriched in chronic type 2 inflammation [9]. Pan-cancer analysis indicates that both TCF7 and LEF1 play significant roles in ccRCC tumorigenesis and progression. Specifically, ccRCC patients exhibiting high expression of TCF7 and LEF1 demonstrate enhanced infiltration capacity for Th2 cells and Treg cells. Cell-cell interaction analysis further suggests that this subpopulation is more prone to differentiate and enrich Th2 cells. Consequently, we reasonably hypothesize that Th2-MPP cells infiltrate and are expressed within ccRCC. Nevertheless, how Th2-MPP cells influence malignant progression in ccRCC and whether they diminish ICI efficacy remain unexplored. Utilizing machine learning-based integrative analysis, we constructed the T2Ms signature to predict prognosis and immunotherapy response in ccRCC patients. The higher T2Ms score was significantly associated with adverse prognosis and advanced disease stage in ccRCC. The signature exhibited robust prognostic performance, which was consistently validated across three independent cohorts. Programmed cell death 1 (PD-1) is a critical immune checkpoint receptor expressed on T cells, where it normally functions to dampen immune responses and maintain self-tolerance [24, 25]. However, tumor cells exploit this pathway by upregulating PD-L1, thereby activating PD-1 signaling to inhibit T cell-mediated anti-tumor activity and facilitate immune escape [26]. Targeting the PD-1/PD-L1 axis is a fundamental aspect of immunotherapy [27, 28]. While anti-PD-1 therapies are now standard first-line and salvage treatments for advanced ccRCC, only a subset of patients derives durable clinical benefit [29]. In this context, our findings demonstrate that ccRCC patients with high T2Ms scores experienced significantly poorer outcomes following Nivolumab treatment. As a result, we identified several potential therapeutic agents as novel alternative treatment options for high-T2Ms score patients beyond ICIs. To our knowledge, this is the first prognostic model to explore the clinical relevance of Th2-MPP cells, providing a potentially actionable framework to guide treatment decisions in ccRCC. Interactions among tumor-infiltrating immune cells are crucial for sustaining tumor progression[30, 31] and play a pivotal role in mediating resistance to ICI therapy [32, 33]; however, the specific mechanistic contributions of Th2-MPP cells are not yet defined. Our analysis indicates that Th2-MPP cells are primarily involved in biological processes including chronic inflammatory responses, type 2 immune responses, and regulatory T cell differentiation. Cell-cell interaction analysis using ssGSEA showed that Th2 cells, acting as adverse prognostic factors in ccRCC, play key roles within these processes. Th2 cells and their secreted cytokines [34, 35] promote an immunosuppressive TME by facilitating the infiltration of immunosuppressive cells and inhibiting the activation and infiltration of Th1-type cytotoxic T lymphocytes (CTLs) [36, 37]. This contributes to a "cold tumor" phenotype, characterized by insufficient effector T cell infiltration and reduced response rates to immune checkpoint inhibitors [38-41]. In ccRCC, a Th2-dominant microenvironment has been consistently associated with adverse clinical outcomes [8]. Moreover, Th2 cells further compromise immune surveillance by activating the STAT6 pathway [42] and upregulating migration-inhibiting factors like RGS1 [43], thereby restricting the homing of anti-tumor T cells to tumor sites and exacerbating immune escape. Collectively, these observations lead us to propose that within the chronically inflamed, Th2-enriched tumor microenvironment, Th2-MPP cells may serve as critical drivers of ccRCC progression and resistance to immunotherapy. By conducting integrative analysis, we have identified Inositol-1,4,5-trisphosphate-3-kinase-A (ITPKA) as a crucial gene associated with ccRCC. These findings align with existing evidence implicating ITPKA as a central regulator in renal cell carcinoma with potential diagnostic and therapeutic relevance [44]. Extending these observations, we found that high ITPKA expression was associated with worse outcomes in ccRCC patients receiving PD-1 blockade therapy, a result further supported by cell-cell interaction analyses. In the TMA cohort, patients with high ITPKA expression were correlated with more advanced clinical stages and poorer prognosis. Additionally, in vitro experiments demonstrated that knockdown of ITPKA expression restricted the growth of ccRCC and reduced the expression of PD-L1. These findings further demonstrate that ITPKA expression not only promotes the progression of renal cancer but also facilitates immune evasion of tumor cells, consequently diminishing the efficacy of immunotherapy in ccRCC patients. Collectively, these findings position ITPKA as a potential biomarker for predicting response to PD-1 blockade and highlight its clinical significance in ccRCC. 5. Conclusion In summary, this research offers a Th2-MPP cell-associated score for forecasting outcomes and response to PD-1 blockade therapy in ccRCC. Th2-MPP cells may play a pro-tumoral function in ccRCC within the long-standing type 2 TME. ITPKA could function as a pivotal biomarker linked to T2Ms. Declarations Authors’ contributions Conceptualization: J.Y., C.C., and W.Z.; Data curation, Formal analysis and Visualization: Y.F., Z.T. and Z.W; Funding acquisition: W.Z., J.Y., and Y.D.; Investigation and Methodology: Z.T., B.W., Z.W., H.X., and HC.H; Writing - original draft: Y.F., Y.L. and Y.D; Writing – review: Y.F., Z.T. and B.W.. All authors contributed to the article and approved the submitted version. Funding This work was supported by Science and Technology Development Fund (FDCT) of Macau SAR (0116/2023/RIA2, 006/2023/SKL, 0074/2025/RIA2) awarded to Weide Zhong; Guangdong Medical Science Research Foundation (B2025039) to YL Deng. Data availability All original data in this research are available upon reasonable request from the corresponding authors. Consent to participate Informed consent was obtained from all individual participants included in the study. Declaration of competing interest None Declared. 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Zhang, N. Huo, R. Cong, L. Ma, et al., ITPKA1 Promotes Growth, Migration and Invasion of Renal Cell Carcinoma via Activation of mTOR Signaling Pathway, Onco Targets Ther, 13 (2020) 10515-10523. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9010954","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601965094,"identity":"52ba6d7a-018b-4687-824d-21734636de54","order_by":0,"name":"Yuanfa Feng","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanfa","middleName":"","lastName":"Feng","suffix":""},{"id":601965095,"identity":"93cb1acf-846e-416b-8b48-d7ca0c4ee0ad","order_by":1,"name":"Zeheng Tan","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zeheng","middleName":"","lastName":"Tan","suffix":""},{"id":601965096,"identity":"b536200c-e294-47de-b39d-0c9ca80f9112","order_by":2,"name":"Biyan Wen","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Biyan","middleName":"","lastName":"Wen","suffix":""},{"id":601965097,"identity":"1611bf78-9b5d-462d-b9cc-31442814e5d8","order_by":3,"name":"Zhenjie Wu","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenjie","middleName":"","lastName":"Wu","suffix":""},{"id":601965098,"identity":"fc1398a4-d22d-48c3-b958-5d43dae184f0","order_by":4,"name":"Haiyin Xiao","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haiyin","middleName":"","lastName":"Xiao","suffix":""},{"id":601965099,"identity":"7e2db554-20bf-45f0-9607-dd54d3ded64c","order_by":5,"name":"YiZe Li","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"YiZe","middleName":"","lastName":"Li","suffix":""},{"id":601965101,"identity":"c20949cc-9572-44c8-aae4-3c35433972e1","order_by":6,"name":"Yulin Deng","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yulin","middleName":"","lastName":"Deng","suffix":""},{"id":601965103,"identity":"3a0ac7b4-6245-411b-bc32-87aabd16e857","order_by":7,"name":"Jianheng Ye","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianheng","middleName":"","lastName":"Ye","suffix":""},{"id":601965106,"identity":"341cc201-653c-4bfe-bb9e-48c5890df804","order_by":8,"name":"Huichan He","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Huichan","middleName":"","lastName":"He","suffix":""},{"id":601965107,"identity":"0b43763f-d3eb-4da3-965e-3ce1913c4a55","order_by":9,"name":"Chao Cai","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Cai","suffix":""},{"id":601965110,"identity":"dae2f240-a18e-4860-96f2-5b7c5e31f09f","order_by":10,"name":"Weide Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACCTDJLMfGzHzwwQMDErQY87O3JRskkKIlcWbPGTOJBGJ0yM9uPvbwa5s144YbOWYVCQWH5RjYzx7Aq4VxzrF0Y9m2dGaDG2llNxIMDhsz8OTht4xZIsdMWnLbYTaDG8nbgFrSEhskePD7iA2qhcfgRoJZAVBLPUEtPEAtkh+3HZaQ7DlixpBgYJPAQEiLhERamjTjv3QDUCBLALUYtvHk4NciPyP5mOSPM9b1bcCo/PDhj4Q8P/sZwrHDzIPiO4LqgYDxBzGqRsEoGAWjYOQCAD9jQJEhOv0kAAAAAElFTkSuQmCC","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Weide","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2026-03-02 13:53:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9010954/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9010954/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104311311,"identity":"2e3b044c-0bb5-4ea2-b302-dd804a327dfc","added_by":"auto","created_at":"2026-03-10 10:57:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1252323,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression and prognostic value of Th2-MPP characteristic regulator in pan-cancer. (A) Differential expression analysis of LEF1 between normal and cancer tissue in pan-cancer from TCGA. (B) Differential expression analysis of TCF7 between normal and cancer tissue in pan-cancer from TCGA. (C)The relationship between LEF1 and overall survival for pan-cancer. (D) The relationship between TCF7 and overall survival for pan-cancer. (E) The intersection of statistically significant genes in univariate LEF1-COX-sig, LEF1-DEG-sig, TCF7-COX-sig and TCF7-DEG-sig.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9010954/v1/89c7cebe753d5d51b35bc7f8.png"},{"id":104311267,"identity":"0917d7e7-1966-4f83-8412-744423330e44","added_by":"auto","created_at":"2026-03-10 10:57:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1344106,"visible":true,"origin":"","legend":"\u003cp\u003eClustering ccRCC patients associated with Th2-MPP characters. (A) The BIC change curve of GMM analysis. (B) Clustering ccRCC patients associated with Th2-MPP characters by applying GMM. (C) OS survival analysis between PCa patients of different Th2-MPP characters clusters. (D) The expression of LEF1 and TCF7 among the three clusters. (E) Immune infiltration analysis of the three clusters. (F) Immune checkpoints expression of the three clusters. (G) Dot plot of the top 10 cell–cell interactions of ssGSEA ranked by FDR. (H)Chord diagram of all cell–cell interactions of ssGSEA. (I)Bar plot of the favorability score to evaluate each cell type’s favorable or unfavorable role in antitumor immunity and prognosis of ssGSEA.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9010954/v1/5e283886bc7385499ca56cd6.png"},{"id":104311289,"identity":"00ba7051-1854-47e6-bae0-c09285f32ab7","added_by":"auto","created_at":"2026-03-10 10:57:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":689306,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the suitable module associated with Th2-MPP cell. (A) The DEGs of clusters 1 and 2. (B-D) The scale-free fit index and the average connectivity of soft threshold power and hierarchical clustering tree of the DEGs of cluster 1 and cluster 2. (E) The correlation with T2Ms and clinical characteristics of these modules.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9010954/v1/2cbc4d494df001e3cea4f7f8.png"},{"id":104311292,"identity":"d3f9659b-c47d-4e25-ad96-aeb342b0b800","added_by":"auto","created_at":"2026-03-10 10:57:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1611725,"visible":true,"origin":"","legend":"\u003cp\u003eA consensus T2Ms was developed and validated via the machine learning-based integrative procedure. (A) The C-indexes of machine-learning algorithm combinations in the 3 cohorts. (B-C) Profles of Lasso coeffcients. (D-E) Optimal tree number selection and variable importance ranking for RSF model. (F-H) OS analysis of T2Ms in the 3 cohorts. (I-K) The 1-, 3-, and 5-year survival ROC curves of T2Ms in the 3 cohorts.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9010954/v1/639c2b3612c3c5ad46f4cd90.png"},{"id":104311269,"identity":"d00fab39-a380-46fa-9847-712d4a1e1cbf","added_by":"auto","created_at":"2026-03-10 10:57:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1354832,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune function, cell interactions, and PD-1 blockade treatment response prediction by the T2Ms. (A) The volcano plot represents the DEGs of the highand low-risk groups. (B) The GSEA results in certain c5 terms. (C) Immune infiltration analysis of the high- and low-T2Ms in TCGA-KIRC. (D) Immune checkpoints expression of the high- and low-T2Ms in TCGA-KIRC. (E) Survival after PD-1 blockade treatment analysis based on T2Ms. (F) Analysis of immune infiltration after PD-1 blockade treatment in high versus low T2Ms. (G) Dot plot of the top 10 cell–cell interactions of ssGSEA ranked by FDR. (H) Chord diagram of all cell–cell interactions of ssGSEA. (I) Bar plot of the favorability score to evaluate each cell type’s favorable or unfavorable role in antitumor immunity and prognosis of ssGSEA.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9010954/v1/29c83f847c9daf3b0b35b06b.png"},{"id":104311255,"identity":"37c51510-5b1e-4202-8b86-46633bc5e87a","added_by":"auto","created_at":"2026-03-10 10:57:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":681737,"visible":true,"origin":"","legend":"\u003cp\u003ePredication of drug sensity associated with T2Ms. (A) The correlation between T2Ms and drug sensitivity in CTRP dataset. (B) The correlation between T2Ms and drug sensitivity in GDSC dataset. (C) Box plot showing the relationship between T2Ms and the sensitivity of top drugs in CTRP dataset. (D) Box plot showing the relationship between T2Ms and the sensitivity of top drugs in GDSC datase.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9010954/v1/b6c3517472992cf4947bcb01.png"},{"id":104311260,"identity":"3dcbe38b-062a-44a1-a4ca-f6c145c251d0","added_by":"auto","created_at":"2026-03-10 10:57:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1094173,"visible":true,"origin":"","legend":"\u003cp\u003eITPKA may play a key role related to T2Ms. (A) The expression of T2Ms genes in normal and tumor tissues in the TCGA-KIRC cohort. (B) The ROC curves of T2Ms genes in normal and tumor tissues in the TCGA-KIRC cohort. (C) Correlation analysis between T2Ms genes. (D) The univariate Cox regression analysis of T2Ms genes. (E) Survival after PD-1 blockade treatment analysis based on ITPKA expression level. (F) Analysis of immune infiltration after PD-1 blockade treatment in high versus low expression of ITPKA. (G) Dot plot of the top 10 cell–cell interactions of ssGSEA ranked by FDR. (H) Chord diagram of all cell–cell interactions of ssGSEA. (I) Bar plot of the favorability score to evaluate each cell type’s favorable or unfavorable role in antitumor immunity and prognosis of ssGSEA.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9010954/v1/51ece7e07ce3467866f20727.png"},{"id":104311185,"identity":"a00cb2e3-3d1d-4fe3-9837-011852f10d83","added_by":"auto","created_at":"2026-03-10 10:57:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1197464,"visible":true,"origin":"","legend":"\u003cp\u003eITPKA knockdown suppresses ccRCC proliferation and reduces PD-L1 expression. The expression of ITPKA (A) and the OS analysis based on the expression of ITPKA (B) in TMA cohort. (C) Knockdown efficiency of ITPKA in 786-O cells confirmed by Western blot. (D, E) CCK‑8 assays and Colony formation showing reduced proliferation upon ITPKA knockdown. (E) Eexpression of PD-L1 decreased in the ITPKA-downregulated 786-O cell line. ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-9010954/v1/b0508205a58b5aac63e2aa1f.png"},{"id":105337308,"identity":"14330055-2960-4316-ae6e-f168fec7b366","added_by":"auto","created_at":"2026-03-25 01:25:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8993022,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9010954/v1/a023693c-e838-4c51-bce6-c3bd02918c14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Th2-MPP Cells Shape an Immunosuppressive Microenvironment in ccRCC: Development of a Prognostic Signature and Functional Validation of ITPKA","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRenal cell carcinoma (RCC) ranks among the top ten most commonly diagnosed malignancies in the United States [1]. Clear cell RCC (ccRCC) is the most common pathological subtype, accounting for over 70% of cases\u0026nbsp;[2]. It is a malignant neoplasm originating from the renal tubular epithelium, making up 80-90% of renal malignancies\u0026nbsp;[3]. Over the past decade, the therapeutic landscape for ccRCC has shifted considerably with the integration of immune checkpoint inhibitor (ICI) therapy into adjuvant treatment regimens. However, the pronounced heterogeneity of the ccRCC tumor microenvironment (TME) limits clinical benefit to only a subset of patients\u0026nbsp;[4].\u003c/p\u003e\n\u003cp\u003eCD4+ T helper (Th) cells are broadly classified into Th1 and Th2 subsets, which exist in a dynamic equilibrium maintained by the reciprocal inhibition of their signature cytokines\u0026nbsp;[5]. Perturbations in the cytokine milieu within the TME can disrupt this homeostasis, often skewing the balance from a Th1- toward a Th2-dominant response\u0026nbsp;[6]. This imbalance is recognized as a determinant factor in the development of malignant tumors\u0026nbsp;[7]. In RCC, Th2 cells are notably more abundant than other T-cell subsets and their infiltration levels correlate positively with poor patient outcomes\u0026nbsp;[8].\u003c/p\u003e\n\u003cp\u003eA study by Radomir Kratchmarov et al. identified a population of Th2 cells co-expressing TCF7 and LEF1 in chronic type 2 inflammatory diseases, naming them Th2 multipotent progenitor (Th2-MPP) cells\u0026nbsp;[9]. These cells possess the capacity for self-renewal and differentiation into effector cells, regulatory T (Treg) cells, and follicular helper T cells. Whether Th2-MPP cells are present in cancers, and their significance in tumorigenesis and progression, along with the underlying mechanisms, remain to be elucidated.\u003c/p\u003e\n\u003cp\u003eThis study aims to investigate the expression of Th2-MPP cells in cancer (specifically ccRCC) and assess their impact on prognosis and response to immunotherapy through bioinformatic analyses; it further seeks to explore the potential mechanisms by which this cell population may contribute to immune evasion in ccRCC.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Ethical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data utilized in this study were obtained from publicly available datasets. No ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data resources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-sequencing data and curated clinical phenotypes for 33 prevalent cancer types were sourced from The Cancer Genome Atlas (TCGA) pan-cancer data, available for download from the Pan-Cancer Atlas Hub at University of California Santa Cruz (UCSC) Xena dataset (https:xenabrowser.net). The transcriptome data for Th2-MPP characteristic regulator in 33 tumor types, along with relevant paracancerous tissues, were extracted for further analysis. Additionally, gene expression profiles and clinical information for the E-MTAB-1980 cohort were obtained from the ArrayExpress website (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1980/), and expression data for GSE167573 was sourced from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). Furthermore, normalized transcriptomic and clinical data for ccRCC patients treated with Nivolumab (anti-PD-1) therapy\u0026nbsp;[10], as reported in a published article, were utilized to enhance the clinical significance of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Cell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe human ccRCC cell line 786-O was obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). Cells were maintained in RPMI-1640 medium (MA0215, Meilunbio, Dalian, China) supplemented with 10% fetal bovine serum, penicillin, and streptomycin, and cultured at 37°C in a 5% CO₂ incubator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Analysis of differential expression and survival in pan-cancer for regulators of Th2-MPP cell characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Wilcoxon signed-rank test was used to identify differences in the expression of LEF1 and TCF7 between normal and tumor tissues across various types of cancer. The prognostic significance of Th2-MPP cell characteristic regulators in pan-cancer was assessed through Univariate Cox regression analysis and visualized using a forest plot. Overlapping candidates from different analyses were identified using the Venny 2.1.0 online platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Patient clustering using Gaussian mixture model (GMM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients within the Kidney renal clear cell carcinoma (KIRC) cohort were grouped into three clusters using the GMM analysis with the R package mclust. Kaplan-Meier (KM) analysis was conducted to compare survival outcomes between these groups using the survival package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Characterization of the immune landscape in the patient clusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-sample gene set enrichment analysis (ssGSEA) was employed to characterize the immune landscape within the three patient clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Analysis of immune-cell interactions enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between immune-cell relative abundance and prognosis was determined for the high-risk group using TimiGP\u0026nbsp;[11], a robust computational method for inferring cell-cell interactions within the TME. This analysis involved twenty-two cell types reported by Bindea et al\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Weighted gene coexpression network analysis (WGCNA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) between clusters 1 and 2 were first identified from the TCGA-KIRC cohort using a threshold of |log₂ fold change (logFC)| \u0026gt; 1. Based on these DEGs, a weighted gene co-expression network was constructed using the WGCNA package\u0026nbsp;[13],\u0026nbsp;with a soft-thresholding power of 7. Network connectivity was assessed via the topological overlap matrix (TOM), and modules were subsequently identified through hierarchical clustering. The relationships between each module and immune cell infiltration, Th2-MPP characteristic regulators, as well as clinical parameters, were then evaluated and visualized in a heatmap.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Development of a Th2-MPP cell-related signature (T2Ms) for predicting outcomes in patients with ccRCC through machine learning-based integration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to create a consistent T2Ms for accurate outcome prediction in patients with ccRCC, 10 machine-learning algorithms and 63 algorithm combinations were integrated. The 10 machine-learning algorithms utilized in this study included elastic network (Enet), random survival forest (RSF), generalized boosted regression modeling (GBM), the least absolute shrinkage and selection operator (Lasso), Ridge, CoxBoost, partial least squares regression for Cox (plsRcox), stepwise Cox, survival support vector machine (survival-SVM), and supervised principal components (SuperPC). Prognostic DEGs in the TCGA-KIRC cohort were identified using univariate Cox regression. Subsequently, the prognostic DEGs were subjected to the 63 algorithm combinations to construct prediction models based on leave-one-out cross-validation (LOOCV) in the TCGA-KIRC cohort. Harrell’s concordance index (C-index) was calculated in three validation datasets, including the TCGA dataset, E-MTAB-1980 dataset, and the GSE167573 dataset. Finally, the model with the highest average C-index was chosen for use in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Evaluation of the T2Ms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TCGA-KIRC cohort was divided into high risk score and low risk score groups based on the optimal cutoff value. Univariate Cox regression was performed to assess the prognostic significance of T2Ms in the three cohorts. K-M survival analysis and receiver operating characteristic (ROC) analysis were conducted to evaluate the accuracy of T2Ms in predicting patient outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 Enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDEGs between the high-risk and low-risk groups of T2Ms were identified using the R package ‘limma’, with thresholds of log2 fold change (FC) \u0026gt; 1 and adjusted P \u0026lt; 0.05. Subsequently, gene ontology (GO) enrichment analysis was conducted to explore the primary biological processes (BP) associated with immune activity and cancer using the R packages ‘ggplot2' and ‘GOplot’. Validation of the GO analysis results was achieved through Gene Set Enrichment Analysis (GSEA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 Prediction of drug sensitivity associated with the signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \"oncoPredict\" R language package was utilized to predict the half-maximal inhibitory concentration (IC50) of anti-cancer drugs for each patient in the TCGA-KIRC database. The Genomics of Drug Sensitivity in Cancer (GDSC, https:www.cancerrxgene.org) and the Cancer Therapeutics Response Portal (CTRP, https:portals.broadinstitute.orgctrp.v2.1) databases served as the training sets. The calcPhenotype function was then employed to predict the IC50 of different drugs for each TCGA-KIRC patient. Subsequently, the Pearson correlation analysis was used to determine the correlations between the risk score of each patient and the IC50 of different drugs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.13 Immunohistochemistry (IHC) assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA tissue microarray containing 90 cases of ccRCC and paired adjacent normal tissues (Cat#HKidE180Su04, OUTDO BIOTECH, Shanghai, China) was used for the IHC assay. Detail information of the tissue microarray was displayed on the website (https://www.superchip.com.cn/biology/tissue.html). The ITPKA protein expression in kidney tissues was detected by the antibody (1:1,200; bs-17184R, BIOSS, Beijing, China). Patients in the TMA cohort were divided into high expression and low expression of ITPKA groups according the median ccRCC IHC score = 5.5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.14 Human ITPKA-specific siRNA and negative control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003esiRNA were purchased from Tsingke Biotechnology Co., Ltd. (Beijing, China). \u0026nbsp;Western blot assay was conducted 72 h after transfection to test the transfection efficacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.15 Western blot\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein expression levels were determined by Western blot analysis following a standard protocol. Membranes were probed with primary antibodies against ITPKA (1:1,000;\u0026nbsp;bs-17184R, BIOSS, Beijing, China),\u0026nbsp;PD-L1 (1:1000, PTM-5075, PTM BIO,\u003c/p\u003e\n\u003cp\u003eHangzhou, China), and GAPDH (1:5,000; 60004-1-Ig, Proteintech, Chicago, IL, USA), the latter serving as an internal loading control. Band intensities were quantified using ImageJ software (National Institutes of Health, USA), and the relative expression of each target protein was normalized to that of GAPDH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.16 Colony formation assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed colony formation assays to evaluate long-term proliferative ability. Briefly, 786-O cells were plated at 2.5×10³ per well in 12-well plates and cultured for 7 days. To maintain knockdown efficiency throughout the experiment, we re-transfected the cells every 72 hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.17 Cell proliferation assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell proliferation was measured using the CCK-8 kit (MA0218, Meilunbio) following the manufacturer’s instructions. We seeded 786-O cells at 1×10³ per well in 96-well plates. Absorbance at 450 nm was recorded every 24 hours up to 96 hours using a microplate reader, and growth curves were plotted from the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.18 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (version 4.3.0). Continuous variables are presented as the mean ± standard deviation (mean ± SD). The statistical significance of quantitative data was determined using Student’s t test or analysis of variance (ANOVA). A threshold of P \u0026lt; 0.05 was considered to be statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 The Th2-MPP characteristic regulators exhibit differential mRNA expression and prognostic value in pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo comprehensively analyze the transcriptome expression pattern of Th2-MPP regulators in pan-cancer, data from TCGA, which includes 33 common cancer types, were utilized. Figure 1A and B illustrate the significant differential expression of LEF1 and TCF7 in multiple cancer types, including UCEC, THCA, STAD, READ, PRAD, PCPG, PAAD, LUSC, LUAD, LIHC, KIRP, KIRC, KICH, HNSC, ESCA, COAD, CHOL, and BRCA. Survival analysis revealed that LEF1 expression levels were associated with OS in ACC, UCEC, KIRP, LUSC, KIRC, BRCA, GBM, LGG, UVM, and THYM, while TCF7 expression levels were associated with relapse-free survival in PAAD, KIRC, LGG, PCPG, and THYM (Figure 1C and D). The Th2-MPP regulators showed a significant role in KIRC based on the intersection of the differential expression analysis and Cox regression analysis (Figure 1E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Clustering ccRCC patients associated with Th2-MPP characters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the Bayesian Information Criterion (BIC) change curve, the VEV model was selected as the optimal fit for the GMM analysis (Figure 2A). The TCGA-KIRC cohort was divided into three clusters based on the expression of Th2-MPP regulators (Figure 2B). OS survival analysis indicated that Th2-MPP cluster 1 and Th2-MPP cluster 2 exhibited significant differences (Figure 2C). The expression of TCF7 and LEF1 also showed significant differences in the three Th2-MPP clusters (Figure 2D). The abundance of immune cells and expression of checkpoints were significantly lower in cluster 1 compared to cluster 2 (Figure 2E and F).\u003c/p\u003e\n\u003cp\u003eTo dissect the immune landscape of cluster 1, we employed the ssGSEA database and TimiGP analysis, which revealed a series of cell-cell interactions predominantly directed toward Th2 cells (Figure 2G). Specifically, we identified interactions from ten immune subsets to Th2 cells, including immature dendritic cells (iDCs), mast cells, T central memory (Tcm) cells, CD8+ T cells, B cells, natural killer (NK) cells, eosinophils, neutrophils, T follicular helper (Tfh) cells, and T helper (Th) cells. Subsequently, a directed interaction network was formed based on these cell-cell interactions (Figure 2H). DC cells and iDC cells displayed the largest outdegree, while Th2 cells exhibited large indegrees. The bottom layer (indegree = 0) comprised DC cells, mast cells, and macrophage cells, which were linked to a favorable prognosis. Conversely, the top layer (outdegree = 0) consisted of cell types associated with an unfavorable prognosis such as Th2 cells. The favorability score was then calculated (Figure 2I). DC cells demonstrated the highest favorable score, indicating a positive prognostic association and potential anti-tumor function. On the other hand, Th2 cells showed the highest unfavorable score, signifying a negative prognostic association and a possible pro-tumor function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIdentification of the suitable module associated with Th2-MPP cell\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DEGs between cluster 1 and 2 were computed and visualized in a volcano plot (Figure 3A). Subsequently, the 436 most variable genes were retained for WGCNA. All 321 samples from the selected GMM clusters passed quality control, as hierarchical clustering detected no outliers (Figure 3B). To construct a scale-free co-expression network, we selected a soft-thresholding power of \u0026beta; = 7, where the scale-free topology fit index plateaued above 0.85 and mean connectivity decreased to an appropriate level (Figure 3C). Finally, the adjacency matrix was converted into a TOM to reduce the impact of spurious correlations. Hierarchical clustering based on the TOM then grouped genes with similar expression patterns into modules, as depicted in the clustering dendrogram (Figure 3D). To identify modules significantly associated with immune characteristics and clinical traits, we computed correlations between module eigengenes (MEs) and these phenotypic data. As illustrated in Figure 3E, the turquoise module exhibited significant negative correlations with tumor stage, Th2 cell infiltration, and the expression of LEF1 and TCF7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Establishment and Integration of a Consensus T2Ms using Machine Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilizing the expression profiles of Th2-MPP in the turquoise module, a machine learning-based approach was employed to develop a precise and robust T2Ms. Using the TCGA-KIRC cohort as the training set, 63 prediction models were fitted within a LOOCV framework, and their performance was evaluated by calculating the C-index across three independent datasets: TCGA-KIRC, E-MTAB-1980, and GSE167573. Among all candidate models, the combination of LASSO regression and RSF emerged as the optimal approach, achieving the highest average C-index of 0.832 (Figure 4A). In Lasso regression, the optimal \u0026lambda; was determined when the partial likelihood of deviance reached the minimum value (Figure 4B-C). Subsequently, the RSF algorithm was utilized to pinpoint core genes for T2Ms construction, with the prediction error rate stabilizing after the construction of 1000 survival trees (Figure 4D). Variable importance (VIMP) scores were calculated for all genes incorporated in the tree-building process, where higher scores indicated greater contributions to overall survival prediction (Figure 4E). This process led to the identification of a final set of 8 core genes (SLC16A12, ITPKA, CUBN, EMX2, IGF2BP2, WDR72, CRYL1, and ALDH6A1) for inclusion in the T2Ms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Assessment of the precision and resilience of T2Ms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all three independent cohorts: TCGA-KIRC, E-MTAB-1980, and GSE167573, patients stratified into the high-risk group based on the optimal cutoff value exhibited significantly poorer OS (Figure 4F-H). Time-dependent ROC analysis demonstrated that the T2Ms signature achieved robust predictive performance, with area under the curve (AUC) values exceeding 0.7 for 1-, 3-, and 5-year OS in each cohort (Figure 4I-K).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Immune Function and Cell Interactions of T2Ms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential gene expression analysis revealed significant differences between high-risk and low-risk groups of T2Ms, as illustrated in a volcano plot (Figure 5A). GSEA identified key immune-related biological processes, including chronic inflammatory response, Tregs cell differentiation, and type 2 immune response (Figure 5B). Additionally, the abundance of immune cells in the high-risk group of T2Ms from the TCGA-KIRC cohort was significantly higher compared to the low-risk group (Figure 5C). Furthermore, the expression of immune checkpoints was found to be significantly higher in the low-risk group of T2Ms compared to the high-risk group (Figure 5D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the predictive value of T2Ms in the NIHMS1611472 cohort treated with PD-1 blockade, we performed Kaplan-Meier survival analysis and tumor response assessment, which revealed that patients with high risk scores had significantly poorer overall survival (Figure 5E). The abundance of immune cells in the high-risk group of T2Ms in the PD-1 blockade-treated cohort was also significantly higher in the low-risk group (Figure 5F).\u003c/p\u003e\n\u003cp\u003eUtilizing the ssGSEA database and TimiGP analysis with high-T2Ms patients in the NIHMS1611472 cohort, we identified the top 10 cell-cell interactions, including neutrophil cells \u0026rarr; Tcm cells, neutrophil cells \u0026rarr; Th2 cells, mast cells \u0026rarr; T cells, neutrophil cells \u0026rarr; CD8+ T cells, neutrophil cells \u0026rarr; T cells, mast cells \u0026rarr; tumor cells, mast cells \u0026rarr; Tcm cells, neutrophil cells \u0026rarr; macrophage cells, neutrophil cells \u0026rarr; Tfh cells, and neutrophil cells \u0026rarr; tumor cells (Figure 5G). Subsequently, a directed interaction network was constructed based on these cell-cell interactions (Figure 5H). Notably, mast cells exhibited the largest outdegree, while T cells had prominent indegrees. The bottom layer comprised mast cells, neutrophil cells, effective memory T (Tem) cells, eosinophil cells, and DC cells, which were associated with a favorable prognosis. Conversely, the top layer included cell types such as Th2 cells, cytotoxic cells, and T cells, which were associated with an unfavorable prognosis. The favorability score was calculated to further assess the prognostic association of these cell types (Figure 5I). Mast cells demonstrated the highest favorable score, indicating a positive prognostic association and potential anti-tumor function, while Th2 cells exhibited one of the highest unfavorable scores, suggesting a poor prognosis and potential pro-tumor functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Predicting drug sensitivity linked to the signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis was utilized to identify the most relevant compounds for T2Ms, and the top 10 drugs with the highest correlation to T2Ms in both databases are depicted in Figure 6A and B. In the CTRP database (Figure 6C), 5 compounds (BCL-LZH-4, cimetidine, cucurbitacin I, PF-4800567 hydrochloride, and tubastain A) demonstrated higher IC50 values in the high T2Ms group, while 5 compounds (BRD-K99584050, niclosamide, fluvastatin, pevonedistat, NSC23766) showed higher IC50 values in the low T2Ms group. Similarly, in the GDSC database (Figure 6D), 6 compounds (SB5051241194, Ibrutinib1799, AZD37591915, OF-11853, LY21097611852, and Osimertinib1919) exhibited higher IC50 values in the high T2Ms group, whereas 4 compounds (Topotecan1808, AZD77621022, ERK66041714, and ULK149891733) showed higher IC50 values in the low T2Ms group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 ITPKA may play a crucial role in relation to T2Ms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen compared to the mRNA expression of T2Ms in normal kidney tissues, SLC16A12, EMX2, IGF2BP2, WDR72, CRYL1, and ALDH61A were found to be downregulated, while ITPKA was upregulated in ccRCC (Figure 7A). There was no significant difference observed in CUBN. Subsequently, the AUC values of T2Ms in the diagnosis of ccRCC were calculated (Figure 7B). Regarding the relationship among the 8 genes of T2Ms, it was demonstrated that ITPKA had a strong positive correlation with other genes (Figure 7C). Univariate Cox regression analysis showed that ITPKA and IGF2BP2 were unfavorable prognostic factors for OS (Figure 7D). Patients were divided into two groups based on the expression of ITPKA determined by the optimal cutoff point, and it was found that patients with higher ITPKA expression had worse OS survival rates (HR = 1.90, P = 0.002) in the NIHMS1611472 cohort (Figure 7E). The immune infiltration was more abundant in the high-expression group of ITPKA compared to the low-expression group (Figure 7F). Utilizing the TimiGP analysis in high-ITPKA group, the top10 cell-cell interactions were identified as Th1 cells \u0026rarr; Tcm cells, Th1 cells \u0026rarr; Th2 cells, Th1 cells \u0026rarr; T cells, Th1 cells \u0026rarr; macrophage cells, Tcm cells \u0026rarr; macrophage cells, cytotoxic cells \u0026rarr; Th1 cells, Th1 cells \u0026rarr; tumor cell, neutrophil cells \u0026rarr; Th1 cells, NK cells \u0026rarr; Mast cells, eosinophil cells \u0026rarr; Tcm cells (Figure 7G). Subsequently, a directed interaction network was constructed based on the cell-cell interactions (Figure 7H). The bottom layer (indegree = 0) consisted of neutrophil cells, Tem cells, and NK cells, which tended to be associated with a favorable prognosis. In contrast, the top layer (outdegree = 0) contained cell types associated with an unfavorable prognosis, including T cells, Th2 cells, and tumor cells. The favorability score was calculated, with neutrophil cells showing the highest favorable score, indicating a positive prognostic association and a potential role in tumor suppression, while tumor cells showed the highest unfavorable score with the opposite role of neutrophil cells (Figure 7I).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 ITPKA overexpression in ccRCC correlates with advanced stage and promotes tumor growth via PD-L1 regulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA TMA cohort was used to validate the expression of ITPKA in renal tissues through an IHC assay (Figure 8A). In our TMA cohort, ITPKA was over-expressed in ccRCC compared with normal tissues. ITPKA was upregulated in ccRCC of \u0026ge;T2 stage. Overexpressed ITPKA was detected in ccRCC of \u0026ge; clinical stage 2 (Figure 8B). The patients with high expression of ITPKA tended to have a poor survival time after surgery, though the difference did not reach a statistical threshold (Figure 8C). siRNAs were used to knock down the expression of ITPKA in the 786-O cell line. The transfection efficiency was measured by WB, and we showed that si-1 and si-3 significantly interfered with the expression of ITPKA (Figure 8D, P\u0026lt; 0.01). The growth curve suggested that the knockdown of ITPKA inhibited the growth of the 786-O cell line (Figure 8E, P\u0026lt; 0.01). Dampened expression of ITPKA reduced the colony formation ability (Figure 8F). We found that the knockdown of ITPKA resulted in decreased expression of PD-L1 (Figure 8G, P\u0026lt; 0.01).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eccRCC is a highly immunogenic tumor characterized by substantial immune cell infiltration within its TME\u0026nbsp;[14]. In 2019, PD-(L)1 ICI combination therapy was formally approved as a first-line treatment\u0026nbsp;[15-17]; however, only approximately half of patients derive clinical benefit\u0026nbsp;[18]. Despite the development of various predictive biomarkers aimed at enhancing the accuracy of immunotherapy benefit prediction in ccRCC patients\u0026nbsp;[19-21], a widely accepted comprehensive predictive framework remains elusive.\u003c/p\u003e\n\u003cp\u003eOur team has previously reported distinct predictive signatures: one based on tumor-infiltrating lymphocyte (TIL)-associated long non-coding RNAs (lncRNAs), revealing the critical impact of TIL-tumor cell interactions on disease progression\u0026nbsp;[22]; and another based on stressed T cell (Tstr)-associated messenger RNAs (mRNAs), highlighting the significant influence of Tstr-tumor cell interactions on patient prognosis and ICI efficacy\u0026nbsp;[23]. These studies collectively underscore the pivotal role of immune cells in ccRCC. Th2-MPP cells, characterized by co-expression of TCF7 and LEF1, are enriched in chronic type 2 inflammation\u0026nbsp;[9]. Pan-cancer analysis indicates that both TCF7 and LEF1 play significant roles in ccRCC tumorigenesis and progression. Specifically, ccRCC patients exhibiting high expression of TCF7 and LEF1 demonstrate enhanced infiltration capacity for Th2 cells and Treg cells. Cell-cell interaction analysis further suggests that this subpopulation is more prone to differentiate and enrich Th2 cells. Consequently, we reasonably hypothesize that Th2-MPP cells infiltrate and are expressed within ccRCC. Nevertheless, how Th2-MPP cells influence malignant progression in ccRCC and whether they diminish ICI efficacy remain unexplored.\u003c/p\u003e\n\u003cp\u003eUtilizing machine learning-based integrative analysis, we constructed the T2Ms signature to predict prognosis and immunotherapy response in ccRCC patients. The higher T2Ms score was significantly associated with adverse prognosis and advanced disease stage in ccRCC. The signature exhibited robust prognostic performance, which was consistently validated across three independent cohorts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProgrammed cell death 1 (PD-1) is a critical immune checkpoint receptor expressed on T cells, where it normally functions to dampen immune responses and maintain self-tolerance\u0026nbsp;[24, 25]. However, tumor cells exploit this pathway by upregulating PD-L1, thereby activating PD-1 signaling to inhibit T cell-mediated anti-tumor activity and facilitate immune escape\u0026nbsp;[26]. Targeting the PD-1/PD-L1 axis is a fundamental aspect of immunotherapy\u0026nbsp;[27, 28]. While anti-PD-1 therapies are now standard first-line and salvage treatments for advanced ccRCC, only a subset of patients derives durable clinical benefit\u0026nbsp;[29]. In this context, our findings demonstrate that ccRCC patients with high T2Ms scores experienced significantly poorer outcomes following Nivolumab treatment. As a result, we identified several potential therapeutic agents as novel alternative treatment options for high-T2Ms score patients beyond ICIs. To our knowledge, this is the first prognostic model to explore the clinical relevance of Th2-MPP cells, providing a potentially actionable framework to guide treatment decisions in ccRCC.\u003c/p\u003e\n\u003cp\u003eInteractions among tumor-infiltrating immune cells are crucial for sustaining tumor progression[30, 31] and play a pivotal role in mediating resistance to ICI therapy\u0026nbsp;[32, 33]; however, the specific mechanistic contributions of Th2-MPP cells are not yet defined. Our analysis indicates that Th2-MPP cells are primarily involved in biological processes including chronic inflammatory responses, type 2 immune responses, and regulatory T cell differentiation. Cell-cell interaction analysis using ssGSEA showed that Th2 cells, acting as adverse prognostic factors in ccRCC, play key roles within these processes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTh2 cells and their secreted cytokines [34, 35] promote an immunosuppressive TME by facilitating the infiltration of immunosuppressive cells and inhibiting the activation and infiltration of Th1-type cytotoxic T lymphocytes (CTLs)\u0026nbsp;[36, 37]. This contributes to a \u0026quot;cold tumor\u0026quot; phenotype, characterized by insufficient effector T cell infiltration and reduced response rates to immune checkpoint inhibitors\u0026nbsp;[38-41]. In ccRCC, a Th2-dominant microenvironment has been consistently associated with adverse clinical outcomes\u0026nbsp;[8]. Moreover, Th2 cells further compromise immune surveillance by activating the STAT6 pathway\u0026nbsp;[42]\u0026nbsp;and upregulating migration-inhibiting factors like RGS1\u0026nbsp;[43], thereby restricting the homing of anti-tumor T cells to tumor sites and exacerbating immune escape. Collectively, these observations lead us to propose that within the chronically inflamed, Th2-enriched tumor microenvironment, Th2-MPP cells may serve as critical drivers of ccRCC progression and resistance to immunotherapy.\u003c/p\u003e\n\u003cp\u003eBy conducting integrative analysis, we have identified Inositol-1,4,5-trisphosphate-3-kinase-A (ITPKA) as a crucial gene associated with ccRCC. These findings align with existing evidence implicating ITPKA as a central regulator in renal cell carcinoma with potential diagnostic and therapeutic relevance [44]. Extending these observations, we found that high ITPKA expression was associated with worse outcomes in ccRCC patients receiving PD-1 blockade therapy, a result further supported by cell-cell interaction analyses. In the TMA cohort, patients with high ITPKA expression were correlated with more advanced clinical stages and poorer prognosis. Additionally, in vitro experiments demonstrated that knockdown of ITPKA expression restricted the growth of ccRCC and reduced the expression of PD-L1. These findings further demonstrate that ITPKA expression not only promotes the progression of renal cancer but also facilitates immune evasion of tumor cells, consequently diminishing the efficacy of immunotherapy in ccRCC patients. Collectively, these findings position ITPKA as a potential biomarker for predicting response to PD-1 blockade and highlight its clinical significance in ccRCC.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, this research offers a Th2-MPP cell-associated score for forecasting outcomes and response to PD-1 blockade therapy in ccRCC. Th2-MPP cells may play a pro-tumoral function in ccRCC within the long-standing type 2 TME. ITPKA could function as a pivotal biomarker linked to T2Ms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: J.Y., C.C., and W.Z.; Data curation, Formal analysis and Visualization: Y.F., Z.T. and Z.W; Funding acquisition: W.Z., J.Y., and Y.D.; Investigation and Methodology: Z.T., B.W., Z.W., H.X., and HC.H; Writing - original draft: Y.F., Y.L. and Y.D; Writing – review: Y.F., Z.T. and B.W.. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Science and Technology Development Fund (FDCT) of Macau SAR (0116/2023/RIA2, 006/2023/SKL, 0074/2025/RIA2) awarded to Weide Zhong; Guangdong Medical Science Research Foundation (B2025039) to YL Deng.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll original data in this research are available upon reasonable request from the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone Declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all public databases.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eR.L. 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Ma, et al., ITPKA1 Promotes Growth, Migration and Invasion of Renal Cell Carcinoma via Activation of mTOR Signaling Pathway, Onco Targets Ther, 13 (2020) 10515-10523.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"machine learning, type 2 helper T multipotent progenitor cell, clear cell renal cell carcinoma, prognosis, bioinformatics, ITPKA","lastPublishedDoi":"10.21203/rs.3.rs-9010954/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9010954/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Clear cell renal cell carcinoma (ccRCC), frequently characterized by the infiltration of type II T helper (Th2) cells, necessitates the development of reliable prognostic models and the discovery of new biomarkers to advance personalized treatment approaches. Th2-multipotent progenitor (Th2-MPP) cells, a recently identified subset of T-cells, have been linked to chronic type II inflammation.\u003c/p\u003e\n\u003cp\u003eMethods: Multiple machine learning algorithms and their combinations were utilized to establish a robust Th2-MPP-related score (T2Ms) for predicting prognosis and response to PD-1 blockade therapy in ccRCC patients. Functional enrichment analysis and the TimiGP algorithm were used to investigate the potential mechanisms of T2Ms in ccRCC, and integrative analysis was employed to identify key T2Ms-associated genes.\u003c/p\u003e\n\u003cp\u003eResults: Pan-cancer analysis revealed elevated expression of Th2-MPP signature genes in malignant ccRCC. A stable T2Ms scoring system was constructed and validated using three independent cohorts. T2Ms accurately predicted patient prognosis and response to PD-1 blockade therapy. Core biological processes associated with Th2-MPP were linked to chronic type II inflammatory responses. ITPKA was identified as a key T2Ms-related gene and an independent predictor of poor prognosis, with high expression correlating with inferior response to PD-1 blockade. The consistent upregulation of ITPKA at the protein level, corroborating our mRNA findings, prompted us to further investigate its functional role in ccRCC cell lines.\u003c/p\u003e\n\u003cp\u003eConclusion: This study suggests that Th2-MPP cells may be enriched in ccRCC and provides a Th2-MPP-based signature for predicting prognosis and therapeutic response to PD-1 inhibition. ITPKA was identified as a critical factor in the T2Ms model and as a potential biomarker for tumor-associated Th2-MPP cells.\u003c/p\u003e","manuscriptTitle":"Th2-MPP Cells Shape an Immunosuppressive Microenvironment in ccRCC: Development of a Prognostic Signature and Functional Validation of ITPKA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 10:56:18","doi":"10.21203/rs.3.rs-9010954/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0af4aa3-b2d2-4c0d-a10f-f8b13f2a877f","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T01:24:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 10:56:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9010954","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9010954","identity":"rs-9010954","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}