Using the TCGA database (https://portal.gdc.cancer.gov/), gene expression patterns and clinical information from 542 patients with KIRC and 72 normal kidney tissue samples were obtained from the TCGA-KIRC dataset (34). The Tumor Immune Estimation Resource (TIMER) database was used to determine the mRNA expression levels of TXNIP in 33 different cancer types (https://cistrome.shinyapps.io/timer/). The Human Protein Atlas (HPA) database (http://www.proteinatlas.org) was used to obtain immunohistochemical data on protein expression of TXNIP in KIRC and normal tissues.

A total of five genes co-expressed with TXNIP were screened, with P<0.001 used as a significant correlation cutoff. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of co-expressed genes were performed using the R package clusterProfiler v4.6.2. with P<0.05 considered the significance cutoff (35,36). The files ‘c2.cp.kegg.v7.4.symbols’ and ‘c5.go.v7.4.symbols’ were used for gene set variation analysis (GSVA). The ‘limma’ R package v3.54.2 was used to identify biological functions (https://bioinf.wehi.edu.au/limma/). A GSVA score t-value >2 was considered significantly altered.

The proportion of infiltrating immune cells in 542 tumor samples was assessed using the CIBERSORT database (http://cibersort.stanford.edu/), and the CIBERSORT R v1.03 and LM22 R software packages were used as tools for algorithmic ensembles (37). Based on the median TXNIP mRNA expression level of the patients with KIRC, the patients were divided into TXNIP low and high expression groups (P<0.05 was considered as a statistically significant screening condition), and the level of infiltration of the different immune cells was subsequently confirmed using the TIMER 2.0 algorithm (38).

A significant correlation of P<0.001 was used as a screening condition and the R package ‘corrplot’ v0.92 (https://github.com/taiyun/corrplot) was used to assess the correlation between the expression data of immune checkpoint-related genes and TXNIP mRNA expression.

The Tumor Immune Single-Cell Hub (http://tisch.comp-genomics.org/home/) is a publicly available and comprehensive web resource site. The KIRC_GSE139555 and KIRC_GSE111360 datasets were selected in the ‘Datasets’ module to visualize and assess the variations in TXNIP expression at the single-cell level between different immune cells.

Drug-related data were obtained from the CellMiner database (39), which includes records of drug sensitivity analysis of drugs validated by clinical trials and approved by the U.S. Food and Drug Administration. Subsequently, Pearson correlation coefficients were used to analyze the relationship between mRNA expression levels of TXNIP and drug sensitivity in the TCGA-KIRC dataset.

Human kidney cancer A498 cells (cat. no. CL-0254) and normal kidney tissue HK-2 cells (cat. no. CM-0109) were obtained from Procell Life Science & Technology Co., Ltd. The cells were resuscitated and cultured with complete minimal essential medium, including MEM basal medium (cat. no. PM150410; Life Science & Technology Co., Ltd.), 1% penicillin mixture (cat. no. P1400; Beijing Solarbio Science & Technology Co., Ltd.), and 10% neonatal fetal bovine serum [cat. no. CF-01P-02; Cell-Box (HK) Biological products Trading Co., Ltd.]. The cell cultures were kept at 37°C in a 5% CO₂ cell incubator. Before transfection, the cells were cultured and cultivated until they reached ~70% confluence. A498 cells were then transfected with 4 µg each of TXNIP-overexpression plasmid (A498-LV-TXNIP) or empty vector plasmid (A498-LV-Empty). The plasmids were purchased from GeneCopoeia, Inc. The TXNIP overexpression and empty vector plasmids were added into MEM basal medium and HighGene plus transfection reagent (cat. no. RM09014P; ABclonal Biotech Co., Ltd.) was then added into the wells containing cells after thorough mixing. After transfection, the cells were placed in a 5% CO₂ cell culture incubator at 37°C for 4–6 h, and then half of the medium was replaced and the cells were incubated again for 24–48 h before the cells were used for subsequent experiments.

An RNA Fast Small Extraction Kit (cat. no. TR154-50; Jianshi Biotechnology Co., Ltd) was used to extract total RNA and cDNA was synthesized using the SureScript™ First-Strand cDNA Synthesis Kit (cat. no. QP056; GeneCopoeia, Inc.). mRNA expression levels were determined using the LightCycler^® 96 Instrument (SW 1.1; Roche Diagnostics GmBH) and the BlazeTaq™ SYBR Green qPCR Mix 2.0 kit (cat. no. QP031; GeneCopoeia, Inc.). RNA extraction, cDNA synthesis and qPCR were performed according to the manufacturers' protocols. The synthesis of cDNA was performed at 25°C for 5 min, 42°C for 15 min and 85°C for 5 min, and then annealed at 4°C to finish. qPCR was performed at 95°C for 10 min, followed by 40 cycles at 95°C for 10 sec, 60°C for 20 sec and extension at 72°C for 15 sec, with a final extension step at 72°C for 10 min. GAPDH was used as an endogenous control and the results were quantified using the 2^−∆∆Cq method (40). A498 cells (~5×10⁵ cells) transfected with empty vector and TXNIP-overexpression plasmids were used as control and treatment groups, respectively, and this experiment was repeated three times. The primer sequences (Shanghai Sangon Pharmaceutical Co., Ltd.) used were as follows: GAPDH forward (F), 5′-GGTGAAGGTCGGAGTCAACG-3′ and GAPDH reverse (R), 5′-CAAAGTTGTCATGGATGACC-3′; TXNIP F, 5′-GGCAATCATATTATCTCAGGGAC-3′ and TXNIP R, 5′-CAGGAACGCTAACATAGATCAGTAA-3′; CD25 F, 5′-TTCGTGGTGGGGCAGATGGT-3′ and CD25 R, 5′-TCTTCCCGTGGGTCATTTTG-3′; and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) F, 5′-AACCTACATGATGGGGAATGAG-3′ and CTLA4 R, 5′-AGGTAGTATGGCGGTGGGTAC-3′.

Total proteins were extracted from HK-2, A498, A498-LV-TXNIP and A498-LV-Empty cells (~5.5×10⁶ cells) using RIPA lysis buffer (Beyotime Institute of Biotechnology) mixed with phenylmethanesulfonyl fluoride (cat. no. P6730; Beijing Solarbio Science & Technology Co., Ltd.) at a ratio of 1:100. The protein concentration was assessed using a BCA protein assay kit (cat. no. PC0020; Beijing Solarbio Science & Technology Co., Ltd.). Subsequently, 70 µg protein/lane were separated on 10% precast gels using SDS-PAGE (Invitrogen; Thermo Fisher Scientific, Inc.) and transferred to PVDF transfer membranes (Biosharp Life Sciences). The membranes were blocked using 5% skim milk powder (cat. no. 1172GR100; BioFroxx; neoFroxx GmbH) for 2 h at room temperature and then incubated with anti-TXNIP (1:1,000; cat. no. A11682; Nature Biosciences Ltd.) and GAPDH antibodies (1:1,000; cat. no. RA0003; Nature Biosciences Ltd.) overnight at 4°C. The following day, these membranes were washed 3 times using TBST (contains 0.05% Tween) and incubated again with HRP-conjugated goat anti-rabbit IgG (H+L) secondary antibodies (1:3,000; cat. no. AS014; ABclonal Biotech Co., Ltd.) for 1 h at room temperature. Luminescence development was performed using MonPro™ ECL Ultrasensitive Substrate Pro (cat. no. PW30701S; Monad Biotech Co., Ltd.) and a Tanon-5200Multi gel imager was used to capture protein images (Tanon Science and Technology Co., Ltd.). Protein expression levels were semi-quantified using ImageJ v1.8.0 (National Institutes of Health). The GAPDH signal was used to normalize the TXNIP band intensity and the experiment was repeated three times.

The CCK-8 was purchased from Dojindo Laboratories, Inc. A498-LV-Empty and A498-LV-TXNIP cells (~2×10⁴ cells) were placed in 96-well plates (3×10³ cells per well) and five wells of each were replicated and cultured in an incubator at 37°C for 0, 24, 48, 72 and 96 h. CCK-8 reagent (10 µl) was added to each well and then the cells were incubated for another 2 h. Finally, using an ELISA microplate reader [Biobase Biodusty (Shandong) Co., Ltd.], the absorbance values were determined at 450 nm. The experiment was repeated three times.

Data handling and statistical analysis was performed using R software version 4.2.1 (https://www.r-project.org/), Strawberry Perl version 5.30.1.1 (https://strawberryperl.com/), SPSS version 25 (IBM Corp.) and GraphPad Prism version 9.0 (Dotmatics). To determine if there were significant differences in TXNIP, CD25 and CTLA4 mRNA expression levels between subgroups, analysis was performed using the unpaired t-test. Kruskal-Wallis and Dunn's test were used to analyze the relationship between KIRC clinicopathological variables and TXNIP mRNA expression levels. Statistical analysis of Kaplan-Meier and other survival analyses were performed using Log-rank tests. For survival analyses, univariate and multivariate Cox regression models were used. P<0.05 was considered to indicate a statistically significant difference.

The differences in mRNA level expression of TXNIP in 33 tumor and normal tissues were compared using data from TCGA database and the TIMER online web tool. TCGA database results showed that the mRNA expression level of TXNIP in KIRC was significantly lower than that in normal tissues (Fig. 1A). The 605 samples of KIRC were further evaluated using the TIMER online database, in which the mRNA expression level of TXNIP was significantly reduced in 533 tumor samples, which was consistent with the results shown in TCGA database (Fig. 1B). The aforementioned results indicated that TXNIP mRNA levels were expressed at a low level in most cancers.

Patients with KIRC were matched for clinical data and separated into low and high TXNIP expression groups according to their median TXNIP mRNA expression level. Kaplan-Meier survival curves and TIMER data (Fig. 2A) demonstrated a positive association between the mRNA expression level of TXNIP and cumulative survival. The mRNA expression levels of TXNIP in 542 KIRC samples and 72 normal samples were evaluated using TCGA data, and the results showed that KIRC patients with high TXNIP expression had significantly improved DSS, OS and progression-free survival (PFS) compared to those with low TXNIP mRNA expression levels (Fig. 2B-D). Additionally, the levels of TXNIP expression significantly decreased with higher tumor grade, stage and TNM stage (Fig. 2E-I). Thus, it can be seen that the high expression of TXNIP could improve the survival rate of patients with KIRC, and that its expression level was negatively associated with the clinicopathological stage.

The TXNIP protein expression level and its clinical significance was assessed using micrographs from patients with KIRC which were included HPA database. TXNIP protein expression was observed to be markedly lower in KIRC tissue samples compared with normal kidney tissue samples, based on immunohistochemistry analysis data from the HPA database (Fig. 3). This result indicates that the protein level of TXNIP in KIRC was also lower than that in normal kidney tissue compared with the mRNA level.

Univariate Cox regression analysis demonstrated a significant association between grade and risk scores with OS in 529 patients with KIRC with TXNIP, age, grade and stage being statistically significant in KIRC (P<0.001), showing good prognostic value (Fig. 4A). Multivariate Cox regression analysis showed that grade and risk scores were independent prognostic indicators for KIRC, and TXNIP had a high prognostic value in KIRC (P=0.012), while age, grade and stage still maintained good prognostic value (P<0.001) (Fig. 4B). Additionally, nomogram plots were constructed that incorporated age, TNM stage, stage, sex and grade to forecast the survival of patients with KIRC at 1, 3 and 5 years, and survival rate was found to decrease significantly with time (Fig. 4C and D). The results suggest that TXNIP can be used as an independent prognostic indicator, as well as age, T staging and stage.

By analyzing 211 genes co-expressed with TXNIP, the co-expressed genes were screened using P<0.001 and corFilter=0.7 as threshold conditions (where P<0.001 was an indication of a significant correlation and corFilter=0.7 was used as a criterion for identifying co-expressed genes to filter out irrelevant genes). The results demonstrated that the gene expression of tudor domain containing 7, cold shock domain containing E1, enhancer of polycomb homolog 2, round spermatid basic protein 1 and ribonuclease L had strong and significant positive correlations with TXNIP mRNA expression (Fig. 5A-F). Furthermore, Kaplan-Meier analysis demonstrated that a high expression of these five genes was significantly associated with a good prognosis, compared with a low expression (Fig. 5G-K).

GO analysis results demonstrated that the function of TXNIP was mainly enriched in the ‘acute-phase response’, ‘anatomical structure maturation’ and ‘acute-inflammatory response’. It was also associated with ‘endopeptidase activity’ and ‘serine-type endopeptidase activity’ (Fig. 5L). Moreover, the KEGG analysis showed there was a high association between the mRNA expression level of TXNIP and ‘neuroactive ligand-receptor interactions’ (Fig. 5M). KEGG genomic analyses demonstrated that five signaling pathways, including chemokine and T-cell receptor signaling pathways, were markedly differentially enriched at high mRNA expression levels of TXNIP (Fig. 5N). This finding suggests that TXNIP and its co-expressed genes have some association with the immune response of the organism.

Fig. 6A demonstrates the distribution of the 21 tumor-infiltrating immune cells (TIICs) in the TCGA-KIRC dataset samples. Additionally, a comparative analysis of the TXNIP high and low expression groups was performed to assess the proportion of TIICs in the two groups. The results showed that seven TIICs differed significantly between the two groups (Fig. 6B). Among them, in the TXNIP high expression group, macrophages M1 (P=0.00045), dendritic cells resting (P=0.00028), monocytes (P=0.0014), macrophages M2 (P=0.0076), neutrophils (P=0.008), T cells CD4 memory resting (P<0.001) and mast cells resting (P<0.001) had significantly increased levels of infiltration, compared with that in the low expression group. In the TXNIP low expression group, the infiltration levels of macrophages M0 (P<0.001), T cells regulatory (Tregs; P<0.001) and T cells follicular helper (P<0.001) were significantly increased, compared with that in the high expression group. Furthermore, correlation analysis demonstrated that macrophages M1, mast cells resting, T cells CD4 memory resting and dendritic cells resting showed a significant positive association with TXNIP expression, and T cells follicular helper, Tregs and macrophages M0 exhibited a strong negative association with TXNIP expression (Fig. 6C-I). Fig. 6J not only reaffirms the high association of TXNIP mRNA expression levels with the immune cells aforementioned, but also visualizes the positive and negative association between various types of immune cells and TXNIP expression. These results indicated that TXNIP was a key player that regulated the immunological microenvironment of KIRC.

Following correlation analysis of the expression levels of immune checkpoint genes and TXNIP expression, 34 immune checkpoint-related genes were found to be strongly associated with the mRNA expression levels of TXNIP. With the exception of tumor necrosis factor superfamily member 14, a positive correlation between the mRNA expression levels of TXNIP and almost all the immune checkpoint genes was demonstrated. neuropilin 1, programmed cell death 1 ligand 2, CD200 and CD274 had the most notable positive associations with TXNIP expression of those genes assessed (Fig. 7A and B). The aforementioned results suggest that TXNIP is closely associated with the expression of most immune checkpoint-related genes.

The association between TXNIP mRNA expression and several immune cells was assessed using the TISCH public database. The results demonstrated that TXNIP was mainly expressed in CD4Tconv cells (Fig. 8A and B). In addition, it was demonstrated that TXNIP was predominantly expressed in immune cells and stromal cells in KIRC (Fig. 8C). The aforementioned results suggest that TXNIP is associated with CD4⁺ T cells to a greater extent than other immune cells.

The CellMiner TM database was used to analyze whether there was a correlation between TXNIP expression and drug sensitivity. A total of 12 drugs were screened from 46 drugs, which were found to be significantly associated with the mRNA expression level of TXNIP, and the drug sensitivity was significantly enhanced in the group with high TXNIP expression compared to the group with low TXNIP expression (Fig. 9). Among these, afuresertib, ipatasertib and MK-2206 are AKT kinase inhibitors (41), entinostat and vorinostat are histone deacetylase (HDAC) inhibitors (42,43), and WIKI4 and XAV939 are tankyrase inhibitors and have inhibitory effects on the Wnt/β-catenin signaling pathway (44,45). Therefore, we hypothesized that the increased expression of TXNIP may have some effect on the presence of the AKT, HDAC, tankyrase and Wnt/β-catenin signaling pathways compared to the generally low expression of TXNIP in cancer.

TXNIP expression levels in HK-2 and A498 cells differed substantially. The mRNA and protein expression levels of TXNIP in A498 cells were significantly lower than those in HK-2 cells, and these levels were significantly increased following the overexpression of TXNIP, compared with that in the A498-LV-Empty cells (Fig. 10A-C). Furthermore, the mRNA levels of CD25 and CTLA4, which are surface markers of CD4⁺ T cells (46), were significantly reduced after the overexpression of TXNIP, compared with that in the A498-LV-Empty cells (Fig. 10C). Additionally, results from the CCK-8 assay showed that TXNIP overexpression significantly reduced the capacity of A498 cells to proliferate (Fig. 10D). These findings indicate that TXNIP acts as an important oncogene in KIRC, exerting inhibitory effects on immune escape and the rapid proliferation of cancer cells.