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Introduction

The global incidence of kidney cancer is increasing year by year, and it is highly invasive and metastatic. The most common type of kidney cancer is kidney renal clear cell carcinoma (KIRC) (1,2). Surgery remains the first choice for early treatment due to the fact that KIRC is insensitive to conventional radiotherapy and chemotherapy (3); however, the disease has an insidious onset, progresses rapidly and is poorly treated with late-stage surgery, resulting in an extremely low late-stage survival rate (4,5). Despite the promising results of targeted therapies (6), the issue of resistance to targeted therapies has arisen. For example, Chatterjee and Bivona (7) found that reversible proteomic and epigenetic mechanisms, tumor microenvironment-mediated mechanisms, and tumor heterogeneity may all contribute to the emergence of resistance, thereby affecting the therapeutic efficacy of cancer treatment.

The use of immunotherapy in cancer has provided novel ideas for the treatment of KIRC, which exhibits a stronger immune response compared with other cancers (8–12). Immunotherapy is effective in prolonging the overall survival (OS) of patients and tumor, node and metastasis (TNM) staging is considered to be the most appropriate prognostic indicator (13–21). However, there are few studies on KIRC immune infiltration and its biomarkers (22). Hence, the search for specific immune biomarkers holds great clinical significance to provide more personalized and precise treatments to improve the prognosis of patients with KIRC.

Thioredoxin-interacting protein (TXNIP), a multifunctional protein that inhibits the production of glucose transporter proteins, enzymes involved in glycolysis and associated genes, is crucial in preventing tumor aerobic glycolysis (23–25). TXNIP is associated with the cell cycle process and its upregulation inhibits the function of the cell cycle protein A promoter, thereby suppressing the cell cycle (26). Under oxidative stress, TXNIP in the nucleus is transported to the mitochondria, where it binds to thioredoxin-2, which in turn triggers apoptosis and inhibits the proliferation of tumor cells (27,28). Additionally, TXNIP is closely associated with inflammatory immune responses, in which TXNIP binds to the nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 inflammasome to induce inflammation (29). Although there are many studies on TXNIP, information on immune infiltration and clinical prognosis is scarce (30). Previous studies have reported the relationship between TXNIP and angiogenesis, as well as clinical prognosis in KIRC (31,32); however, the relationship between the expression level of TXNIP and immune infiltration in KIRC has rarely been reported (33).

In the present study, the mRNA expression level of TXNIP in patients with KIRC were assessed using The Cancer Genome Atlas (TCGA) database to evaluate the association with overall survival and clinicopathological characteristics. Additionally, the correlation between TXNIP expression level, immune cell infiltration and prognosis was assessed using CIBERSORT and univariate and multivariate Cox regression analysis.

Materials and methods

Data gathering

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.

Identification and enrichment analysis of genes co-expressed with TXNIP

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.

Evaluation of immune infiltration

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).

Immune checkpoint correlation

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.

Expression levels of TXNIP at the single cell level

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 susceptibility analysis

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.

Cell culture and transfection

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.

RNA extraction and reverse transcription (RT)-quantitative (q)PCR

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′.

Western blotting

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.

Cell Counting Kit (CCK)-8 cell proliferation assay

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.

Statistical analysis

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.

Results

mRNA expression of TXNIP in certain cancers

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.

Figure 1. Differential expression of TXNIP in different cancers. (A) Differential expression of TXNIP in KIRC tumor and normal tissues. (B) Analysis of the differential expression of TXNIP in 39 tumors based data from the Tumor Immune Estimation Resource database. *P<0.05, **P<0.01 and ***P<0.001. TXNIP, thioredoxin-interacting protein; KIRC, kidney renal clear cell carcinoma; TPM, transcripts per million; ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.

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.

Figure 2. Association between TXNIP expression with clinicopathological characteristics of KIRC. (A) Association between OS and the level of TXNIP expression using data from the Tumor Immune Estimation Resource database. Reduced expression of TXNIP was significantly associated with worse (B) OS, (C) DSS and (D) PFS in patients with KIRC, compared with those with increased expression. TXNIP expression significantly decreased with increasing (E) grade, (F) stage, (G) T stage and (H) the occurrence of distant metastases. (I) Relationship between low TXNIP expression and age, sex, grade, stage and metastasis. ***P<0.001. TXNIP, thioredoxin-interacting protein; KIRC, kidney renal clear cell carcinoma; OS, overall survival; G, grade; T, tumor; M, metastasis; N, node.

Protein expression of TXNIP in two types of tissue samples

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.

Figure 3. Immunohistochemical analysis data of thioredoxin-interacting protein expression in normal kidney and kidney renal clear cell carcinoma tissue from the Human Protein Atlas database. Magnification, ×100. NOS, not otherwise specified.

Independent prognostic value of TXNIP in KIRC

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.

Figure 4. Univariate and multivariate Cox regression analysis of the prognostic value of TXNIP in KIRC. (A) Univariate Cox regression analysis and (B) Multivariate Cox regression analysis of TXNIP in KIRC. (C) Nomogram for predicting KIRC 1-year, 3-year and 5-year survival. (D) Calibration curves of the KIRC 1-year, 3-year and 5-year model. *P<0.05 and ***P<0.001. TXNIP, thioredoxin-interacting protein; KIRC, kidney renal clear cell carcinoma; OS, overall survival; N, node; M, metastasis; T, tumor; Pr, partial response.

TXNIP co-expression analysis

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).

Figure 5. Identification and functional enrichment analysis of co-expressed genes. (A) Degree of association between 11 genes with a strong correlation with TXNIP (gene-gene associations are connected by shading, with red representing the degree of positive correlation and green representing the degree of negative correlation). Correlation between TXNIP and (B) TDRD7, (C) EPC2, (D) RNASEL, (E) RSBN1 and (F) CSDE1. Kaplan-Meier curves of the five co-expressed genes: (G) TDRD7, (H) EPC2, (I) RNASEL, (J) RSBN1 and (K) CSDE1. (L) Results of Gene Ontology enrichment analysis of co-expressed genes. (M) Results of Kyoto Encyclopedia of Genes and Genomes enrichment analysis of co-expressed genes (q-value is the optimized P-value). (N) The gene set enrichment analysis results show the five selected representative pathways. TXNIP, thioredoxin-interacting protein; TDRD7, tudor domain containing 7; CSDE1, cold shock domain containing E1; EPC2, enhancer of polycomb homolog 2; RSBN1, round spermatid basic protein 1; RNASEL, ribonuclease L.

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.

Relationship between mRNA expression of TXNIP and KIRC immune cell infiltration

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.

Figure 6. Relationship between TXNIP expression and immune cell infiltration in KIRC. (A) Characteristics of 21 tumor-infiltrating immune cells in the TCGA-KIRC samples. (B) Differences in immune infiltrating cells between high and low TXNIP expression subgroups. Correlation between TXNIP expression (C) T cells follicular helper, (D) T cells regulatory, (E) Macrophages M0, (F) T cells CD4 memory resting, (G) Macrophages M1, (H) Dendritic cells resting and (I) mast cells resting. (J) Correlation of TXNIP expression with 21 immune cells. *P<0.05, **P<0.01 and ***P<0.001. TXNIP, thioredoxin-interacting protein; KIRC, kidney renal clear cell carcinoma; Abs, absolute value; Cor, correlation coefficient.

Correlation analysis of immune checkpoints

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.

Figure 7. Correlation analysis of TXNIP expression in KIRC with immune checkpoints. (A) Degree of correlation between immune checkpoints and TXNIP expression. (B) Detailed demonstration of the correlation between different immune checkpoints. TXNIP, thioredoxin-interacting protein; KIRC, kidney renal clear cell carcinoma.

TXNIP-specific expression in conventional CD4+ T cells (CD4Tconv)

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.

Figure 8. Characterization of TXNIP expression in different immune cells of KIRC. UMAP graphs presenting the distribution of immune cell populations (left) and their degree of association with TXNIP (right) analyzed through the (A) KIRC-GSE139555 and (B) KIRC-GSE111360 datasets using the TISCH database. (C) Degree of relationship between TXNIP expression and immune cells in different data sets. TXNIP, thioredoxin-interacting protein; KIRC, kidney renal clear cell carcinoma; TPM, transcipts per million; CD4Tconv, CD4T conventional lymphocytes; Treg, regulatory T cells; Tprolif, proliferating T cells; CD8Tex, exhausted CD8T cells; NK, natural killer cells; B, blymphocytes; Mono/Macro, monocytes/macrophages.

Drug sensitivity analysis of TXNIP

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.

Figure 9. Drug sensitivity analysis of TXNIP expression in kidney renal clear cell carcinoma. TXNIP, thioredoxin-interacting protein.

Cellular experimental validation

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.

Figure 10. Cellular assay validation in KIRC. (A) Differences in TXNIP mRNA expression levels in HK-2 and A498 cells, assessed using RT-qPCR. (B) Differences in protein expression levels of TXNIP in HK-2 and A498 cells, and its protein expression changes in A498 after overexpression of TXNIP, assessed using western blotting. (C) mRNA expression changes of CD25 and CTLA4, the surface markers of CD4+ T cells, after overexpression of TXNIP, assessed using RT-qPCR. (D) Effect of overexpression of TXNIP on the proliferation of kidney cancer A498 cells, assessed using the Cell Counting Kit-8 assay. *P<0.05; **P<0.01; ****P<0.0001. TXNIP, thioredoxin-interacting protein; KIRC, kidney renal clear cell carcinoma; RT-qPCR, reverse transcription-quantitative PCR; CTLA4, cytotoxic T-lymphocyte-associated protein 4; OD, optical density.

Discussion

KIRC is a heterogeneous disease with a poor prognosis (47). The limited predictors to assess the risk of KIRC may result in inaccurate grading, lowering the survival rate of patients with KIRC (48). Hence, the identification of reliable biomarkers for the prognosis and treatment of KIRC is urgently required. In the present study, high-throughput RNA sequencing data from the TCGA database was used and was further validated using RT-qPCR and western blot analyses. The findings revealed that TXNIP expression was significantly associated with OS, DSS and PFS, as well as the infiltration levels of TIICs. These findings highlight the potential for the use of TXNIP as a prognostic biomarker and therapeutic target for KIRC.

The primary structure of TXNIP includes an inhibitor-like N-terminus (10–152 aa) and a C-terminus (175–298 aa), indicating its role in the inhibition of the function of binding proteins (49–51). TXNIP is a recognized oncogene repressor in several types of breast cancer (52,53). Moreover, TXNIP expression has been reported to be significantly downregulated in breast, liver and lung cancers (54–56). In the present study, the expression of TXNIP in 33 tumor types was demonstrated, and it was compared with that of normal tissues using data from online databases. The results revealed that TXNIP was significantly downregulated in most cancers (Fig. 1B). Furthermore, RT-qPCR and western blotting demonstrated that TXNIP expression was significantly reduced in KIRC.

The significance of TXNIP as a prognostic factor has been demonstrated in patients with cancer. In hepatocellular carcinoma, lower TXNIP expression was notably associated with a worse prognosis (55). TXNIP was also identified as an independent prognostic factor for distant metastasis-free survival and OS in gastroesophageal adenocarcinoma (57) and the prognostic and predictive value of TXNIP have been established in human breast cancer (58,59). The present study found that reduced mRNA expression of TXNIP was associated with unfavorable clinicopathological features, including high histological grade, stage and the occurrence of distant metastases (Fig. 2E-I). Multivariate Cox regression analysis demonstrated that TXNIP was an independent prognostic factor that significantly impacted PFS, DSS and OS. Reduced TXNIP expression was also associated with adverse clinical outcomes in KIRC. Furthermore, the analyses, stratified by sex, age, TNM stage, stage and grade, demonstrated that patients with high mRNA expression levels of TXNIP expression experienced significantly greater OS in comparison with those patients with low levels of expression. The findings by Gao et al (32) similarly showed that reduced TXNIP mRNA expression was significantly associated with clinical stage. In addition, the study also showed that TXNIP was an independent prognostic factor for KIRC by univariate and multivariate Cox analysis (32). In contrast to this study, the present study not only used TCGA, the TIMER and HPA databases and related experiments (Fig. 10A and B) for further assessment to ensure the reliability of the results, but also screened for co-expressed genes and performed Kaplan-Meier analyses on the co-expressed genes (Fig. 5G-K) to evaluate their association with prognosis.

In the tumor microenvironment, the ecosystem established by TIICs serves a crucial role in the regulation of cancer progression in KIRC, and the immune responses are a critical determinant of survival outcomes. Hence, the proportion of TIICs in cancer potentially has prognostic value (60–62). Further analysis using TCGA and CIBERSORT revealed that certain immune cells associated with TXNIP expression were significantly associated with the survival of patients with KIRC. Increased TXNIP mRNA expression was associated with an increase in macrophages M1, T cells CD4 memory resting, mast cells resting and dendritic cells resting. Conversely, there was a decrease in the proportion of T cells follicular helper, Tregs and macrophages M0 (Fig. 6B). The ability of the chemokine signaling pathway to control T cell migration toward chemokine sources has been demonstrated. In the context of cancer, ligands for C-C chemokine receptor type 5 and CXC motif chemokine receptor 3 have also been reported to be associated with the degree of tumor-infiltrating lymphocyte (TIL) infiltration in cancer (63–65). Reactive oxygen species (ROS) metabolism is controlled by the essential component TXNIP and cytosolic ROS in CD4+ T lymphocytes rapidly decrease during the contraction phase. Once CD4+ T cells activation is attenuated, TXNIP has been reported to be quickly upregulated, and furthermore, TXNIP expression has also been directly associated with the development of allergen-specific memory Th2 cells (66). Results from the present study demonstrated that the T-cell receptor and chemokine pathways were notably differentially enriched in KIRC with high TXNIP mRNA expression, according to the gene set enrichment analysis data (Fig. 5N). Additionally, the outcomes of RT-qPCR assays revealed that TXNIP mRNA expression was significantly associated with CD4+ T cells in KIRC (Fig. 10C). These findings demonstrate that TXNIP regulates immune infiltration and may affect the levels of ROS in patients with KIRC.

Additionally, the present study found a significant association between the mRNA levels of TXNIP and sensitivity to 12 antitumor drugs (Fig. 9). WIKI4 and XAV-939 are known to suppress tankyrase activity, inhibiting Wnt/β-catenin signaling pathway-mediated transcription (44,45). Afuresertib, ipatasertib and MK-2206 are key antitumor drugs as they potentially inhibit AKT kinase (41). Elevated levels of TXNIP expression can suppress the activity of the Wnt/β-catenin and PI3K/AKT/mTOR signaling pathways (67–69). This mechanism may therefore be related to enhanced drug sensitivity, but further evaluation is needed.

Whilst the present study offers insights on the potential function of TXNIP on KIRC prognosis and immune infiltration, certain limitations merit attention. Primarily, the sample size was only 614 cases and a larger data set is needed to confirm the accuracy of the findings. Additionally, further experimental studies are needed to confirm the functional role of TXNIP in KIRC.

In summary, the present study indicated that there is a strong association between decreased TXNIP expression levels, insufficient immune cell infiltration and poor prognosis in patients with KIRC, and that the reduction in TXNIP expression levels may impair the antitumor activity of the immune system in patients with KIRC. These findings provide insights that may prove beneficial in the development of novel immunotherapeutic approaches.

Acknowledgements

Not applicable.

Funding

Funding was received from the Guangxi Zhuang Autonomous Region Health and Family Planning Commission (grant. no. Z-L20221837).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The data accessed in the present study may be found in the TCGA (https://portal.gdc.cancer.gov/), HPA (http://www.proteinatls.org), and TIMER (https://cistrome.shinyapps.io/timer/) databases.

Authors' contributions

WL, ZX and MD contributed to the development of the statistical analysis strategies to collect, evaluate and organize data from the databases; design, implementation and processing of experimental data; analysis of data; and drafting and revision of the manuscript. XL and ZH designed and directed the study, provided experimental design ideas, guidance on data processing and advice on manuscript revision. WL and ZX wrote the main manuscript text and prepared Fig. 1, Fig. 2, Fig. 3, Fig. 4. MD prepared Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10. All authors have read and approved the final manuscript. WL and ZX confirm the authenticity of all the raw data.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

References